Very High Spatial Resolution Downscaled SMAP Radiometer Soil Moisture in the CONUS Using VIIRS/MODIS Data

Bin Fang, Venkat Lakshmi, Michael Cosh, Christopher Hain
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Satellite remote sensing has been providing passive microwave soil moisture (SM) retrievals of a global spatial coverage and a 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, the spatial resolution of SM data is restricted to tens of kilometers, which is insufficient for regional or watershed scale studies. In this study, a SM downscaling algorithm was developed
more » ... ed on the vegetation modulated apparent thermal inertia (ATI) relationship between SM and changes in land surface temperature (LST). The algorithm used data sets from the North America Land Data Assimilation System (NLDAS) Noah model outputs and the Advanced Very High Resolution Radiometer (AVHRR) data of the Long Term Data Record (LTDR) from 1981-2018. Here, the downscaling model was applied to VISible/InfRed (VIS/IR) LST data from the Visible Infrared Imaging Radiometer Suite (VIIRS) at 400-m and the Moderate Resolution Imaging Spectroradiometer (MODIS) at 1 km to downscale the L2 radiometer half-orbit 9 km SMAP SM from 2018-2019 for the Contiguous United States (CONUS). The 400-m/1-km downscaled SM products were validated using 125 in situ SM ground measurements acquired from the International Soil Moisture Network (ISMN). The validation results summarized by SM network show that the overall unbiased RMSE for 400-m of the improved/original downscaling algorithms and 1-km SM outperform 9-km SM by 0.01 m 3 /m 3 , 0.007 m 3 /m 3 , and 0.012 m 3 /m 3 volumetric soil moisture, respectively, which indicates a fairly good performance of the downscaling algorithm. It is also found that precipitation has an impact on the 9-km SMAP SM. Index Terms-downscaling algorithm, SMAP, NLDAS, VIIRS, MODIS
doi:10.1109/jstars.2021.3076026 fatcat:kamd5c36b5aq7dexnoh5hdhn7m