Long Term Global Surface Soil Moisture Fields Using an SMOS-Trained Neural Network Applied to AMSR-E Data

Long Term Global Surface Soil Moisture Fields Using an SMOS-Trained Neural Network Applied to AMSR-E Data

A method to retrieve soil moisture (SM) from Advanced Scanning Microwave Radiometer—Earth Observing System Sensor (AMSR-E) observations using Soil Moisture and Ocean Salinity (SMOS) Level 3 SM as a reference is discussed. The goal is to obtain longer time series of SM with no significant bias and wi...

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Bibliographic Details
Main Authors: Nemesio J. Rodríguez-Fernández, Yann H. Kerr, Robin van der Schalie, Amen Al-Yaari, Jean-Pierre Wigneron, Richard de Jeu, Philippe Richaume, Emanuel Dutra, Arnaud Mialon, Matthias Drusch
Format: Article
Language:English
Published: MDPI AG 2016-11-01
Series:Remote Sensing
Subjects:
soil moisture
passive radiometry
neural networks
SMOS (Soil Moisture and Ocean Salinity) mission
Online Access:http://www.mdpi.com/2072-4292/8/11/959

Internet

http://www.mdpi.com/2072-4292/8/11/959

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