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Evaluations of a Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations
Content Provider | MDPI |
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Author | Senyurek, Volkan Lei, Fangni Boyd, Dylan Gurbuz, Ali Kurum, Mehmet Moorhead, Robert |
Copyright Year | 2020 |
Abstract | 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 |
Starting Page | 3503 |
e-ISSN | 20724292 |
DOI | 10.3390/rs12213503 |
Journal | Remote Sensing |
Issue Number | 21 |
Volume Number | 12 |
Language | English |
Publisher | MDPI |
Publisher Date | 2020-10-25 |
Access Restriction | Open |
Subject Keyword | Remote Sensing Imaging Science Gnss-reflectometry Random Forest Cygnss Soil Moisture Retrieval Ismn Smap |
Content Type | Text |
Resource Type | Article |