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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 #381747

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: SMAP radiometer soil moisture downscaling using VIIRS/MODIS data in CONUS

item FANG, B. - University Of Virginia
item LAKSHMI, V. - University Of Virginia
item Cosh, Michael
item HAIN, C. - Nasa Marshall Space Flight Center

Submitted to: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/21/2021
Publication Date: 4/27/2021
Citation: Fang, B., Lakshmi, V., Cosh, M.H., Hain, C. 2021. SMAP radiometer soil moisture downscaling using VIIRS/MODIS data in CONUS. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 14:4946-4965.

Interpretive Summary: Passive microwave remote sensing for soil moisture suffers from very large footprints, making effective use of the data difficult for management scales in agriculture. Higher resolution visible products can be used to help downscale the accurate microwave product to more valuable resolutions. Using a 1 km land surface temperature dataset and the thermal inertia relationship, two summer seasons of low resolution soil moisture data were downscaled to a 1 km product. The 1 km product slightly outperformed the 9 km gridded product from the Soil Moisture Active Passive (SMAP) mission. This will be of benefit to land surface modelers and basin modelers who require a better spatial resolution than 9 km.

Technical Abstract: Satellite remote sensing has been providing passive microwave soil moisture (SM) retrievals of wide spatial coverage and high revisit frequency for research and applications in earth and environmental sciences, specifically after the Soil Moisture Active and Passive (SMAP) was launched in 2015. But, due to the limitation of the passive radiometer instrument, the spatial resolution of SM data is restricted to tens of kilometers, which is insufficient for regional or watershed scale studies. In this study, an SM downscaling algorithm which was developed based on the vegetation modulated thermal inertia relationship between SM and change in land surface temperature (LST) and used data sets from the North America Land Data Assimilation System (NLDAS) Noah model output and the Long Term Data Record (LTDR) AVHRR (Advanced Very High Resolution Radiometer) in 1981-2018. The downscaling model was applied using the high resolution VISible/InfRed (VIS/IR) LST data from Visible Infrared Imaging Radiometer Suite (VIIRS) at 400 m and Moderate Resolution Imaging Spectroradiometer (MODIS) at 1 km to downscale the enhanced L2 radiometer half-orbit 9 km SMAP SM on April-September, 2018-2019 for the Contiguous United States (CONUS) region. The validation of the 400m downscaled SM products was conducted using 303 in-situ SM ground measurements acquired from the International Soil Moisture Network (ISMN). The validation results showed that the overall unbiased RMSE for 400 m and 1 km SM outperformed 9 km SM by 0.009 and 0.007 m3/m3 volumetric soil moisture, respectively, which indicated the fairly good performance of the downscaling algorithm. It was also found that precipitation had impact on both 1 and 9 km downscaled SMAP SM products.