Testing regression equations to derive long-term global soil moisture datasets from passive microwave observations-Reference-Cited by-同舟云学术

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Testing regression equations to derive long-term global soil moisture datasets from passive microwave observations

Published:2016-07 Issue: Volume:180 Page:453-464
ISSN:0034-4257
Container-title:Remote Sensing of Environment
language:en
Short-container-title:Remote Sensing of Environment

Author:

Al-Yaari A.,Wigneron J.P.,Kerr Y.,de Jeu R.,Rodriguez-Fernandez N.,van der Schalie R.,Al Bitar A.,Mialon A.,Richaume P.,Dolman A.,Ducharne A.

Funder

TOSCA

European Space Agency

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Geology,Soil Science

Reference75 articles.

1. Global-scale comparison of passive (SMOS) and active (ASCAT) satellite based microwave soil moisture retrievals with soil moisture simulations (MERRA-Land);Al-Yaari;Remote Sens. Environ.,2014

2. Global-scale evaluation of two satellite-based passive microwave soil moisture datasets (SMOS and AMSR-E) with respect to Land Data Assimilation System estimates;Al-Yaari;Remote Sens. Environ.,2014

3. Evaluation of SMOS soil moisture products over continental U.S. using the SCAN/SNOTEL network;Al Bitar;Geoscience and Remote Sensing, IEEE Transactions on,2012

4. A first assessment of the SMOS data in southwestern France using in situ and airborne soil moisture estimates: The CAROLS airborne campaign;Albergel;Remote Sens. Environ.,2011

5. Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations;Albergel;Remote Sens. Environ.,2012

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