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Regularized Dual-Channel Algorithm for the Retrieval of Soil Moisture and Vegetation Optical Depth From SMAP Measurements.

Authors :
Chaubell, Julian
Yueh, Simon
Dunbar, R. Scott
Colliander, Andreas
Entekhabi, Dara
Chan, Steven K.
Chen, Fan
Xu, Xiaolan
Bindlish, Rajat
OaNeill, Peggy
Asanuma, Jun
Berg, Aaron A.
Bosch, David D.
Caldwell, Todd
Cosh, Michael H.
Collins, Chandra Holifield
Jensen, Karsten H.
Martinez-Fernandez, Jose
Seyfried, Mark
Starks, Patrick J.
Source :
IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing; Jul/Dec2022, Vol. 20 Issue 2, p102-114, 13p
Publication Year :
2022

Abstract

In August 2020, soil moisture active passive (SMAP) released a new version of its soil moisture and vegetation optical depth (VOD) retrieval products. In this article, we review the methodology followed by the SMAP regularized dual-channel retrieval algorithm. We show that the new implementation generates SM retrievals that not only satisfy the SMAP accuracy requirements, but also show a performance comparable to the single-channel algorithm that uses the V polarized brightness temperature. Due to a lack of in situ measurements we cannot evaluate the accuracy of the VOD. In this article, we show analyses with the intention of providing an understanding of the VOD product. We compare the VOD results with those from SMOS. We also study the relation of the SMAP VOD with two vegetation parameters: tree height and biomass. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19391404
Volume :
20
Issue :
2
Database :
Complementary Index
Journal :
IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing
Publication Type :
Academic Journal
Accession number :
154265647
Full Text :
https://doi.org/10.1109/JSTARS.2021.3123932