Quasi-Global Assessment of Deep Learning-Based CYGNSS Soil Moisture Retrieval

A high spatial and temporal resolution global soil moisture product is essential for understanding hydrologic and meteorological processes and enhancing agricultural applications. Global navigation satellite system (GNSS) signals at L-band frequencies that reflect off the land surface can convey hig...

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Bibliografische Detailangaben

Hauptverfasser:	M M Nabi, Volkan Senyurek, Fangni Lei, Mehmet Kurum, Ali Cafer Gurbuz
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	IEEE 2023-01-01
Schriftenreihe:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Schlagworte:	Convolutional neural network (CNN) cyclone global navigation satellite system (CYGNSS) deep learning (DL) global navigation satellite system-reflectometry (GNSS-R) soil moisture active passive (SMAP) soil moisture retrieval
Online-Zugang:	https://ieeexplore.ieee.org/document/10157977/
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