{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T12:27:17Z","timestamp":1764937637818,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T00:00:00Z","timestamp":1699488000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42101376","2021YFD1900902","2021YFD1900902"],"award-info":[{"award-number":["42101376","2021YFD1900902","2021YFD1900902"]}]},{"name":"National Key Research and Development Program of China","award":["42101376","2021YFD1900902","2021YFD1900902"],"award-info":[{"award-number":["42101376","2021YFD1900902","2021YFD1900902"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The Cyclone Global Navigation Satellite System (CYGNSS), a publicly accessible spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data, provides a new alternative opportunity for large-scale soil moisture (SM) retrieval, but with interference from complex environmental conditions (i.e., vegetation cover and ground roughness). This study aims to develop a high-accuracy model for CYGNSS SM retrieval. The normalized surface reflectivity calculated by CYGNSS is fused with variables that are highly related to the SM obtained from optical\/microwave remote sensing to solve the problem of the influence of complicated environmental conditions. The Gradient Boost Regression Tree (GBRT) model aided by land-type data is then used to construct a multi-variables SM retrieval model with six different land types of multiple models. The methodology is tested in southeastern China, and the results correlate very well with the existing satellite remote sensing products and in situ SM data (R = 0.765, ubRMSE = 0.054 m3m\u22123 vs. SMAP; R = 0.653, ubRMSE = 0.057 m3 m\u22123 vs. ERA5 SM; R = 0.691, ubRMSE = 0.057 m3m\u22123 vs. in situ SM). This study makes contributions from two aspects: (1) improves the accuracy of the CYGNSS retrieval of SM based on fusion with other auxiliary data; (2) constructs the SM retrieval model with multi-layer multiple models, which is suitable for different land properties.<\/jats:p>","DOI":"10.3390\/s23229066","type":"journal-article","created":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T10:19:27Z","timestamp":1699525167000},"page":"9066","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Daily Soil Moisture Retrieval by Fusing CYGNSS and Multi-Source Auxiliary Data Using Machine Learning Methods"],"prefix":"10.3390","volume":"23","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":"Jundong","family":"Wang","sequence":"additional","affiliation":[{"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"}]},{"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"}]},{"given":"Sen","family":"Li","sequence":"additional","affiliation":[{"name":"National Meteorological Center, China Meteorological Administration, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2136","DOI":"10.3390\/s8042136","article-title":"Relationship Between Remotely-sensed Vegetation Indices, Canopy Attributes and Plant Physiological Processes: What Vegetation Indices Can and Cannot Tell Us About the Landscape","volume":"8","author":"Glenn","year":"2008","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"70","DOI":"10.3390\/s8010070","article-title":"Assessment of Evapotranspiration and Soil Moisture Content across Different Scales of Observation","volume":"8","author":"Verstraeten","year":"2008","journal-title":"Sensors"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2539","DOI":"10.1016\/j.rse.2017.08.025","article-title":"Development and assessment of the smap enhanced passive soil moisture product","volume":"204","author":"Chan","year":"2018","journal-title":"Remote Sens. 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