부산대학교 도서관

Soil moisture is one of the significant variables in the fields of meteorological, hydrologic, and agriculture applications. This article utilizes the SENTINEL-1 SAR (synthetic aperture radar) and SENTINEL-2 multispectral satellite data to evaluate the spatial and temporal soil moisture distribution in the Piliao area. This study employs the water cloud model to distinguish the backscattering coefficient from the effects of soil moisture and vegetation coverage, and to estimate soil moisture. Furthermore, the integration of artificial neural network and a radiative transfer model, i.e., PROSAIL, was used to extract the vegetation related parameters for the water cloud model, i.e., LAI and canopy water content. Results show that the r-square value between radar backscattering coefficient and soil moisture increased from 0.33 to 0.53 with considering the vegetation coverage effect. The spatial estimation of soil moisture in Piliao area was conducted at selected times. It shows that soil moisture is related to crop types and their growth periods, which is identified by comparing LAI and soil moisture estimation. Our results can serve as a useful reference for irrigation water management in the study area.