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• W3107777305 abstract "This work presents a novel scheme to retrieve soil moisture (SM) from the Cyclone Global Navigation Satellite System (CyGNSS) data, which is accomplished by using a bagged regression trees (BRT) algorithm with the inputs being the CyGNSS-derived products, the corresponding geolocation, and associated climate type. This algorithm is validated with the <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>in situ</i> hourly SM data acquired by China’s automatic SM observation stations throughout the year 2018. High consistency between the retrieved SM results and the measured SM is achieved, with a correlation coefficient of 0.86 and a root-mean-square error of 0.05 cm <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>3</sup> /cm <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>3</sup> . The results obtained in this work indicate that the proposed BRT-based method can effectively estimate SM from CyGNSS data in different scenarios of various station locations and climate types in a near real-time manner." @default.
• W3107777305 created "2020-12-07" @default.
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• W3107777305 date "2022-01-01" @default.
• W3107777305 modified "2023-10-15" @default.
• W3107777305 title "Near Real-Time Soil Moisture in China Retrieved From CyGNSS Reflectivity" @default.
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• W3107777305 doi "https://doi.org/10.1109/lgrs.2020.3039519" @default.
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