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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 #387439

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: Thermal hydraulic disaggregation of SMAP soil moisture over continental United States

item LIU, P.W. - National Aeronautics And Space Administration (NASA)
item BINDLISH, R. - National Aeronautics And Space Administration (NASA)
item O'NEIL, P.E. - National Aeronautics And Space Administration (NASA)
item FANG, B. - University Of Virginia
item LAKSHMI, V. - University Of Virginia
item YANG, ZHENGWEI - National Agricultural Statistical Service (NASS, USDA)
item Cosh, Michael
item BONGIOVANNI, T. - University Of Texas At Austin
item Holifield Collins, Chandra
item Starks, Patrick
item Prueger, John
item Bosch, David - Dave
item Seyfried, Mark
item Williams, Mark

Submitted to: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/30/2022
Publication Date: 4/7/2022
Citation: Liu, P., Bindlish, R., O'Neil, P., Fang, B., Lakshmi, V., Yang, Z., Cosh, M.H., Bongiovanni, T., Holifield Collins, C.D., Starks, P.J., Prueger, J.H., Bosch, D.D., Seyfried, M.S., Williams, M.R. 2022. Thermal hydraulic disaggregation of SMAP soil moisture over continental United States. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 15:4072-4093.

Interpretive Summary: Soil moisture remote sensing has been limited by the abilities of passive radiometers to have a fine enough resolution to get to field scales for agriculture. However, by adding optical and thermal information into a downscaling algorithm for the Soil Moisture Active Passive (SMAP) Mission, it is possible to reduce the field scale to a fine as 1 km. A review of the algorithm and the validation of the 1 km product are provided. This new downscaling algorithm exceeds the performance of the best available product from the SMAP – Sentinel combined 1km product, which uses a radiometer and radar combination at low temporal resolution. This algorithm and product will advance the science of soil moisture into agricultural management at the field and basin scale.

Technical Abstract: A Thermal Hydraulic disaggregation of Soil Moisture (THySM) algorithm was implemented to downscale NASA’s Soil Moisture Active Passive (SMAP) Enhanced soil moisture (SM) product (SPL2SMP_E) to 1 km over the Continental United States (US). This algorithm combines thermal inertia theory with a soil hydraulic-based approach to consider SM spatial distribution at fine scale driven by both heat fluxes and water drainage under various vegetation conditions. Ancillary datasets used in this approach include land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, which was used to estimate relative soil wetness using thermal inertia theory, and soil texture and properties from SoilGrid were applied to estimate the soil wetness from a hydraulic model. The relative soil wetness values at 1 km from the two models were then combined by using weighting functions based on the notion that thermal fluxes govern SM distribution during times of strong heat transport, so it is weighted higher during these times. During colder seasons and for wet soil conditions, the spatial SM distribution is governed by vertical drainage in soils, so the soil hydraulic approach is weighted higher and spatial SM patterns are more influenced by soil patterns. Inclusion of the soil hydraulic approach compensates for the data gaps due to occurrence of clouds that blocks surface temperature retrievals from satellite sensors and hence the use of the optical/thermal-based approach. THySM values obtained by disaggregating SPL2SMP_E using the proposed algorithm were evaluated in terms of spatial representativeness and accuracy using in situ SM measurements from several SMAP Core Validation Sites (CVS), the USDA Soil Climate Analysis Network (USDA SCAN), and the NOAA Climate Reference Network (NOAA CRN). Results were also compared with the SMAP / Sentinel-1 (SPL2SMAP_S) 1 km SM product. THySM shows higher accuracy than SPL2SMAP_S based on daily spatial unbiased root mean square deviation (ubRMSDSpt) when compared to in situ CVS stations. The accuracy of THySM is 0.048 m3/m3 based on unbiased root mean square error (ubRMSE), outperforming SPL2SMAP_S by about 0.01-0.02 m3/m3. The ubRMSE of THySM at 1 km over the SMAP grassland/rangeland-dominated CVS sites is less than 0.04 m3/m3, which meets the SMAP mission SM accuracy requirement at 9 and 36 km. Although the ubRMSE of THySM is relatively higher in agriculture/cropland than in grassland/rangeland areas, it shows an improvement of more than 0.02 m3/m3 compared to SPL2SMAP_S.