DOWNSCALING OF SOIL MOISTURE PRODUCT OF SMAP
Keywords: Downscaling, Neural Network, Regression, SMAP, Soil Moisture
Abstract. Soil moisture as a variable parameter of the Earth's surface plays a very important role in many applications such as meteorology, climatology, water resources management and hydrology. Therefore, access to soil surface moisture product with high spatial resolution is very important. Due to the lack of access to soil moisture information with high spatial resolution, the main goal in this article is to downscale the existing soil moisture products and improve their spatial resolution into 1 square kilometer. For this purpose, two methods based on regression and neural network have been used for downscaling the 3 km soil moisture products of SMAP satellite. To this end, other available satellite data and products including various combinations of land surface temperature (LST), normalized difference vegetation index (NDVI), brightness temperature in different polarizations of (TBH and TBV) passive microwave sensor data, digital elevation model (DEM) and short-wavelength infrared (SWIR) data of MODIS and Sentinel 3 have been used. In this study, two regions in the north and south of Iran, Golestan and Fars provinces, have been examined, due to the lack of ground measurements of soil moisture, the SMAP product with the resolution of 1 km which has been already downscaled by exploiting the Sentinel-1 radar data, was used to evaluate the results. The evaluation results in Golestan and Fars provinces showed the correlation coefficient of 0.82 to 0.93 and 0.72 to 0.77, respectively, and the average percentage of absolute error in both regression and neural network methods was less than 21 to 30 and 42 to 46 percent.