DOWNSCALING SMAP PASSIVE SOIL MOISTURE PRODUCT WITH MODIS PRODUCTS OVER MOUNTAINOUS REGION

To solve the limitation of coarse spatial resolution of passive microwave surface soil moisture (SSM) product, many spatial downscaling methods has been proposed under the theoretic basis of the land surface temperature (LST)/vegetation index triangle space. However, in most studies, the topographic influences on the downscaling results is rarely analyzed but the impacts should be significant due to the strong effect of topographic changes on LST. To effectively solve this issue, a downscaling approach was developed in this study to disaggregate the Soil Moisture Active and Passive (SMAP) SSM product with the use of the Optical/Thermal infrared (TIR) observations from the Moderate-Resolution Imaging Spectro-radiometer (MODIS) onboard the Terra satellite for a typical mountain region located in the west of U.S. Two steps are included in the approach: (1) normalizing the terrain effect on LST and (2) downscaling passive SSM product based on a machine learning approach. The SNOTEL soil moisture observation network located in the study area was used to validate the downscaled SSM.