Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations

This paper presents a machine learning (ML) framework to derive a quasi-global soil moisture (SM) product by direct use of the Cyclone Global Navigation Satellite System (CYGNSS)’s high spatio-temporal resolution observations over the tropics (within <inline-formula><math display="inline"><semantics...

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主要な著者:	Volkan Senyurek, Fangni Lei, Dylan Boyd, Ali Cafer Gurbuz, Mehmet Kurum, Robert Moorhead
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2020-10-01
シリーズ:	Remote Sensing
主題:	GNSS-reflectometry random forest CYGNSS soil moisture retrieval ISMN SMAP
オンライン･アクセス:	https://www.mdpi.com/2072-4292/12/21/3503
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