Assessing the performance of GNSS-R observations in drought monitoring: a case study in Jiangxi and Hunan, China

The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1080/10106049.2024.2333351

Detalles Bibliográficos
Autores: Liu, Y., Min, Rong, Du, Hao, Guo, Wenfei
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/378776
Acceso en línea:http://hdl.handle.net/10261/378776
https://api.elsevier.com/content/abstract/scopus_id/85189071292
Access Level:acceso abierto
Palabra clave:Cyclone global navigation satellite system (CYGNSS)
Drought monitoring
Global navigation satellite system reflectometry (GNSS-R)
Soil moisture (SM)
Water detection
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spelling Assessing the performance of GNSS-R observations in drought monitoring: a case study in Jiangxi and Hunan, ChinaLiu, Y.Min, RongDu, HaoGuo, WenfeiCyclone global navigation satellite system (CYGNSS)Drought monitoringGlobal navigation satellite system reflectometry (GNSS-R)Soil moisture (SM)Water detectionThe underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1080/10106049.2024.2333351Drought is a disaster that seriously constrains economic development and endangers human life. This paper explores the potential of Global Navigation Satellite System Reflectometry (GNSS-R) for drought monitoring, using Cyclone Global Navigation Satellite System (CYGNSS) data to monitor drought in Jiangxi and Hunan Provinces, China, in 2022. This study applies the Random Under-sampling Boosting (RUSBoost) algorithm to detect waterbodies and linear regression to retrieve soil moisture (SM). Result shows that drought in September was heaviest, with the area of Poyang Lake in Jiangxi and Dongting Lake in Hunan decreasing by 70.2% and 76.9%, respectively, compared to that in June. The variation in retrieved SM shows that the Poyang Lake Plain and Jitai Basin in Jiangxi and the Dongting Lake, Yuanjiang River, and Xiangjiang River basins in Hunan suffered from the most serious drought. The variation in retrievals shows high consistency with various reference datasets, including Soil Moisture Active Passive (SMAP) SM data and vegetation condition index (VCI). The correlation coefficient between retrieved SM and VCI is 0.93 in Jiangxi and 0.94 in Hunan.This work was supported in part by the National Key Research and Development Program of Chinaunder Grant 2016YFB0501804, in part by the National Natural Science Foundation of China under Grant41604021 and Grant 41974031Peer reviewedTaylor & FrancisNational Key Research and Development Program (China)National Natural Science Foundation of ChinaLiu, Y. [0009-0008-2855-9590]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/378776https://api.elsevier.com/content/abstract/scopus_id/85189071292reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttps://doi.org/10.1080/10106049.2024.2333351Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3787762026-05-22T06:33:51Z
dc.title.none.fl_str_mv Assessing the performance of GNSS-R observations in drought monitoring: a case study in Jiangxi and Hunan, China
title Assessing the performance of GNSS-R observations in drought monitoring: a case study in Jiangxi and Hunan, China
spellingShingle Assessing the performance of GNSS-R observations in drought monitoring: a case study in Jiangxi and Hunan, China
Liu, Y.
Cyclone global navigation satellite system (CYGNSS)
Drought monitoring
Global navigation satellite system reflectometry (GNSS-R)
Soil moisture (SM)
Water detection
title_short Assessing the performance of GNSS-R observations in drought monitoring: a case study in Jiangxi and Hunan, China
title_full Assessing the performance of GNSS-R observations in drought monitoring: a case study in Jiangxi and Hunan, China
title_fullStr Assessing the performance of GNSS-R observations in drought monitoring: a case study in Jiangxi and Hunan, China
title_full_unstemmed Assessing the performance of GNSS-R observations in drought monitoring: a case study in Jiangxi and Hunan, China
title_sort Assessing the performance of GNSS-R observations in drought monitoring: a case study in Jiangxi and Hunan, China
dc.creator.none.fl_str_mv Liu, Y.
Min, Rong
Du, Hao
Guo, Wenfei
author Liu, Y.
author_facet Liu, Y.
Min, Rong
Du, Hao
Guo, Wenfei
author_role author
author2 Min, Rong
Du, Hao
Guo, Wenfei
author2_role author
author
author
dc.contributor.none.fl_str_mv National Key Research and Development Program (China)
National Natural Science Foundation of China
Liu, Y. [0009-0008-2855-9590]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Cyclone global navigation satellite system (CYGNSS)
Drought monitoring
Global navigation satellite system reflectometry (GNSS-R)
Soil moisture (SM)
Water detection
topic Cyclone global navigation satellite system (CYGNSS)
Drought monitoring
Global navigation satellite system reflectometry (GNSS-R)
Soil moisture (SM)
Water detection
description The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1080/10106049.2024.2333351
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/378776
https://api.elsevier.com/content/abstract/scopus_id/85189071292
url http://hdl.handle.net/10261/378776
https://api.elsevier.com/content/abstract/scopus_id/85189071292
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.1080/10106049.2024.2333351
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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