Regularized Dual-Channel Algorithm for the Retrieval of Soil Moisture and Vegetation Optical Depth From SMAP Measurements
Julián Chaubell, Simon Yueh, R. S. Dunbar, Andreas Colliander, Dara Entekhabi, S. Chan, Fan Chen, Xiaolan Xu, Rajat Bindlish, Peggy O’Neill, Jun Asanuma, Aaron Berg, David D. Bosch, Todd G. Caldwell, Michael H. Cosh, Chandra Holifield Collins, Karsten H. Jensen, José Martínez‐Fernández, M. S. Seyfried, Patrick J. Starks, Zhongbo Su, M. Thibeault, Jeffrey P. Walker
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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i> polarized brightness temperature. Due to a lack of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> 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.