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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #349648

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: An initial assessment of SMAP soil moisture disaggregation scheme using TIR surface evaporation data over the continental United States

item MISHRA, V. - University Of Alabama
item ELLENBURG, W.L. - University Of Alabama
item GRIFFIN, R.E. - University Of Alabama
item MECIKALSKI, J. - University Of Alabama
item HAIN, C. - Goddard Space Flight Center
item Anderson, Martha

Submitted to: International Journal of Applied Earth Observation and Geoinformation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/5/2018
Publication Date: 3/16/2018
Citation: Mishra, V., Ellenburg, W., Griffin, R., Mecikalski, J., Hain, C., Anderson, M.C. 2018. An initial assessment of SMAP soil moisture disaggregation scheme using TIR surface evaporation data over the continental United States. International Journal of Applied Earth Observation and Geoinformation. 68:92-104.

Interpretive Summary: Accurate and frequently updated maps of soil moisture content across the United States, derived from satellite remote sensing, will have a wide range of applications for agricultural management and yield estimation. To be of optimal benefit, these maps should be at as fine a spatial resolution as possible to represent local variability in soil moisture conditions. The Soil Moisture Active Passive (SMAP) mission launched in 2015 included a dual microwave sensor satellite system, with a passive sensor component providing coarse scale (36-km pixels) maps and an active radar intended for downscaling to 9-km and 3-km pixels. Unfortunately, the active radar failed months into the mission, and other techniques had to be developed to enhance the SMAP resolution to more operationally meaningful spatial scales. This paper tests a method that uses relatively high resolution (4-km pixels) maps of land-surface evaporation rate, developed using thermal-infrared satellite imagery, to spatially sharpen the SMAP passive microwave derived soil moisture maps to 9- and 3-km. The method builds relationships between the Soil Evaporative Efficiency (SEE), derived from the thermal surface evaporation maps aggregated to SMAP resolution (36-km), and the coarse resolution SMAP soil moisture data. These relationships are then applied to the thermal SEE at 3- and 9-km resolution to estimate soil moisture distributions at these finer scales. The downscaled maps were compared to maps generated using the radar technique during the first months of SMAP operation, prior to radar failure, and to ground-based measurements collected at soil moisture monitoring sites across the United States. The thermally sharpened maps agreed well with radar derived products, and performed similarly in comparison with ground-based measurements. This indicates that the thermal sharpening method could be useful for developing higher resolution soil moisture data for the remainder of the SMAP mission life, post radar failure.

Technical Abstract: The Soil Moisture Active Passive (SMAP) mission is dedicated toward global soil moisture mapping. Typically, an L-band microwave radiometer has a spatial resolution on the order of 36-40 km, which is too coarse for many specific hydro-meteorological and agricultural applications. With the failure of the SMAP active radar within three months of becoming operational, an intermediate (9-km) and finer (3-km) scale soil moisture product solely from the SMAP mission is no longer possible. Therefore, the focus of this study is a disaggregation of the 36-km resolution SMAP passive-only surface soil moisture (SSM) using the Soil Evaporative Efficiency (SEE) approach to spatial scales of 3-km and 9-km. The SEE was computed using thermal-infrared (TIR) estimation of surface evaporation from the Atmosphere Land Exchange Inverse (ALEXI) model over CONUS. The disaggregation results were compared with the 3 months of SMAP-Active (SMAP-A) and Active/Passive (AP) products, while comparisons with SMAP-Enhanced (SMAP-E), SMAP-Passive (SMAP-P), as well as with more than 180 Soil Climate Analysis Network (SCAN) stations across CONUS were performed for a 19 month period. At the 9-km spatial scale, the TIR-Downscaled data correlated strongly with the SMAP-E SSM both spatially (r = 0.90) and temporally (r = 0.87). In comparison with SCAN observations, overall correlations of 0.49 and 0.47; bias of -0.022 and -0.019 and unbiased RMSE of 0.105 and 0.100 were found for SMAP-E and TIR-Downscaled SSM across the Continental U.S., respectively. At the 3-km scale, TIR-Downscaled and SMAP-A had a mean temporal r of only 0.27. In terms of gain statistics, the highest percentage of SCAN sites with positive gains (<55%) was observed with the TIR-Downscaled SSM at 9-km. Overall, the TIR-based downscaled SSM showed strong correspondence with SMAP-E; compared to SCAN, and overall both SMAP-E and TIR-Downscaled performed similarly; however, gain statistics shows that TIR-Downscaled SSM slightly outperformed SMAP-E.