SMAP Soil Moisture Downscaling using ECOSTRESS data in CONUS

Abstract:

Remotely sensing technique can provide high accurate and wide coverage soil moisture estimates for many applications, such as: hydrology, agriculture, ecology and weather forecast. The Soil Moisture Active Passive (SMAP) satellite was launched in 2015 and is now providing global coverage L-band microwave radiometer soil moisture retrievals with a 2-3 day revisit at a spatial resolution of 36 km. However, such resolution is too coarse for watershed hydrology studies which require much finer resolution. In this study, we will apply a remotely sensed soil moisture downscaling algorithm based on the thermal inertia relationship between surface soil moisture and LST (Land Surface Temperature) change between day and night. We will build a model using NLDAS (North America Land Data Assimilation System) Noah model output, and AVHRR (Advanced Very High-Resolution Radiometer) NDVI (Normalized Difference Vegetation Index) data from LTDR (Land Long Term Data Record) project from 1981 – 2018. The downscaling model will be applied on 400 m resolution LST and LAI (Leaf Area Index) products derived from VIIRS (Visible Infrared Imaging Radiometer Suite) brightness temperature observations in CONUS (CONtiguous United States). The downscaled soil moisture observations will be compared and validated with ISMN (International Soil Moisture Networks) in situ soil moisture measurements and NASA (National Aeronautics and Space Administration) LIS (Land Information System) model output.