{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:29:07Z","timestamp":1760149747942,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T00:00:00Z","timestamp":1693785600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41876204","2022YFC3104901"],"award-info":[{"award-number":["41876204","2022YFC3104901"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["41876204","2022YFC3104901"],"award-info":[{"award-number":["41876204","2022YFC3104901"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Wind measurement using spaceborne scatterometers has been used for various scientific and operational purposes. However, the major problem of such measurements is contamination by rain. To improve the wind measurement using the HY-2A scatterometer under rainy conditions, a neural network-based model was established in this study. The model is almost autonomous in that it only needs the backscatter coefficient measurement data and the observation geometry information from the HY-2A scatterometer itself. The model can distinguish between rain-contaminated wind pixels and rain-free wind pixels and significantly improve the accuracy of wind speed measurements using HY-2A scatterometer alone. TAO data and linearly calibrated ECMWF data were used in the study to validate the neural network-inverted wind speed. Under no rain conditions, the RMS of the neural network-inverted wind speed and TAO wind speed was 1.06 m\/s, with a deviation of \u22120.21 m\/s, which is a small difference from the standard method inverted wind speed. Under rain conditions, the RMS and deviation were 1.94 m\/s and 0.66 m\/s, respectively, which were better than the statistical results of the conventional maximum likelihood estimation method. The validated results using linearly calibrated data also indicate that the neural network-inverted wind speed is closer to the validation data under rain conditions.<\/jats:p>","DOI":"10.3390\/rs15174357","type":"journal-article","created":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T10:24:30Z","timestamp":1693823070000},"page":"4357","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Neural Network-Based Wind Measurements in Rainy Conditions Using the HY-2A Scatterometer"],"prefix":"10.3390","volume":"15","author":[{"given":"Jing","family":"Wang","sequence":"first","affiliation":[{"name":"School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1261-7003","authenticated-orcid":false,"given":"Xuetong","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4560-2000","authenticated-orcid":false,"given":"Ruru","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5468-1591","authenticated-orcid":false,"given":"Mingsen","family":"Lin","sequence":"additional","affiliation":[{"name":"National Satellite Ocean Application Service, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1303-195X","authenticated-orcid":false,"given":"Xiankun","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1015832919110","article-title":"Progress in Scatterometer Application","volume":"58","author":"Liu","year":"2002","journal-title":"J. Oceanogr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1175\/MWR-2861.1","article-title":"Scatterometer-Based Assessment of 10-m Wind Analyses from the Operational ECMWF and NCEP Numerical Weather Prediction Models","volume":"133","author":"Chelton","year":"2005","journal-title":"Mon. Weather. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1175\/1520-0426(2002)019<0738:EORRAW>2.0.CO;2","article-title":"Effects of Rain Rate and Wind Magnitude on SeaWinds Scatterometer Wind Speed Errors","volume":"19","author":"Weissman","year":"2002","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1109\/TGRS.2004.830169","article-title":"Simultaneous wind and rain retrieval using SeaWinds data","volume":"42","author":"Draper","year":"2004","journal-title":"EEE Trans. Geosci. Remote. Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Weissman, D.E., and Bourassa, M.A. (2007, January 23\u201328). Measurements of the Effect of Rain-Induced Sea Surface Roughness on the QuikSCAT Scatterometer Radar Cross Section. Proceedings of the IEEE Transactions on Geoscience and Remote Sensing, Barcelona, Spain.","DOI":"10.1109\/IGARSS.2007.4422726"},{"key":"ref_6","unstructured":"Verhoef, A., Vogelzang, J., and Stoffelen, A.D. (2022, January 26). ScatSat-1 Wind Validation Report 25 km (OSI-112-a) and 50 km (OSI-112-b) Wind Products, EUMETSAT Ocean and Sea Ice Satellite Application Facility Document SAF\/OSI\/CDOP3\/KNMI\/TEC\/RP\/324, Version 1.0, KNMI, De Bilt, The Netherlands. Available online: https:\/\/scatterometer.knmi.nl\/publications\/pdf\/osisaf_cdop3_ss3_valrep_scatsat1_winds.pdf."},{"key":"ref_7","first-page":"2173","article-title":"Comparison of Ocean Surface Rain Rates From the Global Precipitation Mission and the Meteosat Second-Generation Satellite for Wind Scatterometer Quality Control","volume":"13","author":"Xu","year":"2020","journal-title":"IEEE JSTARS"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3165","DOI":"10.1029\/2001JC001255","article-title":"Effects of rain on Ku-band backscatter from the ocean","volume":"108","author":"Contreras","year":"2003","journal-title":"J. Geophys. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1175\/1520-0426(2001)018<1171:RDAQCO>2.0.CO;2","article-title":"Rain Detection and Quality Control of SeaWinds","volume":"18","author":"Portabella","year":"2001","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.rse.2016.10.015","article-title":"Rain footprints on C- band synthetic aperture radar images of the ocean\u2013Revisited","volume":"187","author":"Alpers","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/JOE.1983.1145541","article-title":"Errors in Scatterometer\u2013Radiometer Wind Measurement Due to Rain","volume":"8","author":"Moore","year":"1983","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1567","DOI":"10.1016\/S0273-1177(99)00072-1","article-title":"Understanding the effects of rain on radar altimeter waveforms","volume":"22","author":"Quartly","year":"1998","journal-title":"Adv. Space Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1175\/1520-0426(1998)015<0387:DORCCF>2.0.CO;2","article-title":"Determination of Rain Cell Characteristics from the Analysis of TOPEX Altimeter Echo Waveforms","volume":"15","author":"Tournadre","year":"1998","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3225","DOI":"10.1029\/2002JC001428","article-title":"Impact of rain cell on scatterometer data: 1. Theory and modeling","volume":"108","author":"Tournadre","year":"2003","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7023","DOI":"10.1029\/2004JC002766","article-title":"Impact of rain cell on scatterometer data: 2. Correction of Seawinds measured backscatter and wind and rain flagging","volume":"110","author":"Tournadre","year":"2005","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2495","DOI":"10.1109\/TGRS.2012.2185933","article-title":"Rain Effects on ASCAT-Retrieved Winds: Toward an Improved Quality Control","volume":"50","author":"Portabella","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2494","DOI":"10.1109\/TGRS.2019.2951726","article-title":"Improved Rain Screening for Ku-Band Wind Scatterometry","volume":"58","author":"Xu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","unstructured":"Huddleston, J., and Stiles, B. (2000, January 24\u201328). A multidimensional histogram rain-flagging technique for SeaWinds on QuikSCAT. Proceedings of the 2000 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ahmad, K., Jones, W., and Kasparis, T. (2007, January 23\u201327). Oceanic Rainfall Retrievals using passive and active measurements from SeaWinds Remote Sensor. Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain.","DOI":"10.1109\/IGARSS.2007.4423601"},{"key":"ref_20","unstructured":"Moore, R., Braaten, D., Natarajakumar, B., and Kurisunkal, V.J. (2003, January 21\u201325). Correcting scatterometer ocean measurements for rain effects using radiometer data: Application to SeaWinds on ADEOS-2. Proceedings of the 2003 IEEE International Geoscience and Remote Sensing Symposium, Toulouse, France."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1175\/JAM2357.1","article-title":"Correcting active scatterometer data for the effects of rain using passive mi-crowave data","volume":"45","author":"Hilburn","year":"2006","journal-title":"J. Appl. Meteorol. Clim."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Draper, D., and Long, D. (2004). Evaluating the effect of rain on SeaWinds scatterometer measurements. J. Geophys. Res, 109.","DOI":"10.1029\/2002JC001741"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1973","DOI":"10.1109\/TGRS.2002.803846","article-title":"Impact of rain on spaceborne Ku-band wind scatterometer data","volume":"40","author":"Stiles","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3114","DOI":"10.1109\/TGRS.2010.2049362","article-title":"A Neural Network Technique for Improving the Accuracy of Scatterometer Winds in Rainy Conditions","volume":"48","author":"Stiles","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","unstructured":"Verhoef, A., Vogelzang, J., Verspeek, J., and Stoffelen, A. (2010). AWDP User Manual and Reference Guide, EU-METSAT. NWPSAF-KN-UD-005, version 2.0."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1109\/LGRS.2014.2298095","article-title":"Rain Identification in ASCAT Winds Using Singularity Analysis","volume":"11","author":"Lin","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1298","DOI":"10.1175\/1520-0426(1997)014<1298:SDIMSA>2.0.CO;2","article-title":"Scatterometer data interpretation: Measurement space and inversion","volume":"14","author":"Stoffelen","year":"1997","journal-title":"J. Atmos. Ocean. Technol"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1893","DOI":"10.1109\/36.851772","article-title":"On the assimilation of Ku-band scatterometer winds for weather analysis and forecasting","volume":"38","author":"Figa","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.rse.2019.03.005","article-title":"Inconsistencies in scatterometer wind products based on ASCAT and OSCAT-2 collocations","volume":"225","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_30","unstructured":"Verhoef, A., Vogelzang, J., Verspeek, J., and Stoffelen, A. (2021, October 10). PenWP User Manual and Reference Guide, EUMETSAT NWP Satellite Application Facility Document NWPSAF-KN-UD-009, Version 2.2, KNMI, De Bilt, The Netherlands. Available online: https:\/\/nwpsaf.eumetsat.int\/site\/download\/documentation\/scatterometer\/penwp\/NWPSAF-KN-UD009_PenWP_User_Guide_v2.2.pdf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"20521","DOI":"10.1029\/91JC02216","article-title":"Wind ambiguity removal by the use of neural network techniques","volume":"96","author":"Badran","year":"1991","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Deep learning and process understanding for data-driven Earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"12853","DOI":"10.1029\/97JC02178","article-title":"Determination of the geophysical model function of the ERS-1 scat-terometer by the use of neural networks","volume":"103","author":"Mejia","year":"1998","journal-title":"J. Geophys. Res"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Jones, W.L., Park, J.D., Donnelly, W.J., Carswell, J.R., Mclntosh, R.E., Zec, J., and Yueh, S. (1998, January 6\u201310). An improved NASA Scatterometer ge-ophysical model function for tropical cyclones. Proceedings of the 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings, Seattle, WA, USA.","DOI":"10.1109\/IGARSS.1998.703719"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"8737","DOI":"10.1029\/1999JC900225","article-title":"Neural network wind retrieval from ERS-1 scatterometer data","volume":"105","author":"Richaume","year":"2000","journal-title":"J. Geophys. Res"},{"key":"ref_36","first-page":"35","article-title":"Neural network wind retrieval from ERS-1\/2 scatterometer data","volume":"25","author":"Lin","year":"2006","journal-title":"Acta Oceanol. Sin.-Engl. Ed."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Xu, X., and Stoffelen, A. (August, January 28). Wind Retrieval for Cfoscat Edge and Nadir Observations Based on Neural Networks and Improved Principle Component Analysis. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898097"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1648","DOI":"10.3390\/rs12101648","article-title":"A Neural Network-Based Rain Effect Correction Method for HY-2A Scatterometer Backscatter Measurements","volume":"12","author":"Xie","year":"2020","journal-title":"Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"831","DOI":"10.5194\/os-15-831-2019","article-title":"Characterizing ERA-Interim and ERA5 surface wind biases using ASCAT","volume":"15","author":"Rivas","year":"2019","journal-title":"Ocean Sci"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1002\/joc.1176","article-title":"Methods to homogenize wind speeds from ships and buoys","volume":"25","author":"Thomas","year":"2005","journal-title":"Int. J. Climatol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"7340","DOI":"10.1029\/2017JD028010","article-title":"Effects of wind direction on variations in friction velocity with wind speed under conditions of strong onshore wind","volume":"123","author":"Fang","year":"2018","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"265","DOI":"10.5194\/os-4-265-2008","article-title":"Characterization of ASCAT measurements based on buoy and QuikSCAT wind vector observations","volume":"4","author":"Bentamy","year":"2008","journal-title":"Ocean Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5-1","DOI":"10.1029\/2001JC000850","article-title":"Mechanisms of the 1997\u20131998 El Ni\u00f1o\u2013La Ni\u00f1a, as inferred from space-based observations","volume":"107","author":"Picaut","year":"2002","journal-title":"J. Geophys. Res."},{"key":"ref_44","unstructured":"Zou, J., Xie, X., Yi, Z., and Lin, M. (2014, January 13\u201318). Wind Retrieval Processing for HY-2A Microwave Scatterometer. Proceedings of the 2014 IEEE International Geo-science and Remote Sensing Symposium, Quebec City, QC, Canada."},{"key":"ref_45","unstructured":"Freilich, M.H. (2022, May 19). SeaWinds Algorithm Theoretical Basis Document. NASA JPL, Los Angeles, CA, USA, Tech. Rep. NASA ATBD-SWS-01, Available online: https:\/\/ntrs.nasa.gov\/api\/citations\/20160003317\/downloads\/20160003317.pdf."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3006","DOI":"10.1029\/2006JC003743","article-title":"An improved C-band scatterometer ocean geophysical model function: CMOD5","volume":"112","author":"Hersbach","year":"2007","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"11499","DOI":"10.1029\/98JC02148","article-title":"A model function for the ocean-normalized radar cross section at 14 GHz derived from NSCAT ob-servations","volume":"104","author":"Wentz","year":"1999","journal-title":"J. Geophys. Res"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2123","DOI":"10.1109\/JSTARS.2017.2681806","article-title":"The CMOD7 geophysical model function for ASCAT and ERS wind retrievals","volume":"10","author":"Stoffelen","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Lin, W., Portabella, M., Stoffelen, A., Verhoef, A., and Wang, Z. (2018, January 22\u201327). Validation of the NSCAT-5 Geophysical Model Function for Scatsat-1 Wind Scatterometer. Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517739"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1256\/qj.02.205","article-title":"A probabilistic approach for SeaWinds data assimilation","volume":"130","author":"Portabella","year":"2004","journal-title":"Q. J. Roy. Meteor. Soc"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/17\/4357\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:46:22Z","timestamp":1760129182000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/17\/4357"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,4]]},"references-count":50,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15174357"],"URL":"https:\/\/doi.org\/10.3390\/rs15174357","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,9,4]]}}}