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

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 gen...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2022-01, Vol.15, p.102-114
Main Authors: Chaubell, Julian, Yueh, Simon, Dunbar, R. Scott, Colliander, Andreas, Entekhabi, Dara, Chan, Steven K., Chen, Fan, Xu, Xiaolan, Bindlish, Rajat, O'Neill, 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., Su, Zhongbo, Thibeault, Marc, Walker, Jeffrey P.
Format: Article
Language:English
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Summary: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.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2021.3123932