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Research Project: Enhancing Water Resources, Production Efficiency and Ecosystem Services in Gulf Atlantic Coastal Plain Agricultural Watersheds

Location: Southeast Watershed Research

Title: Improved SMAP dual-channel algorithm for the retrieval of soil moisture

Author
item CHAUBELL, JULIAN - Jet Propulsion Laboratory
item YUEH, SIMON - Jet Propulsion Laboratory
item DUNBAR, SCOTT - Jet Propulsion Laboratory
item COLLIANDER, ANDREAS - Jet Propulsion Laboratory
item CHEN, FAN - US Department Of Agriculture (USDA)
item CHAN, STEVEN - Jet Propulsion Laboratory
item ENTEKHABI, DARA - Massachusetts Institute Of Technology
item BINDLISH, RAJAT - Goddard Space Flight Center
item O'NEILL, PEGGY - Goddard Space Flight Center
item ASANSUMA, JUN - University Of Tsukuba
item BERG, AARON - University Of Guelph
item Bosch, David
item CALDWELL, TODD - University Of Texas
item Cosh, Michael
item Holifield Collins, Chandra
item MARTINEZ-FERNANDEZ, JOSE - University Of Salamanca
item Seyfried, Mark
item Starks, Patrick
item SU, ZHONGBO - University Of Twente
item THIBEAULT, MARC - South African National Space Agency
item WALKER, JEFFREY - Monash University

Submitted to: IEEE Transactions on Geoscience and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/9/2019
Publication Date: 1/15/2020
Citation: Chaubell, J., Yueh, S., Dunbar, S., Colliander, A., Chen, F., Chan, S., Entekhabi, D., Bindlish, R., O'Neill, P., Asansuma, J., Berg, A., Bosch, D.D., Caldwell, T., Cosh, M.H., Holifield Collins, C.D., Martinez-Fernandez, J., Seyfried, M.S., Starks, P.J., Su, Z., Thibeault, M., Walker, J. 2020. Improved SMAP dual-channel algorithm for the retrieval of soil moisture. IEEE Transactions on Geoscience and Remote Sensing. 58(6):3894-3905. https://doi.org/10.1109/TGRS.2019.2959239.
DOI: https://doi.org/10.1109/TGRS.2019.2959239

Interpretive Summary: Accurate measurements of surface soil moisture 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. SMAP acquires estimates of soil moisture using single channel (SCA) and dual-channel (DCA) algorithms. The SCA has been providing satisfactory soil moisture retrievals. However, the DCA using pre-launch design and algorithm parameters has yielded marginal results. Research was conducted to yield a modified dual-channel algorithm (MDCA) which can achieve improved accuracy over the original DCA. The performance of MDCA was assessed and compared with SCA and DCA using four years (April 1, 2015–March 31, 2019) of collected field data. While improvements were made with the MDCA, the assessment shows that SCA still outperforms all the dual-channel algorithms.

Technical Abstract: The Soil Moisture Active Passive (SMAP) mission was designed to acquire L-band radiometer measurements for the estimation of soil moisture (SM) with an average ubRMSD of no more than 0.04 m3/m3 volumetric accuracy in the top 5 cm for vegetation with water content of less than 5 kg/m2. Single Channel Algorithm (SCA) and Dual-Channel Algorithm (DCA) are implemented for the processing of SMAP radiometer data. The SCA using the vertically polarized brightness temperature (SCA-V) has been providing satisfactory soil moisture retrievals. However, the DCA using pre-launch design and algorithm parameters for vertical and horizontal polarization data has a marginal performance. In this work, we show that with the updates of the roughness parameter h and the polarization mixing parameters Q, a Modified Dual-Channel Algorithm (MDCA) can achieve improved accuracy over DCA; it also allows for the retrieval of vegetation optical depth (VOD or t). The retrieval performance of MDCA is assessed and compared with SCA-V and DCA using four years (April 1, 2015–March 31, 2019) of in situ data from core validation sites (CVS) and sparse networks. The assessment shows that SCA-V still outperforms all the implemented algorithms.