Application of KOMPSAT-3A and KOMPSAT-5 SAR Image to Soil Moisture Estimation and Validation Test Using SMAP

The research of using synthetic aperture radar (SAR) satellite imagery to estimate soil moisture is an important application of satellite image sets. In this study, a soil moisture estimation study was conducted by integrating Sentinel-1 SAR imagery provided by Google Earth Engine (GEE) and X-band KOMPSAT-5 SAR. Vegetation indices calculated from high-resolution KOMPSAT-3A optical imagery were used as vegetation inputs required by the water cloud model used for soil moisture estimation. Meanwhile, accurate land cover maps provided by the Korea Environmental Information Service were used as objects for soil moisture estimation. The study area is part of the Gimje Plain, a major rice growing area along the west coast of the Korean peninsula. The estimated results were validated using data from the Soil Moisture Active Passive (SMAP) satellite, as a test. The differences between SMAP L-3 data and L-4 data with estimated result were found to be 4.84±6.78%p and 3.09±5.30%p, respectively, showing significant accuracy levels. This experimental result suggests the potential of integrating SAR imagery with GEE for soil moisture estimation applications.