{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:29:52Z","timestamp":1771698592242,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,5,26]],"date-time":"2020-05-26T00:00:00Z","timestamp":1590451200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41971377"],"award-info":[{"award-number":["41971377"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2017YFD0300101"],"award-info":[{"award-number":["2017YFD0300101"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA23050102"],"award-info":[{"award-number":["XDA23050102"]}]},{"name":"the Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA19040303"],"award-info":[{"award-number":["XDA19040303"]}]},{"name":"the National Key Research and Development Program of China","award":["2017YFC0503805"],"award-info":[{"award-number":["2017YFC0503805"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) provides a new opportunity for land observation. This study is the first to compare and evaluate the performance of the only two spaceborne GNSS-R satellite missions whose data are publicly available, i.e., the UK\u2019s TechdemoSat-1 (TDS-1) and the US\u2019s Cyclone Global Navigation Satellite System (CYGNSS), for sensitivity analysis with SMAP SM on a daily basis and soil moisture (SM) estimates on a monthly basis over Mainland China. For daily sensitivity analysis, the two data were matched up and compared for the period (i.e., May 2017 through April 2018) when they coexisted (R = 0.561 vs. R = 0.613). For monthly SM estimates, a back-propagation artificial neural network (BP-ANN) was used to construct a model using data from more than two years. The model was subsequently used to derive long-term and continuous SM maps over Mainland China. The results showed that TDS-1 and CYGNSS agree and correlate very well with the SMAP SM in Mainland China (R = 0.676, MAE = 0.052 m3m\u22123, and ubRMSE = 0.060 m3m\u22123 for TDS-1; R = 0.798, MAE = 0.040 m3m\u22123, and ubRMSE = 0.062 m3m\u22123 for CYGNSS). The retrieved results were further validated using monthly in situ SM data from dense sites across Mainland China. It was found that the SM derived from the TDS-1\/CYGNSS also correlated well with in situ SM (R = 0.687, MAE = 0.066 m3m\u22123, and ubRMSE = 0.056 m3m\u22123 for TDS-1; R = 0.724, MAE = 0.052 m3m\u22123, and ubRMSE = 0.053 m3m\u22123 for CYGNSS). The results in this study suggested that TDS-1\/CYGNSS and the upcoming spaceborne GNSS-R mission could be new and powerful data sources to produce SM data set at a large scale and with relatively high precision.<\/jats:p>","DOI":"10.3390\/rs12111699","type":"journal-article","created":{"date-parts":[[2020,5,28]],"date-time":"2020-05-28T12:36:58Z","timestamp":1590669418000},"page":"1699","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Comprehensive Evaluation of Using TechDemoSat-1 and CYGNSS Data to Estimate Soil Moisture over Mainland China"],"prefix":"10.3390","volume":"12","author":[{"given":"Ting","family":"Yang","sequence":"first","affiliation":[{"name":"CAS Engineering Laboratory for Yellow River Delta Modern Agriculture, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Shandong Dongying Institute of Geographic Sciences, Dongying 257000, China"}]},{"given":"Wei","family":"Wan","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, China"},{"name":"Engineering Research Center of Earth Observation and Navigation (CEON), Ministry of Education of the PRC, Peking University, Beijing 100871, China"}]},{"given":"Zhigang","family":"Sun","sequence":"additional","affiliation":[{"name":"CAS Engineering Laboratory for Yellow River Delta Modern Agriculture, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Shandong Dongying Institute of Geographic Sciences, Dongying 257000, China"},{"name":"Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2390-2889","authenticated-orcid":false,"given":"Baojian","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, China"}]},{"given":"Sen","family":"Li","sequence":"additional","affiliation":[{"name":"National Meteorological Center, China Meteorological Administration, Beijing 100081, China"}]},{"given":"Xiuwan","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, China"},{"name":"Engineering Research Center of Earth Observation and Navigation (CEON), Ministry of Education of the PRC, Peking University, Beijing 100871, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,26]]},"reference":[{"key":"ref_1","first-page":"71","article-title":"Land geophysical parameters retrieval using the interference pattern GNSS-R technique","volume":"49","author":"Camps","year":"2010","journal-title":"IEEE Trans. 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