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

Author
item CHAUBELL, JULIAN - Jet Propulsion Laboratory
item CHAN, STEVEN - Jet Propulsion Laboratory
item DUNBAR, SCOTT - Jet Propulsion Laboratory
item ANDREAS, COLLIANDER - Jet Propulsion Laboratory
item YUEH, SIMON - Jet Propulsion Laboratory
item XU, AIAOLAN - Jet Propulsion Laboratory
item CHEN, FAN - US Department Of Agriculture (USDA)
item ENTEKHABI, DARA - Massachusetts Institute Of Technology
item BINDLISH, RAJAT - Goddard Space Flight Center
item O'NEILL, PEGGY - Goddard Space Flight Center
item ASANUMA, JUN - National Institute Of Agrobiological Sciences (NIAS)
item BERG, AARON - University Of Guelph
item Bosch, David - Dave
item CALDWELL, TODD - University Of Texas
item Cosh, Michael
item Holifield Collins, Chandra
item JENSEN, KARSTEN - University Of Copenhagen
item MARTINEZ-FERN, JOSE - University Of Salamanca
item MCNARIN, HEATHER - Agriculture And Agri-Food Canada
item Seyfried, Mark
item Starks, Patrick - Pat
item SU, ZHONGBO - University Of Twente
item THIBEAULT, MARC - Estacao Agronomica Nacional
item WALKER, JEFFREY - Monash University

Submitted to: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/15/2021
Publication Date: 10/29/2022
Citation: Chaubell, J., Chan, S., Dunbar, S., Andreas, C., Yueh, S., Xu, A., Chen, F., Entekhabi, D., Bindlish, R., O'Neill, P., Asanuma, J., Berg, A., Bosch, D.D., Caldwell, T., Cosh, M.H., Holifield Collins, C.D., Jensen, K., Martinez-Fern, J., Mcnarin, H., Seyfried, M.S., Starks, P.J., Su, Z., Thibeault, M., Walker, J. 2022. Regularized Dual-Channel Algorithm for the Retrieval of Soil Moisture and Vegetation Optical Depth for SMAP. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/JSTARS.2021.3124743.
DOI: https://doi.org/10.1109/JSTARS.2021.3124743

Interpretive Summary: Interpretive Summary Accurate measurements of surface soil moisture and vegetation condition are valuable for a wide range of agricultural applications, including: irrigation scheduling, crop yield forecasting, drought assessment, and fertilizer management. In January 2015 NASA launched the Soil Moisture Active/Passive (SMAP) satellite with the goal of improving our ability to globally measure surface soil moisture from space. In August of 2020, a new algorithm was established to better estimate soil moisture and vegetation condition. Results indicated that the new soil moisture estimates satisfy the original accuracy requirements of the SMAP mission. Furthermore, early results of the vegetation estimates indicate SMAP data will be useful for evaluating vegetation conditions across the globe. These datasets will provide modelers and land managers with improved estimates for decision making.

Technical Abstract: Technical Abstract In August 2020, SMAP released a new version of its soil moisture (SM) and vegetation optical depth (VOD) products. In this work, we review the methodology followed by the SMAP regularized dual-implementation generated SM retrievals satisfy the SMAP accuracy requirements and show a performance comparable to the single-channel algorithm that uses the V polarized brightness temperature (SCA-V). Due to a lack of in situ measurements we cannot evaluate theaccuracy of the VOD, but in this work, we will show analysis with the intention of providing an understanding of the VOD product and compare the VOD results with those from SMOS.