https://wiki.icingcentre.eu/api.php?action=feedcontributions&feedformat=atom&user=UwasaIcingWiki - User contributions [en]2026-11-01T12:41:25ZUser contributionsMediaWiki 1.44.5https://wiki.icingcentre.eu/index.php?title=GNSS_Reflectometry&diff=829GNSS Reflectometry2022-02-09T15:51:01Z<p>Uwasa: </p>
<hr />
<div>Global Navigation Satellite System (GNSS) satellites transmits navigation signals from Earth orbits to terrestial receivers to help them [https://www.telesens.co/2017/07/17/calculating-position-from-raw-gps-data/ find their location]. Many nations have deployed their own satellite constellations for positioning, and new receivers can use all of them. Currently available constellations are <br />
<br />
* '''G:''' [https://www.gps.gov/ GPS], Global Positioning System, United States<br />
* '''R:''' [[wikipedia:GLONASS|GLONASS]], Russia<br />
* '''E:''' [https://www.esa.int/Applications/Navigation/Galileo/What_is_Galileo Galileo], European Space Agency<br />
* '''J:''' [https://qzss.go.jp/en/overview/services/sv01_what.html QZSS,] Japan<br />
* '''C:''' BDS, [http://en.beidou.gov.cn/SYSTEMS/System/ Beidou], China<br />
* '''I:''' [https://www.isro.gov.in/irnss-programme IRNSS] Indian Regional Navigation Satellite System<br />
* '''S:''' SBAS payload<br />
* '''M:''' Mixed<br />
The preceding letters are use as a satellite systems shortcuts in many systems, such as [https://www.igs.org/wg/rinex/ Rinex] GNSS data files.<br />
<br />
The GNSS signals are affected by the ionosphere <ref>{{Cite journal| doi = 10.3390/rs11020171| issn = 2072-4292| volume = 11| issue = 2| pages = 171| last1 = Su| first1 = Ke| last2 = Jin| first2 = Shuanggen| last3 = Hoque| first3 = M.| title = Evaluation of Ionospheric Delay Effects on Multi-GNSS Positioning Performance| journal = Remote Sensing| accessdate = 2022-02-09| date = 2019-01-17| url = http://www.mdpi.com/2072-4292/11/2/171}}</ref> and weather conditions in troposhpere. For example rain <ref>{{Cite journal| doi = 10.1109/TGRS.2014.2320309| issn = 0196-2892, 1558-0644| volume = 53| issue = 1| pages = 190–206| last1 = Cardellach| first1 = Estel| last2 = Tomas| first2 = Sergio| last3 = Oliveras| first3 = Santi| last4 = Padulles| first4 = Ramon| last5 = Rius| first5 = Antonio| last6 = de la Torre-Juarez| first6 = Manuel| last7 = Turk| first7 = Francis Joseph| last8 = Ao| first8 = Chi O.| last9 = Kursinski| first9 = E. Robert| last10 = Schreiner| first10 = Bill| last11 = Ector| first11 = Dave| last12 = Cucurull| first12 = Lidia| title = Sensitivity of PAZ LEO Polarimetric GNSS Radio-Occultation Experiment to Precipitation Events| journal = IEEE Transactions on Geoscience and Remote Sensing| accessdate = 2022-02-09| date = 2015-01| url = http://ieeexplore.ieee.org/document/6816062/}}</ref> and other atmospheric water content <ref>{{Cite journal| doi = 10.1016/j.jestch.2019.11.006| issn = 2215-0986| volume = 23| issue = 5| pages = 967–972| last = Selbesoglu| first = Mahmut Oguz| title = Prediction of tropospheric wet delay by an artificial neural network model based on meteorological and GNSS data| journal = Engineering Science and Technology, an International Journal| accessdate = 2021-02-15| date = 2020-10-01| url = https://www.sciencedirect.com/science/article/pii/S2215098619316003}}</ref> and other weather conditions cause delay to the GNSS signal propagation and it is then possible to monitor these phenomena by GNSS signals.<br />
<br />
==== Reflectometry ====<br />
<br />
<br />
The GNSS radio signals transmitted from satellites are also reflected from the ground and by monitoring these reflections, the properties of the ground and atmosphere can be examined <ref name="King_2020" />. This kind of analysis is called as GNSS reflectometry (GNSS-R) <ref name="Jia-2021" />.<br />
<br />
Terrestial GNSS-R measurements include two antennas, on of which is receiving the direct signal from the satellite, and the other forward-scattered reflected signal. Satellite-based GSNS-R measures the GNSS signal backscatter from the Earth. GNSS-R backscatter data has been also collected by ESA's [https://earth.esa.int/web/eoportal/satellite-missions/t/techdemosat-1 TechDemoSat-1] (TDS-1) satellite and with NASA's [https://www.nasa.gov/cygnss/the-science-of-cygnss CYGNSS] satellite constellation. Observation data can be downloaded from their portals. <br />
<br />
GNSS reflectometry has been used for detecting sea ice thickness <ref name=Yan_2017/> <ref name=Alonso-Arroyo_2017 /> <ref name=Semmling_2019 /><ref name=Yan_2016 /> <ref name=Yan_2018/><ref name=Southwell_2020/> <ref name=Strandberg_2017/><ref name=Zhu_2020/><ref name=Yan_2020/><ref name=Southwell_2020b/> GNSS-R is also popular for other sea target analysis which is not directly related to icing. <ref name=King_2020/> <ref name=Di_2017/><ref name=Valencia_2013/>. Many research articles concentrating in method develoment, are also indirectly related to icing <ref name=Cheong_2019/> <ref name=Li_2020/><br />
<br />
=== Links ===<br />
<br />
* The description of [https://www.engineersgarage.com/tutorials/gps-receivers-and-nmea-standards/ NMEA messages]<br />
* Documentation for [http://acc.igs.org/misc/rinex304.pdf RINEX 3.04]<br />
* RINEX 3 and RINEX 2 reader python library [https://pypi.org/project/georinex/ GeoRinex]<br />
* [https://www.unavco.org/ UNAVCO]<br />
* RINEX 3 [ftp://data-out.unavco.org/pub/rinex3/obs/ OBS data] **(from 2016 to NOW)** <br />
* UNAVCO RINEX 3 [ftp://data-out.unavco.org/pub/rinex3/nav/ NAV data] **(from 2016 to NOW)** <br />
* UNAVCO [https://www.unavco.org/instrumentation/networks/map/map.html#!/ network monitoring website] <br />
* GNSS Planning [https://www.gnssplanning.com/#/charts TRIMBLE]<br />
* [https://gssc.esa.int/navipedia//index.php/Reference_Frames_in_GNSS#GLONASS_reference_frame_PZ-90 Reference Frames]in GNSS<br />
<br />
== References ==<br />
<references><br />
<ref name=Yan_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2689009| issn = 2151-1535| volume = 10| issue = 8| pages = 3789–3798| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| last3 = Moloney| first3 = Cecilia| title = Neural networks based sea ice detection and concentration retrieval from GNSS-R delay-doppler maps| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=King_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2997199| issn = 2151-1535| volume = 13| pages = 2916–2924| last1 = King| first1 = Lucinda S.| last2 = Unwin| first2 = Martin| last3 = Rawlinson| first3 = Jonathan| last4 = Guida| first4 = Raffaella| last5 = Underwood| first5 = Craig| title = Processing of raw GNSS reflectometry data from TDS-1 in a backscattering configuration| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Cheong_2019>{{Cite journal| doi = 10.1109/JSTARS.2019.2956183| issn = 2151-1535| volume = 12| issue = 12| pages = 5373–5378| last1 = Cheong| first1 = Joon Wayn| last2 = Southwell| first2 = Benjamin J.| last3 = Dempster| first3 = Andrew G.| title = Blind sea clutter suppression for spaceborne GNSS-R target detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Alonso-Arroyo_2017>{{Cite journal| doi = 10.1109/TGRS.2017.2699122| issn = 1558-0644| volume = 55| issue = 9| pages = 4989–5001| last1 = Alonso-Arroyo| first1 = Alberto| last2 = Zavorotny| first2 = Valery U.| last3 = Camps| first3 = Adriano| title = Sea ice detection using U.K. TDS-1 GNSS-R data| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Semmling_2019>{{Cite journal| doi = 10.1109/TGRS.2019.2933911| issn = 1558-0644| volume = 57| issue = 12| pages = 10350–10361| last1 = Semmling| first1 = A. Maximilian| last2 = Rösel| first2 = Anja| last3 = Divine| first3 = Dmitry V.| last4 = Gerland| first4 = Sebastian| last5 = Stienne| first5 = Georges| last6 = Reboul| first6 = Serge| last7 = Ludwig| first7 = Marcel| last8 = Wickert| first8 = Jens| last9 = Schuh| first9 = Harald| title = Sea-ice concentration derived from GNSS reflection measurements in fram strait| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Yan_2016>{{Cite journal| doi = 10.1109/JSTARS.2016.2582690| issn = 2151-1535| volume = 9| issue = 10| pages = 4795–4801| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Spaceborne GNSS-R sea ice detection using delay-doppler maps: First results from the U.K. TechDemoSat-1 mission| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2016}}<br />
</ref><br />
<ref name=Yan_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2966880| issn = 2151-1535| volume = 13| pages = 577–587| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice thickness measurement using spaceborne GNSS-R: First results with TechDemoSat-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Di_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2705350| issn = 2151-1535| volume = 10| issue = 9| pages = 4237–4255| last1 = Di Simone| first1 = Alessio| last2 = Park| first2 = Hyuk| last3 = Riccio| first3 = Daniele| last4 = Camps| first4 = Adriano| title = Sea target detection using spaceborne GNSS-R delay-doppler maps: Theory and experimental proof of concept using TDS-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Li_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2992037| issn = 2151-1535| volume = 13| pages = 1833–1841| last1 = Li| first1 = Bowen| last2 = Yang| first2 = Lei| last3 = Zhang| first3 = Bo| last4 = Yang| first4 = Dongkai| last5 = Wu| first5 = Di| title = Modeling and simulation of GNSS-R observables with effects of swell| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Yan_2018>{{Cite journal| doi = 10.1109/LGRS.2018.2852143| issn = 1558-0571| volume = 15| issue = 10| pages = 1510–1514| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice sensing from GNSS-R data using convolutional neural networks| journal = IEEE Geoscience and Remote Sensing Letters| date = 2018}}<br />
</ref><br />
<ref name=Southwell_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2943510| issn = 2151-1535| volume = 13| pages = 14–20| last1 = Southwell| first1 = Benjamin J.| last2 = Dempster| first2 = Andrew G.| title = Sea ice transition detection using incoherent integration and deconvolution| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Strandberg_2017>{{Cite journal| doi = 10.1109/LGRS.2017.2722041| issn = 1558-0571| volume = 14| issue = 9| pages = 1552–1556| last1 = Strandberg| first1 = Joakim| last2 = Hobiger| first2 = Thomas| last3 = Haas| first3 = Rüdiger| title = Coastal sea ice detection using ground-based GNSS-R| journal = IEEE Geoscience and Remote Sensing Letters| date = 2017}}<br />
</ref><br />
<ref name=Valencia_2013>{{Cite journal| doi = 10.1109/JSTARS.2012.2210392| issn = 2151-1535| volume = 6| issue = 1| pages = 217–223| last1 = Valencia| first1 = Enric| last2 = Camps| first2 = Adriano| last3 = Rodriguez-Alvarez| first3 = Nereida| last4 = Park| first4 = Hyuk| last5 = Ramos-Perez| first5 = Isaac| title = Using GNSS-R imaging of the ocean surface for oil slick detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2013}}<br />
</ref><br />
<ref name=Zhu_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2955175| issn = 2151-1535| volume = 13| pages = 217–226| last1 = Zhu| first1 = Yongchao| last2 = Tao| first2 = Tingye| last3 = Yu| first3 = Kegen| last4 = Li| first4 = Zhenxuan| last5 = Qu| first5 = Xiaochuan| last6 = Ye| first6 = Zhourun| last7 = Geng| first7 = Jun| last8 = Zou| first8 = Jingui| last9 = Semmling| first9 = Maximilian| last10 = Wickert| first10 = Jens| title = Sensing sea ice based on doppler spread analysis of spaceborne GNSS-R data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Southwell_2020b>{{Cite journal| doi = 10.1109/TGRS.2020.2973142| issn = 1558-0644| volume = 58| issue = 8| pages = 5922–5931| last1 = Southwell| first1 = Benjamin J.| last2 = Cheong| first2 = Joon Wayn| last3 = Dempster| first3 = Andrew G.| title = A matched filter for spaceborne GNSS-R based sea-target detection| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Jia-2021>{{Cite book| publisher = IntechOpen| isbn = 978-1-78923-537-1| last1 = Jia| first1 = Yan| last2 = Pei| first2 = Yuekun| title = Remote Sensing in Land Applications by Using GNSS-Reflectometry| accessdate = 2021-12-09| date = 2018-07-25| url = https://www.intechopen.com/chapters/59160}}<br />
</ref><br />
</references></div>Uwasahttps://wiki.icingcentre.eu/index.php?title=GNSS_Reflectometry&diff=828GNSS Reflectometry2022-02-09T15:45:08Z<p>Uwasa: Added text about GNSS measurements</p>
<hr />
<div>Global Navigation Satellite System (GNSS) satellites transmits navigation signals from Earth orbits to terrestial receivers to help them find their location. Many nations have deployed their own satellite constellations for positioning, and new receivers can use all of them. Currently available constellations are <br />
<br />
* '''G:''' [https://www.gps.gov/ GPS], Global Positioning System, United States<br />
* '''R:''' [[wikipedia:GLONASS|GLONASS]], Russia<br />
* '''E:''' [https://www.esa.int/Applications/Navigation/Galileo/What_is_Galileo Galileo], European Space Agency<br />
* '''J:''' [https://qzss.go.jp/en/overview/services/sv01_what.html QZSS,] Japan<br />
* '''C:''' BDS, [http://en.beidou.gov.cn/SYSTEMS/System/ Beidou], China<br />
* '''I:''' [https://www.isro.gov.in/irnss-programme IRNSS] Indian Regional Navigation Satellite System<br />
* '''S:''' SBAS payload<br />
* '''M:''' Mixed<br />
The preceding letters are use as a satellite systems shortcuts in many systems, such as [https://www.igs.org/wg/rinex/ Rinex] GNSS data files.<br />
<br />
The GNSS signals are affected by the ionosphere <ref>{{Cite journal| doi = 10.3390/rs11020171| issn = 2072-4292| volume = 11| issue = 2| pages = 171| last1 = Su| first1 = Ke| last2 = Jin| first2 = Shuanggen| last3 = Hoque| first3 = M.| title = Evaluation of Ionospheric Delay Effects on Multi-GNSS Positioning Performance| journal = Remote Sensing| accessdate = 2022-02-09| date = 2019-01-17| url = http://www.mdpi.com/2072-4292/11/2/171}}</ref> and weather conditions in troposhpere. For example rain <ref>{{Cite journal| doi = 10.1109/TGRS.2014.2320309| issn = 0196-2892, 1558-0644| volume = 53| issue = 1| pages = 190–206| last1 = Cardellach| first1 = Estel| last2 = Tomas| first2 = Sergio| last3 = Oliveras| first3 = Santi| last4 = Padulles| first4 = Ramon| last5 = Rius| first5 = Antonio| last6 = de la Torre-Juarez| first6 = Manuel| last7 = Turk| first7 = Francis Joseph| last8 = Ao| first8 = Chi O.| last9 = Kursinski| first9 = E. Robert| last10 = Schreiner| first10 = Bill| last11 = Ector| first11 = Dave| last12 = Cucurull| first12 = Lidia| title = Sensitivity of PAZ LEO Polarimetric GNSS Radio-Occultation Experiment to Precipitation Events| journal = IEEE Transactions on Geoscience and Remote Sensing| accessdate = 2022-02-09| date = 2015-01| url = http://ieeexplore.ieee.org/document/6816062/}}</ref> and other atmospheric water content <ref>{{Cite journal| doi = 10.1016/j.jestch.2019.11.006| issn = 2215-0986| volume = 23| issue = 5| pages = 967–972| last = Selbesoglu| first = Mahmut Oguz| title = Prediction of tropospheric wet delay by an artificial neural network model based on meteorological and GNSS data| journal = Engineering Science and Technology, an International Journal| accessdate = 2021-02-15| date = 2020-10-01| url = https://www.sciencedirect.com/science/article/pii/S2215098619316003}}</ref> and other weather conditions cause delay to the GNSS signal propagation and it is then possible to monitor these phenomena by GNSS signals.<br />
<br />
==== Reflectometry ====<br />
<br />
<br />
The GNSS radio signals transmitted from satellites are also reflected from the ground and by monitoring these reflections, the properties of the ground and atmosphere can be examined <ref name="King_2020" />. This kind of analysis is called as GNSS reflectometry (GNSS-R) <ref name="Jia-2021" />.<br />
<br />
Terrestial GNSS-R measurements include two antennas, on of which is receiving the direct signal from the satellite, and the other forward-scattered reflected signal. Satellite-based GSNS-R measures the GNSS signal backscatter from the Earth. GNSS-R backscatter data has been also collected by ESA's [https://earth.esa.int/web/eoportal/satellite-missions/t/techdemosat-1 TechDemoSat-1] (TDS-1) satellite and with NASA's [https://www.nasa.gov/cygnss/the-science-of-cygnss CYGNSS] satellite constellation. Observation data can be downloaded from their portals. <br />
<br />
GNSS reflectometry has been used for detecting sea ice thickness <ref name=Yan_2017/> <ref name=Alonso-Arroyo_2017 /> <ref name=Semmling_2019 /><ref name=Yan_2016 /> <ref name=Yan_2018/><ref name=Southwell_2020/> <ref name=Strandberg_2017/><ref name=Zhu_2020/><ref name=Yan_2020/><ref name=Southwell_2020b/> GNSS-R is also popular for other sea target analysis which is not directly related to icing. <ref name=King_2020/> <ref name=Di_2017/><ref name=Valencia_2013/>. Many research articles concentrating in method develoment, are also indirectly related to icing <ref name=Cheong_2019/> <ref name=Li_2020/><br />
<br />
=== Links ===<br />
<br />
* The description of [https://www.engineersgarage.com/tutorials/gps-receivers-and-nmea-standards/ NMEA messages]<br />
* Documentation for [http://acc.igs.org/misc/rinex304.pdf RINEX 3.04]<br />
* RINEX 3 and RINEX 2 reader python library [https://pypi.org/project/georinex/ GeoRinex]<br />
* [https://www.unavco.org/ UNAVCO]<br />
* RINEX 3 [ftp://data-out.unavco.org/pub/rinex3/obs/ OBS data] **(from 2016 to NOW)** <br />
* UNAVCO RINEX 3 [ftp://data-out.unavco.org/pub/rinex3/nav/ NAV data] **(from 2016 to NOW)** <br />
* UNAVCO [https://www.unavco.org/instrumentation/networks/map/map.html#!/ network monitoring website] <br />
* GNSS Planning [https://www.gnssplanning.com/#/charts TRIMBLE]<br />
* [https://gssc.esa.int/navipedia//index.php/Reference_Frames_in_GNSS#GLONASS_reference_frame_PZ-90 Reference Frames]in GNSS<br />
<br />
== References ==<br />
<references><br />
<ref name=Yan_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2689009| issn = 2151-1535| volume = 10| issue = 8| pages = 3789–3798| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| last3 = Moloney| first3 = Cecilia| title = Neural networks based sea ice detection and concentration retrieval from GNSS-R delay-doppler maps| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=King_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2997199| issn = 2151-1535| volume = 13| pages = 2916–2924| last1 = King| first1 = Lucinda S.| last2 = Unwin| first2 = Martin| last3 = Rawlinson| first3 = Jonathan| last4 = Guida| first4 = Raffaella| last5 = Underwood| first5 = Craig| title = Processing of raw GNSS reflectometry data from TDS-1 in a backscattering configuration| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Cheong_2019>{{Cite journal| doi = 10.1109/JSTARS.2019.2956183| issn = 2151-1535| volume = 12| issue = 12| pages = 5373–5378| last1 = Cheong| first1 = Joon Wayn| last2 = Southwell| first2 = Benjamin J.| last3 = Dempster| first3 = Andrew G.| title = Blind sea clutter suppression for spaceborne GNSS-R target detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Alonso-Arroyo_2017>{{Cite journal| doi = 10.1109/TGRS.2017.2699122| issn = 1558-0644| volume = 55| issue = 9| pages = 4989–5001| last1 = Alonso-Arroyo| first1 = Alberto| last2 = Zavorotny| first2 = Valery U.| last3 = Camps| first3 = Adriano| title = Sea ice detection using U.K. TDS-1 GNSS-R data| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Semmling_2019>{{Cite journal| doi = 10.1109/TGRS.2019.2933911| issn = 1558-0644| volume = 57| issue = 12| pages = 10350–10361| last1 = Semmling| first1 = A. Maximilian| last2 = Rösel| first2 = Anja| last3 = Divine| first3 = Dmitry V.| last4 = Gerland| first4 = Sebastian| last5 = Stienne| first5 = Georges| last6 = Reboul| first6 = Serge| last7 = Ludwig| first7 = Marcel| last8 = Wickert| first8 = Jens| last9 = Schuh| first9 = Harald| title = Sea-ice concentration derived from GNSS reflection measurements in fram strait| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Yan_2016>{{Cite journal| doi = 10.1109/JSTARS.2016.2582690| issn = 2151-1535| volume = 9| issue = 10| pages = 4795–4801| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Spaceborne GNSS-R sea ice detection using delay-doppler maps: First results from the U.K. TechDemoSat-1 mission| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2016}}<br />
</ref><br />
<ref name=Yan_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2966880| issn = 2151-1535| volume = 13| pages = 577–587| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice thickness measurement using spaceborne GNSS-R: First results with TechDemoSat-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Di_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2705350| issn = 2151-1535| volume = 10| issue = 9| pages = 4237–4255| last1 = Di Simone| first1 = Alessio| last2 = Park| first2 = Hyuk| last3 = Riccio| first3 = Daniele| last4 = Camps| first4 = Adriano| title = Sea target detection using spaceborne GNSS-R delay-doppler maps: Theory and experimental proof of concept using TDS-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Li_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2992037| issn = 2151-1535| volume = 13| pages = 1833–1841| last1 = Li| first1 = Bowen| last2 = Yang| first2 = Lei| last3 = Zhang| first3 = Bo| last4 = Yang| first4 = Dongkai| last5 = Wu| first5 = Di| title = Modeling and simulation of GNSS-R observables with effects of swell| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Yan_2018>{{Cite journal| doi = 10.1109/LGRS.2018.2852143| issn = 1558-0571| volume = 15| issue = 10| pages = 1510–1514| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice sensing from GNSS-R data using convolutional neural networks| journal = IEEE Geoscience and Remote Sensing Letters| date = 2018}}<br />
</ref><br />
<ref name=Southwell_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2943510| issn = 2151-1535| volume = 13| pages = 14–20| last1 = Southwell| first1 = Benjamin J.| last2 = Dempster| first2 = Andrew G.| title = Sea ice transition detection using incoherent integration and deconvolution| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Strandberg_2017>{{Cite journal| doi = 10.1109/LGRS.2017.2722041| issn = 1558-0571| volume = 14| issue = 9| pages = 1552–1556| last1 = Strandberg| first1 = Joakim| last2 = Hobiger| first2 = Thomas| last3 = Haas| first3 = Rüdiger| title = Coastal sea ice detection using ground-based GNSS-R| journal = IEEE Geoscience and Remote Sensing Letters| date = 2017}}<br />
</ref><br />
<ref name=Valencia_2013>{{Cite journal| doi = 10.1109/JSTARS.2012.2210392| issn = 2151-1535| volume = 6| issue = 1| pages = 217–223| last1 = Valencia| first1 = Enric| last2 = Camps| first2 = Adriano| last3 = Rodriguez-Alvarez| first3 = Nereida| last4 = Park| first4 = Hyuk| last5 = Ramos-Perez| first5 = Isaac| title = Using GNSS-R imaging of the ocean surface for oil slick detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2013}}<br />
</ref><br />
<ref name=Zhu_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2955175| issn = 2151-1535| volume = 13| pages = 217–226| last1 = Zhu| first1 = Yongchao| last2 = Tao| first2 = Tingye| last3 = Yu| first3 = Kegen| last4 = Li| first4 = Zhenxuan| last5 = Qu| first5 = Xiaochuan| last6 = Ye| first6 = Zhourun| last7 = Geng| first7 = Jun| last8 = Zou| first8 = Jingui| last9 = Semmling| first9 = Maximilian| last10 = Wickert| first10 = Jens| title = Sensing sea ice based on doppler spread analysis of spaceborne GNSS-R data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Southwell_2020b>{{Cite journal| doi = 10.1109/TGRS.2020.2973142| issn = 1558-0644| volume = 58| issue = 8| pages = 5922–5931| last1 = Southwell| first1 = Benjamin J.| last2 = Cheong| first2 = Joon Wayn| last3 = Dempster| first3 = Andrew G.| title = A matched filter for spaceborne GNSS-R based sea-target detection| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Jia-2021>{{Cite book| publisher = IntechOpen| isbn = 978-1-78923-537-1| last1 = Jia| first1 = Yan| last2 = Pei| first2 = Yuekun| title = Remote Sensing in Land Applications by Using GNSS-Reflectometry| accessdate = 2021-12-09| date = 2018-07-25| url = https://www.intechopen.com/chapters/59160}}<br />
</ref><br />
</references></div>Uwasahttps://wiki.icingcentre.eu/index.php?title=GNSS_Reflectometry&diff=827GNSS Reflectometry2022-02-09T14:38:48Z<p>Uwasa: Added gnss systms</p>
<hr />
<div>Global Navigation Satellite System (GNSS) satellites transmits navigation signals from Earth orbits to terrestial receivers to help them find their location. Many nations have deployed their own satellite constellations for positioning, and new receivers can use all of them. Currently available constellations are <br />
<br />
* '''G:''' [https://www.gps.gov/ GPS], Global Positioning System, United States<br />
* '''R:''' [[wikipedia:GLONASS|GLONASS]], Russia<br />
* '''E:''' [https://www.esa.int/Applications/Navigation/Galileo/What_is_Galileo Galileo], European Space Agency<br />
* '''J:''' QZSS, Japan<br />
* '''C:''' BDS, [http://en.beidou.gov.cn/SYSTEMS/System/ Beidou], China<br />
* '''I:''' [https://www.isro.gov.in/irnss-programme IRNSS] Indian Regional Navigation Satellite System<br />
* '''S:''' SBAS payload<br />
* '''M:''' Mixed<br />
<br />
<br />
These radio signals are also reflecting from the ground and by monitoring these reflections, the properties of the ground can be examined <ref name="King_2020" />. This kind of analysis is called as GNSS reflectometry (GNSS-R) <ref name="Jia-2021" />.<br />
<br />
Terrestial GNSS-R measurements include two antennas, on of which is receiving the direct signal from the satellite, and the other forward-scattered reflected signal. Satellite-based GSNS-R measures the GNSS signal backscatter from the Earth. GNSS-R bacscatter data has been collected by ESA's [https://earth.esa.int/web/eoportal/satellite-missions/t/techdemosat-1 TechDemoSat-1] (TDS-1) satellite and with NASA's [https://www.nasa.gov/cygnss/the-science-of-cygnss CYGNSS] satellite constellation. Observation data can be downloaded from their portals. <br />
<br />
GNSS reflectometry has been used for detecting sea ice thickness <ref name=Yan_2017/> <ref name=Alonso-Arroyo_2017 /> <ref name=Semmling_2019 /><ref name=Yan_2016 /> <ref name=Yan_2018/><ref name=Southwell_2020/> <ref name=Strandberg_2017/><ref name=Zhu_2020/><ref name=Yan_2020/><ref name=Southwell_2020b/> GNSS-R is also popular for other sea target analysis which is not directly related to icing. <ref name=King_2020/> <ref name=Di_2017/><ref name=Valencia_2013/>. Many research articles concentrating in method develoment, are also indirectly related to icing <ref name=Cheong_2019/> <ref name=Li_2020/><br />
<br />
=== Links ===<br />
<br />
* The description of [https://www.engineersgarage.com/tutorials/gps-receivers-and-nmea-standards/ NMEA messages]<br />
* Documentation for [http://acc.igs.org/misc/rinex304.pdf RINEX 3.04]<br />
* RINEX 3 and RINEX 2 reader python library [https://pypi.org/project/georinex/ GeoRinex]<br />
* [https://www.unavco.org/ UNAVCO]<br />
* RINEX 3 [ftp://data-out.unavco.org/pub/rinex3/obs/ OBS data] **(from 2016 to NOW)** <br />
* UNAVCO RINEX 3 [ftp://data-out.unavco.org/pub/rinex3/nav/ NAV data] **(from 2016 to NOW)** <br />
* UNAVCO [https://www.unavco.org/instrumentation/networks/map/map.html#!/ network monitoring website] <br />
* GNSS Planning [https://www.gnssplanning.com/#/charts TRIMBLE]<br />
* [https://gssc.esa.int/navipedia//index.php/Reference_Frames_in_GNSS#GLONASS_reference_frame_PZ-90 Reference Frames]in GNSS<br />
<br />
== References ==<br />
<references><br />
<ref name=Yan_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2689009| issn = 2151-1535| volume = 10| issue = 8| pages = 3789–3798| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| last3 = Moloney| first3 = Cecilia| title = Neural networks based sea ice detection and concentration retrieval from GNSS-R delay-doppler maps| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=King_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2997199| issn = 2151-1535| volume = 13| pages = 2916–2924| last1 = King| first1 = Lucinda S.| last2 = Unwin| first2 = Martin| last3 = Rawlinson| first3 = Jonathan| last4 = Guida| first4 = Raffaella| last5 = Underwood| first5 = Craig| title = Processing of raw GNSS reflectometry data from TDS-1 in a backscattering configuration| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Cheong_2019>{{Cite journal| doi = 10.1109/JSTARS.2019.2956183| issn = 2151-1535| volume = 12| issue = 12| pages = 5373–5378| last1 = Cheong| first1 = Joon Wayn| last2 = Southwell| first2 = Benjamin J.| last3 = Dempster| first3 = Andrew G.| title = Blind sea clutter suppression for spaceborne GNSS-R target detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Alonso-Arroyo_2017>{{Cite journal| doi = 10.1109/TGRS.2017.2699122| issn = 1558-0644| volume = 55| issue = 9| pages = 4989–5001| last1 = Alonso-Arroyo| first1 = Alberto| last2 = Zavorotny| first2 = Valery U.| last3 = Camps| first3 = Adriano| title = Sea ice detection using U.K. TDS-1 GNSS-R data| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Semmling_2019>{{Cite journal| doi = 10.1109/TGRS.2019.2933911| issn = 1558-0644| volume = 57| issue = 12| pages = 10350–10361| last1 = Semmling| first1 = A. Maximilian| last2 = Rösel| first2 = Anja| last3 = Divine| first3 = Dmitry V.| last4 = Gerland| first4 = Sebastian| last5 = Stienne| first5 = Georges| last6 = Reboul| first6 = Serge| last7 = Ludwig| first7 = Marcel| last8 = Wickert| first8 = Jens| last9 = Schuh| first9 = Harald| title = Sea-ice concentration derived from GNSS reflection measurements in fram strait| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Yan_2016>{{Cite journal| doi = 10.1109/JSTARS.2016.2582690| issn = 2151-1535| volume = 9| issue = 10| pages = 4795–4801| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Spaceborne GNSS-R sea ice detection using delay-doppler maps: First results from the U.K. TechDemoSat-1 mission| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2016}}<br />
</ref><br />
<ref name=Yan_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2966880| issn = 2151-1535| volume = 13| pages = 577–587| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice thickness measurement using spaceborne GNSS-R: First results with TechDemoSat-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Di_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2705350| issn = 2151-1535| volume = 10| issue = 9| pages = 4237–4255| last1 = Di Simone| first1 = Alessio| last2 = Park| first2 = Hyuk| last3 = Riccio| first3 = Daniele| last4 = Camps| first4 = Adriano| title = Sea target detection using spaceborne GNSS-R delay-doppler maps: Theory and experimental proof of concept using TDS-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Li_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2992037| issn = 2151-1535| volume = 13| pages = 1833–1841| last1 = Li| first1 = Bowen| last2 = Yang| first2 = Lei| last3 = Zhang| first3 = Bo| last4 = Yang| first4 = Dongkai| last5 = Wu| first5 = Di| title = Modeling and simulation of GNSS-R observables with effects of swell| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Yan_2018>{{Cite journal| doi = 10.1109/LGRS.2018.2852143| issn = 1558-0571| volume = 15| issue = 10| pages = 1510–1514| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice sensing from GNSS-R data using convolutional neural networks| journal = IEEE Geoscience and Remote Sensing Letters| date = 2018}}<br />
</ref><br />
<ref name=Southwell_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2943510| issn = 2151-1535| volume = 13| pages = 14–20| last1 = Southwell| first1 = Benjamin J.| last2 = Dempster| first2 = Andrew G.| title = Sea ice transition detection using incoherent integration and deconvolution| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Strandberg_2017>{{Cite journal| doi = 10.1109/LGRS.2017.2722041| issn = 1558-0571| volume = 14| issue = 9| pages = 1552–1556| last1 = Strandberg| first1 = Joakim| last2 = Hobiger| first2 = Thomas| last3 = Haas| first3 = Rüdiger| title = Coastal sea ice detection using ground-based GNSS-R| journal = IEEE Geoscience and Remote Sensing Letters| date = 2017}}<br />
</ref><br />
<ref name=Valencia_2013>{{Cite journal| doi = 10.1109/JSTARS.2012.2210392| issn = 2151-1535| volume = 6| issue = 1| pages = 217–223| last1 = Valencia| first1 = Enric| last2 = Camps| first2 = Adriano| last3 = Rodriguez-Alvarez| first3 = Nereida| last4 = Park| first4 = Hyuk| last5 = Ramos-Perez| first5 = Isaac| title = Using GNSS-R imaging of the ocean surface for oil slick detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2013}}<br />
</ref><br />
<ref name=Zhu_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2955175| issn = 2151-1535| volume = 13| pages = 217–226| last1 = Zhu| first1 = Yongchao| last2 = Tao| first2 = Tingye| last3 = Yu| first3 = Kegen| last4 = Li| first4 = Zhenxuan| last5 = Qu| first5 = Xiaochuan| last6 = Ye| first6 = Zhourun| last7 = Geng| first7 = Jun| last8 = Zou| first8 = Jingui| last9 = Semmling| first9 = Maximilian| last10 = Wickert| first10 = Jens| title = Sensing sea ice based on doppler spread analysis of spaceborne GNSS-R data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Southwell_2020b>{{Cite journal| doi = 10.1109/TGRS.2020.2973142| issn = 1558-0644| volume = 58| issue = 8| pages = 5922–5931| last1 = Southwell| first1 = Benjamin J.| last2 = Cheong| first2 = Joon Wayn| last3 = Dempster| first3 = Andrew G.| title = A matched filter for spaceborne GNSS-R based sea-target detection| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Jia-2021>{{Cite book| publisher = IntechOpen| isbn = 978-1-78923-537-1| last1 = Jia| first1 = Yan| last2 = Pei| first2 = Yuekun| title = Remote Sensing in Land Applications by Using GNSS-Reflectometry| accessdate = 2021-12-09| date = 2018-07-25| url = https://www.intechopen.com/chapters/59160}}<br />
</ref><br />
</references></div>Uwasahttps://wiki.icingcentre.eu/index.php?title=GNSS_Reflectometry&diff=826GNSS Reflectometry2022-02-09T09:58:09Z<p>Uwasa: </p>
<hr />
<div>Global Navigation Satellite System (GNSS) satellites transmits navigation signals from Earth orbits to terrestial receivers to help them find their location. These radio signals are also reflecting from the ground and by monitoring these reflections, the properties of the ground can be examined <ref name=King_2020 />. This kind of analysis is called as GNSS reflectometry (GNSS-R) <ref name=Jia-2021/>.<br />
<br />
Terrestial GNSS-R measurements include two antennas, on of which is receiving the direct signal from the satellite, and the other forward-scattered reflected signal. Satellite-based GSNS-R measures the GNSS signal backscatter from the Earth. GNSS-R bacscatter data has been collected by ESA's [https://earth.esa.int/web/eoportal/satellite-missions/t/techdemosat-1 TechDemoSat-1] (TDS-1) satellite and with NASA's [https://www.nasa.gov/cygnss/the-science-of-cygnss CYGNSS] satellite constellation. Observation data can be downloaded from their portals. <br />
<br />
GNSS reflectometry has been used for detecting sea ice thickness <ref name=Yan_2017/> <ref name=Alonso-Arroyo_2017 /> <ref name=Semmling_2019 /><ref name=Yan_2016 /> <ref name=Yan_2018/><ref name=Southwell_2020/> <ref name=Strandberg_2017/><ref name=Zhu_2020/><ref name=Yan_2020/><ref name=Southwell_2020b/> GNSS-R is also popular for other sea target analysis which is not directly related to icing. <ref name=King_2020/> <ref name=Di_2017/><ref name=Valencia_2013/>. Many research articles concentrating in method develoment, are also indirectly related to icing <ref name=Cheong_2019/> <ref name=Li_2020/><br />
<br />
=== Links ===<br />
<br />
* The description of [https://www.engineersgarage.com/tutorials/gps-receivers-and-nmea-standards/ NMEA messages]<br />
* Documentation for [http://acc.igs.org/misc/rinex304.pdf RINEX 3.04]<br />
* RINEX 3 and RINEX 2 reader python library [https://pypi.org/project/georinex/ GeoRinex]<br />
* [https://www.unavco.org/ UNAVCO]<br />
* RINEX 3 [ftp://data-out.unavco.org/pub/rinex3/obs/ OBS data] **(from 2016 to NOW)** <br />
* UNAVCO RINEX 3 [ftp://data-out.unavco.org/pub/rinex3/nav/ NAV data] **(from 2016 to NOW)** <br />
* UNAVCO [https://www.unavco.org/instrumentation/networks/map/map.html#!/ network monitoring website] <br />
* GNSS Planning [https://www.gnssplanning.com/#/charts TRIMBLE]<br />
* [https://gssc.esa.int/navipedia//index.php/Reference_Frames_in_GNSS#GLONASS_reference_frame_PZ-90 Reference Frames]in GNSS<br />
<br />
== References ==<br />
<references><br />
<ref name=Yan_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2689009| issn = 2151-1535| volume = 10| issue = 8| pages = 3789–3798| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| last3 = Moloney| first3 = Cecilia| title = Neural networks based sea ice detection and concentration retrieval from GNSS-R delay-doppler maps| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=King_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2997199| issn = 2151-1535| volume = 13| pages = 2916–2924| last1 = King| first1 = Lucinda S.| last2 = Unwin| first2 = Martin| last3 = Rawlinson| first3 = Jonathan| last4 = Guida| first4 = Raffaella| last5 = Underwood| first5 = Craig| title = Processing of raw GNSS reflectometry data from TDS-1 in a backscattering configuration| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Cheong_2019>{{Cite journal| doi = 10.1109/JSTARS.2019.2956183| issn = 2151-1535| volume = 12| issue = 12| pages = 5373–5378| last1 = Cheong| first1 = Joon Wayn| last2 = Southwell| first2 = Benjamin J.| last3 = Dempster| first3 = Andrew G.| title = Blind sea clutter suppression for spaceborne GNSS-R target detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Alonso-Arroyo_2017>{{Cite journal| doi = 10.1109/TGRS.2017.2699122| issn = 1558-0644| volume = 55| issue = 9| pages = 4989–5001| last1 = Alonso-Arroyo| first1 = Alberto| last2 = Zavorotny| first2 = Valery U.| last3 = Camps| first3 = Adriano| title = Sea ice detection using U.K. TDS-1 GNSS-R data| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Semmling_2019>{{Cite journal| doi = 10.1109/TGRS.2019.2933911| issn = 1558-0644| volume = 57| issue = 12| pages = 10350–10361| last1 = Semmling| first1 = A. Maximilian| last2 = Rösel| first2 = Anja| last3 = Divine| first3 = Dmitry V.| last4 = Gerland| first4 = Sebastian| last5 = Stienne| first5 = Georges| last6 = Reboul| first6 = Serge| last7 = Ludwig| first7 = Marcel| last8 = Wickert| first8 = Jens| last9 = Schuh| first9 = Harald| title = Sea-ice concentration derived from GNSS reflection measurements in fram strait| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Yan_2016>{{Cite journal| doi = 10.1109/JSTARS.2016.2582690| issn = 2151-1535| volume = 9| issue = 10| pages = 4795–4801| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Spaceborne GNSS-R sea ice detection using delay-doppler maps: First results from the U.K. TechDemoSat-1 mission| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2016}}<br />
</ref><br />
<ref name=Yan_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2966880| issn = 2151-1535| volume = 13| pages = 577–587| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice thickness measurement using spaceborne GNSS-R: First results with TechDemoSat-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Di_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2705350| issn = 2151-1535| volume = 10| issue = 9| pages = 4237–4255| last1 = Di Simone| first1 = Alessio| last2 = Park| first2 = Hyuk| last3 = Riccio| first3 = Daniele| last4 = Camps| first4 = Adriano| title = Sea target detection using spaceborne GNSS-R delay-doppler maps: Theory and experimental proof of concept using TDS-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Li_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2992037| issn = 2151-1535| volume = 13| pages = 1833–1841| last1 = Li| first1 = Bowen| last2 = Yang| first2 = Lei| last3 = Zhang| first3 = Bo| last4 = Yang| first4 = Dongkai| last5 = Wu| first5 = Di| title = Modeling and simulation of GNSS-R observables with effects of swell| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Yan_2018>{{Cite journal| doi = 10.1109/LGRS.2018.2852143| issn = 1558-0571| volume = 15| issue = 10| pages = 1510–1514| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice sensing from GNSS-R data using convolutional neural networks| journal = IEEE Geoscience and Remote Sensing Letters| date = 2018}}<br />
</ref><br />
<ref name=Southwell_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2943510| issn = 2151-1535| volume = 13| pages = 14–20| last1 = Southwell| first1 = Benjamin J.| last2 = Dempster| first2 = Andrew G.| title = Sea ice transition detection using incoherent integration and deconvolution| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Strandberg_2017>{{Cite journal| doi = 10.1109/LGRS.2017.2722041| issn = 1558-0571| volume = 14| issue = 9| pages = 1552–1556| last1 = Strandberg| first1 = Joakim| last2 = Hobiger| first2 = Thomas| last3 = Haas| first3 = Rüdiger| title = Coastal sea ice detection using ground-based GNSS-R| journal = IEEE Geoscience and Remote Sensing Letters| date = 2017}}<br />
</ref><br />
<ref name=Valencia_2013>{{Cite journal| doi = 10.1109/JSTARS.2012.2210392| issn = 2151-1535| volume = 6| issue = 1| pages = 217–223| last1 = Valencia| first1 = Enric| last2 = Camps| first2 = Adriano| last3 = Rodriguez-Alvarez| first3 = Nereida| last4 = Park| first4 = Hyuk| last5 = Ramos-Perez| first5 = Isaac| title = Using GNSS-R imaging of the ocean surface for oil slick detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2013}}<br />
</ref><br />
<ref name=Zhu_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2955175| issn = 2151-1535| volume = 13| pages = 217–226| last1 = Zhu| first1 = Yongchao| last2 = Tao| first2 = Tingye| last3 = Yu| first3 = Kegen| last4 = Li| first4 = Zhenxuan| last5 = Qu| first5 = Xiaochuan| last6 = Ye| first6 = Zhourun| last7 = Geng| first7 = Jun| last8 = Zou| first8 = Jingui| last9 = Semmling| first9 = Maximilian| last10 = Wickert| first10 = Jens| title = Sensing sea ice based on doppler spread analysis of spaceborne GNSS-R data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Southwell_2020b>{{Cite journal| doi = 10.1109/TGRS.2020.2973142| issn = 1558-0644| volume = 58| issue = 8| pages = 5922–5931| last1 = Southwell| first1 = Benjamin J.| last2 = Cheong| first2 = Joon Wayn| last3 = Dempster| first3 = Andrew G.| title = A matched filter for spaceborne GNSS-R based sea-target detection| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Jia-2021>{{Cite book| publisher = IntechOpen| isbn = 978-1-78923-537-1| last1 = Jia| first1 = Yan| last2 = Pei| first2 = Yuekun| title = Remote Sensing in Land Applications by Using GNSS-Reflectometry| accessdate = 2021-12-09| date = 2018-07-25| url = https://www.intechopen.com/chapters/59160}}<br />
</ref><br />
</references></div>Uwasahttps://wiki.icingcentre.eu/index.php?title=GNSS_Reflectometry&diff=825GNSS Reflectometry2022-02-09T09:52:45Z<p>Uwasa: Links</p>
<hr />
<div>Global Navigation Satellite System (GNSS) satellites transmits navigation signals from Earth orbits to terrestial receivers to help them find their location. These radio signals are also reflecting from the ground and by monitoring these reflections, the properties of the ground can be examined <ref name=King_2020 />. This kind of analysis is called as GNSS reflectometry (GNSS-R) <ref name=Jia-2021/>.<br />
<br />
Terrestial GNSS-R measurements include two antennas, on of which is receiving the direct signal from the satellite, and the other forward-scattered reflected signal. Satellite-based GSNS-R measures the GNSS signal backscatter from the Earth. GNSS-R bacscatter data has been collected by ESA's [https://earth.esa.int/web/eoportal/satellite-missions/t/techdemosat-1 TechDemoSat-1] (TDS-1) satellite and with NASA's [https://www.nasa.gov/cygnss/the-science-of-cygnss CYGNSS] satellite constellation. Observation data can be downloaded from their portals. <br />
<br />
GNSS reflectometry has been used for detecting sea ice thickness <ref name=Yan_2017/> <ref name=Alonso-Arroyo_2017 /> <ref name=Semmling_2019 /><ref name=Yan_2016 /> <ref name=Yan_2018/><ref name=Southwell_2020/> <ref name=Strandberg_2017/><ref name=Zhu_2020/><ref name=Yan_2020/><ref name=Southwell_2020b/> GNSS-R is also popular for other sea target analysis which is not directly related to icing. <ref name=King_2020/> <ref name=Di_2017/><ref name=Valencia_2013/>. Many research articles concentrating in method develoment, are also indirectly related to icing <ref name=Cheong_2019/> <ref name=Li_2020/><br />
<br />
=== Links ===<br />
<br />
* The description of [https://www.engineersgarage.com/tutorials/gps-receivers-and-nmea-standards/ NMEA messages] "https://www.engineersgarage.com/tutorials/gps-receivers-and-nmea-standards/") <br />
* Documentation for [RINEX 3.04](http://acc.igs.org/misc/rinex304.pdf "http://acc.igs.org/misc/rinex304.pdf") -RINEX 3 and RINEX 2 reader python library [GeoRinex](https://pypi.org/project/georinex/ "https://pypi.org/project/georinex/") -[UNAVCO](https://www.unavco.org/ "https://www.unavco.org/") RINEX 3 [OBS data](ftp://data-out.unavco.org/pub/rinex3/obs/ "ftp://data-out.unavco.org/pub/rinex3/obs/") **(from 2016 to NOW)** -UNAVCO RINEX 3 [NAV data](ftp://data-out.unavco.org/pub/rinex3/nav/ "ftp://data-out.unavco.org/pub/rinex3/nav/") **(from 2016 to NOW)** -UNAVCO [network monitoring website](https://www.unavco.org/instrumentation/networks/map/map.html#!/ "https://www.unavco.org/instrumentation/networks/map/map.html#!/") -GNSS Planning [TRIMBLE](https://www.gnssplanning.com/#/charts "https://www.gnssplanning.com/#/charts") -[Reference Frames](https://gssc.esa.int/navipedia//index.php/Reference_Frames_in_GNSS#GLONASS_reference_frame_PZ-90 "https://gssc.esa.int/navipedia//index.php/reference_frames_in_gnss#glonass_reference_frame_pz-90") in GNSS<br />
<br />
== References ==<br />
<references><br />
<ref name=Yan_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2689009| issn = 2151-1535| volume = 10| issue = 8| pages = 3789–3798| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| last3 = Moloney| first3 = Cecilia| title = Neural networks based sea ice detection and concentration retrieval from GNSS-R delay-doppler maps| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=King_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2997199| issn = 2151-1535| volume = 13| pages = 2916–2924| last1 = King| first1 = Lucinda S.| last2 = Unwin| first2 = Martin| last3 = Rawlinson| first3 = Jonathan| last4 = Guida| first4 = Raffaella| last5 = Underwood| first5 = Craig| title = Processing of raw GNSS reflectometry data from TDS-1 in a backscattering configuration| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Cheong_2019>{{Cite journal| doi = 10.1109/JSTARS.2019.2956183| issn = 2151-1535| volume = 12| issue = 12| pages = 5373–5378| last1 = Cheong| first1 = Joon Wayn| last2 = Southwell| first2 = Benjamin J.| last3 = Dempster| first3 = Andrew G.| title = Blind sea clutter suppression for spaceborne GNSS-R target detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Alonso-Arroyo_2017>{{Cite journal| doi = 10.1109/TGRS.2017.2699122| issn = 1558-0644| volume = 55| issue = 9| pages = 4989–5001| last1 = Alonso-Arroyo| first1 = Alberto| last2 = Zavorotny| first2 = Valery U.| last3 = Camps| first3 = Adriano| title = Sea ice detection using U.K. TDS-1 GNSS-R data| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Semmling_2019>{{Cite journal| doi = 10.1109/TGRS.2019.2933911| issn = 1558-0644| volume = 57| issue = 12| pages = 10350–10361| last1 = Semmling| first1 = A. Maximilian| last2 = Rösel| first2 = Anja| last3 = Divine| first3 = Dmitry V.| last4 = Gerland| first4 = Sebastian| last5 = Stienne| first5 = Georges| last6 = Reboul| first6 = Serge| last7 = Ludwig| first7 = Marcel| last8 = Wickert| first8 = Jens| last9 = Schuh| first9 = Harald| title = Sea-ice concentration derived from GNSS reflection measurements in fram strait| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Yan_2016>{{Cite journal| doi = 10.1109/JSTARS.2016.2582690| issn = 2151-1535| volume = 9| issue = 10| pages = 4795–4801| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Spaceborne GNSS-R sea ice detection using delay-doppler maps: First results from the U.K. TechDemoSat-1 mission| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2016}}<br />
</ref><br />
<ref name=Yan_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2966880| issn = 2151-1535| volume = 13| pages = 577–587| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice thickness measurement using spaceborne GNSS-R: First results with TechDemoSat-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Di_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2705350| issn = 2151-1535| volume = 10| issue = 9| pages = 4237–4255| last1 = Di Simone| first1 = Alessio| last2 = Park| first2 = Hyuk| last3 = Riccio| first3 = Daniele| last4 = Camps| first4 = Adriano| title = Sea target detection using spaceborne GNSS-R delay-doppler maps: Theory and experimental proof of concept using TDS-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Li_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2992037| issn = 2151-1535| volume = 13| pages = 1833–1841| last1 = Li| first1 = Bowen| last2 = Yang| first2 = Lei| last3 = Zhang| first3 = Bo| last4 = Yang| first4 = Dongkai| last5 = Wu| first5 = Di| title = Modeling and simulation of GNSS-R observables with effects of swell| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Yan_2018>{{Cite journal| doi = 10.1109/LGRS.2018.2852143| issn = 1558-0571| volume = 15| issue = 10| pages = 1510–1514| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice sensing from GNSS-R data using convolutional neural networks| journal = IEEE Geoscience and Remote Sensing Letters| date = 2018}}<br />
</ref><br />
<ref name=Southwell_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2943510| issn = 2151-1535| volume = 13| pages = 14–20| last1 = Southwell| first1 = Benjamin J.| last2 = Dempster| first2 = Andrew G.| title = Sea ice transition detection using incoherent integration and deconvolution| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Strandberg_2017>{{Cite journal| doi = 10.1109/LGRS.2017.2722041| issn = 1558-0571| volume = 14| issue = 9| pages = 1552–1556| last1 = Strandberg| first1 = Joakim| last2 = Hobiger| first2 = Thomas| last3 = Haas| first3 = Rüdiger| title = Coastal sea ice detection using ground-based GNSS-R| journal = IEEE Geoscience and Remote Sensing Letters| date = 2017}}<br />
</ref><br />
<ref name=Valencia_2013>{{Cite journal| doi = 10.1109/JSTARS.2012.2210392| issn = 2151-1535| volume = 6| issue = 1| pages = 217–223| last1 = Valencia| first1 = Enric| last2 = Camps| first2 = Adriano| last3 = Rodriguez-Alvarez| first3 = Nereida| last4 = Park| first4 = Hyuk| last5 = Ramos-Perez| first5 = Isaac| title = Using GNSS-R imaging of the ocean surface for oil slick detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2013}}<br />
</ref><br />
<ref name=Zhu_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2955175| issn = 2151-1535| volume = 13| pages = 217–226| last1 = Zhu| first1 = Yongchao| last2 = Tao| first2 = Tingye| last3 = Yu| first3 = Kegen| last4 = Li| first4 = Zhenxuan| last5 = Qu| first5 = Xiaochuan| last6 = Ye| first6 = Zhourun| last7 = Geng| first7 = Jun| last8 = Zou| first8 = Jingui| last9 = Semmling| first9 = Maximilian| last10 = Wickert| first10 = Jens| title = Sensing sea ice based on doppler spread analysis of spaceborne GNSS-R data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Southwell_2020b>{{Cite journal| doi = 10.1109/TGRS.2020.2973142| issn = 1558-0644| volume = 58| issue = 8| pages = 5922–5931| last1 = Southwell| first1 = Benjamin J.| last2 = Cheong| first2 = Joon Wayn| last3 = Dempster| first3 = Andrew G.| title = A matched filter for spaceborne GNSS-R based sea-target detection| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Jia-2021>{{Cite book| publisher = IntechOpen| isbn = 978-1-78923-537-1| last1 = Jia| first1 = Yan| last2 = Pei| first2 = Yuekun| title = Remote Sensing in Land Applications by Using GNSS-Reflectometry| accessdate = 2021-12-09| date = 2018-07-25| url = https://www.intechopen.com/chapters/59160}}<br />
</ref><br />
</references></div>Uwasahttps://wiki.icingcentre.eu/index.php?title=GNSS_Reflectometry&diff=824GNSS Reflectometry2022-02-09T09:51:24Z<p>Uwasa: </p>
<hr />
<div>Global Navigation Satellite System (GNSS) satellites transmits navigation signals from Earth orbits to terrestial receivers to help them find their location. These radio signals are also reflecting from the ground and by monitoring these reflections, the properties of the ground can be examined <ref name=King_2020 />. This kind of analysis is called as GNSS reflectometry (GNSS-R) <ref name=Jia-2021/>.<br />
<br />
Terrestial GNSS-R measurements include two antennas, on of which is receiving the direct signal from the satellite, and the other forward-scattered reflected signal. Satellite-based GSNS-R measures the GNSS signal backscatter from the Earth. GNSS-R bacscatter data has been collected by ESA's [https://earth.esa.int/web/eoportal/satellite-missions/t/techdemosat-1 TechDemoSat-1] (TDS-1) satellite and with NASA's [https://www.nasa.gov/cygnss/the-science-of-cygnss CYGNSS] satellite constellation. Observation data can be downloaded from their portals. <br />
<br />
GNSS reflectometry has been used for detecting sea ice thickness <ref name=Yan_2017/> <ref name=Alonso-Arroyo_2017 /> <ref name=Semmling_2019 /><ref name=Yan_2016 /> <ref name=Yan_2018/><ref name=Southwell_2020/> <ref name=Strandberg_2017/><ref name=Zhu_2020/><ref name=Yan_2020/><ref name=Southwell_2020b/> GNSS-R is also popular for other sea target analysis which is not directly related to icing. <ref name=King_2020/> <ref name=Di_2017/><ref name=Valencia_2013/>. Many research articles concentrating in method develoment, are also indirectly related to icing <ref name=Cheong_2019/> <ref name=Li_2020/><br />
<br />
## Links<br />
<br />
-The [description of NMEA messages](https://www.engineersgarage.com/tutorials/gps-receivers-and-nmea-standards/ "https://www.engineersgarage.com/tutorials/gps-receivers-and-nmea-standards/") <br />
-Documentation for [RINEX 3.04](http://acc.igs.org/misc/rinex304.pdf "http://acc.igs.org/misc/rinex304.pdf")<br />
-RINEX 3 and RINEX 2 reader python library [GeoRinex](https://pypi.org/project/georinex/ "https://pypi.org/project/georinex/")<br />
-[UNAVCO](https://www.unavco.org/ "https://www.unavco.org/") RINEX 3 [OBS data](ftp://data-out.unavco.org/pub/rinex3/obs/ "ftp://data-out.unavco.org/pub/rinex3/obs/") **(from 2016 to NOW)**<br />
-UNAVCO RINEX 3 [NAV data](ftp://data-out.unavco.org/pub/rinex3/nav/ "ftp://data-out.unavco.org/pub/rinex3/nav/") **(from 2016 to NOW)**<br />
-UNAVCO [network monitoring website](https://www.unavco.org/instrumentation/networks/map/map.html#!/ "https://www.unavco.org/instrumentation/networks/map/map.html#!/")<br />
-GNSS Planning [TRIMBLE](https://www.gnssplanning.com/#/charts "https://www.gnssplanning.com/#/charts")<br />
-[Reference Frames](https://gssc.esa.int/navipedia//index.php/Reference_Frames_in_GNSS#GLONASS_reference_frame_PZ-90 "https://gssc.esa.int/navipedia//index.php/reference_frames_in_gnss#glonass_reference_frame_pz-90") in GNSS<br />
<br />
== References ==<br />
<references><br />
<ref name=Yan_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2689009| issn = 2151-1535| volume = 10| issue = 8| pages = 3789–3798| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| last3 = Moloney| first3 = Cecilia| title = Neural networks based sea ice detection and concentration retrieval from GNSS-R delay-doppler maps| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=King_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2997199| issn = 2151-1535| volume = 13| pages = 2916–2924| last1 = King| first1 = Lucinda S.| last2 = Unwin| first2 = Martin| last3 = Rawlinson| first3 = Jonathan| last4 = Guida| first4 = Raffaella| last5 = Underwood| first5 = Craig| title = Processing of raw GNSS reflectometry data from TDS-1 in a backscattering configuration| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Cheong_2019>{{Cite journal| doi = 10.1109/JSTARS.2019.2956183| issn = 2151-1535| volume = 12| issue = 12| pages = 5373–5378| last1 = Cheong| first1 = Joon Wayn| last2 = Southwell| first2 = Benjamin J.| last3 = Dempster| first3 = Andrew G.| title = Blind sea clutter suppression for spaceborne GNSS-R target detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Alonso-Arroyo_2017>{{Cite journal| doi = 10.1109/TGRS.2017.2699122| issn = 1558-0644| volume = 55| issue = 9| pages = 4989–5001| last1 = Alonso-Arroyo| first1 = Alberto| last2 = Zavorotny| first2 = Valery U.| last3 = Camps| first3 = Adriano| title = Sea ice detection using U.K. TDS-1 GNSS-R data| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Semmling_2019>{{Cite journal| doi = 10.1109/TGRS.2019.2933911| issn = 1558-0644| volume = 57| issue = 12| pages = 10350–10361| last1 = Semmling| first1 = A. Maximilian| last2 = Rösel| first2 = Anja| last3 = Divine| first3 = Dmitry V.| last4 = Gerland| first4 = Sebastian| last5 = Stienne| first5 = Georges| last6 = Reboul| first6 = Serge| last7 = Ludwig| first7 = Marcel| last8 = Wickert| first8 = Jens| last9 = Schuh| first9 = Harald| title = Sea-ice concentration derived from GNSS reflection measurements in fram strait| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Yan_2016>{{Cite journal| doi = 10.1109/JSTARS.2016.2582690| issn = 2151-1535| volume = 9| issue = 10| pages = 4795–4801| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Spaceborne GNSS-R sea ice detection using delay-doppler maps: First results from the U.K. TechDemoSat-1 mission| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2016}}<br />
</ref><br />
<ref name=Yan_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2966880| issn = 2151-1535| volume = 13| pages = 577–587| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice thickness measurement using spaceborne GNSS-R: First results with TechDemoSat-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Di_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2705350| issn = 2151-1535| volume = 10| issue = 9| pages = 4237–4255| last1 = Di Simone| first1 = Alessio| last2 = Park| first2 = Hyuk| last3 = Riccio| first3 = Daniele| last4 = Camps| first4 = Adriano| title = Sea target detection using spaceborne GNSS-R delay-doppler maps: Theory and experimental proof of concept using TDS-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Li_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2992037| issn = 2151-1535| volume = 13| pages = 1833–1841| last1 = Li| first1 = Bowen| last2 = Yang| first2 = Lei| last3 = Zhang| first3 = Bo| last4 = Yang| first4 = Dongkai| last5 = Wu| first5 = Di| title = Modeling and simulation of GNSS-R observables with effects of swell| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Yan_2018>{{Cite journal| doi = 10.1109/LGRS.2018.2852143| issn = 1558-0571| volume = 15| issue = 10| pages = 1510–1514| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice sensing from GNSS-R data using convolutional neural networks| journal = IEEE Geoscience and Remote Sensing Letters| date = 2018}}<br />
</ref><br />
<ref name=Southwell_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2943510| issn = 2151-1535| volume = 13| pages = 14–20| last1 = Southwell| first1 = Benjamin J.| last2 = Dempster| first2 = Andrew G.| title = Sea ice transition detection using incoherent integration and deconvolution| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Strandberg_2017>{{Cite journal| doi = 10.1109/LGRS.2017.2722041| issn = 1558-0571| volume = 14| issue = 9| pages = 1552–1556| last1 = Strandberg| first1 = Joakim| last2 = Hobiger| first2 = Thomas| last3 = Haas| first3 = Rüdiger| title = Coastal sea ice detection using ground-based GNSS-R| journal = IEEE Geoscience and Remote Sensing Letters| date = 2017}}<br />
</ref><br />
<ref name=Valencia_2013>{{Cite journal| doi = 10.1109/JSTARS.2012.2210392| issn = 2151-1535| volume = 6| issue = 1| pages = 217–223| last1 = Valencia| first1 = Enric| last2 = Camps| first2 = Adriano| last3 = Rodriguez-Alvarez| first3 = Nereida| last4 = Park| first4 = Hyuk| last5 = Ramos-Perez| first5 = Isaac| title = Using GNSS-R imaging of the ocean surface for oil slick detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2013}}<br />
</ref><br />
<ref name=Zhu_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2955175| issn = 2151-1535| volume = 13| pages = 217–226| last1 = Zhu| first1 = Yongchao| last2 = Tao| first2 = Tingye| last3 = Yu| first3 = Kegen| last4 = Li| first4 = Zhenxuan| last5 = Qu| first5 = Xiaochuan| last6 = Ye| first6 = Zhourun| last7 = Geng| first7 = Jun| last8 = Zou| first8 = Jingui| last9 = Semmling| first9 = Maximilian| last10 = Wickert| first10 = Jens| title = Sensing sea ice based on doppler spread analysis of spaceborne GNSS-R data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Southwell_2020b>{{Cite journal| doi = 10.1109/TGRS.2020.2973142| issn = 1558-0644| volume = 58| issue = 8| pages = 5922–5931| last1 = Southwell| first1 = Benjamin J.| last2 = Cheong| first2 = Joon Wayn| last3 = Dempster| first3 = Andrew G.| title = A matched filter for spaceborne GNSS-R based sea-target detection| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Jia-2021>{{Cite book| publisher = IntechOpen| isbn = 978-1-78923-537-1| last1 = Jia| first1 = Yan| last2 = Pei| first2 = Yuekun| title = Remote Sensing in Land Applications by Using GNSS-Reflectometry| accessdate = 2021-12-09| date = 2018-07-25| url = https://www.intechopen.com/chapters/59160}}<br />
</ref><br />
</references></div>Uwasahttps://wiki.icingcentre.eu/index.php?title=SAR_Imaging&diff=823SAR Imaging2022-02-09T09:01:02Z<p>Uwasa: /* Frequency bands and some of their usage */</p>
<hr />
<div>Synthetic Aperture Radar (SAR) imaging is an Earth Observation (EO) method, where a satellite sends radar pulses to the Earth and monitors it's reflections. The moving satellite forms so called synthetic aperture, which can be used for studying the signal reflections from different points on earth. The time of flight of signal provides a distance between satellite and the ground surface. Because the exact location of the satellite is known, the distance can be used for estimating Earth topography. On the other hand, the strength of the reflected signals carries information about the properties of the Earth surface. <br />
<br />
There are many SAR satellites in the orbit, such as <br />
<br />
* Two [https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-1 Sentinel 1] satellites from [https://www.esa.int/Applications/Observing_the_Earth/Copernicus/ Copernicus program] of the [https://www.esa.int/ European Space Agency (ESA)].<br />
*[https://asf.alaska.edu/data-sets/sar-data-sets/alos-palsar/ ALOS PALSAR]<br />
*[https://www.iceye.com/ IceEye]<br />
*JERS<br />
*TERRASAR-X<br />
*TanDEM-X<br />
<br />
<br />
Some of the SAR satellite data is available from public sources<br />
<br />
* Data from all satellites from [https://www.copernicus.eu/en Copernicus program] can be downloaded from [https://scihub.copernicus.eu/ SciHub], instructions for usage can be found from the [https://mooc.copernicus.eu/ Copernicus MOOC] course.<br />
* Data from many publicly open satellites can be accessed using [https://code.earthengine.google.com/ Google Earth Engine], and it can be also used for making and running your own algorithms in the cloud. One example is the [https://akinnunenuva.users.earthengine.app/view/nightlightstest night light test application].<br />
<br />
=== Some useful SAR resources ===<br />
<br />
# ESA, free EO and SAR data:<br />
## ALOS, JERS, TERRASAR-X/TandDEM-X<br />
## <nowiki>https://earth.esa.int/web/guest/pi-community/apply-for-data</nowiki><br />
## [https://earth.esa.int/eogateway/ Earth observation gateway]<br />
## [https://earth.esa.int/web/guest/pi-community/apply-for-data/3rd-party Earth Online 3rd party data products]<br />
# The [http://spaceportal.uwasa.fi/ space portal of University of Vaasa]<br />
# NASA data sources, for example [https://worldview.earthdata.nasa.gov/ NASA Earth data]<br />
# Commercial ones, [https://www.planet.com/pulse/planet-introduces-new-high-resolution-pelican-satellites-and-fusion-with-sar/ Planet], [http://syntheticapertureradar.com/category/spaceborne/satellite/airbus/ Airbus], [https://www.iceye.com/ ICEye] etc. You can test and learn with the free data but it’s limited.<br />
#[https://asf.alaska.edu/information/sar-information/what-is-sar/ University of Alaska's SAR satellites]<br />
<br />
=== SAR information ===<br />
The wavelength of the sensor determines the penetration depth of the transmitted signal into the vegetation layer of the terrain surface. The longer the wavelength, the deeper the penetration can be, particularly in forests.<br />
<br />
==== Frequency bands and some of their usage ====<br />
<br />
* L-band: 1-2 GHz, Wavelength 15-30 m (GPS, 3G74G)<br />
* S-Band: 2-4 GHz, 7.5-15 m (weather radars, 5G)<br />
* C-band: 4-8 GHz, 3.75 - 7.5 m (Satellite communication, satellite television)<br />
* X-band: 8-12 GHz, 2.5-3.75 m (SAR, military radars)<br />
* Ku-band: 12-18 GHz 1.7 - 2.5 m (Satellite communication)<br />
* Ka-band: 26-40 GHz, 0.75 - 1.15 m (Satellite communication)<br />
* <br />
<br />
The energy of an X-band sensor is mainly returned at the top layer of the canopies<br />
<br />
Most of the L-band signal penetrates through the upper vegetation layer and is returned at the ground surface.<br />
<br />
The backscatter behavior of C-band is less predictable. Due to volume scattering effects, the layer of backscattering is less determined and does not correspond directly to a terrain surface — neither the vegetation surface nor the ground surface.<br />
<br />
The SAR satellites usually send the radio signal either vertically (V) or horizontally (H) polarized. They can also receive the reflected signal back using either vertical or horizontal polarizer. The polarization angles used in transmission and reception can therefore form the following four combinations:<br />
<br />
* VV: the signal is send as vertically polarized and received as vertically polarized. This leads strong reflection from <br />
* HH: The signal is send as horizontally polarized and received as horizontally polarized. This leads strong reception from smooth specular surfaces.<br />
* VH: The signal is send as vertically polarized by the receiver only collects horizontally polarized signals. This cross-polarization combination mostly blocks the pure specular reflections which is keeping the polarization angle. On the contrary, the reflections from more complex surfaces having multiple scattering looses the polarization angle and is therefore partly received.<br />
*HV: Similar cross polarization method than VH. <br />
<br />
More in depth discussion of [https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/product-overview/polarimetry polarimetric SAR].<br />
<br />
==== Sentinel 1 ====<br />
<br />
* Two satellites in polar orbits, 693 km height<br />
* 12 days repeat cycle (each)<br />
* The C-SAR’s advantage appears to be an ability to operate at C-band wavelength that cannot be blocked by cloud cover or insufficient light.<br />
* 5.405 GHz, bandwidth 0-100 MHz, polarization HH+HV, VV+VH, VV, HH<br />
* Azimuth beam width 0.23”<br />
* Available data products with different level of preprocessing<br />
* Level-0 Products: condensed source packets which have additional annotations and lists auxiliary information that supports processing. Includes manifest.safe file, measurement and index data components.<br />
* Level-1 Products<br />
** Slant-range single-look complex products (SLC): sdf phase an amplitude, I-Q values of each pixel.<br />
** ground range Detected geo-referenced products (GRD): All data is projected to ground range using an Earth ellipsoid model corrected with specified terrain heigh<br />
* Level-2 Products: geo-located geophysical products copied from Level-1. Level-2 Ocean (OCN) products applicable for wind, wave and currents can have geophysical components obtained from the SAR data like: Ocean Wind field (OWI), Ocean Swell spectral range (OSW), Surface Radial Velocity (RVL), Availability of each one highly depends on mode of acquisition<br />
<br />
=== Tutorials and learning resources ===<br />
<br />
* [http://step.esa.int/main/doc/tutorials/ ESA tutorials]<br />
* [https://asf.alaska.edu/information/sar-information/what-is-sar/ What is SAR?], material from the University of Alaska<br />
* Tutorial for [https://www.youtube.com/watch?v=CBIlTOl2po4&t=1760s downloading data from SentinelHUB]<br />
* How to get free research access to Copernicus using [https://www.sentinel-hub.com/Network-of-Resources/ Network of Resources (NOR)]<br />
* Custom [https://custom-scripts.sentinel-hub.com/sentinel-1/sar-ice/ scripts for ice evaluation using] S1 SAR data<br />
<br />
=== Google tools for accessing and processing EO data ===<br />
<br />
* [https://developers.google.com/earth-engine Google Earth Engine] online notebooks and Python and JavasScript APIs<br />
<br />
=== ESA tools for accessing and processing EO data (These are installed in the RUS environment) ===<br />
<br />
* Access Sentinel Hub from [https://medium.com/sentinel-hub/control-sentinel-hub-from-within-qgis-2a83eb7f13db QGIS], also [https://docs.csc.fi/apps/qgis/ available in Puhti in CSC] for those who have access to CSC services.<br />
* [https://github.com/sentinel-hub/example-notebooks/blob/master/Slippy%20maps%20in%20Jupyter.ipynb Python Sentinel API in Jupyter notebook] (example)<br />
* Python Sentinel [https://sentinelhub-py.readthedocs.io/en/latest/examples/process_request.html API for downloading Sentinel data] by Sentinel HUB<br />
* [https://www.sentinel-hub.com/develop/integrate/python-integrate/ Python and JavaScript Sentinel API:s]<br />
* [http://step.esa.int/main/toolboxes/snap/ SNAP], also available in [https://docs.csc.fi/apps/snap/ Puhti in CSC] for those who have access to CSC services<br />
* BRAT ([http://www.altimetry.info/ Basic Radar Altimetry Toolbox])<br />
* Libraries:<br />
** [https://gdal.org/ GDAL] (raster and vector geospatial data library), [https://docs.csc.fi/apps/gdal/ also available in Puhti in CSC] for those who have access to CSC services<br />
** [https://www.orfeo-toolbox.org/ Orfeo Toolbox (OTB)] (Open Source processing of remote sensing images) [https://docs.csc.fi/apps/otb/ also available in CSC]<br />
** [http://step.esa.int/main/snap-supported-plugins/sen2cor/ Sen2Cor], SNAP plugin, processor for Sentinel-2 Level 2A product generation and formatting, [https://docs.csc.fi/apps/sen2cor/ also available in Puhti in CSC] for those who have access to CSC services<br />
** [http://step.esa.int/main/snap-supported-plugins/snaphu/ SNAPHU], SNAP plugin, two-dimensional phase unwrapping to recover unambiguous phase data from a 2-D array of phase values known only modulo 2pi rad.<br />
<br />
RUS environment can be used as a virtual server in ESA:s server room or as a Docker image. Both are Linux environments, with all above tools and many others already installed. RUS is only available for EU citizens. The tools can be also installed in normal Linux computer, and then there is no need for Docker or other virtualization platform, and the resources of the computer can therefore used more efficiently.</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=SAR_Imaging&diff=822SAR Imaging2022-02-09T08:33:17Z<p>Uwasa: /* Frequency bands and some of their usage */</p>
<hr />
<div>Synthetic Aperture Radar (SAR) imaging is an Earth Observation (EO) method, where a satellite sends radar pulses to the Earth and monitors it's reflections. The moving satellite forms so called synthetic aperture, which can be used for studying the signal reflections from different points on earth. The time of flight of signal provides a distance between satellite and the ground surface. Because the exact location of the satellite is known, the distance can be used for estimating Earth topography. On the other hand, the strength of the reflected signals carries information about the properties of the Earth surface. <br />
<br />
There are many SAR satellites in the orbit, such as <br />
<br />
* Two [https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-1 Sentinel 1] satellites from [https://www.esa.int/Applications/Observing_the_Earth/Copernicus/ Copernicus program] of the [https://www.esa.int/ European Space Agency (ESA)].<br />
*[https://asf.alaska.edu/data-sets/sar-data-sets/alos-palsar/ ALOS PALSAR]<br />
*[https://www.iceye.com/ IceEye]<br />
*JERS<br />
*TERRASAR-X<br />
*TanDEM-X<br />
<br />
<br />
Some of the SAR satellite data is available from public sources<br />
<br />
* Data from all satellites from [https://www.copernicus.eu/en Copernicus program] can be downloaded from [https://scihub.copernicus.eu/ SciHub], instructions for usage can be found from the [https://mooc.copernicus.eu/ Copernicus MOOC] course.<br />
* Data from many publicly open satellites can be accessed using [https://code.earthengine.google.com/ Google Earth Engine], and it can be also used for making and running your own algorithms in the cloud. One example is the [https://akinnunenuva.users.earthengine.app/view/nightlightstest night light test application].<br />
<br />
=== Some useful SAR resources ===<br />
<br />
# ESA, free EO and SAR data:<br />
## ALOS, JERS, TERRASAR-X/TandDEM-X<br />
## <nowiki>https://earth.esa.int/web/guest/pi-community/apply-for-data</nowiki><br />
## [https://earth.esa.int/eogateway/ Earth observation gateway]<br />
## [https://earth.esa.int/web/guest/pi-community/apply-for-data/3rd-party Earth Online 3rd party data products]<br />
# The [http://spaceportal.uwasa.fi/ space portal of University of Vaasa]<br />
# NASA data sources, for example [https://worldview.earthdata.nasa.gov/ NASA Earth data]<br />
# Commercial ones, [https://www.planet.com/pulse/planet-introduces-new-high-resolution-pelican-satellites-and-fusion-with-sar/ Planet], [http://syntheticapertureradar.com/category/spaceborne/satellite/airbus/ Airbus], [https://www.iceye.com/ ICEye] etc. You can test and learn with the free data but it’s limited.<br />
#[https://asf.alaska.edu/information/sar-information/what-is-sar/ University of Alaska's SAR satellites]<br />
<br />
=== SAR information ===<br />
The wavelength of the sensor determines the penetration depth of the transmitted signal into the vegetation layer of the terrain surface. The longer the wavelength, the deeper the penetration can be, particularly in forests.<br />
<br />
==== Frequency bands and some of their usage ====<br />
<br />
* L-band: 1-2 GHz, Wavelength 15-30 m (GPS, 3G74G)<br />
* S-Band: 2-4 GHz, 7.5-15 m (weather radars, 5G)<br />
* C-band: 4-8 GHz, 3.75 - 7.5 m (Satellite communication, satellite television)<br />
* X-band: 8-12 GHz, 2.5-3.75 m (SAR, military radars)<br />
* Ku-band: 12-18 GHz 1.7 - 2.5 m (Satellite communication)<br />
* Ka-band: 26-40 GHz, 0.75 - 1.15 m (Satellite communication)<br />
* <br />
<br />
The energy of an X-band sensor is mainly returned at the top layer of the canopies<br />
<br />
Most of the L-band signal penetrates through the upper vegetation layer and is returned at the ground surface.<br />
<br />
The backscatter behavior of C-band is less predictable. Due to volume scattering effects, the layer of backscattering is less determined and does not correspond directly to a terrain surface — neither the vegetation surface nor the ground surface.<br />
<br />
The SAR satellites usually send the radio signal either vertically (V) or horizontally (H) polarized. They can also receive the reflected signal back using either vertical or horizontal polarizer. The polarization angles used in transmission and reception can therefore form the following four combinations:<br />
<br />
* VV: the signal is send as vertically polarized and received as vertically polarized. This leads strong reflection from <br />
* HH: The signal is send as horizontally polarized and received as horizontally polarized. This leads strong reception from smooth specular surfaces.<br />
* VH: The signal is send as vertically polarized by the receiver only collects horizontally polarized signals. This combination mostly blocks the <br />
<br />
More in depth discussion of [https://sentinel.esa.int/web/sentinel/user-guides/sentinel-1-sar/product-overview/polarimetry polarimetric SAR].<br />
<br />
==== Sentinel 1 ====<br />
<br />
* Two satellites in polar orbits, 693 km height<br />
* 12 days repeat cycle (each)<br />
* The C-SAR’s advantage appears to be an ability to operate at C-band wavelength that cannot be blocked by cloud cover or insufficient light.<br />
* 5.405 GHz, bandwidth 0-100 MHz, polarization HH+HV, VVV+VH, VV, HH<br />
* Azimuth beam width 0.23”<br />
* Available data products with different level of preprocessing<br />
* Level-0 Products: condensed source packets which have additional annotations and lists auxiliary information that supports processing. Includes manifest.safe file, measurement and index data components.<br />
* Level-1 Products<br />
** Slant-range single-look complex products (SLC): sdf phase an amplitude, I-Q values of each pixel.<br />
** ground range Detected geo-referenced products (GRD): All data is projected to ground range using an Earth ellipsoid model corrected with specified terrain heigh<br />
* Level-2 Products: geo-located geophysical products copied from Level-1. Level-2 Ocean (OCN) products applicable for wind, wave and currents can have geophysical components obtained from the SAR data like: Ocean Wind field (OWI), Ocean Swell spectral range (OSW), Surface Radial Velocity (RVL), Availability of each one highly depends on mode of acquisition<br />
<br />
=== Tutorials and learning resources ===<br />
<br />
* [http://step.esa.int/main/doc/tutorials/ ESA tutorials]<br />
* [https://asf.alaska.edu/information/sar-information/what-is-sar/ What is SAR?], material from the University of Alaska<br />
* Tutorial for [https://www.youtube.com/watch?v=CBIlTOl2po4&t=1760s downloading data from SentinelHUB]<br />
* How to get free research access to Copernicus using [https://www.sentinel-hub.com/Network-of-Resources/ Network of Resources (NOR)]<br />
* Custom [https://custom-scripts.sentinel-hub.com/sentinel-1/sar-ice/ scripts for ice evaluation using] S1 SAR data<br />
<br />
=== Google tools for accessing and processing EO data ===<br />
<br />
* [https://developers.google.com/earth-engine Google Earth Engine] online notebooks and Python and JavasScript APIs<br />
<br />
=== ESA tools for accessing and processing EO data (These are installed in the RUS environment) ===<br />
<br />
* Access Sentinel Hub from [https://medium.com/sentinel-hub/control-sentinel-hub-from-within-qgis-2a83eb7f13db QGIS], also [https://docs.csc.fi/apps/qgis/ available in Puhti in CSC] for those who have access to CSC services.<br />
* [https://github.com/sentinel-hub/example-notebooks/blob/master/Slippy%20maps%20in%20Jupyter.ipynb Python Sentinel API in Jupyter notebook] (example)<br />
* Python Sentinel [https://sentinelhub-py.readthedocs.io/en/latest/examples/process_request.html API for downloading Sentinel data] by Sentinel HUB<br />
* [https://www.sentinel-hub.com/develop/integrate/python-integrate/ Python and JavaScript Sentinel API:s]<br />
* [http://step.esa.int/main/toolboxes/snap/ SNAP], also available in [https://docs.csc.fi/apps/snap/ Puhti in CSC] for those who have access to CSC services<br />
* BRAT ([http://www.altimetry.info/ Basic Radar Altimetry Toolbox])<br />
* Libraries:<br />
** [https://gdal.org/ GDAL] (raster and vector geospatial data library), [https://docs.csc.fi/apps/gdal/ also available in Puhti in CSC] for those who have access to CSC services<br />
** [https://www.orfeo-toolbox.org/ Orfeo Toolbox (OTB)] (Open Source processing of remote sensing images) [https://docs.csc.fi/apps/otb/ also available in CSC]<br />
** [http://step.esa.int/main/snap-supported-plugins/sen2cor/ Sen2Cor], SNAP plugin, processor for Sentinel-2 Level 2A product generation and formatting, [https://docs.csc.fi/apps/sen2cor/ also available in Puhti in CSC] for those who have access to CSC services<br />
** [http://step.esa.int/main/snap-supported-plugins/snaphu/ SNAPHU], SNAP plugin, two-dimensional phase unwrapping to recover unambiguous phase data from a 2-D array of phase values known only modulo 2pi rad.<br />
<br />
RUS environment can be used as a virtual server in ESA:s server room or as a Docker image. Both are Linux environments, with all above tools and many others already installed. RUS is only available for EU citizens. The tools can be also installed in normal Linux computer, and then there is no need for Docker or other virtualization platform, and the resources of the computer can therefore used more efficiently.</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=SAR_Imaging&diff=821SAR Imaging2022-02-09T08:26:06Z<p>Uwasa: Added more links</p>
<hr />
<div>Synthetic Aperture Radar (SAR) imaging is an Earth Observation (EO) method, where a satellite sends radar pulses to the Earth and monitors it's reflections. The moving satellite forms so called synthetic aperture, which can be used for studying the signal reflections from different points on earth. The time of flight of signal provides a distance between satellite and the ground surface. Because the exact location of the satellite is known, the distance can be used for estimating Earth topography. On the other hand, the strength of the reflected signals carries information about the properties of the Earth surface. <br />
<br />
There are many SAR satellites in the orbit, such as <br />
<br />
* Two [https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-1 Sentinel 1] satellites from [https://www.esa.int/Applications/Observing_the_Earth/Copernicus/ Copernicus program] of the [https://www.esa.int/ European Space Agency (ESA)].<br />
*[https://asf.alaska.edu/data-sets/sar-data-sets/alos-palsar/ ALOS PALSAR]<br />
*[https://www.iceye.com/ IceEye]<br />
*JERS<br />
*TERRASAR-X<br />
*TanDEM-X<br />
<br />
<br />
Some of the SAR satellite data is available from public sources<br />
<br />
* Data from all satellites from [https://www.copernicus.eu/en Copernicus program] can be downloaded from [https://scihub.copernicus.eu/ SciHub], instructions for usage can be found from the [https://mooc.copernicus.eu/ Copernicus MOOC] course.<br />
* Data from many publicly open satellites can be accessed using [https://code.earthengine.google.com/ Google Earth Engine], and it can be also used for making and running your own algorithms in the cloud. One example is the [https://akinnunenuva.users.earthengine.app/view/nightlightstest night light test application].<br />
<br />
=== Some useful SAR resources ===<br />
<br />
# ESA, free EO and SAR data:<br />
## ALOS, JERS, TERRASAR-X/TandDEM-X<br />
## <nowiki>https://earth.esa.int/web/guest/pi-community/apply-for-data</nowiki><br />
## [https://earth.esa.int/eogateway/ Earth observation gateway]<br />
## [https://earth.esa.int/web/guest/pi-community/apply-for-data/3rd-party Earth Online 3rd party data products]<br />
# The [http://spaceportal.uwasa.fi/ space portal of University of Vaasa]<br />
# NASA data sources, for example [https://worldview.earthdata.nasa.gov/ NASA Earth data]<br />
# Commercial ones, [https://www.planet.com/pulse/planet-introduces-new-high-resolution-pelican-satellites-and-fusion-with-sar/ Planet], [http://syntheticapertureradar.com/category/spaceborne/satellite/airbus/ Airbus], [https://www.iceye.com/ ICEye] etc. You can test and learn with the free data but it’s limited.<br />
#[https://asf.alaska.edu/information/sar-information/what-is-sar/ University of Alaska's SAR satellites]<br />
<br />
=== SAR information ===<br />
The wavelength of the sensor determines the penetration depth of the transmitted signal into the vegetation layer of the terrain surface. The longer the wavelength, the deeper the penetration can be, particularly in forests.<br />
<br />
==== Frequency bands and some of their usage ====<br />
<br />
* L-band: 1-2 GHz, Wavelength 15-30 m (GPS, 3G74G)<br />
* S-Band: 2-4 GHz, 7.5-15 m (weather radars, 5G)<br />
* C-band: 4-8 GHz, 3.75 - 7.5 m (Satellite communication, satellite television)<br />
* X-band: 8-12 GHz, 2.5-3.75 m (SAR, military radars)<br />
* Ku-band: 12-18 GHz 1.7 - 2.5 m (Satellite communication)<br />
* Ka-band: 26-40 GHz, 0.75 - 1.15 m (Satellite communication)<br />
* <br />
<br />
The energy of an X-band sensor is mainly returned at the top layer of the canopies<br />
<br />
Most of the L-band signal penetrates through the upper vegetation layer and is returned at the ground surface.<br />
<br />
The backscatter behavior of C-band is less predictable. Due to volume scattering effects, the layer of backscattering is less determined and does not correspond directly to a terrain surface — neither the vegetation surface nor the ground surface.<br />
<br />
==== Sentinel 1 ====<br />
<br />
* Two satellites in polar orbits, 693 km height<br />
* 12 days repeat cycle (each)<br />
* The C-SAR’s advantage appears to be an ability to operate at C-band wavelength that cannot be blocked by cloud cover or insufficient light.<br />
* 5.405 GHz, bandwidth 0-100 MHz, polarization HH+HV, VVV+VH, VV, HH<br />
* Azimuth beam width 0.23”<br />
* Available data products with different level of preprocessing<br />
* Level-0 Products: condensed source packets which have additional annotations and lists auxiliary information that supports processing. Includes manifest.safe file, measurement and index data components.<br />
* Level-1 Products<br />
** Slant-range single-look complex products (SLC): sdf phase an amplitude, I-Q values of each pixel.<br />
** ground range Detected geo-referenced products (GRD): All data is projected to ground range using an Earth ellipsoid model corrected with specified terrain heigh<br />
* Level-2 Products: geo-located geophysical products copied from Level-1. Level-2 Ocean (OCN) products applicable for wind, wave and currents can have geophysical components obtained from the SAR data like: Ocean Wind field (OWI), Ocean Swell spectral range (OSW), Surface Radial Velocity (RVL), Availability of each one highly depends on mode of acquisition<br />
<br />
=== Tutorials and learning resources ===<br />
<br />
* [http://step.esa.int/main/doc/tutorials/ ESA tutorials]<br />
* [https://asf.alaska.edu/information/sar-information/what-is-sar/ What is SAR?], material from the University of Alaska<br />
* Tutorial for [https://www.youtube.com/watch?v=CBIlTOl2po4&t=1760s downloading data from SentinelHUB]<br />
* How to get free research access to Copernicus using [https://www.sentinel-hub.com/Network-of-Resources/ Network of Resources (NOR)]<br />
* Custom [https://custom-scripts.sentinel-hub.com/sentinel-1/sar-ice/ scripts for ice evaluation using] S1 SAR data<br />
<br />
=== Google tools for accessing and processing EO data ===<br />
<br />
* [https://developers.google.com/earth-engine Google Earth Engine] online notebooks and Python and JavasScript APIs<br />
<br />
=== ESA tools for accessing and processing EO data (These are installed in the RUS environment) ===<br />
<br />
* Access Sentinel Hub from [https://medium.com/sentinel-hub/control-sentinel-hub-from-within-qgis-2a83eb7f13db QGIS], also [https://docs.csc.fi/apps/qgis/ available in Puhti in CSC] for those who have access to CSC services.<br />
* [https://github.com/sentinel-hub/example-notebooks/blob/master/Slippy%20maps%20in%20Jupyter.ipynb Python Sentinel API in Jupyter notebook] (example)<br />
* Python Sentinel [https://sentinelhub-py.readthedocs.io/en/latest/examples/process_request.html API for downloading Sentinel data] by Sentinel HUB<br />
* [https://www.sentinel-hub.com/develop/integrate/python-integrate/ Python and JavaScript Sentinel API:s]<br />
* [http://step.esa.int/main/toolboxes/snap/ SNAP], also available in [https://docs.csc.fi/apps/snap/ Puhti in CSC] for those who have access to CSC services<br />
* BRAT ([http://www.altimetry.info/ Basic Radar Altimetry Toolbox])<br />
* Libraries:<br />
** [https://gdal.org/ GDAL] (raster and vector geospatial data library), [https://docs.csc.fi/apps/gdal/ also available in Puhti in CSC] for those who have access to CSC services<br />
** [https://www.orfeo-toolbox.org/ Orfeo Toolbox (OTB)] (Open Source processing of remote sensing images) [https://docs.csc.fi/apps/otb/ also available in CSC]<br />
** [http://step.esa.int/main/snap-supported-plugins/sen2cor/ Sen2Cor], SNAP plugin, processor for Sentinel-2 Level 2A product generation and formatting, [https://docs.csc.fi/apps/sen2cor/ also available in Puhti in CSC] for those who have access to CSC services<br />
** [http://step.esa.int/main/snap-supported-plugins/snaphu/ SNAPHU], SNAP plugin, two-dimensional phase unwrapping to recover unambiguous phase data from a 2-D array of phase values known only modulo 2pi rad.<br />
<br />
RUS environment can be used as a virtual server in ESA:s server room or as a Docker image. Both are Linux environments, with all above tools and many others already installed. RUS is only available for EU citizens. The tools can be also installed in normal Linux computer, and then there is no need for Docker or other virtualization platform, and the resources of the computer can therefore used more efficiently.</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=SAR_Imaging&diff=820SAR Imaging2022-02-09T08:07:52Z<p>Uwasa: Added sentinel 1 information</p>
<hr />
<div>Synthetic Aperture Radar (SAR) imaging is an Earth Observation (EO) method, where a satellite sends radar pulses to the Earth and monitors it's reflections. The moving satellite forms so called synthetic aperture, which can be used for studying the signal reflections from different points on earth. The time of flight of signal provides a distance between satellite and the ground surface. Because the exact location of the satellite is known, the distance can be used for estimating Earth topography. On the other hand, the strength of the reflected signals carries information about the properties of the Earth surface. <br />
<br />
There are many SAR satellites in the orbit, such as <br />
<br />
* Two [https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-1 Sentinel 1] satellites from [https://www.esa.int/Applications/Observing_the_Earth/Copernicus/ Copernicus program] of the [https://www.esa.int/ European Space Agency (ESA)].<br />
*[https://asf.alaska.edu/data-sets/sar-data-sets/alos-palsar/ ALOS PALSAR]<br />
*[https://www.iceye.com/ IceEye]<br />
*JERS<br />
*TERRASAR-X<br />
*TanDEM-X<br />
<br />
<br />
Some of the SAR satellite data is available from public sources<br />
<br />
* Data from all satellites from [https://www.copernicus.eu/en Copernicus program] can be downloaded from [https://scihub.copernicus.eu/ SciHub], instructions for usage can be found from the [https://mooc.copernicus.eu/ Copernicus MOOC] course.<br />
* Data from many publicly open satellites can be accessed using [https://code.earthengine.google.com/ Google Earth Engine], and it can be also used for making and running your own algorithms in the cloud. One example is the [https://akinnunenuva.users.earthengine.app/view/nightlightstest night light test application].<br />
<br />
=== Some useful SAR resources ===<br />
<br />
# ESA, free EO and SAR data:<br />
## ALOS, JERS, TERRASAR-X/TandDEM-X<br />
## <nowiki>https://earth.esa.int/web/guest/pi-community/apply-for-data</nowiki><br />
## [https://earth.esa.int/eogateway/ Earth observation gateway]<br />
## [https://earth.esa.int/web/guest/pi-community/apply-for-data/3rd-party Earth Online 3rd party data products]<br />
# The [http://spaceportal.uwasa.fi/ space portal of University of Vaasa]<br />
# NASA data sources, for example [https://worldview.earthdata.nasa.gov/ NASA Earth data]<br />
# Commercial ones, [https://www.planet.com/pulse/planet-introduces-new-high-resolution-pelican-satellites-and-fusion-with-sar/ Planet], [http://syntheticapertureradar.com/category/spaceborne/satellite/airbus/ Airbus], [https://www.iceye.com/ ICEye] etc. You can test and learn with the free data but it’s limited.<br />
#[https://asf.alaska.edu/information/sar-information/what-is-sar/ University of Alaska's SAR satellites]<br />
<br />
=== SAR information ===<br />
The wavelength of the sensor determines the penetration depth of the transmitted signal into the vegetation layer of the terrain surface. The longer the wavelength, the deeper the penetration can be, particularly in forests.<br />
<br />
==== Frequency bands and some of their usage ====<br />
<br />
* L-band: 1-2 GHz, Wavelength 15-30 m (GPS, 3G74G)<br />
* S-Band: 2-4 GHz, 7.5-15 m (weather radars, 5G)<br />
* C-band: 4-8 GHz, 3.75 - 7.5 m (Satellite communication, satellite television)<br />
* X-band: 8-12 GHz, 2.5-3.75 m (SAR, military radars)<br />
* Ku-band: 12-18 GHz 1.7 - 2.5 m (Satellite communication)<br />
* Ka-band: 26-40 GHz, 0.75 - 1.15 m (Satellite communication)<br />
* <br />
<br />
The energy of an X-band sensor is mainly returned at the top layer of the canopies<br />
<br />
Most of the L-band signal penetrates through the upper vegetation layer and is returned at the ground surface.<br />
<br />
The backscatter behavior of C-band is less predictable. Due to volume scattering effects, the layer of backscattering is less determined and does not correspond directly to a terrain surface — neither the vegetation surface nor the ground surface.<br />
<br />
==== Sentinel 1 ====<br />
<br />
* Two satellites in polar orbits, 693 km height<br />
* 12 days repeat cycle (each)<br />
* The C-SAR’s advantage appears to be an ability to operate at C-band wavelength that cannot be blocked by cloud cover or insufficient light.<br />
* 5.405 GHz, bandwidth 0-100 MHz, polarization HH+HV, VVV+VH, VV, HH<br />
* Azimuth beam width 0.23”<br />
* Available data products with different level of preprocessing<br />
* Level-0 Products: condensed source packets which have additional annotations and lists auxiliary information that supports processing. Includes manifest.safe file, measurement and index data components.<br />
* Level-1 Products<br />
** Slant-range single-look complex products (SLC): sdf phase an amplitude, I-Q values of each pixel.<br />
** ground range Detected geo-referenced products (GRD): All data is projected to ground range using an Earth ellipsoid model corrected with specified terrain heigh<br />
* Level-2 Products: geo-located geophysical products copied from Level-1. Level-2 Ocean (OCN) products applicable for wind, wave and currents can have geophysical components obtained from the SAR data like: Ocean Wind field (OWI), Ocean Swell spectral range (OSW), Surface Radial Velocity (RVL), Availability of each one highly depends on mode of acquisition</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=SAR_Imaging&diff=819SAR Imaging2022-02-09T07:59:50Z<p>Uwasa: Added more links</p>
<hr />
<div>Synthetic Aperture Radar (SAR) imaging is an Earth Observation (EO) method, where a satellite sends radar pulses to the Earth and monitors it's reflections. The moving satellite forms so called synthetic aperture, which can be used for studying the signal reflections from different points on earth. The time of flight of signal provides a distance between satellite and the ground surface. Because the exact location of the satellite is known, the distance can be used for estimating Earth topography. On the other hand, the strength of the reflected signals carries information about the properties of the Earth surface. <br />
<br />
There are many SAR satellites in the orbit, such as <br />
<br />
* Two [https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-1 Sentinel 1] satellites from [https://www.esa.int/Applications/Observing_the_Earth/Copernicus/ Copernicus program] of the [https://www.esa.int/ European Space Agency (ESA)].<br />
*[https://asf.alaska.edu/data-sets/sar-data-sets/alos-palsar/ ALOS PALSAR]<br />
*[https://www.iceye.com/ IceEye]<br />
*JERS<br />
*TERRASAR-X<br />
*TanDEM-X<br />
<br />
<br />
Some of the SAR satellite data is available from public sources<br />
<br />
* Data from all satellites from [https://www.copernicus.eu/en Copernicus program] can be downloaded from [https://scihub.copernicus.eu/ SciHub], instructions for usage can be found from the [https://mooc.copernicus.eu/ Copernicus MOOC] course.<br />
* Data from many publicly open satellites can be accessed using [https://code.earthengine.google.com/ Google Earth Engine], and it can be also used for making and running your own algorithms in the cloud. One example is the [https://akinnunenuva.users.earthengine.app/view/nightlightstest night light test application].<br />
<br />
=== Some useful SAR resources ===<br />
<br />
# ESA, free EO and SAR data:<br />
## ALOS, JERS, TERRASAR-X/TandDEM-X<br />
## <nowiki>https://earth.esa.int/web/guest/pi-community/apply-for-data</nowiki><br />
## <nowiki>https://earth.esa.int/eogateway/</nowiki><br />
## <nowiki>https://earth.esa.int/web/guest/pi-community/apply-for-data/3rd-party</nowiki><br />
## <nowiki>https://earth.esa.int/eogateway/</nowiki> ---> <nowiki>https://eocat.esa.int/sec/#data-services-area</nowiki><br />
# The [http://spaceportal.uwasa.fi/ space portal of University of Vaasa]<br />
# NASA data sources, for example [https://worldview.earthdata.nasa.gov/ NASA Earth data]<br />
# Commercial ones, [https://www.planet.com/pulse/planet-introduces-new-high-resolution-pelican-satellites-and-fusion-with-sar/ Planet], [http://syntheticapertureradar.com/category/spaceborne/satellite/airbus/ Airbus], [https://www.iceye.com/ ICEye] etc. You can test and learn with the free data but it’s limited.</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=Main_Page&diff=785Main Page2022-02-04T07:35:08Z<p>Uwasa: </p>
<hr />
<div><strong>Welcome to the NoICE IcingWiki!</strong><br />
<br />
Consult the [https://www.mediawiki.org/wiki/Special:MyLanguage/Help:Contents User's Guide] for information on using the wiki software.<br />
<br />
To add a new article you can search for the article title you wish to add, or search for "Create an article".<br />
<br />
Instructions for using references in wiki are shown in document [[ImportReferences|Inserting citations]].<br />
<br />
'''The IcingWiki is under construction!''' - PLEASE WRITE YOUR NEW READY MADE PUBLISHED ARTICLE UNDER TOPICS - '''The IcingWiki is under construction!''' <br />
<br />
== Topics ==<br />
<br />
* '''FORMING OF ICE AND ICE FEATURES:''' - [[icing]], [[ice type]], [[ice feature]], [[ice adhesion]], [[wind]], [[freezing rain]], [[nucleation]], [[supercooled water]], [[in-cloud icing]], [[precipitation icing]], [[icing on airplane wings]], [[icing on ships]], [[icing on wind turbine blades]], [[icing on masts]], [[icing on powerlines]], [[LWC]], [[wenzel state]], [[wet snow]], [[Cassie-baxter state|Cassie-Baxter state]], [[Atmospheric Icing|atmospheric icing]], [[SLD]], [[CC|CC (cold climate)]], [[IC]], [[LTC]], [[icing event]]<br />
*'''ICE PREVENTION, ANTI-ICING AND DE-ICING METHODS:''' - [[ice prevention]], [[superhydrophobic surface]], [[coating material]], [[Anti-freeze substances|anti-freeze substances (railway traffic)]], [[railway traffic de-icing]], [[railway traffic anti-icing]], [[Ice-phobic surface|ice-phobic surfaces]], [[De-icing chemicals|de-icing chemicals (aviation)]], [[ice density]], [[icephobic]], [[microroughness and nanoroughness]], [[manual ice removal]],<br />
*'''ICE RESEARCH, TESTS AND MODELS:''' - [[modelling of icing]], [[modelling programs]], [[ice adhesion test]], [[Icing Wind Tunnel (IWiT)|icing wind tunnel]], [[Makkonen´s model]], [[Lozowski model]], [[ice classes]], [[instrumental icing]], [[WRF]], [[T19IceLossMethod|T19IceLossMethod,]] <br />
*'''RAILWAY TRAFFIC:''' - [[winter effects in railway traffic]], [[track switches, derailment]], [[Powerlines, overhead lines, pantograph|power lines, overhead lines, pantograph]], [[train brakes]], [[rail gaps]], [[track smoothness]], [[frost heaving]], [[track brittleness]]<br />
*'''SPACE BASED DATA:''' [[SAR Imaging|SAR imaging]], [[GNSS Reflectometry|GNSS reflectometry]]<br />
*'''OTHER:''' [[AEP]], [[IEA]]<br />
<br />
== Getting started ==<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:Configuration_settings Configuration settings list]<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:FAQ MediaWiki FAQ]<br />
* [https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Localisation#Translation_resources Localise MediaWiki for your language]<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:Combating_spam Learn how to combat spam on your wiki]<br />
*[[ImportReferences|How to import references into MediaWiki]]</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=Main_Page&diff=784Main Page2022-02-04T07:34:05Z<p>Uwasa: Added space based data links</p>
<hr />
<div><strong>Welcome to the NoICE IcingWiki!</strong><br />
<br />
Consult the [https://www.mediawiki.org/wiki/Special:MyLanguage/Help:Contents User's Guide] for information on using the wiki software.<br />
<br />
To add a new article you can search for the article title you wish to add, or search for "Create an article".<br />
<br />
Instructions for using references in wiki are shown in document [[ImportReferences|Inserting citations]].<br />
<br />
'''The IcingWiki is under construction!''' - PLEASE WRITE YOUR NEW READY MADE PUBLISHED ARTICLE UNDER TOPICS - '''The IcingWiki is under construction!''' <br />
<br />
== Topics ==<br />
<br />
* '''FORMING OF ICE AND ICE FEATURES:''' - [[icing]], [[ice type]], [[ice feature]], [[ice adhesion]], [[wind]], [[freezing rain]], [[nucleation]], [[supercooled water]], [[in-cloud icing]], [[precipitation icing]], [[icing on airplane wings]], [[icing on ships]], [[icing on wind turbine blades]], [[icing on masts]], [[icing on powerlines]], [[LWC]], [[wenzel state]], [[wet snow]], [[Cassie-baxter state|Cassie-Baxter state]], [[Atmospheric Icing|atmospheric icing]], [[SLD]], [[CC|CC (cold climate)]], [[IC]], [[LTC]], [[icing event]]<br />
*'''ICE PREVENTION, ANTI-ICING AND DE-ICING METHODS:''' - [[ice prevention]], [[superhydrophobic surface]], [[coating material]], [[Anti-freeze substances|anti-freeze substances (railway traffic)]], [[railway traffic de-icing]], [[railway traffic anti-icing]], [[Ice-phobic surface|ice-phobic surfaces]], [[De-icing chemicals|de-icing chemicals (aviation)]], [[ice density]], [[icephobic]], [[microroughness and nanoroughness]], [[manual ice removal]],<br />
*'''ICE RESEARCH, TESTS AND MODELS:''' - [[modelling of icing]], [[modelling programs]], [[ice adhesion test]], [[Icing Wind Tunnel (IWiT)|icing wind tunnel]], [[Makkonen´s model]], [[Lozowski model]], [[ice classes]], [[instrumental icing]], [[WRF]], [[T19IceLossMethod|T19IceLossMethod,]] <br />
*'''RAILWAY TRAFFIC:''' - [[winter effects in railway traffic]], [[track switches, derailment]], [[Powerlines, overhead lines, pantograph|power lines, overhead lines, pantograph]], [[train brakes]], [[rail gaps]], [[track smoothness]], [[frost heaving]], [[track brittleness]]<br />
*'''SPACE BASED DATA:''' [[SAR Imaging|SAR imaging]], [[GNSS Reflectometry|GNSS observations]]<br />
*'''OTHER:''' [[AEP]], [[IEA]]<br />
<br />
== Getting started ==<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:Configuration_settings Configuration settings list]<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:FAQ MediaWiki FAQ]<br />
* [https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Localisation#Translation_resources Localise MediaWiki for your language]<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:Combating_spam Learn how to combat spam on your wiki]<br />
*[[ImportReferences|How to import references into MediaWiki]]</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=SAR_Imaging&diff=773SAR Imaging2022-02-01T12:58:41Z<p>Uwasa: Added more links</p>
<hr />
<div>Synthetic Aperture Radar (SAR) imaging is an Earth Observation (EO) method, where a satellite sends radar pulses to the Earth and monitors it's reflections. The moving satellite forms so called synthetic aperture, which can be used for studying the signal reflections from different points on earth. The time of flight of signal provides a distance between satellite and the ground surface. Because the exact location of the satellite is known, the distance can be used for estimating Earth topography. On the other hand, the strength of the reflected signals carries information about the properties of the Earth surface. <br />
<br />
There are many SAR satellites in the orbit, such as <br />
<br />
* Two [https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-1 Sentinel 1] satellites from [https://www.esa.int/Applications/Observing_the_Earth/Copernicus/ Copernicus program] of the [https://www.esa.int/ European Space Agency (ESA)].<br />
*[https://asf.alaska.edu/data-sets/sar-data-sets/alos-palsar/ ALOS PALSAR]<br />
*[https://www.iceye.com/ IceEye]<br />
*JERS<br />
*TERRASAR-X<br />
*TanDEM-X<br />
<br />
<br />
Some of the SAR satellite data is available from public sources<br />
<br />
* Data from all satellites from Copernicus program can be downloaded from [https://scihub.copernicus.eu/ SciHub]<br />
* Data from many publicly open satellites can be accessed using [https://code.earthengine.google.com/ Google Earth Engine]</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=SAR_Imaging&diff=772SAR Imaging2022-02-01T12:52:47Z<p>Uwasa: Added somewhat text already</p>
<hr />
<div>Synthetic Aperture Radar (SAR) imaging is an Earth Observation (EO) method, where a satellite sends radar pulses to the Earth and monitors it's reflections. The moving satellite forms so called synthetic aperture, which can be used for studying the signal reflections from different points on earth. The time of flight of signal provides a distance between satellite and the ground surface. Because the exact location of the satellite is known, the distance can be used for estimating Earth topography. On the other hand, the strength of the reflected signals carries information about the properties of the Earth surface. <br />
<br />
There are many SAR satellites in the orbit, such as <br />
<br />
* Two Sentinel 1 satellites from Copernicus program.<br />
<br />
<br />
Some of the SAR satellite data is available from public sources<br />
<br />
* Data from all satellites from Copernicus program can be downloaded from [https://scihub.copernicus.eu/ SciHub]<br />
* Data from many publicly open satellites can be accessed using [https://code.earthengine.google.com/ Google Earth Engine]</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=Makkonen%C2%B4s_model&diff=750Makkonen´s model2022-01-31T11:30:11Z<p>Uwasa: </p>
<hr />
<div>The international ISO standard Atmospheric Icing on Structures (ISO, E 2017<ref>{{Cite web| title = ISO 12494:2017 - Atmospheric icing of structures| work = iTeh Standards Store| accessdate = 2022-01-31| url = https://standards.iteh.ai/catalog/standards/iso/e35bb939-fd4b-4b3f-83cc-3d4dbb1e71ab/iso-12494-2017}}</ref>) is based on Makkonen icing model <ref>{{Cite journal| doi = 10.1098/rsta.2000.0690| volume = 358| issue = 1776| pages = 2913–2939| last1 = Poots| first1 = G.| last2 = Makkonen| first2 = Lasse| title = Models for the growth of rime, glaze, icicles and wet snow on structures| journal = Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences| accessdate = 2021-08-12| date = 2000-11-15| url = https://royalsocietypublishing.org/doi/10.1098/rsta.2000.0690}}</ref>. The icing model calculates the amount of ice accumulated over a 1 m high vertically oriented, freely rotating cylinder with a diameter of 3 cm. A threshold value of 10 g/h for the modeled icing intensity is often used <ref>{{Cite journal| doi = 10.1002/we.1998| issn = 1099-1824| volume = 20| issue = 1| pages = 171–189| last1 = Hämäläinen| first1 = Karoliina| last2 = Niemelä| first2 = Sami| title = Production of a Numerical Icing Atlas for Finland| journal = Wind Energy| accessdate = 2021-08-09| date = 2017| url = https://onlinelibrary.wiley.com/doi/abs/10.1002/we.1998}}</ref> to distinguish between icing and non-icing conditions. <br />
<br />
In Makkonen’s model icing rate can be modeled with an equation <br />
<br />
<math>\frac{{\rm d}M}{{\rm d}t} = \alpha_1 \;\alpha_2 \;\alpha_3 \;\omega \;v \;A</math><br />
<br />
where <math>\alpha_1</math> is '''collision efficiency''', describing how likely a droplet hit the cylinder, and it varies between 0..1. It can be set to 1 for large droplet sizes.<br />
<br />
<math>\alpha_2</math> '''sticking efficiency,''' describes how likely a droplet sticks in the cylinder when hit. It ranges from 0..1 and can be set to 1 for freezing rain<br />
<br />
and <math>\alpha_3</math> represent '''ice accretion efficiency''', varying between 0..1<br />
<br />
<math>\alpha_1</math>, <math>\alpha_2</math> and <math>\alpha_3</math> are correction factors, and their value is between 0 and 1. Empirical data is used to determine the correction factors and they are very dependent on the icing conditions.<br />
<br />
<br />
A is area and<br />
<br />
v is the velocity of the particles<br />
<br />
and ω is the mass concentration of particles, Liquid Water Content, [[LWC]]<br />
<br />
<br />
<br />
'''Collision efficiency <math>\alpha_1</math>''' is the ratio at which water droplets hit the icing surface, as some droplets, especially the smaller ones, can be directed by airflow to miss the icing object.<br />
<br />
'''Sticking efficiency <math>\alpha_2</math>''' is the measure of how much of the water droplets, or snow, that hit the surface, stick to the ice layer. Supercooled water droplets do not bounce but freeze immediately. Adversely, snow can bounce off. Snow increases the complexity of icing and therefore many icing models have a hard time modelling icing events with snowflakes.<br />
<br />
'''Ice accretion efficiency''' <math>\alpha_3</math> is a measure of how many of the particles contacting the material surface form ice on it. Some droplets do not freeze to from ice, leading to wet ice growth. The heat balance of the object surface affects the number of droplets that freeze. The surface heat balance follows equation:<br />
<br />
<math>Q_f+Q_v = Q_c+Q_e+Q_l+Q_s</math><br />
<br />
<br />
where <math>𝑄_𝑓</math> is latent heat of freezing, <br />
<br />
<math>𝑄_𝑣</math> is frictional heating of air, <br />
<br />
<math>𝑄_𝑐</math> is loss of sensible heat to air, <br />
<br />
<math>𝑄_𝑒</math> is heat loss due to evaporation, <br />
<br />
<math>𝑄_𝑙</math> is heat loss because of warming of supercooled water to freezing point, <br />
<br />
and <math>𝑄_𝑠</math> is heat loss due to radiation.<br />
<br />
<br />
The ice accretion efficiency is strongly affected by how well the latent heat of freezing can leave the surface.<br />
<br />
<br />
<ref>Ingvaldsen, K. (2017) Atmospheric icing in a changing climate: Impact of higher boundary temperatures on simulations of atmospheric ice accretion on structures during the 2015-2016 icing winter in West-Norway.</ref> <ref>Stenroos, C. (2015) Properties of icephobic surfaces in different icing conditions.</ref> <ref>Makkonen, L. (2000). Models for the growth of rime, glaze, icicles and wet snow on structures. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical, and Engineering Sciences, 358(1776), 2913–2939.</ref></div>Uwasahttps://wiki.icingcentre.eu/index.php?title=Makkonen%C2%B4s_model&diff=749Makkonen´s model2022-01-31T11:28:00Z<p>Uwasa: </p>
<hr />
<div>The international ISO standard Atmospheric Icing on Structures (ISO, E 2017<ref>{{Cite web| title = ISO 12494:2017 - Atmospheric icing of structures| work = iTeh Standards Store| accessdate = 2022-01-31| url = https://standards.iteh.ai/catalog/standards/iso/e35bb939-fd4b-4b3f-83cc-3d4dbb1e71ab/iso-12494-2017}}</ref>) is based on Makkonen icing model <ref>{{Cite journal| doi = 10.1098/rsta.2000.0690| volume = 358| issue = 1776| pages = 2913–2939| last1 = Poots| first1 = G.| last2 = Makkonen| first2 = Lasse| title = Models for the growth of rime, glaze, icicles and wet snow on structures| journal = Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences| accessdate = 2021-08-12| date = 2000-11-15| url = https://royalsocietypublishing.org/doi/10.1098/rsta.2000.0690}}</ref>. The icing model calculates the amount of ice accumulated over a 1 m high vertically oriented, freely rotating cylinder with a diameter of 3 cm. A threshold value of 10 g/h for the modeled icing intensity is often used <ref>{{Cite journal| doi = 10.1002/we.1998| issn = 1099-1824| volume = 20| issue = 1| pages = 171–189| last1 = Hämäläinen| first1 = Karoliina| last2 = Niemelä| first2 = Sami| title = Production of a Numerical Icing Atlas for Finland| journal = Wind Energy| accessdate = 2021-08-09| date = 2017| url = https://onlinelibrary.wiley.com/doi/abs/10.1002/we.1998}}</ref> to distinguish between icing and non-icing conditions. <br />
<br />
In Makkonen’s model icing rate can be modeled with an equation <br />
<br />
<math>\frac{{\rm d}M}{{\rm d}t} = \alpha_1 \;\alpha_2 \;\alpha_3 \;\omega \;v \;A</math><br />
<br />
where 𝛼1 is '''collision efficiency''', describing how likely a droplet hit the cylinder, and it varies between 0..1. It can be set to 1 for large droplet sizes.<br />
<br />
𝛼2 '''sticking efficiency,''' describes how likely a droplet sticks in the cylinder when hit. It ranges from 0..1 and can be set to 1 for freezing rain<br />
<br />
and 𝛼3 represent '''ice accretion efficiency''', varying between 0..1<br />
<br />
𝛼1, 𝛼2 and 𝛼3 are correction factors, and their value is between 0 and 1. Empirical data is used to determine the correction factors and they are very dependent on the icing conditions.<br />
<br />
<br />
A is area and<br />
<br />
v is the velocity of the particles<br />
<br />
and ω is the mass concentration of particles, Liquid Water Content, [[LWC]]<br />
<br />
<br />
<br />
'''Collision efficiency 𝛼1''' is the ratio at which water droplets hit the icing surface, as some droplets, especially the smaller ones, can be directed by airflow to miss the icing object.<br />
<br />
'''Sticking efficiency 𝛼2''' is the measure of how much of the water droplets, or snow, that hit the surface, stick to the ice layer. Supercooled water droplets do not bounce but freeze immediately. Adversely, snow can bounce off. Snow increases the complexity of icing and therefore many icing models have a hard time modelling icing events with snowflakes.<br />
<br />
'''Ice accretion efficiency''' '''𝛼3''' is a measure of how many of the particles contacting the material surface form ice on it. Some droplets do not freeze to from ice, leading to wet ice growth. The heat balance of the object surface affects the number of droplets that freeze. The surface heat balance follows equation:<br />
<br />
<math>Q_f+Q_v = Q_c+Q_e+Q_l+Q_s</math><br />
<br />
<br />
where <math>𝑄_𝑓</math> is latent heat of freezing, <br />
<br />
<math>𝑄_𝑣</math> is frictional heating of air, <br />
<br />
<math>𝑄_𝑐</math> is loss of sensible heat to air, <br />
<br />
<math>𝑄_𝑒</math> is heat loss due to evaporation, <br />
<br />
<math>𝑄_𝑙</math> is heat loss because of warming of supercooled water to freezing point, <br />
<br />
and <math>𝑄_𝑠</math> is heat loss due to radiation.<br />
<br />
<br />
The ice accretion efficiency is strongly affected by how well the latent heat of freezing can leave the surface.<br />
<br />
<br />
<ref>Ingvaldsen, K. (2017) Atmospheric icing in a changing climate: Impact of higher boundary temperatures on simulations of atmospheric ice accretion on structures during the 2015-2016 icing winter in West-Norway.</ref> <ref>Stenroos, C. (2015) Properties of icephobic surfaces in different icing conditions.</ref> <ref>Makkonen, L. (2000). Models for the growth of rime, glaze, icicles and wet snow on structures. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical, and Engineering Sciences, 358(1776), 2913–2939.</ref></div>Uwasahttps://wiki.icingcentre.eu/index.php?title=Makkonen%C2%B4s_model&diff=748Makkonen´s model2022-01-31T11:27:13Z<p>Uwasa: Some changes to formulas</p>
<hr />
<div>The international ISO standard Atmospheric Icing on Structures (ISO, E 2017<ref>{{Cite web| title = ISO 12494:2017 - Atmospheric icing of structures| work = iTeh Standards Store| accessdate = 2022-01-31| url = https://standards.iteh.ai/catalog/standards/iso/e35bb939-fd4b-4b3f-83cc-3d4dbb1e71ab/iso-12494-2017}}</ref>) is based on Makkonen icing model <ref>{{Cite journal| doi = 10.1098/rsta.2000.0690| volume = 358| issue = 1776| pages = 2913–2939| last1 = Poots| first1 = G.| last2 = Makkonen| first2 = Lasse| title = Models for the growth of rime, glaze, icicles and wet snow on structures| journal = Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences| accessdate = 2021-08-12| date = 2000-11-15| url = https://royalsocietypublishing.org/doi/10.1098/rsta.2000.0690}}</ref>. The icing model calculates the amount of ice accumulated over a 1 m high vertically oriented, freely rotating cylinder with a diameter of 3 cm. A threshold value of 10 g/h for the modeled icing intensity is often used <ref>{{Cite journal| doi = 10.1002/we.1998| issn = 1099-1824| volume = 20| issue = 1| pages = 171–189| last1 = Hämäläinen| first1 = Karoliina| last2 = Niemelä| first2 = Sami| title = Production of a Numerical Icing Atlas for Finland| journal = Wind Energy| accessdate = 2021-08-09| date = 2017| url = https://onlinelibrary.wiley.com/doi/abs/10.1002/we.1998}}</ref> to distinguish between icing and non-icing conditions. <br />
<br />
In Makkonen’s model icing rate can be modeled with an equation <br />
<br />
<math>\frac{{\rm d}M}{{\rm d}t} = \alpha_1 \alpha_2 \alpha_3 \omega v A</math><br />
<br />
where 𝛼1 is '''collision efficiency''', describing how likely a droplet hit the cylinder, and it varies between 0..1. It can be set to 1 for large droplet sizes.<br />
<br />
𝛼2 '''sticking efficiency,''' describes how likely a droplet sticks in the cylinder when hit. It ranges from 0..1 and can be set to 1 for freezing rain<br />
<br />
and 𝛼3 represent '''ice accretion efficiency''', varying between 0..1<br />
<br />
𝛼1, 𝛼2 and 𝛼3 are correction factors, and their value is between 0 and 1. Empirical data is used to determine the correction factors and they are very dependent on the icing conditions.<br />
<br />
<br />
A is area and<br />
<br />
v is the velocity of the particles<br />
<br />
and ω is the mass concentration of particles, Liquid Water Content, [[LWC]]<br />
<br />
<br />
<br />
'''Collision efficiency 𝛼1''' is the ratio at which water droplets hit the icing surface, as some droplets, especially the smaller ones, can be directed by airflow to miss the icing object.<br />
<br />
'''Sticking efficiency 𝛼2''' is the measure of how much of the water droplets, or snow, that hit the surface, stick to the ice layer. Supercooled water droplets do not bounce but freeze immediately. Adversely, snow can bounce off. Snow increases the complexity of icing and therefore many icing models have a hard time modelling icing events with snowflakes.<br />
<br />
'''Ice accretion efficiency''' '''𝛼3''' is a measure of how many of the particles contacting the material surface form ice on it. Some droplets do not freeze to from ice, leading to wet ice growth. The heat balance of the object surface affects the number of droplets that freeze. The surface heat balance follows equation:<br />
<br />
<math>Q_f+Q_v = Q_c+Q_e+Q_l+Q_s</math><br />
<br />
<br />
where <math>𝑄_𝑓</math> is latent heat of freezing, <br />
<br />
<math>𝑄_𝑣</math> is frictional heating of air, <br />
<br />
<math>𝑄_𝑐</math> is loss of sensible heat to air, <br />
<br />
<math>𝑄_𝑒</math> is heat loss due to evaporation, <br />
<br />
<math>𝑄_𝑙</math> is heat loss because of warming of supercooled water to freezing point, <br />
<br />
and <math>𝑄_𝑠</math> is heat loss due to radiation.<br />
<br />
<br />
The ice accretion efficiency is strongly affected by how well the latent heat of freezing can leave the surface.<br />
<br />
<br />
<ref>Ingvaldsen, K. (2017) Atmospheric icing in a changing climate: Impact of higher boundary temperatures on simulations of atmospheric ice accretion on structures during the 2015-2016 icing winter in West-Norway.</ref> <ref>Stenroos, C. (2015) Properties of icephobic surfaces in different icing conditions.</ref> <ref>Makkonen, L. (2000). Models for the growth of rime, glaze, icicles and wet snow on structures. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical, and Engineering Sciences, 358(1776), 2913–2939.</ref></div>Uwasahttps://wiki.icingcentre.eu/index.php?title=Makkonen%C2%B4s_model&diff=747Makkonen´s model2022-01-31T11:20:54Z<p>Uwasa: Added description of the measurements for Makkonen's model</p>
<hr />
<div>The international ISO standard Atmospheric Icing on Structures (ISO, E 2017<ref>{{Cite web| title = ISO 12494:2017 - Atmospheric icing of structures| work = iTeh Standards Store| accessdate = 2022-01-31| url = https://standards.iteh.ai/catalog/standards/iso/e35bb939-fd4b-4b3f-83cc-3d4dbb1e71ab/iso-12494-2017}}</ref>) is based on Makkonen icing model <ref>{{Cite journal| doi = 10.1098/rsta.2000.0690| volume = 358| issue = 1776| pages = 2913–2939| last1 = Poots| first1 = G.| last2 = Makkonen| first2 = Lasse| title = Models for the growth of rime, glaze, icicles and wet snow on structures| journal = Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences| accessdate = 2021-08-12| date = 2000-11-15| url = https://royalsocietypublishing.org/doi/10.1098/rsta.2000.0690}}</ref>. The icing model calculates the amount of ice accumulated over a 1 m high vertically oriented, freely rotating cylinder with a diameter of 3 cm. A threshold value of 10 g/h for the modeled icing intensity is often used <ref>{{Cite journal| doi = 10.1002/we.1998| issn = 1099-1824| volume = 20| issue = 1| pages = 171–189| last1 = Hämäläinen| first1 = Karoliina| last2 = Niemelä| first2 = Sami| title = Production of a Numerical Icing Atlas for Finland| journal = Wind Energy| accessdate = 2021-08-09| date = 2017| url = https://onlinelibrary.wiley.com/doi/abs/10.1002/we.1998}}</ref> to distinguish between icing and non-icing conditions. <br />
<br />
In Makkonen’s model icing rate can be modeled with an equation <br />
<br />
[[File:Makkonen´s model.jpg|left|300x300px|Makkonen´s equation]]<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
where 𝛼1 is '''collision efficiency''', describing how likely a droplet hit the cylinder, and it varies between 0..1. It can be set to 1 for large droplet sizes.<br />
<br />
𝛼2 '''sticking efficiency,''' describes how likely a droplet sticks in the cylinder when hit. It ranges from 0..1 and can be set to 1 for freezing rain<br />
<br />
and 𝛼3 represent '''ice accretion efficiency''', varying between 0..1<br />
<br />
𝛼1, 𝛼2 and 𝛼3 are correction factors, and their value is between 0 and 1. Empirical data is used to determine the correction factors and they are very dependent on the icing conditions.<br />
<br />
<br />
A is area and<br />
<br />
v is the velocity of the particles<br />
<br />
and ω is the mass concentration of particles, Liquid Water Content, [[LWC]]<br />
<br />
<br />
<br />
'''Collision efficiency 𝛼1''' is the ratio at which water droplets hit the icing surface, as some droplets, especially the smaller ones, can be directed by airflow to miss the icing object.<br />
<br />
'''Sticking efficiency 𝛼2''' is the measure of how much of the water droplets, or snow, that hit the surface, stick to the ice layer. Supercooled water droplets do not bounce but freeze immediately. Adversely, snow can bounce off. Snow increases the complexity of icing and therefore many icing models have a hard time modelling icing events with snowflakes.<br />
<br />
'''Ice accretion efficiency''' '''𝛼3''' is a measure of how many of the particles contacting the material surface form ice on it. Some droplets do not freeze to from ice, leading to wet ice growth. The heat balance of the object surface affects the number of droplets that freeze. The surface heat balance follows equation:<br />
<br />
[[File:The surface heat balance.jpg|left|329x329px]]<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
where 𝑄𝑓 is latent heat of freezing, <br />
<br />
𝑄𝑣 is frictional heating of air, <br />
<br />
𝑄𝑐 is loss of sensible heat to air, <br />
<br />
𝑄𝑒 is heat loss due to evaporation, <br />
<br />
𝑄𝑙 is heat loss because of warming of supercooled water to freezing point, <br />
<br />
and 𝑄𝑠 is heat loss due to radiation.<br />
<br />
<br />
The ice accretion efficiency is strongly affected by how well the latent heat of freezing can leave the surface.<br />
<br />
<br />
<ref>Ingvaldsen, K. (2017) Atmospheric icing in a changing climate: Impact of higher boundary temperatures on simulations of atmospheric ice accretion on structures during the 2015-2016 icing winter in West-Norway.</ref> <ref>Stenroos, C. (2015) Properties of icephobic surfaces in different icing conditions.</ref> <ref>Makkonen, L. (2000). Models for the growth of rime, glaze, icicles and wet snow on structures. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical, and Engineering Sciences, 358(1776), 2913–2939.</ref></div>Uwasahttps://wiki.icingcentre.eu/index.php?title=Makkonen%C2%B4s_model&diff=746Makkonen´s model2022-01-31T11:06:14Z<p>Uwasa: Added some text and link to LWC</p>
<hr />
<div>In Makkonen’s model icing rate can be modeled with an equation <br />
<br />
[[File:Makkonen´s model.jpg|left|300x300px|Makkonen´s equation]]<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
where 𝛼1 is '''collision efficiency''', describing how likely a droplet hit the cylinder, and it varies between 0..1. It can be set to 1 for large droplet sizes.<br />
<br />
𝛼2 '''sticking efficiency,''' describes how likely a droplet sticks in the cylinder when hit. It ranges from 0..1 and can be set to 1 for freezing rain<br />
<br />
and 𝛼3 represent '''ice accretion efficiency''', varying between 0..1<br />
<br />
𝛼1, 𝛼2 and 𝛼3 are correction factors, and their value is between 0 and 1. Empirical data is used to determine the correction factors and they are very dependent on the icing conditions.<br />
<br />
<br />
A is area and<br />
<br />
v is the velocity of the particles<br />
<br />
and ω is the mass concentration of particles, Liquid Water Content, [[LWC]]<br />
<br />
<br />
<br />
'''Collision efficiency 𝛼1''' is the ratio at which water droplets hit the icing surface, as some droplets, especially the smaller ones, can be directed by airflow to miss the icing object.<br />
<br />
'''Sticking efficiency 𝛼2''' is the measure of how much of the water droplets, or snow, that hit the surface, stick to the ice layer. Supercooled water droplets do not bounce but freeze immediately. Adversely, snow can bounce off. Snow increases the complexity of icing and therefore many icing models have a hard time modelling icing events with snowflakes.<br />
<br />
'''Ice accretion efficiency''' '''𝛼3''' is a measure of how many of the particles contacting the material surface form ice on it. Some droplets do not freeze to from ice, leading to wet ice growth. The heat balance of the object surface affects the number of droplets that freeze. The surface heat balance follows equation:<br />
<br />
[[File:The surface heat balance.jpg|left|329x329px]]<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
where 𝑄𝑓 is latent heat of freezing, <br />
<br />
𝑄𝑣 is frictional heating of air, <br />
<br />
𝑄𝑐 is loss of sensible heat to air, <br />
<br />
𝑄𝑒 is heat loss due to evaporation, <br />
<br />
𝑄𝑙 is heat loss because of warming of supercooled water to freezing point, <br />
<br />
and 𝑄𝑠 is heat loss due to radiation.<br />
<br />
<br />
The ice accretion efficiency is strongly affected by how well the latent heat of freezing can leave the surface.<br />
<br />
<br />
<ref>Ingvaldsen, K. (2017) Atmospheric icing in a changing climate: Impact of higher boundary temperatures on simulations of atmospheric ice accretion on structures during the 2015-2016 icing winter in West-Norway.</ref> <ref>Stenroos, C. (2015) Properties of icephobic surfaces in different icing conditions.</ref> <ref>Makkonen, L. (2000). Models for the growth of rime, glaze, icicles and wet snow on structures. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical, and Engineering Sciences, 358(1776), 2913–2939.</ref></div>Uwasahttps://wiki.icingcentre.eu/index.php?title=LWC&diff=745LWC2022-01-31T10:59:55Z<p>Uwasa: </p>
<hr />
<div>Liquid water content, <math>\rho_{LW}</math>. It's unit is <math>\rm{g/m}^3</math>.<br />
<br />
In a cloud, <math>\rho_{LW}</math> varies between [0.03-3.0] g/cm&sup3;, while it is approximately 0.06 g/cm&sup3; in fog.</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=LWC&diff=744LWC2022-01-31T10:54:48Z<p>Uwasa: </p>
<hr />
<div>Liquid water content, <math>\rho_{LW}</math>. It's unit is <math>\rm{g/m}^3</math>.</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=Main_Page&diff=720Main Page2022-01-18T11:22:06Z<p>Uwasa: Added instructions for adding citations</p>
<hr />
<div><strong>Welcome to the NoICE IcingWiki!</strong><br />
<br />
Consult the [https://www.mediawiki.org/wiki/Special:MyLanguage/Help:Contents User's Guide] for information on using the wiki software.<br />
<br />
To add a new article you can search for the article title you wish to add, or search for "Create an article".<br />
<br />
Instructions for using references in wiki are shown in document [[ImportReferences|Inserting citations]].<br />
<br />
The IcingWiki is under construction!<br />
<br />
<br />
'''Content (to be moved to topics later?)'''<br />
<br />
<br />
<br />
'''FORMING OF ICE AND ICE FEATURES''' - icing, ice type, ice feature, ice adhesion, wind, freezing rain, nucleation, supercooled water, in-cloud icing, precipitation icing, icing on airplane wings, icing on ships, icing on wind turbine blades, icing on masts, icing on powerlines, LWC, icephobic, <br />
<br />
'''ICE PREVENTION, ANTI-ICING AND DE-ICING METHODS''' - ice prevention, superhydrophobic surface, coating material, anti-freeze substances (railway traffic), railway traffic de-icing, railway traffic anti-icing, ice-phobic surfaces, de-icing chemicals (aviation), ice density, <br />
<br />
'''ICE RESEARCH, TESTS AND MODELS''' - modelling of icing, modelling programs, ice adhesion test, icing wind tunnel, Makkonen´s model, Lozowski model, ice classes, <br />
<br />
'''RAILWAY TRAFFIC -''' winter effects in railway traffic, track switches, derailment, power lines, overhead lines, pantograph, train brakes, rail gaps, track smoothness, frost heaving, track brittleness<br />
<br />
== Topics ==<br />
<br />
* [[IcingSensors]]<br />
* [[Atmospheric Icing]]<br />
* [[Traffic]]<br />
* [[Ice Detection and Monitoring]]<br />
* [[Sea Ice Monitoring]]<br />
<br />
== Getting started ==<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:Configuration_settings Configuration settings list]<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:FAQ MediaWiki FAQ]<br />
* [https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Localisation#Translation_resources Localise MediaWiki for your language]<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:Combating_spam Learn how to combat spam on your wiki]<br />
*[[ImportReferences|How to import references into MediaWiki]]</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=ImportReferences&diff=719ImportReferences2022-01-18T11:17:30Z<p>Uwasa: /* Adding citations directly from Zotero */</p>
<hr />
<div>=== Adding citations directly from Zotero ===<br />
[[File:ZoteroQuickCopy.png.png|thumb|'''Figure 1.''' Zotero Quick copy configuration dialog]]<br />
The easiest way to add citations to Wiki is to do it directly from a reference management system, such as [https://www.zotero.org/ Zotero]. First configure the Zotero quick copy configuration using the preferences dialog by selecting ''Preferences'' from ''Edit'' menu, as shown in the Figure 1 on the right. Configure ''Wikipedia Citation Templates'' from the export options. Having done this, you can drag the citations directly from your Zotero collections into the Basic reference dialog opened from ''Cite'' menu, shown in Figure 3. <br />
<br />
=== Batch converting the list of references ===<br />
[[File:ZoteroExportWiki.png|thumb|'''Figure 2.''' A dialog showing how to export citations from Zotero in MediaWiki form]]A large amount of references can be also batch converted into Wikipedia format and then added by copy-pasting them from the text file. To do this, first collect the references using suitable reference management systems, such as [https://www.zotero.org/ Zotero]. Then export the references from the reference management system in Wikipedia format. From Zotero, they references can be exported using using ''Export Collection'' function. Selecting the export format as "''Wikipedia Citation Templates''", the references are exported in Wikipedia format, and can be copy-pasted in Wiki, see Figure 2.<br />
<br />
=== Adding citations in visual editor ===<br />
[[File:InsertReference.png|alt=A dialog for adding a citation|thumb|'''Figure 3.''' A dialog for adding a citation]]<br />
With visual editor, the adding of citation is rather simple. First add ''References list'' from the ''Insert-''menu in the end of the page. After this, the reference can be easily added by starting to type ''< ref'' in the visual editor or select ''Basic'' from the ''Cite'' menu. Then copy and paste suitable Wikimedia formatted reference data into the dialog box or drag it directly from Zotero. If the citation is already in the list of references, you can re-use it by selecting ''Re-Use'' from the ''Cite'' menu.<br />
<br />
Here are some sample citations <ref name=":0">{{Cite journal| doi = 10.1109/LGRS.2009.2032304| issn = 1558-0571| volume = 7| issue = 2| pages = 246–250| last1 = Laird| first1 = Claude M.| last2 = Blake| first2 = William A.| last3 = Matsuoka| first3 = Kenichi| last4 = Conway| first4 = Howard| last5 = Allen| first5 = Christopher T.| last6 = Leuschen| first6 = Carl J.| last7 = Gogineni| first7 = Sivaprasad| title = Deep ice stratigraphy and basal conditions in central west antarctica revealed by coherent radar| journal = IEEE Geoscience and Remote Sensing Letters| date = 2010-04}}</ref><ref>{{Cite journal| doi = 10.1109/TMI.2009.2036258| issn = 1558-254X| volume = 29| issue = 4| pages = 998–1011| last1 = Figueiredo| first1 = Isabel N.| last2 = Figueiredo| first2 = Pedro N.| last3 = Stadler| first3 = Georg| last4 = Ghattas| first4 = Omar| last5 = Araujo| first5 = Adérito| title = Variational image segmentation for endoscopic human colonic aberrant crypt foci| journal = IEEE Transactions on Medical Imaging| date = 2010-04}}</ref> and re use of <ref name=":0" />. Let's add one more directly from Zotero <ref>{{Cite journal| volume = 9| pages = 97| last = Seifert| first = Henry| title = Technical requirements for rotor blades operating in cold climate| journal = BOREAS VI| date = 2003}}</ref> <ref>{{Cite journal| doi = 10.1016/j.coldregions.2006.06.005| issn = 0165-232X| volume = 46| issue = 2| pages = 125â131| last1 = Homola| first1 = Matthew C.| last2 = Nicklasson| first2 = Per J.| last3 = Sundsbø| first3 = Per A.| title = Ice sensors for wind turbines| journal = Cold Regions Science and Technology| accessdate = 2017-08-04| date = 2006-11-01| url = http://www.sciencedirect.com/science/article/pii/S0165232X06000760}}</ref>. <br />
<br />
=== Using Raw editor, and author year citation format ===<br />
It seems that the methods for using citations with Wikimedia editor are great, and nothing else is usually needed. Here is still some script which can be useful when entering a large number of citations in a page.<br />
<br />
To make citing of the references easier, it is good to add a citation key to the references. The citation keys can be added manually, but for a larger list of references, the [https://github.com/UniVaasaDigiEco/Scripts/blob/main/WikiAuthorYearCitations.py WikiAuthorYearCitations.py] -script can be used. <br />
<br />
The script takes two input parameters, the name of the input and output files. The input file should be a Wikimedia-formatted list of references, exported from Zotero, for example. The same list of references will be written in the output file in Wikimedia format, supplemented with unique author-year citation keys. The script also removes duplicates from the list of references. <br />
<br />
The citations can be copy-pasted in the Wiki-page and cited using the following syntax:<br />
<br />
<code>< ref name=Author_year / ></code><br />
<br />
=== References ===<br />
<references /></div>Uwasahttps://wiki.icingcentre.eu/index.php?title=ImportReferences&diff=718ImportReferences2022-01-18T11:13:25Z<p>Uwasa: Updated Zotero quick copy method</p>
<hr />
<div>=== Adding citations directly from Zotero ===<br />
[[File:ZoteroQuickCopy.png.png|thumb|'''Figure 1.''' Zotero Quick copy configuration dialog]]<br />
The easiest way to add citations to Wiki is to do it directly from a reference management system, such as Zotero. First configure the Zotero quick copy configuration using the preferences dialog by selecting ''Preferences'' from ''Edit'' menu, as shown in the Figure 1 on the right. Configure ''Wikipedia Citation Templates'' from the export options. Having done this, you can drag the citations directly from your Zotero collections into the Basic reference dialog opened from ''Cite'' menu, shown in Figure 3. <br />
<br />
=== Batch converting the list of references ===<br />
[[File:ZoteroExportWiki.png|thumb|'''Figure 2.''' A dialog showing how to export citations from Zotero in MediaWiki form]]A large amount of references can be also batch converted into Wikipedia format and then added by copy-pasting them from the text file. To do this, first collect the references using suitable reference management systems, such as [https://www.zotero.org/ Zotero]. Then export the references from the reference management system in Wikipedia format. From Zotero, they references can be exported using using ''Export Collection'' function. Selecting the export format as "''Wikipedia Citation Templates''", the references are exported in Wikipedia format, and can be copy-pasted in Wiki, see Figure 2.<br />
<br />
=== Adding citations in visual editor ===<br />
[[File:InsertReference.png|alt=A dialog for adding a citation|thumb|'''Figure 3.''' A dialog for adding a citation]]<br />
With visual editor, the adding of citation is rather simple. First add ''References list'' from the ''Insert-''menu in the end of the page. After this, the reference can be easily added by starting to type ''< ref'' in the visual editor or select ''Basic'' from the ''Cite'' menu. Then copy and paste suitable Wikimedia formatted reference data into the dialog box or drag it directly from Zotero. If the citation is already in the list of references, you can re-use it by selecting ''Re-Use'' from the ''Cite'' menu.<br />
<br />
Here are some sample citations <ref name=":0">{{Cite journal| doi = 10.1109/LGRS.2009.2032304| issn = 1558-0571| volume = 7| issue = 2| pages = 246–250| last1 = Laird| first1 = Claude M.| last2 = Blake| first2 = William A.| last3 = Matsuoka| first3 = Kenichi| last4 = Conway| first4 = Howard| last5 = Allen| first5 = Christopher T.| last6 = Leuschen| first6 = Carl J.| last7 = Gogineni| first7 = Sivaprasad| title = Deep ice stratigraphy and basal conditions in central west antarctica revealed by coherent radar| journal = IEEE Geoscience and Remote Sensing Letters| date = 2010-04}}</ref><ref>{{Cite journal| doi = 10.1109/TMI.2009.2036258| issn = 1558-254X| volume = 29| issue = 4| pages = 998–1011| last1 = Figueiredo| first1 = Isabel N.| last2 = Figueiredo| first2 = Pedro N.| last3 = Stadler| first3 = Georg| last4 = Ghattas| first4 = Omar| last5 = Araujo| first5 = Adérito| title = Variational image segmentation for endoscopic human colonic aberrant crypt foci| journal = IEEE Transactions on Medical Imaging| date = 2010-04}}</ref> and re use of <ref name=":0" />. Let's add one more directly from Zotero <ref>{{Cite journal| volume = 9| pages = 97| last = Seifert| first = Henry| title = Technical requirements for rotor blades operating in cold climate| journal = BOREAS VI| date = 2003}}</ref> <ref>{{Cite journal| doi = 10.1016/j.coldregions.2006.06.005| issn = 0165-232X| volume = 46| issue = 2| pages = 125â131| last1 = Homola| first1 = Matthew C.| last2 = Nicklasson| first2 = Per J.| last3 = Sundsbø| first3 = Per A.| title = Ice sensors for wind turbines| journal = Cold Regions Science and Technology| accessdate = 2017-08-04| date = 2006-11-01| url = http://www.sciencedirect.com/science/article/pii/S0165232X06000760}}</ref>. <br />
<br />
=== Using Raw editor, and author year citation format ===<br />
It seems that the methods for using citations with Wikimedia editor are great, and nothing else is usually needed. Here is still some script which can be useful when entering a large number of citations in a page.<br />
<br />
To make citing of the references easier, it is good to add a citation key to the references. The citation keys can be added manually, but for a larger list of references, the [https://github.com/UniVaasaDigiEco/Scripts/blob/main/WikiAuthorYearCitations.py WikiAuthorYearCitations.py] -script can be used. <br />
<br />
The script takes two input parameters, the name of the input and output files. The input file should be a Wikimedia-formatted list of references, exported from Zotero, for example. The same list of references will be written in the output file in Wikimedia format, supplemented with unique author-year citation keys. The script also removes duplicates from the list of references. <br />
<br />
The citations can be copy-pasted in the Wiki-page and cited using the following syntax:<br />
<br />
<code>< ref name=Author_year / ></code><br />
<br />
=== References ===<br />
<references /></div>Uwasahttps://wiki.icingcentre.eu/index.php?title=File:ZoteroQuickCopy.png.png&diff=711File:ZoteroQuickCopy.png.png2022-01-18T11:05:12Z<p>Uwasa: </p>
<hr />
<div>Zotero Quic copy configuration dialog</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=ImportReferences&diff=710ImportReferences2022-01-18T10:58:31Z<p>Uwasa: test</p>
<hr />
<div>[[File:ZoteroExportWiki.png|thumb|A dialog showing how to export citations from Zotero in MediaWiki form]]References can be imported semi-automatically using the following instructions. First collect the references using suitable reference management systems, such as [https://www.zotero.org/ Zotero]. Then export the references from the reference management system in Wikipedia format. From Zotero, they references can be exported using using ''Export Collection'' function. Selecting the export format as "''Wikipedia Citation Templates''", the references are exported in Wikipedia format, and can be copy-pasted in Wiki.<br />
<br />
=== Using visual editor ===<br />
[[File:InsertReference.png|alt=A dialog for adding a citation|thumb|A dialog for adding a citation]]<br />
With visual editor, the adding of citation is rather simple. First add ''References list'' from the ''Insert-''menu in the end of the page. After this, the reference can be easily added by starting to type ''< ref'' in the visual editor or select ''Basic'' from the ''Cite'' menu. Then copy and paste suitable Wikimedia formatted reference data into the dialog box. If the citation is already in the list of references, you can re-use it by selecting ''Re-Use'' from the ''Cite'' menu.<br />
<br />
Here are some sample citations <ref name=":0">{{Cite journal| doi = 10.1109/LGRS.2009.2032304| issn = 1558-0571| volume = 7| issue = 2| pages = 246–250| last1 = Laird| first1 = Claude M.| last2 = Blake| first2 = William A.| last3 = Matsuoka| first3 = Kenichi| last4 = Conway| first4 = Howard| last5 = Allen| first5 = Christopher T.| last6 = Leuschen| first6 = Carl J.| last7 = Gogineni| first7 = Sivaprasad| title = Deep ice stratigraphy and basal conditions in central west antarctica revealed by coherent radar| journal = IEEE Geoscience and Remote Sensing Letters| date = 2010-04}}</ref><ref>{{Cite journal| doi = 10.1109/TMI.2009.2036258| issn = 1558-254X| volume = 29| issue = 4| pages = 998–1011| last1 = Figueiredo| first1 = Isabel N.| last2 = Figueiredo| first2 = Pedro N.| last3 = Stadler| first3 = Georg| last4 = Ghattas| first4 = Omar| last5 = Araujo| first5 = Adérito| title = Variational image segmentation for endoscopic human colonic aberrant crypt foci| journal = IEEE Transactions on Medical Imaging| date = 2010-04}}</ref> and re use of <ref name=":0" />. Let's add one more directly from Zotero <ref>{{Cite journal| volume = 9| pages = 97| last = Seifert| first = Henry| title = Technical requirements for rotor blades operating in cold climate| journal = BOREAS VI| date = 2003}}</ref>. <br />
<br />
=== Using Raw editor, and author year citation format ===<br />
It seems that the methods for using citations with Wikimedia editor are great, and nothing else is usually needed. Here is still some script which can be useful when entering a large number of citations in a page.<br />
<br />
To make citing of the references easier, it is good to add a citation key to the references. The citation keys can be added manually, but for a larger list of references, the [https://github.com/UniVaasaDigiEco/Scripts/blob/main/WikiAuthorYearCitations.py WikiAuthorYearCitations.py] -script can be used. <br />
<br />
The script takes two input parameters, the name of the input and output files. The input file should be a Wikimedia-formatted list of references, exported from Zotero, for example. The same list of references will be written in the output file in Wikimedia format, supplemented with unique author-year citation keys. The script also removes duplicates from the list of references. <br />
<br />
The citations can be copy-pasted in the Wiki-page and cited using the following syntax:<br />
<br />
<code>< ref name=Author_year / ></code><br />
<br />
=== References ===<br />
<references /></div>Uwasahttps://wiki.icingcentre.eu/index.php?title=ImportReferences&diff=705ImportReferences2022-01-18T10:18:34Z<p>Uwasa: Finished the instructions</p>
<hr />
<div>[[File:ZoteroExportWiki.png|thumb|A dialog showing how to export citations from Zotero in MediaWiki form]]References can be imported semi-automatically using the following instructions. First collect the references using suitable reference management systems, such as [https://www.zotero.org/ Zotero]. Then export the references from the reference management system in Wikipedia format. From Zotero, they references can be exported using using ''Export Collection'' function. Selecting the export format as "''Wikipedia Citation Templates''", the references are exported in Wikipedia format, and can be copy-pasted in Wiki.<br />
<br />
=== Using visual editor ===<br />
[[File:InsertReference.png|alt=A dialog for adding a citation|thumb|A dialog for adding a citation]]<br />
With visual editor, the adding of citation is rather simple. First add ''References list'' from the ''Insert-''menu in the end of the page. After this, the reference can be easily added by starting to type ''< ref'' in the visual editor or select ''Basic'' from the ''Cite'' menu. Then copy and paste suitable Wikimedia formatted reference data into the dialog box. If the citation is already in the list of references, you can re-use it by selecting ''Re-Use'' from the ''Cite'' menu.<br />
<br />
Here are some sample citations <ref name=":0">{{Cite journal| doi = 10.1109/LGRS.2009.2032304| issn = 1558-0571| volume = 7| issue = 2| pages = 246–250| last1 = Laird| first1 = Claude M.| last2 = Blake| first2 = William A.| last3 = Matsuoka| first3 = Kenichi| last4 = Conway| first4 = Howard| last5 = Allen| first5 = Christopher T.| last6 = Leuschen| first6 = Carl J.| last7 = Gogineni| first7 = Sivaprasad| title = Deep ice stratigraphy and basal conditions in central west antarctica revealed by coherent radar| journal = IEEE Geoscience and Remote Sensing Letters| date = 2010-04}}</ref><ref>{{Cite journal| doi = 10.1109/TMI.2009.2036258| issn = 1558-254X| volume = 29| issue = 4| pages = 998–1011| last1 = Figueiredo| first1 = Isabel N.| last2 = Figueiredo| first2 = Pedro N.| last3 = Stadler| first3 = Georg| last4 = Ghattas| first4 = Omar| last5 = Araujo| first5 = Adérito| title = Variational image segmentation for endoscopic human colonic aberrant crypt foci| journal = IEEE Transactions on Medical Imaging| date = 2010-04}}</ref> and re use of <ref name=":0" />.<br />
<br />
=== Using Raw editor, and author year citation format ===<br />
It seems that the methods for using citations with Wikimedia editor are great, and nothing else is usually needed. Here is still some script which can be useful when entering a large number of citations in a page.<br />
<br />
To make citing of the references easier, it is good to add a citation key to the references. The citation keys can be added manually, but for a larger list of references, the [https://github.com/UniVaasaDigiEco/Scripts/blob/main/WikiAuthorYearCitations.py WikiAuthorYearCitations.py] -script can be used. <br />
<br />
The script takes two input parameters, the name of the input and output files. The input file should be a Wikimedia-formatted list of references, exported from Zotero, for example. The same list of references will be written in the output file in Wikimedia format, supplemented with unique author-year citation keys. The script also removes duplicates from the list of references. <br />
<br />
The citations can be copy-pasted in the Wiki-page and cited using the following syntax:<br />
<br />
<code>< ref name=Author_year / ></code><br />
<br />
=== References ===<br />
<references><ref name="Laird_2010">{{Cite journal| doi = 10.1109/LGRS.2009.2032304| issn = 1558-0571| volume = 7| issue = 2| pages = 246–250| last1 = Laird| first1 = Claude M.| last2 = Blake| first2 = William A.| last3 = Matsuoka| first3 = Kenichi| last4 = Conway| first4 = Howard| last5 = Allen| first5 = Christopher T.| last6 = Leuschen| first6 = Carl J.| last7 = Gogineni| first7 = Sivaprasad| title = Deep ice stratigraphy and basal conditions in central west antarctica revealed by coherent radar| journal = IEEE Geoscience and Remote Sensing Letters| date = 2010-04}}<br />
</ref> <ref name="Figueiredo_2010">{{Cite journal| doi = 10.1109/TMI.2009.2036258| issn = 1558-254X| volume = 29| issue = 4| pages = 998–1011| last1 = Figueiredo| first1 = Isabel N.| last2 = Figueiredo| first2 = Pedro N.| last3 = Stadler| first3 = Georg| last4 = Ghattas| first4 = Omar| last5 = Araujo| first5 = Adérito| title = Variational image segmentation for endoscopic human colonic aberrant crypt foci| journal = IEEE Transactions on Medical Imaging| date = 2010-04}}<br />
</ref> <ref name="Zirizzotti_2010">{{Cite journal| doi = 10.1109/TGRS.2009.2038900| issn = 1558-0644| volume = 48| issue = 5| pages = 2343–2348| last1 = Zirizzotti| first1 = Achille| last2 = Cafarella| first2 = Lili| last3 = Baskaradas| first3 = James Arokasami| last4 = Tabacco| first4 = Ignazio Ezio| last5 = Urbini| first5 = Stefano| last6 = Mangialetti| first6 = Morena| last7 = Bianchi| first7 = Cesidio| title = Dry–Wet bedrock interface detection by radio echo sounding measurements| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2010-05}}<br />
</ref></references></div>Uwasahttps://wiki.icingcentre.eu/index.php?title=ImportReferences&diff=703ImportReferences2022-01-18T10:16:00Z<p>Uwasa: </p>
<hr />
<div>[[File:ZoteroExportWiki.png|thumb|A dialog showing how to export citations from Zotero in MediaWiki form]]References can be imported semi-automatically using the following instructions. First collect the references using suitable reference management systems, such as [https://www.zotero.org/ Zotero]. Then export the references from the reference management system in Wikipedia format. From Zotero, they references can be exported using using ''Export Collection'' function. Selecting the export format as "''Wikipedia Citation Templates''", the references are exported in Wikipedia format, and can be copy-pasted in Wiki.<br />
<br />
=== Using visual editor ===<br />
[[File:InsertReference.png|alt=A dialog for adding a citation|thumb|A dialog for adding a citation]]<br />
With visual editor, the adding of citation is rather simple. First add ''References list'' from the ''Insert-''menu in the end of the page. After this, the reference can be easily added by starting to type ''< ref'' in the visual editor or select ''Basic'' from the ''Cite'' menu. Then copy and paste suitable Wikimedia formatted reference data into the dialog box. If the citation is already in the list of references, you can re-use it by selecting ''Re-Use'' from the ''Cite'' menu.<br />
<br />
=== Using Raw editor, and author year citation format ===<br />
It seems that the methods for using citations with Wikimedia editor are great, and nothing else is usually needed. Here is still some script which can be useful when entering a large number of citations in a page.<br />
<br />
To make citing of the references easier, it is good to add a citation key to the references. The citation keys can be added manually, but for a larger list of references, the [https://github.com/UniVaasaDigiEco/Scripts/blob/main/WikiAuthorYearCitations.py WikiAuthorYearCitations.py] -script can be used. <br />
<br />
The script takes two input parameters, the name of the input and output files. The input file should be a Wikimedia-formatted list of references, exported from Zotero, for example. The same list of references will be written in the output file in Wikimedia format, supplemented with unique author-year citation keys. The script also removes duplicates from the list of references. <br />
<br />
The citations can be copy-pasted in the Wiki-page and cited using the following syntax:<br />
<br />
<code>< ref name=Author_year / ></code><br />
<br />
Like this <ref name="Laird_2010"/><br />
<br />
=== References ===<br />
<references><ref name="Laird_2010">{{Cite journal| doi = 10.1109/LGRS.2009.2032304| issn = 1558-0571| volume = 7| issue = 2| pages = 246–250| last1 = Laird| first1 = Claude M.| last2 = Blake| first2 = William A.| last3 = Matsuoka| first3 = Kenichi| last4 = Conway| first4 = Howard| last5 = Allen| first5 = Christopher T.| last6 = Leuschen| first6 = Carl J.| last7 = Gogineni| first7 = Sivaprasad| title = Deep ice stratigraphy and basal conditions in central west antarctica revealed by coherent radar| journal = IEEE Geoscience and Remote Sensing Letters| date = 2010-04}}<br />
</ref> <ref name="Figueiredo_2010">{{Cite journal| doi = 10.1109/TMI.2009.2036258| issn = 1558-254X| volume = 29| issue = 4| pages = 998–1011| last1 = Figueiredo| first1 = Isabel N.| last2 = Figueiredo| first2 = Pedro N.| last3 = Stadler| first3 = Georg| last4 = Ghattas| first4 = Omar| last5 = Araujo| first5 = Adérito| title = Variational image segmentation for endoscopic human colonic aberrant crypt foci| journal = IEEE Transactions on Medical Imaging| date = 2010-04}}<br />
</ref> <ref name="Zirizzotti_2010">{{Cite journal| doi = 10.1109/TGRS.2009.2038900| issn = 1558-0644| volume = 48| issue = 5| pages = 2343–2348| last1 = Zirizzotti| first1 = Achille| last2 = Cafarella| first2 = Lili| last3 = Baskaradas| first3 = James Arokasami| last4 = Tabacco| first4 = Ignazio Ezio| last5 = Urbini| first5 = Stefano| last6 = Mangialetti| first6 = Morena| last7 = Bianchi| first7 = Cesidio| title = Dry–Wet bedrock interface detection by radio echo sounding measurements| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2010-05}}<br />
</ref></references></div>Uwasahttps://wiki.icingcentre.eu/index.php?title=ImportReferences&diff=702ImportReferences2022-01-18T10:14:59Z<p>Uwasa: Updated the visual editor instructions</p>
<hr />
<div>[[File:ZoteroExportWiki.png|thumb|A dialog showing how to export citations from Zotero in MediaWiki form]]References can be imported semi-automatically using the following instructions. First collect the references using suitable reference management systems, such as [https://www.zotero.org/ Zotero]. Then export the references from the reference management system in Wikipedia format. From Zotero, they references can be exported using using ''Export Collection'' function. Selecting the export format as "''Wikipedia Citation Templates''", the references are exported in Wikipedia format, and can be copy-pasted in Wiki.<br />
<br />
=== Using visual editor ===<br />
[[File:InsertReference.png|alt=A dialog for adding a citation|thumb|A dialog for adding a citation]]<br />
With visual editor, the adding of citation is rather simple. First add ''References list'' from the ''Insert-''menu in the end of the page. After this, the reference can be easily added by starting to type ''< ref'' in the visual editor or select ''Basic'' from the ''Cite'' menu. Then copy and paste suitable Wikimedia formatted reference data into the dialog box. If the citation is already in the list of references, you can re-use it by selecting ''Re-Use'' from the ''Cite'' menu.<br />
<br />
=== Using Raw editor, and author year citation format ===<br />
It seems that the methods for using citations with Wikimedia editor are great, and nothing else is usually needed. Here is still some script which can be useful when entering a large number of citations in a page.<br />
<br />
To make citing of the references easier, it is good to add a citation key to the references. The citation keys can be added manually, but for a larger list of references, the [https://github.com/UniVaasaDigiEco/Scripts/blob/main/WikiAuthorYearCitations.py WikiAuthorYearCitations.py] -script can be used. <br />
<br />
The script takes two input parameters, the name of the input and output files. The input file should be a Wikimedia-formatted list of references, exported from Zotero, for example. The same list of references will be written in the output file in Wikimedia format, supplemented with unique author-year citation keys. The script also removes duplicates from the list of references. <br />
<br />
The citations can be copy-pasted in the Wiki-page and cited using the following syntax:<br />
<br />
<code>< ref name=Author_year / ></code><br />
<br />
=== References ===<br />
<references><ref name="Laird_2010">{{Cite journal| doi = 10.1109/LGRS.2009.2032304| issn = 1558-0571| volume = 7| issue = 2| pages = 246–250| last1 = Laird| first1 = Claude M.| last2 = Blake| first2 = William A.| last3 = Matsuoka| first3 = Kenichi| last4 = Conway| first4 = Howard| last5 = Allen| first5 = Christopher T.| last6 = Leuschen| first6 = Carl J.| last7 = Gogineni| first7 = Sivaprasad| title = Deep ice stratigraphy and basal conditions in central west antarctica revealed by coherent radar| journal = IEEE Geoscience and Remote Sensing Letters| date = 2010-04}}<br />
</ref> <ref name="Figueiredo_2010">{{Cite journal| doi = 10.1109/TMI.2009.2036258| issn = 1558-254X| volume = 29| issue = 4| pages = 998–1011| last1 = Figueiredo| first1 = Isabel N.| last2 = Figueiredo| first2 = Pedro N.| last3 = Stadler| first3 = Georg| last4 = Ghattas| first4 = Omar| last5 = Araujo| first5 = Adérito| title = Variational image segmentation for endoscopic human colonic aberrant crypt foci| journal = IEEE Transactions on Medical Imaging| date = 2010-04}}<br />
</ref> <ref name="Zirizzotti_2010">{{Cite journal| doi = 10.1109/TGRS.2009.2038900| issn = 1558-0644| volume = 48| issue = 5| pages = 2343–2348| last1 = Zirizzotti| first1 = Achille| last2 = Cafarella| first2 = Lili| last3 = Baskaradas| first3 = James Arokasami| last4 = Tabacco| first4 = Ignazio Ezio| last5 = Urbini| first5 = Stefano| last6 = Mangialetti| first6 = Morena| last7 = Bianchi| first7 = Cesidio| title = Dry–Wet bedrock interface detection by radio echo sounding measurements| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2010-05}}<br />
</ref></references></div>Uwasahttps://wiki.icingcentre.eu/index.php?title=File:InsertReference.png&diff=701File:InsertReference.png2022-01-18T10:09:18Z<p>Uwasa: </p>
<hr />
<div>A dialog box for citing a reference</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=ImportReferences&diff=693ImportReferences2022-01-18T09:41:07Z<p>Uwasa: Added the WikiAuthorYearCitations script</p>
<hr />
<div>References can be imported semi-automatically using the following instructions. First collect the references using suitable reference management systems, such as [https://www.zotero.org/ Zotero]. Then export the references from the reference management system in Wikipedia format. From Zotero, they references can be exported using using ''Export Collection'' function. Selecting the export format as "''Wikipedia Citation Templates''", the references are exported in Wikipedia format, and can be copy-pasted in Wiki.<br />
<br />
[[File:ZoteroExportWiki.png|thumb|A dialog showing how to export citations from Zotero in MediaWiki form]]<br />
To make citing of the references easier, it is good to add a citation key to the references. The citation keys can be added manually, but for a larger list of references, the [https://github.com/UniVaasaDigiEco/Scripts/blob/main/WikiAuthorYearCitations.py WikiAuthorYearCitations.py] -script can be used. <br />
<br />
The script takes two input parameters, the name of the input and output files. The input file should be a Wikimedia-formatted list of references, exported from Zotero, for example. The same list of references will be written in the output file in Wikimedia format, supplemented with unique author-year citation keys. The script also removes duplicates from the list of references. <br />
<br />
The citations can be copy-pasted in the Wiki-page and cited using the following syntax:<br />
<br />
<code>< ref name=Author_year / ></code></div>Uwasahttps://wiki.icingcentre.eu/index.php?title=ImportReferences&diff=692ImportReferences2022-01-18T07:51:02Z<p>Uwasa: Instructions for adding citations in Wiki</p>
<hr />
<div>References can be imported semi-automatically using the following instructions. First collect the references using suitable reference management systems, such as [https://www.zotero.org/ Zotero]. Then export the references from the reference management system in Wikipedia format. From Zotero, they references can be exported using using ''Export Collection'' function. Selecting the export format as "''Wikipedia Citation Templates''", the references are exported in Wikipedia format, and can be copy-pasted in Wiki.<br />
<br />
[[File:ZoteroExportWiki.png|thumb|A dialog showing how to export citations from Zotero in MediaWiki form]]<br />
To make citing of the references easier, it is good to add a citation key to the references. The citation keys can be added manually, but for a larger list of references, the following script can be used. The script uses ....</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=File:ZoteroExportWiki.png&diff=691File:ZoteroExportWiki.png2022-01-18T07:27:49Z<p>Uwasa: </p>
<hr />
<div>Shows how to export citations in Wiki form from Zotero</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=Main_Page&diff=690Main Page2022-01-18T07:22:24Z<p>Uwasa: /* Getting started */ Added instructions for adding referecenes</p>
<hr />
<div><strong>Welcome to the NoICE IcingWiki!</strong><br />
<br />
Consult the [https://www.mediawiki.org/wiki/Special:MyLanguage/Help:Contents User's Guide] for information on using the wiki software.<br />
<br />
To add a new article you can search for the article title you wish to add, or search for "Create an article".<br />
<br />
The IcingWiki is under construction!<br />
<br />
<br />
'''Content (to be moved to topics later?)'''<br />
<br />
<br />
<br />
'''FORMING OF ICE AND ICE FEATURES''' - icing, ice type, ice feature, ice adhesion, wind, freezing rain, nucleation, supercooled water, in-cloud icing, precipitation icing, icing on airplane wings, icing on ships, icing on wind turbine blades, icing on masts, icing on powerlines, LWC, icephobic, <br />
<br />
'''ICE PREVENTION, ANTI-ICING AND DE-ICING METHODS''' - ice prevention, superhydrophobic surface, coating material, anti-freeze substances (railway traffic), railway traffic de-icing, railway traffic anti-icing, ice-phobic surfaces, de-icing chemicals (aviation), polymeric coating, ice density, <br />
<br />
'''ICE RESEARCH, TESTS AND MODELS''' - modelling of icing, modelling programs, ice adhesion test, icing wind tunnel, Makkonen´s model, Lozowski model, ice classes, <br />
<br />
'''RAILWAY TRAFFIC -''' winter effects in railway traffic, track switches, derailment, power lines, overhead lines, pantograph, train brakes, rail gaps, track smoothness, frost heaving, track brittleness<br />
<br />
== Topics ==<br />
<br />
* [[IcingSensors]]<br />
* [[Atmospheric Icing]]<br />
* [[Traffic]]<br />
* [[Ice Detection and Monitoring]]<br />
* [[Sea Ice Monitoring]]<br />
<br />
== Getting started ==<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:Configuration_settings Configuration settings list]<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:FAQ MediaWiki FAQ]<br />
* [https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Localisation#Translation_resources Localise MediaWiki for your language]<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:Combating_spam Learn how to combat spam on your wiki]<br />
*[[ImportReferences|How to import references into MediaWiki]]</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=IcingSensors&diff=684IcingSensors2022-01-17T10:48:23Z<p>Uwasa: </p>
<hr />
<div>{| class="wikitable"<br />
|+Yes yet<br />
!'''FIrst'''<br />
!'''Second'''<br />
!'''Third'''<br />
!'''Fourth'''<br />
|-<br />
!1<br />
!2<br />
!3<br />
|4<br />
|-<br />
!Monday<br />
!Tuesday<br />
!Wed<br />
|Ttuy<br />
|-<br />
|a<br />
|b<br />
|c<br />
|d<br />
|}<br />
{{Cite journal| doi = 10.5772/37749| last1 = Troiano| first1 = Amedeo| last2 = Pasero| first2 = Eros| last3 = Mesin| first3 = Luca| title = A Road Ice Sensor| journal = New Advances in Vehicular Technology and Automotive Engineering| accessdate = 2020-10-18| date = 2012-08-01| url = https://www.intechopen.com/books/new-advances-in-vehicular-technology-and-automotive-engineering/a-road-ice-sensor}}<br />
{{Cite journal| doi = 10.1175/1520-0426(2001)018<0515:ATRIDW>2.0.CO;2| issn = 0739-0572| volume = 18| issue = 4| pages = 515–528| last1 = Cober| first1 = Stewart G.| last2 = Isaac| first2 = George A.| last3 = Korolev| first3 = Alexei V.| title = Assessing the Rosemount Icing Detector with In Situ Measurements| journal = Journal of Atmospheric and Oceanic Technology| accessdate = 2019-01-30| date = 2001-04-01| url = https://journals.ametsoc.org/doi/abs/10.1175/1520-0426(2001)018%3C0515:ATRIDW%3E2.0.CO%3B2}}<br />
{{Cite journal| doi = 10.1175/JTECH-D-17-0074.1| issn = 0739-0572| volume = 35| issue = 3| pages = 459–472| last1 = Waugh| first1 = Sean| last2 = Schuur| first2 = Terry J.| title = On the Use of Radiosondes in Freezing Precipitation| journal = Journal of Atmospheric and Oceanic Technology| accessdate = 2019-01-30| date = 2018-01-25| url = https://journals.ametsoc.org/doi/abs/10.1175/JTECH-D-17-0074.1}}<br />
{{Cite conference| last1 = Wadham-Gagnon| first1 = M| last2 = Swytink-Binnema| first2 = N| last3 = Bolduc| first3 = D| last4 = Tété| first4 = K| last5 = Arbez| first5 = C| title = Ice Detection Methods and Measurement of Atmospheric Icing| booktitle = 16th International Workshop on Atmospheric Icing of Structures (IWAIS)| date = 2015}}<br />
{{Cite journal| doi = 10.1175/JAM2535.1| issn = 1558-8424| volume = 46| issue = 9| pages = 1423–1437| last1 = Ryerson| first1 = Charles C.| last2 = Ramsay| first2 = Allan C.| title = Quantitative Ice Accretion Information from the Automated Surface Observing System| journal = Journal of Applied Meteorology and Climatology| accessdate = 2019-01-30| date = 2007-09-01| url = https://journals.ametsoc.org/doi/abs/10.1175/JAM2535.1}}<br />
{{Cite journal| doi = 10.1016/j.coldregions.2007.12.003| issn = 0165-232X| volume = 53| issue = 2| pages = 150–161| last1 = Drage| first1 = Magne A.| last2 = Hauge| first2 = Gard| title = Atmospheric icing in a coastal mountainous terrain. Measurements and numerical simulations, a case study| journal = Cold Regions Science and Technology| accessdate = 2019-01-30| date = 2008-07-01| url = http://www.sciencedirect.com/science/article/pii/S0165232X07002121}}<br />
{{Cite journal| doi = 10.1016/j.atmosres.2013.03.005| issn = 0169-8095| volume = 128| pages = 57–75| last1 = Lamraoui| first1 = Fayçal| last2 = Fortin| first2 = Guy| last3 = Benoit| first3 = Robert| last4 = Perron| first4 = Jean| last5 = Masson| first5 = Christian| title = Atmospheric icing severity: Quantification and mapping| journal = Atmospheric Research| accessdate = 2019-01-30| date = 2013-07-01| url = http://www.sciencedirect.com/science/article/pii/S0169809513000896}}<br />
{{Cite journal| last1 = Fortin| first1 = Guy| last2 = Perron| first2 = Jean| last3 = Ilinca| first3 = Adrian| title = Behaviour and modeling of cup anemometers under Icing conditions| date = 2005}}<br />
{{Cite journal| doi = 10.1175/1520-0426(2001)018<1457:AIIC>2.0.CO;2| issn = 0739-0572| volume = 18| issue = 9| pages = 1457–1469| last1 = Makkonen| first1 = Lasse| last2 = Lehtonen| first2 = Pertti| last3 = Helle| first3 = Lauri| title = Anemometry in Icing Conditions| journal = Journal of Atmospheric and Oceanic Technology| accessdate = 2019-01-30| date = 2001-09-01| url = https://journals.ametsoc.org/doi/abs/10.1175/1520-0426(2001)018%3C1457:AIIC%3E2.0.CO;2}}</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=IcingSensors&diff=683IcingSensors2022-01-17T10:47:28Z<p>Uwasa: teset table</p>
<hr />
<div>{| class="wikitable"<br />
|+Yes yet<br />
!FIrst<br />
!Second<br />
!Third<br />
!Fourth<br />
|-<br />
!1<br />
!2<br />
!3<br />
|4<br />
|-<br />
!Monday<br />
!Tuesday<br />
!Wed<br />
|Ttuy<br />
|-<br />
|a<br />
|b<br />
|c<br />
|d<br />
|}<br />
{{Cite journal| doi = 10.5772/37749| last1 = Troiano| first1 = Amedeo| last2 = Pasero| first2 = Eros| last3 = Mesin| first3 = Luca| title = A Road Ice Sensor| journal = New Advances in Vehicular Technology and Automotive Engineering| accessdate = 2020-10-18| date = 2012-08-01| url = https://www.intechopen.com/books/new-advances-in-vehicular-technology-and-automotive-engineering/a-road-ice-sensor}}<br />
{{Cite journal| doi = 10.1175/1520-0426(2001)018<0515:ATRIDW>2.0.CO;2| issn = 0739-0572| volume = 18| issue = 4| pages = 515–528| last1 = Cober| first1 = Stewart G.| last2 = Isaac| first2 = George A.| last3 = Korolev| first3 = Alexei V.| title = Assessing the Rosemount Icing Detector with In Situ Measurements| journal = Journal of Atmospheric and Oceanic Technology| accessdate = 2019-01-30| date = 2001-04-01| url = https://journals.ametsoc.org/doi/abs/10.1175/1520-0426(2001)018%3C0515:ATRIDW%3E2.0.CO%3B2}}<br />
{{Cite journal| doi = 10.1175/JTECH-D-17-0074.1| issn = 0739-0572| volume = 35| issue = 3| pages = 459–472| last1 = Waugh| first1 = Sean| last2 = Schuur| first2 = Terry J.| title = On the Use of Radiosondes in Freezing Precipitation| journal = Journal of Atmospheric and Oceanic Technology| accessdate = 2019-01-30| date = 2018-01-25| url = https://journals.ametsoc.org/doi/abs/10.1175/JTECH-D-17-0074.1}}<br />
{{Cite conference| last1 = Wadham-Gagnon| first1 = M| last2 = Swytink-Binnema| first2 = N| last3 = Bolduc| first3 = D| last4 = Tété| first4 = K| last5 = Arbez| first5 = C| title = Ice Detection Methods and Measurement of Atmospheric Icing| booktitle = 16th International Workshop on Atmospheric Icing of Structures (IWAIS)| date = 2015}}<br />
{{Cite journal| doi = 10.1175/JAM2535.1| issn = 1558-8424| volume = 46| issue = 9| pages = 1423–1437| last1 = Ryerson| first1 = Charles C.| last2 = Ramsay| first2 = Allan C.| title = Quantitative Ice Accretion Information from the Automated Surface Observing System| journal = Journal of Applied Meteorology and Climatology| accessdate = 2019-01-30| date = 2007-09-01| url = https://journals.ametsoc.org/doi/abs/10.1175/JAM2535.1}}<br />
{{Cite journal| doi = 10.1016/j.coldregions.2007.12.003| issn = 0165-232X| volume = 53| issue = 2| pages = 150–161| last1 = Drage| first1 = Magne A.| last2 = Hauge| first2 = Gard| title = Atmospheric icing in a coastal mountainous terrain. Measurements and numerical simulations, a case study| journal = Cold Regions Science and Technology| accessdate = 2019-01-30| date = 2008-07-01| url = http://www.sciencedirect.com/science/article/pii/S0165232X07002121}}<br />
{{Cite journal| doi = 10.1016/j.atmosres.2013.03.005| issn = 0169-8095| volume = 128| pages = 57–75| last1 = Lamraoui| first1 = Fayçal| last2 = Fortin| first2 = Guy| last3 = Benoit| first3 = Robert| last4 = Perron| first4 = Jean| last5 = Masson| first5 = Christian| title = Atmospheric icing severity: Quantification and mapping| journal = Atmospheric Research| accessdate = 2019-01-30| date = 2013-07-01| url = http://www.sciencedirect.com/science/article/pii/S0169809513000896}}<br />
{{Cite journal| last1 = Fortin| first1 = Guy| last2 = Perron| first2 = Jean| last3 = Ilinca| first3 = Adrian| title = Behaviour and modeling of cup anemometers under Icing conditions| date = 2005}}<br />
{{Cite journal| doi = 10.1175/1520-0426(2001)018<1457:AIIC>2.0.CO;2| issn = 0739-0572| volume = 18| issue = 9| pages = 1457–1469| last1 = Makkonen| first1 = Lasse| last2 = Lehtonen| first2 = Pertti| last3 = Helle| first3 = Lauri| title = Anemometry in Icing Conditions| journal = Journal of Atmospheric and Oceanic Technology| accessdate = 2019-01-30| date = 2001-09-01| url = https://journals.ametsoc.org/doi/abs/10.1175/1520-0426(2001)018%3C1457:AIIC%3E2.0.CO;2}}</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=Main_Page&diff=531Main Page2021-12-09T13:33:36Z<p>Uwasa: /* Topics */</p>
<hr />
<div><strong>Welcome to the NoICE IcingWiki!</strong><br />
<br />
Consult the [https://www.mediawiki.org/wiki/Special:MyLanguage/Help:Contents User's Guide] for information on using the wiki software.<br />
<br />
To add a new article you can search for the article title you wish to add, or search for "Create an article".<br />
<br />
== Topics ==<br />
<br />
* [[IcingSensors]]<br />
* [[Atmospheric Icing]]<br />
* [[Traffic]]<br />
* [[Ice Detection and Monitoring]]<br />
* [[Sea Ice Monitoring]]<br />
<br />
== Getting started ==<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:Configuration_settings Configuration settings list]<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:FAQ MediaWiki FAQ]<br />
* [https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Localisation#Translation_resources Localise MediaWiki for your language]<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:Combating_spam Learn how to combat spam on your wiki]</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=Ice_Detecting_and_monitoring&diff=530Ice Detecting and monitoring2021-12-09T13:33:04Z<p>Uwasa: Uwasa moved page Ice Detecting and monitoring to Ice Detection and Monitoring: Fixing a typo</p>
<hr />
<div>#REDIRECT [[Ice Detection and Monitoring]]</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=Ice_Detection_and_Monitoring&diff=529Ice Detection and Monitoring2021-12-09T13:33:04Z<p>Uwasa: Uwasa moved page Ice Detecting and monitoring to Ice Detection and Monitoring: Fixing a typo</p>
<hr />
<div>* [[GNSS Reflectometry]]</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=Main_Page&diff=528Main Page2021-12-09T13:31:38Z<p>Uwasa: /* Topics */</p>
<hr />
<div><strong>Welcome to the NoICE IcingWiki!</strong><br />
<br />
Consult the [https://www.mediawiki.org/wiki/Special:MyLanguage/Help:Contents User's Guide] for information on using the wiki software.<br />
<br />
To add a new article you can search for the article title you wish to add, or search for "Create an article".<br />
<br />
== Topics ==<br />
<br />
* [[IcingSensors]]<br />
* [[Atmospheric Icing]]<br />
* [[Traffic]]<br />
* [[Ice Detecting and monitoring]]<br />
* [[Sea Ice Monitoring]]<br />
<br />
== Getting started ==<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:Configuration_settings Configuration settings list]<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:FAQ MediaWiki FAQ]<br />
* [https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Localisation#Translation_resources Localise MediaWiki for your language]<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:Combating_spam Learn how to combat spam on your wiki]</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=SAR_Imaging&diff=527SAR Imaging2021-12-09T13:30:05Z<p>Uwasa: Created page with "TBD"</p>
<hr />
<div>TBD</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=Sea_Ice_Monitoring&diff=526Sea Ice Monitoring2021-12-09T13:29:47Z<p>Uwasa: Created page with "Monitoring of the ice situation in sea is very important for sea traffic. Some methods suitable for sea ice monitoring are GNSS Reflectometry and SAR Imaging."</p>
<hr />
<div>Monitoring of the ice situation in sea is very important for sea traffic. Some methods suitable for sea ice monitoring are [[GNSS Reflectometry]] and [[SAR Imaging]].</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=Main_Page&diff=525Main Page2021-12-09T13:28:03Z<p>Uwasa: /* Topics */ Added Sea ice monitoring topi</p>
<hr />
<div><strong>Welcome to the NoICE IcingWiki!</strong><br />
<br />
Consult the [https://www.mediawiki.org/wiki/Special:MyLanguage/Help:Contents User's Guide] for information on using the wiki software.<br />
<br />
To add a new article you can search for the article title you wish to add, or search for "Create an article".<br />
<br />
== Topics ==<br />
<br />
* [[IcingSensors]]<br />
* [[Atmospheric Icing]]<br />
* [[Traffic]]<br />
* [[Ice Detecting and monitoring]]<br />
*[[Sea Ice Monitoring]]<br />
<br />
== Getting started ==<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:Configuration_settings Configuration settings list]<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:FAQ MediaWiki FAQ]<br />
* [https://lists.wikimedia.org/mailman/listinfo/mediawiki-announce MediaWiki release mailing list]<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Localisation#Translation_resources Localise MediaWiki for your language]<br />
* [https://www.mediawiki.org/wiki/Special:MyLanguage/Manual:Combating_spam Learn how to combat spam on your wiki]</div>Uwasahttps://wiki.icingcentre.eu/index.php?title=GNSS_Reflectometry&diff=524GNSS Reflectometry2021-12-09T11:42:26Z<p>Uwasa: </p>
<hr />
<div>Global Navigation Satellite System (GNSS) satellites transmits navigation signals from Earth orbits to terrestial receivers to help them find their location. These radio signals are also reflecting from the ground and by monitoring these reflections, the properties of the ground can be examined <ref name=King_2020 />. This kind of analysis is called as GNSS reflectometry (GNSS-R) <ref name=Jia-2021/>.<br />
<br />
Terrestial GNSS-R measurements include two antennas, on of which is receiving the direct signal from the satellite, and the other forward-scattered reflected signal. Satellite-based GSNS-R measures the GNSS signal backscatter from the Earth. GNSS-R bacscatter data has been collected by ESA's [https://earth.esa.int/web/eoportal/satellite-missions/t/techdemosat-1 TechDemoSat-1] (TDS-1) satellite and with NASA's [https://www.nasa.gov/cygnss/the-science-of-cygnss CYGNSS] satellite constellation. Observation data can be downloaded from their portals. <br />
<br />
GNSS reflectometry has been used for detecting sea ice thickness <ref name=Yan_2017/> <ref name=Alonso-Arroyo_2017 /> <ref name=Semmling_2019 /><ref name=Yan_2016 /> <ref name=Yan_2018/><ref name=Southwell_2020/> <ref name=Strandberg_2017/><ref name=Zhu_2020/><ref name=Yan_2020/><ref name=Southwell_2020b/> GNSS-R is also popular for other sea target analysis which is not directly related to icing. <ref name=King_2020/> <ref name=Di_2017/><ref name=Valencia_2013/>. Many research articles concentrating in method develoment, are also indirectly related to icing <ref name=Cheong_2019/> <ref name=Li_2020/><br />
<br />
== References ==<br />
<references><br />
<ref name=Yan_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2689009| issn = 2151-1535| volume = 10| issue = 8| pages = 3789–3798| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| last3 = Moloney| first3 = Cecilia| title = Neural networks based sea ice detection and concentration retrieval from GNSS-R delay-doppler maps| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=King_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2997199| issn = 2151-1535| volume = 13| pages = 2916–2924| last1 = King| first1 = Lucinda S.| last2 = Unwin| first2 = Martin| last3 = Rawlinson| first3 = Jonathan| last4 = Guida| first4 = Raffaella| last5 = Underwood| first5 = Craig| title = Processing of raw GNSS reflectometry data from TDS-1 in a backscattering configuration| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Cheong_2019>{{Cite journal| doi = 10.1109/JSTARS.2019.2956183| issn = 2151-1535| volume = 12| issue = 12| pages = 5373–5378| last1 = Cheong| first1 = Joon Wayn| last2 = Southwell| first2 = Benjamin J.| last3 = Dempster| first3 = Andrew G.| title = Blind sea clutter suppression for spaceborne GNSS-R target detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Alonso-Arroyo_2017>{{Cite journal| doi = 10.1109/TGRS.2017.2699122| issn = 1558-0644| volume = 55| issue = 9| pages = 4989–5001| last1 = Alonso-Arroyo| first1 = Alberto| last2 = Zavorotny| first2 = Valery U.| last3 = Camps| first3 = Adriano| title = Sea ice detection using U.K. TDS-1 GNSS-R data| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Semmling_2019>{{Cite journal| doi = 10.1109/TGRS.2019.2933911| issn = 1558-0644| volume = 57| issue = 12| pages = 10350–10361| last1 = Semmling| first1 = A. Maximilian| last2 = Rösel| first2 = Anja| last3 = Divine| first3 = Dmitry V.| last4 = Gerland| first4 = Sebastian| last5 = Stienne| first5 = Georges| last6 = Reboul| first6 = Serge| last7 = Ludwig| first7 = Marcel| last8 = Wickert| first8 = Jens| last9 = Schuh| first9 = Harald| title = Sea-ice concentration derived from GNSS reflection measurements in fram strait| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Yan_2016>{{Cite journal| doi = 10.1109/JSTARS.2016.2582690| issn = 2151-1535| volume = 9| issue = 10| pages = 4795–4801| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Spaceborne GNSS-R sea ice detection using delay-doppler maps: First results from the U.K. TechDemoSat-1 mission| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2016}}<br />
</ref><br />
<ref name=Yan_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2966880| issn = 2151-1535| volume = 13| pages = 577–587| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice thickness measurement using spaceborne GNSS-R: First results with TechDemoSat-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Di_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2705350| issn = 2151-1535| volume = 10| issue = 9| pages = 4237–4255| last1 = Di Simone| first1 = Alessio| last2 = Park| first2 = Hyuk| last3 = Riccio| first3 = Daniele| last4 = Camps| first4 = Adriano| title = Sea target detection using spaceborne GNSS-R delay-doppler maps: Theory and experimental proof of concept using TDS-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Li_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2992037| issn = 2151-1535| volume = 13| pages = 1833–1841| last1 = Li| first1 = Bowen| last2 = Yang| first2 = Lei| last3 = Zhang| first3 = Bo| last4 = Yang| first4 = Dongkai| last5 = Wu| first5 = Di| title = Modeling and simulation of GNSS-R observables with effects of swell| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Yan_2018>{{Cite journal| doi = 10.1109/LGRS.2018.2852143| issn = 1558-0571| volume = 15| issue = 10| pages = 1510–1514| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice sensing from GNSS-R data using convolutional neural networks| journal = IEEE Geoscience and Remote Sensing Letters| date = 2018}}<br />
</ref><br />
<ref name=Southwell_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2943510| issn = 2151-1535| volume = 13| pages = 14–20| last1 = Southwell| first1 = Benjamin J.| last2 = Dempster| first2 = Andrew G.| title = Sea ice transition detection using incoherent integration and deconvolution| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Strandberg_2017>{{Cite journal| doi = 10.1109/LGRS.2017.2722041| issn = 1558-0571| volume = 14| issue = 9| pages = 1552–1556| last1 = Strandberg| first1 = Joakim| last2 = Hobiger| first2 = Thomas| last3 = Haas| first3 = Rüdiger| title = Coastal sea ice detection using ground-based GNSS-R| journal = IEEE Geoscience and Remote Sensing Letters| date = 2017}}<br />
</ref><br />
<ref name=Valencia_2013>{{Cite journal| doi = 10.1109/JSTARS.2012.2210392| issn = 2151-1535| volume = 6| issue = 1| pages = 217–223| last1 = Valencia| first1 = Enric| last2 = Camps| first2 = Adriano| last3 = Rodriguez-Alvarez| first3 = Nereida| last4 = Park| first4 = Hyuk| last5 = Ramos-Perez| first5 = Isaac| title = Using GNSS-R imaging of the ocean surface for oil slick detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2013}}<br />
</ref><br />
<ref name=Zhu_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2955175| issn = 2151-1535| volume = 13| pages = 217–226| last1 = Zhu| first1 = Yongchao| last2 = Tao| first2 = Tingye| last3 = Yu| first3 = Kegen| last4 = Li| first4 = Zhenxuan| last5 = Qu| first5 = Xiaochuan| last6 = Ye| first6 = Zhourun| last7 = Geng| first7 = Jun| last8 = Zou| first8 = Jingui| last9 = Semmling| first9 = Maximilian| last10 = Wickert| first10 = Jens| title = Sensing sea ice based on doppler spread analysis of spaceborne GNSS-R data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Southwell_2020b>{{Cite journal| doi = 10.1109/TGRS.2020.2973142| issn = 1558-0644| volume = 58| issue = 8| pages = 5922–5931| last1 = Southwell| first1 = Benjamin J.| last2 = Cheong| first2 = Joon Wayn| last3 = Dempster| first3 = Andrew G.| title = A matched filter for spaceborne GNSS-R based sea-target detection| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Jia-2021>{{Cite book| publisher = IntechOpen| isbn = 978-1-78923-537-1| last1 = Jia| first1 = Yan| last2 = Pei| first2 = Yuekun| title = Remote Sensing in Land Applications by Using GNSS-Reflectometry| accessdate = 2021-12-09| date = 2018-07-25| url = https://www.intechopen.com/chapters/59160}}<br />
</ref><br />
</references></div>Uwasahttps://wiki.icingcentre.eu/index.php?title=GNSS_Reflectometry&diff=523GNSS Reflectometry2021-12-09T07:48:06Z<p>Uwasa: Fixed a typo</p>
<hr />
<div>Global Navigation Satellite System (GNSS) satellites transmits navigation signals from Earth orbits to terrestial receivers to help them find their location. These radio signals are also reflecting from the ground and by monitoring these reflections, the properties of the ground can be examined <ref name=King_2020 />. This kind of analysis is called as GNSS reflectometry (GNSS-R).<br />
<br />
Terrestial GNSS-R measurements include two antennas, on of which is receiving the direct signal from the satellite, and the other forward-scattered reflected signal. Satellite-based GSNS-R measures the GNSS signal backscatter from the Earth. GNSS-R bacscatter data has been collected by ESA's [https://earth.esa.int/web/eoportal/satellite-missions/t/techdemosat-1 TechDemoSat-1] (TDS-1) satellite and with NASA's [https://www.nasa.gov/cygnss/the-science-of-cygnss CYGNSS] satellite constellation. Observation data can be downloaded from their portals. <br />
<br />
GNSS reflectometry has been used for detecting sea ice thickness <ref name=Yan_2017/> <ref name=Alonso-Arroyo_2017 /> <ref name=Semmling_2019 /><ref name=Yan_2016 /> <ref name=Yan_2018/><ref name=Southwell_2020/> <ref name=Strandberg_2017/><ref name=Zhu_2020/><ref name=Yan_2020/><ref name=Southwell_2020b/> GNSS-R is also popular for other sea target analysis which is not directly related to icing. <ref name=King_2020/> <ref name=Di_2017/><ref name=Valencia_2013/>. Many research articles concentrating in method develoment, are also indirectly related to icing <ref name=Cheong_2019/> <ref name=Li_2020/><br />
<br />
== References ==<br />
<references><br />
<ref name=Yan_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2689009| issn = 2151-1535| volume = 10| issue = 8| pages = 3789–3798| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| last3 = Moloney| first3 = Cecilia| title = Neural networks based sea ice detection and concentration retrieval from GNSS-R delay-doppler maps| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=King_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2997199| issn = 2151-1535| volume = 13| pages = 2916–2924| last1 = King| first1 = Lucinda S.| last2 = Unwin| first2 = Martin| last3 = Rawlinson| first3 = Jonathan| last4 = Guida| first4 = Raffaella| last5 = Underwood| first5 = Craig| title = Processing of raw GNSS reflectometry data from TDS-1 in a backscattering configuration| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Cheong_2019>{{Cite journal| doi = 10.1109/JSTARS.2019.2956183| issn = 2151-1535| volume = 12| issue = 12| pages = 5373–5378| last1 = Cheong| first1 = Joon Wayn| last2 = Southwell| first2 = Benjamin J.| last3 = Dempster| first3 = Andrew G.| title = Blind sea clutter suppression for spaceborne GNSS-R target detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Alonso-Arroyo_2017>{{Cite journal| doi = 10.1109/TGRS.2017.2699122| issn = 1558-0644| volume = 55| issue = 9| pages = 4989–5001| last1 = Alonso-Arroyo| first1 = Alberto| last2 = Zavorotny| first2 = Valery U.| last3 = Camps| first3 = Adriano| title = Sea ice detection using U.K. TDS-1 GNSS-R data| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Semmling_2019>{{Cite journal| doi = 10.1109/TGRS.2019.2933911| issn = 1558-0644| volume = 57| issue = 12| pages = 10350–10361| last1 = Semmling| first1 = A. Maximilian| last2 = Rösel| first2 = Anja| last3 = Divine| first3 = Dmitry V.| last4 = Gerland| first4 = Sebastian| last5 = Stienne| first5 = Georges| last6 = Reboul| first6 = Serge| last7 = Ludwig| first7 = Marcel| last8 = Wickert| first8 = Jens| last9 = Schuh| first9 = Harald| title = Sea-ice concentration derived from GNSS reflection measurements in fram strait| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Yan_2016>{{Cite journal| doi = 10.1109/JSTARS.2016.2582690| issn = 2151-1535| volume = 9| issue = 10| pages = 4795–4801| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Spaceborne GNSS-R sea ice detection using delay-doppler maps: First results from the U.K. TechDemoSat-1 mission| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2016}}<br />
</ref><br />
<ref name=Yan_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2966880| issn = 2151-1535| volume = 13| pages = 577–587| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice thickness measurement using spaceborne GNSS-R: First results with TechDemoSat-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Di_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2705350| issn = 2151-1535| volume = 10| issue = 9| pages = 4237–4255| last1 = Di Simone| first1 = Alessio| last2 = Park| first2 = Hyuk| last3 = Riccio| first3 = Daniele| last4 = Camps| first4 = Adriano| title = Sea target detection using spaceborne GNSS-R delay-doppler maps: Theory and experimental proof of concept using TDS-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Li_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2992037| issn = 2151-1535| volume = 13| pages = 1833–1841| last1 = Li| first1 = Bowen| last2 = Yang| first2 = Lei| last3 = Zhang| first3 = Bo| last4 = Yang| first4 = Dongkai| last5 = Wu| first5 = Di| title = Modeling and simulation of GNSS-R observables with effects of swell| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Yan_2018>{{Cite journal| doi = 10.1109/LGRS.2018.2852143| issn = 1558-0571| volume = 15| issue = 10| pages = 1510–1514| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice sensing from GNSS-R data using convolutional neural networks| journal = IEEE Geoscience and Remote Sensing Letters| date = 2018}}<br />
</ref><br />
<ref name=Southwell_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2943510| issn = 2151-1535| volume = 13| pages = 14–20| last1 = Southwell| first1 = Benjamin J.| last2 = Dempster| first2 = Andrew G.| title = Sea ice transition detection using incoherent integration and deconvolution| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Strandberg_2017>{{Cite journal| doi = 10.1109/LGRS.2017.2722041| issn = 1558-0571| volume = 14| issue = 9| pages = 1552–1556| last1 = Strandberg| first1 = Joakim| last2 = Hobiger| first2 = Thomas| last3 = Haas| first3 = Rüdiger| title = Coastal sea ice detection using ground-based GNSS-R| journal = IEEE Geoscience and Remote Sensing Letters| date = 2017}}<br />
</ref><br />
<ref name=Valencia_2013>{{Cite journal| doi = 10.1109/JSTARS.2012.2210392| issn = 2151-1535| volume = 6| issue = 1| pages = 217–223| last1 = Valencia| first1 = Enric| last2 = Camps| first2 = Adriano| last3 = Rodriguez-Alvarez| first3 = Nereida| last4 = Park| first4 = Hyuk| last5 = Ramos-Perez| first5 = Isaac| title = Using GNSS-R imaging of the ocean surface for oil slick detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2013}}<br />
</ref><br />
<ref name=Zhu_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2955175| issn = 2151-1535| volume = 13| pages = 217–226| last1 = Zhu| first1 = Yongchao| last2 = Tao| first2 = Tingye| last3 = Yu| first3 = Kegen| last4 = Li| first4 = Zhenxuan| last5 = Qu| first5 = Xiaochuan| last6 = Ye| first6 = Zhourun| last7 = Geng| first7 = Jun| last8 = Zou| first8 = Jingui| last9 = Semmling| first9 = Maximilian| last10 = Wickert| first10 = Jens| title = Sensing sea ice based on doppler spread analysis of spaceborne GNSS-R data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Southwell_2020b>{{Cite journal| doi = 10.1109/TGRS.2020.2973142| issn = 1558-0644| volume = 58| issue = 8| pages = 5922–5931| last1 = Southwell| first1 = Benjamin J.| last2 = Cheong| first2 = Joon Wayn| last3 = Dempster| first3 = Andrew G.| title = A matched filter for spaceborne GNSS-R based sea-target detection| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2020}}<br />
</ref><br />
</references></div>Uwasahttps://wiki.icingcentre.eu/index.php?title=GNSS_Reflectometry&diff=522GNSS Reflectometry2021-12-09T07:46:42Z<p>Uwasa: Added reference to CYGNSS constellation</p>
<hr />
<div>Global Navigation Satellite System (GNSS) satellites transmits navigation signals from Earth orbits to terrestial receivers to help them find their location. These radio signals are also reflecting from the ground and by monitoring these reflections, the properties of the ground can be examined <ref name=King_2020 />. This kind of analysis is called as GNSS reflectometry (GNSS-R).<br />
<br />
Terrestial GNSS-R measurements include two antennas, on of which is receiving the direct signal from the satellite, and the other forward-scattered reflected signal. Satellite-based GSNS-R measures the GNSS signal backscatter from the Earth. GNSS-R bacscatter data has been collected by ESA's [https://earth.esa.int/web/eoportal/satellite-missions/t/techdemosat-1 TechSemoSat-1] satellite and with NASA's [https://www.nasa.gov/cygnss/the-science-of-cygnss CYGNSS] satellite constellation. Observation data can be downloaded from their portals. <br />
<br />
GNSS reflectometry has been used for detecting sea ice thickness <ref name=Yan_2017/> <ref name=Alonso-Arroyo_2017 /> <ref name=Semmling_2019 /><ref name=Yan_2016 /> <ref name=Yan_2018/><ref name=Southwell_2020/> <ref name=Strandberg_2017/><ref name=Zhu_2020/><ref name=Yan_2020/><ref name=Southwell_2020b/> GNSS-R is also popular for other sea target analysis which is not directly related to icing. <ref name=King_2020/> <ref name=Di_2017/><ref name=Valencia_2013/>. Many research articles concentrating in method develoment, are also indirectly related to icing <ref name=Cheong_2019/> <ref name=Li_2020/><br />
<br />
== References ==<br />
<references><br />
<ref name=Yan_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2689009| issn = 2151-1535| volume = 10| issue = 8| pages = 3789–3798| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| last3 = Moloney| first3 = Cecilia| title = Neural networks based sea ice detection and concentration retrieval from GNSS-R delay-doppler maps| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=King_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2997199| issn = 2151-1535| volume = 13| pages = 2916–2924| last1 = King| first1 = Lucinda S.| last2 = Unwin| first2 = Martin| last3 = Rawlinson| first3 = Jonathan| last4 = Guida| first4 = Raffaella| last5 = Underwood| first5 = Craig| title = Processing of raw GNSS reflectometry data from TDS-1 in a backscattering configuration| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Cheong_2019>{{Cite journal| doi = 10.1109/JSTARS.2019.2956183| issn = 2151-1535| volume = 12| issue = 12| pages = 5373–5378| last1 = Cheong| first1 = Joon Wayn| last2 = Southwell| first2 = Benjamin J.| last3 = Dempster| first3 = Andrew G.| title = Blind sea clutter suppression for spaceborne GNSS-R target detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Alonso-Arroyo_2017>{{Cite journal| doi = 10.1109/TGRS.2017.2699122| issn = 1558-0644| volume = 55| issue = 9| pages = 4989–5001| last1 = Alonso-Arroyo| first1 = Alberto| last2 = Zavorotny| first2 = Valery U.| last3 = Camps| first3 = Adriano| title = Sea ice detection using U.K. TDS-1 GNSS-R data| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Semmling_2019>{{Cite journal| doi = 10.1109/TGRS.2019.2933911| issn = 1558-0644| volume = 57| issue = 12| pages = 10350–10361| last1 = Semmling| first1 = A. Maximilian| last2 = Rösel| first2 = Anja| last3 = Divine| first3 = Dmitry V.| last4 = Gerland| first4 = Sebastian| last5 = Stienne| first5 = Georges| last6 = Reboul| first6 = Serge| last7 = Ludwig| first7 = Marcel| last8 = Wickert| first8 = Jens| last9 = Schuh| first9 = Harald| title = Sea-ice concentration derived from GNSS reflection measurements in fram strait| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Yan_2016>{{Cite journal| doi = 10.1109/JSTARS.2016.2582690| issn = 2151-1535| volume = 9| issue = 10| pages = 4795–4801| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Spaceborne GNSS-R sea ice detection using delay-doppler maps: First results from the U.K. TechDemoSat-1 mission| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2016}}<br />
</ref><br />
<ref name=Yan_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2966880| issn = 2151-1535| volume = 13| pages = 577–587| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice thickness measurement using spaceborne GNSS-R: First results with TechDemoSat-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Di_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2705350| issn = 2151-1535| volume = 10| issue = 9| pages = 4237–4255| last1 = Di Simone| first1 = Alessio| last2 = Park| first2 = Hyuk| last3 = Riccio| first3 = Daniele| last4 = Camps| first4 = Adriano| title = Sea target detection using spaceborne GNSS-R delay-doppler maps: Theory and experimental proof of concept using TDS-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Li_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2992037| issn = 2151-1535| volume = 13| pages = 1833–1841| last1 = Li| first1 = Bowen| last2 = Yang| first2 = Lei| last3 = Zhang| first3 = Bo| last4 = Yang| first4 = Dongkai| last5 = Wu| first5 = Di| title = Modeling and simulation of GNSS-R observables with effects of swell| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Yan_2018>{{Cite journal| doi = 10.1109/LGRS.2018.2852143| issn = 1558-0571| volume = 15| issue = 10| pages = 1510–1514| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice sensing from GNSS-R data using convolutional neural networks| journal = IEEE Geoscience and Remote Sensing Letters| date = 2018}}<br />
</ref><br />
<ref name=Southwell_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2943510| issn = 2151-1535| volume = 13| pages = 14–20| last1 = Southwell| first1 = Benjamin J.| last2 = Dempster| first2 = Andrew G.| title = Sea ice transition detection using incoherent integration and deconvolution| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Strandberg_2017>{{Cite journal| doi = 10.1109/LGRS.2017.2722041| issn = 1558-0571| volume = 14| issue = 9| pages = 1552–1556| last1 = Strandberg| first1 = Joakim| last2 = Hobiger| first2 = Thomas| last3 = Haas| first3 = Rüdiger| title = Coastal sea ice detection using ground-based GNSS-R| journal = IEEE Geoscience and Remote Sensing Letters| date = 2017}}<br />
</ref><br />
<ref name=Valencia_2013>{{Cite journal| doi = 10.1109/JSTARS.2012.2210392| issn = 2151-1535| volume = 6| issue = 1| pages = 217–223| last1 = Valencia| first1 = Enric| last2 = Camps| first2 = Adriano| last3 = Rodriguez-Alvarez| first3 = Nereida| last4 = Park| first4 = Hyuk| last5 = Ramos-Perez| first5 = Isaac| title = Using GNSS-R imaging of the ocean surface for oil slick detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2013}}<br />
</ref><br />
<ref name=Zhu_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2955175| issn = 2151-1535| volume = 13| pages = 217–226| last1 = Zhu| first1 = Yongchao| last2 = Tao| first2 = Tingye| last3 = Yu| first3 = Kegen| last4 = Li| first4 = Zhenxuan| last5 = Qu| first5 = Xiaochuan| last6 = Ye| first6 = Zhourun| last7 = Geng| first7 = Jun| last8 = Zou| first8 = Jingui| last9 = Semmling| first9 = Maximilian| last10 = Wickert| first10 = Jens| title = Sensing sea ice based on doppler spread analysis of spaceborne GNSS-R data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Southwell_2020b>{{Cite journal| doi = 10.1109/TGRS.2020.2973142| issn = 1558-0644| volume = 58| issue = 8| pages = 5922–5931| last1 = Southwell| first1 = Benjamin J.| last2 = Cheong| first2 = Joon Wayn| last3 = Dempster| first3 = Andrew G.| title = A matched filter for spaceborne GNSS-R based sea-target detection| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2020}}<br />
</ref><br />
</references></div>Uwasahttps://wiki.icingcentre.eu/index.php?title=GNSS_Reflectometry&diff=521GNSS Reflectometry2021-12-09T07:29:58Z<p>Uwasa: </p>
<hr />
<div>Global Navigation Satellite System (GNSS) satellites transmits navigation signals from Earth orbits to terrestial receivers to help them find their location. These radio signals are also reflecting from the ground and by monitoring these reflections, the properties of the ground can be examined <ref name=King_2020 />. This kind of analysis is called as GNSS reflectometry (GNSS-R).<br />
<br />
Terrestial GNSS-R measurements include two antennas, on of which is receiving the direct signal from the satellite, and the other forward-scattered reflected signal. Satellite-based GSNS-R measures the GNSS signal backscatter from the Earth. GNSS-R bacscatter data has been collected by ESA's [https://earth.esa.int/web/eoportal/satellite-missions/t/techdemosat-1 TechSemoSat-1] satellite, and they can be downloaded from the ESA's portal.<br />
<br />
GNSS reflectometry has been used for detecting sea ice thickness <ref name=Yan_2017/> <ref name=Alonso-Arroyo_2017 /> <ref name=Semmling_2019 /><ref name=Yan_2016 /> <ref name=Yan_2018/><ref name=Southwell_2020/> <ref name=Strandberg_2017/><ref name=Zhu_2020/><ref name=Yan_2020/><ref name=Southwell_2020b/> GNSS-R is also popular for other sea target analysis which is not directly related to icing. <ref name=King_2020/> <ref name=Di_2017/><ref name=Valencia_2013/>. Many research articles concentrating in method develoment, are also indirectly related to icing <ref name=Cheong_2019/> <ref name=Li_2020/><br />
<br />
== References ==<br />
<references><br />
<ref name=Yan_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2689009| issn = 2151-1535| volume = 10| issue = 8| pages = 3789–3798| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| last3 = Moloney| first3 = Cecilia| title = Neural networks based sea ice detection and concentration retrieval from GNSS-R delay-doppler maps| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=King_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2997199| issn = 2151-1535| volume = 13| pages = 2916–2924| last1 = King| first1 = Lucinda S.| last2 = Unwin| first2 = Martin| last3 = Rawlinson| first3 = Jonathan| last4 = Guida| first4 = Raffaella| last5 = Underwood| first5 = Craig| title = Processing of raw GNSS reflectometry data from TDS-1 in a backscattering configuration| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Cheong_2019>{{Cite journal| doi = 10.1109/JSTARS.2019.2956183| issn = 2151-1535| volume = 12| issue = 12| pages = 5373–5378| last1 = Cheong| first1 = Joon Wayn| last2 = Southwell| first2 = Benjamin J.| last3 = Dempster| first3 = Andrew G.| title = Blind sea clutter suppression for spaceborne GNSS-R target detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Alonso-Arroyo_2017>{{Cite journal| doi = 10.1109/TGRS.2017.2699122| issn = 1558-0644| volume = 55| issue = 9| pages = 4989–5001| last1 = Alonso-Arroyo| first1 = Alberto| last2 = Zavorotny| first2 = Valery U.| last3 = Camps| first3 = Adriano| title = Sea ice detection using U.K. TDS-1 GNSS-R data| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Semmling_2019>{{Cite journal| doi = 10.1109/TGRS.2019.2933911| issn = 1558-0644| volume = 57| issue = 12| pages = 10350–10361| last1 = Semmling| first1 = A. Maximilian| last2 = Rösel| first2 = Anja| last3 = Divine| first3 = Dmitry V.| last4 = Gerland| first4 = Sebastian| last5 = Stienne| first5 = Georges| last6 = Reboul| first6 = Serge| last7 = Ludwig| first7 = Marcel| last8 = Wickert| first8 = Jens| last9 = Schuh| first9 = Harald| title = Sea-ice concentration derived from GNSS reflection measurements in fram strait| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Yan_2016>{{Cite journal| doi = 10.1109/JSTARS.2016.2582690| issn = 2151-1535| volume = 9| issue = 10| pages = 4795–4801| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Spaceborne GNSS-R sea ice detection using delay-doppler maps: First results from the U.K. TechDemoSat-1 mission| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2016}}<br />
</ref><br />
<ref name=Yan_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2966880| issn = 2151-1535| volume = 13| pages = 577–587| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice thickness measurement using spaceborne GNSS-R: First results with TechDemoSat-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Di_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2705350| issn = 2151-1535| volume = 10| issue = 9| pages = 4237–4255| last1 = Di Simone| first1 = Alessio| last2 = Park| first2 = Hyuk| last3 = Riccio| first3 = Daniele| last4 = Camps| first4 = Adriano| title = Sea target detection using spaceborne GNSS-R delay-doppler maps: Theory and experimental proof of concept using TDS-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Li_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2992037| issn = 2151-1535| volume = 13| pages = 1833–1841| last1 = Li| first1 = Bowen| last2 = Yang| first2 = Lei| last3 = Zhang| first3 = Bo| last4 = Yang| first4 = Dongkai| last5 = Wu| first5 = Di| title = Modeling and simulation of GNSS-R observables with effects of swell| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Yan_2018>{{Cite journal| doi = 10.1109/LGRS.2018.2852143| issn = 1558-0571| volume = 15| issue = 10| pages = 1510–1514| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice sensing from GNSS-R data using convolutional neural networks| journal = IEEE Geoscience and Remote Sensing Letters| date = 2018}}<br />
</ref><br />
<ref name=Southwell_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2943510| issn = 2151-1535| volume = 13| pages = 14–20| last1 = Southwell| first1 = Benjamin J.| last2 = Dempster| first2 = Andrew G.| title = Sea ice transition detection using incoherent integration and deconvolution| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Strandberg_2017>{{Cite journal| doi = 10.1109/LGRS.2017.2722041| issn = 1558-0571| volume = 14| issue = 9| pages = 1552–1556| last1 = Strandberg| first1 = Joakim| last2 = Hobiger| first2 = Thomas| last3 = Haas| first3 = Rüdiger| title = Coastal sea ice detection using ground-based GNSS-R| journal = IEEE Geoscience and Remote Sensing Letters| date = 2017}}<br />
</ref><br />
<ref name=Valencia_2013>{{Cite journal| doi = 10.1109/JSTARS.2012.2210392| issn = 2151-1535| volume = 6| issue = 1| pages = 217–223| last1 = Valencia| first1 = Enric| last2 = Camps| first2 = Adriano| last3 = Rodriguez-Alvarez| first3 = Nereida| last4 = Park| first4 = Hyuk| last5 = Ramos-Perez| first5 = Isaac| title = Using GNSS-R imaging of the ocean surface for oil slick detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2013}}<br />
</ref><br />
<ref name=Zhu_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2955175| issn = 2151-1535| volume = 13| pages = 217–226| last1 = Zhu| first1 = Yongchao| last2 = Tao| first2 = Tingye| last3 = Yu| first3 = Kegen| last4 = Li| first4 = Zhenxuan| last5 = Qu| first5 = Xiaochuan| last6 = Ye| first6 = Zhourun| last7 = Geng| first7 = Jun| last8 = Zou| first8 = Jingui| last9 = Semmling| first9 = Maximilian| last10 = Wickert| first10 = Jens| title = Sensing sea ice based on doppler spread analysis of spaceborne GNSS-R data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Southwell_2020b>{{Cite journal| doi = 10.1109/TGRS.2020.2973142| issn = 1558-0644| volume = 58| issue = 8| pages = 5922–5931| last1 = Southwell| first1 = Benjamin J.| last2 = Cheong| first2 = Joon Wayn| last3 = Dempster| first3 = Andrew G.| title = A matched filter for spaceborne GNSS-R based sea-target detection| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2020}}<br />
</ref><br />
</references></div>Uwasahttps://wiki.icingcentre.eu/index.php?title=GNSS_Reflectometry&diff=520GNSS Reflectometry2021-12-08T22:24:43Z<p>Uwasa: </p>
<hr />
<div>Global Navigation Satellite System (GNSS) satellites transmits navigation signals from Earth orbits to terrestial receivers to help them find their location. These radio signals are also reflecting from the ground and by monitoring these reflections, the properties of the ground can be examined <ref name=King_2020 />. This kind of analysis is called as GNSS reflectometry.<br />
<br />
GNSS reflectometry has been used for detecting sea ice thickness <ref name=Yan_2017/> <ref name=Alonso-Arroyo_2017 /> <ref name=Semmling_2019 /><ref name=Yan_2016 /> <ref name=Yan_2018/><ref name=Southwell_2020/> <ref name=Strandberg_2017/><ref name=Zhu_2020/><ref name=Yan_2020/><ref name=Southwell_2020b/><br />
<br />
GNSS-R is also popular for other sea target analysis which is not directly related to icing. <ref name=King_2020/> <ref name=Di_2017/><ref name=Valencia_2013/><br />
<br />
Many research articles concentrating in method develoment, are also indirectly related to icing <ref name=Cheong_2019/> <ref name=Li_2020/><br />
<br />
== References ==<br />
<references><br />
<ref name=Yan_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2689009| issn = 2151-1535| volume = 10| issue = 8| pages = 3789–3798| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| last3 = Moloney| first3 = Cecilia| title = Neural networks based sea ice detection and concentration retrieval from GNSS-R delay-doppler maps| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=King_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2997199| issn = 2151-1535| volume = 13| pages = 2916–2924| last1 = King| first1 = Lucinda S.| last2 = Unwin| first2 = Martin| last3 = Rawlinson| first3 = Jonathan| last4 = Guida| first4 = Raffaella| last5 = Underwood| first5 = Craig| title = Processing of raw GNSS reflectometry data from TDS-1 in a backscattering configuration| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Cheong_2019>{{Cite journal| doi = 10.1109/JSTARS.2019.2956183| issn = 2151-1535| volume = 12| issue = 12| pages = 5373–5378| last1 = Cheong| first1 = Joon Wayn| last2 = Southwell| first2 = Benjamin J.| last3 = Dempster| first3 = Andrew G.| title = Blind sea clutter suppression for spaceborne GNSS-R target detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Alonso-Arroyo_2017>{{Cite journal| doi = 10.1109/TGRS.2017.2699122| issn = 1558-0644| volume = 55| issue = 9| pages = 4989–5001| last1 = Alonso-Arroyo| first1 = Alberto| last2 = Zavorotny| first2 = Valery U.| last3 = Camps| first3 = Adriano| title = Sea ice detection using U.K. TDS-1 GNSS-R data| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Semmling_2019>{{Cite journal| doi = 10.1109/TGRS.2019.2933911| issn = 1558-0644| volume = 57| issue = 12| pages = 10350–10361| last1 = Semmling| first1 = A. Maximilian| last2 = Rösel| first2 = Anja| last3 = Divine| first3 = Dmitry V.| last4 = Gerland| first4 = Sebastian| last5 = Stienne| first5 = Georges| last6 = Reboul| first6 = Serge| last7 = Ludwig| first7 = Marcel| last8 = Wickert| first8 = Jens| last9 = Schuh| first9 = Harald| title = Sea-ice concentration derived from GNSS reflection measurements in fram strait| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Yan_2016>{{Cite journal| doi = 10.1109/JSTARS.2016.2582690| issn = 2151-1535| volume = 9| issue = 10| pages = 4795–4801| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Spaceborne GNSS-R sea ice detection using delay-doppler maps: First results from the U.K. TechDemoSat-1 mission| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2016}}<br />
</ref><br />
<ref name=Yan_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2966880| issn = 2151-1535| volume = 13| pages = 577–587| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice thickness measurement using spaceborne GNSS-R: First results with TechDemoSat-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Di_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2705350| issn = 2151-1535| volume = 10| issue = 9| pages = 4237–4255| last1 = Di Simone| first1 = Alessio| last2 = Park| first2 = Hyuk| last3 = Riccio| first3 = Daniele| last4 = Camps| first4 = Adriano| title = Sea target detection using spaceborne GNSS-R delay-doppler maps: Theory and experimental proof of concept using TDS-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Li_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2992037| issn = 2151-1535| volume = 13| pages = 1833–1841| last1 = Li| first1 = Bowen| last2 = Yang| first2 = Lei| last3 = Zhang| first3 = Bo| last4 = Yang| first4 = Dongkai| last5 = Wu| first5 = Di| title = Modeling and simulation of GNSS-R observables with effects of swell| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Yan_2018>{{Cite journal| doi = 10.1109/LGRS.2018.2852143| issn = 1558-0571| volume = 15| issue = 10| pages = 1510–1514| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice sensing from GNSS-R data using convolutional neural networks| journal = IEEE Geoscience and Remote Sensing Letters| date = 2018}}<br />
</ref><br />
<ref name=Southwell_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2943510| issn = 2151-1535| volume = 13| pages = 14–20| last1 = Southwell| first1 = Benjamin J.| last2 = Dempster| first2 = Andrew G.| title = Sea ice transition detection using incoherent integration and deconvolution| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Strandberg_2017>{{Cite journal| doi = 10.1109/LGRS.2017.2722041| issn = 1558-0571| volume = 14| issue = 9| pages = 1552–1556| last1 = Strandberg| first1 = Joakim| last2 = Hobiger| first2 = Thomas| last3 = Haas| first3 = Rüdiger| title = Coastal sea ice detection using ground-based GNSS-R| journal = IEEE Geoscience and Remote Sensing Letters| date = 2017}}<br />
</ref><br />
<ref name=Valencia_2013>{{Cite journal| doi = 10.1109/JSTARS.2012.2210392| issn = 2151-1535| volume = 6| issue = 1| pages = 217–223| last1 = Valencia| first1 = Enric| last2 = Camps| first2 = Adriano| last3 = Rodriguez-Alvarez| first3 = Nereida| last4 = Park| first4 = Hyuk| last5 = Ramos-Perez| first5 = Isaac| title = Using GNSS-R imaging of the ocean surface for oil slick detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2013}}<br />
</ref><br />
<ref name=Zhu_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2955175| issn = 2151-1535| volume = 13| pages = 217–226| last1 = Zhu| first1 = Yongchao| last2 = Tao| first2 = Tingye| last3 = Yu| first3 = Kegen| last4 = Li| first4 = Zhenxuan| last5 = Qu| first5 = Xiaochuan| last6 = Ye| first6 = Zhourun| last7 = Geng| first7 = Jun| last8 = Zou| first8 = Jingui| last9 = Semmling| first9 = Maximilian| last10 = Wickert| first10 = Jens| title = Sensing sea ice based on doppler spread analysis of spaceborne GNSS-R data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Southwell_2020b>{{Cite journal| doi = 10.1109/TGRS.2020.2973142| issn = 1558-0644| volume = 58| issue = 8| pages = 5922–5931| last1 = Southwell| first1 = Benjamin J.| last2 = Cheong| first2 = Joon Wayn| last3 = Dempster| first3 = Andrew G.| title = A matched filter for spaceborne GNSS-R based sea-target detection| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2020}}<br />
</ref><br />
</references></div>Uwasahttps://wiki.icingcentre.eu/index.php?title=GNSS_Reflectometry&diff=519GNSS Reflectometry2021-12-08T22:22:23Z<p>Uwasa: </p>
<hr />
<div>Global Navigation Satellite System (GNSS) satellites transmits navigation signals from Earth orbits to terrestial receivers to help them find their location. These radio signals are also reflecting from the ground and by monitoring these reflections, the properties of the ground can be examined <ref name=King_2020 />. This kind of analysis is called as GNSS reflectometry.<br />
<br />
GNSS reflectometry has been used for detecting sea ice thickness <ref name=Yan_2017/> <ref name=Alonso-Arroyo_2017 /> <ref name=Semmling_2019 /><ref name=Yan_2016 /> <ref name=Yan_2018/><ref name=Southwell_2020/> <ref name=Strandberg_2017/><ref name=Zhu_2020/><ref name=Yan_2020/><br />
<br />
GNSS-R is also popular for other sea target analysis which is not directly related to icing. <ref name=King_2020/> <ref name=Di_2017/><ref name=Valencia_2013/><br />
<br />
Many research articles concentrating in method develoment, are also indirectly related to icing <ref name=Cheong_2019/> <ref name=Li_2020/><br />
<br />
== References ==<br />
<references><br />
<ref name=Yan_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2689009| issn = 2151-1535| volume = 10| issue = 8| pages = 3789–3798| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| last3 = Moloney| first3 = Cecilia| title = Neural networks based sea ice detection and concentration retrieval from GNSS-R delay-doppler maps| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=King_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2997199| issn = 2151-1535| volume = 13| pages = 2916–2924| last1 = King| first1 = Lucinda S.| last2 = Unwin| first2 = Martin| last3 = Rawlinson| first3 = Jonathan| last4 = Guida| first4 = Raffaella| last5 = Underwood| first5 = Craig| title = Processing of raw GNSS reflectometry data from TDS-1 in a backscattering configuration| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Cheong_2019>{{Cite journal| doi = 10.1109/JSTARS.2019.2956183| issn = 2151-1535| volume = 12| issue = 12| pages = 5373–5378| last1 = Cheong| first1 = Joon Wayn| last2 = Southwell| first2 = Benjamin J.| last3 = Dempster| first3 = Andrew G.| title = Blind sea clutter suppression for spaceborne GNSS-R target detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Alonso-Arroyo_2017>{{Cite journal| doi = 10.1109/TGRS.2017.2699122| issn = 1558-0644| volume = 55| issue = 9| pages = 4989–5001| last1 = Alonso-Arroyo| first1 = Alberto| last2 = Zavorotny| first2 = Valery U.| last3 = Camps| first3 = Adriano| title = Sea ice detection using U.K. TDS-1 GNSS-R data| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Semmling_2019>{{Cite journal| doi = 10.1109/TGRS.2019.2933911| issn = 1558-0644| volume = 57| issue = 12| pages = 10350–10361| last1 = Semmling| first1 = A. Maximilian| last2 = Rösel| first2 = Anja| last3 = Divine| first3 = Dmitry V.| last4 = Gerland| first4 = Sebastian| last5 = Stienne| first5 = Georges| last6 = Reboul| first6 = Serge| last7 = Ludwig| first7 = Marcel| last8 = Wickert| first8 = Jens| last9 = Schuh| first9 = Harald| title = Sea-ice concentration derived from GNSS reflection measurements in fram strait| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2019}}<br />
</ref><br />
<ref name=Yan_2016>{{Cite journal| doi = 10.1109/JSTARS.2016.2582690| issn = 2151-1535| volume = 9| issue = 10| pages = 4795–4801| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Spaceborne GNSS-R sea ice detection using delay-doppler maps: First results from the U.K. TechDemoSat-1 mission| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2016}}<br />
</ref><br />
<ref name=Yan_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2966880| issn = 2151-1535| volume = 13| pages = 577–587| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice thickness measurement using spaceborne GNSS-R: First results with TechDemoSat-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Di_2017>{{Cite journal| doi = 10.1109/JSTARS.2017.2705350| issn = 2151-1535| volume = 10| issue = 9| pages = 4237–4255| last1 = Di Simone| first1 = Alessio| last2 = Park| first2 = Hyuk| last3 = Riccio| first3 = Daniele| last4 = Camps| first4 = Adriano| title = Sea target detection using spaceborne GNSS-R delay-doppler maps: Theory and experimental proof of concept using TDS-1 data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2017}}<br />
</ref><br />
<ref name=Li_2020>{{Cite journal| doi = 10.1109/JSTARS.2020.2992037| issn = 2151-1535| volume = 13| pages = 1833–1841| last1 = Li| first1 = Bowen| last2 = Yang| first2 = Lei| last3 = Zhang| first3 = Bo| last4 = Yang| first4 = Dongkai| last5 = Wu| first5 = Di| title = Modeling and simulation of GNSS-R observables with effects of swell| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Yan_2018>{{Cite journal| doi = 10.1109/LGRS.2018.2852143| issn = 1558-0571| volume = 15| issue = 10| pages = 1510–1514| last1 = Yan| first1 = Qingyun| last2 = Huang| first2 = Weimin| title = Sea ice sensing from GNSS-R data using convolutional neural networks| journal = IEEE Geoscience and Remote Sensing Letters| date = 2018}}<br />
</ref><br />
<ref name=Southwell_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2943510| issn = 2151-1535| volume = 13| pages = 14–20| last1 = Southwell| first1 = Benjamin J.| last2 = Dempster| first2 = Andrew G.| title = Sea ice transition detection using incoherent integration and deconvolution| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Strandberg_2017>{{Cite journal| doi = 10.1109/LGRS.2017.2722041| issn = 1558-0571| volume = 14| issue = 9| pages = 1552–1556| last1 = Strandberg| first1 = Joakim| last2 = Hobiger| first2 = Thomas| last3 = Haas| first3 = Rüdiger| title = Coastal sea ice detection using ground-based GNSS-R| journal = IEEE Geoscience and Remote Sensing Letters| date = 2017}}<br />
</ref><br />
<ref name=Valencia_2013>{{Cite journal| doi = 10.1109/JSTARS.2012.2210392| issn = 2151-1535| volume = 6| issue = 1| pages = 217–223| last1 = Valencia| first1 = Enric| last2 = Camps| first2 = Adriano| last3 = Rodriguez-Alvarez| first3 = Nereida| last4 = Park| first4 = Hyuk| last5 = Ramos-Perez| first5 = Isaac| title = Using GNSS-R imaging of the ocean surface for oil slick detection| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2013}}<br />
</ref><br />
<ref name=Zhu_2020>{{Cite journal| doi = 10.1109/JSTARS.2019.2955175| issn = 2151-1535| volume = 13| pages = 217–226| last1 = Zhu| first1 = Yongchao| last2 = Tao| first2 = Tingye| last3 = Yu| first3 = Kegen| last4 = Li| first4 = Zhenxuan| last5 = Qu| first5 = Xiaochuan| last6 = Ye| first6 = Zhourun| last7 = Geng| first7 = Jun| last8 = Zou| first8 = Jingui| last9 = Semmling| first9 = Maximilian| last10 = Wickert| first10 = Jens| title = Sensing sea ice based on doppler spread analysis of spaceborne GNSS-R data| journal = IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing| date = 2020}}<br />
</ref><br />
<ref name=Southwell_2020>{{Cite journal| doi = 10.1109/TGRS.2020.2973142| issn = 1558-0644| volume = 58| issue = 8| pages = 5922–5931| last1 = Southwell| first1 = Benjamin J.| last2 = Cheong| first2 = Joon Wayn| last3 = Dempster| first3 = Andrew G.| title = A matched filter for spaceborne GNSS-R based sea-target detection| journal = IEEE Transactions on Geoscience and Remote Sensing| date = 2020}}<br />
</ref><br />
</references></div>Uwasa