Soil moisture monitoring by downscaling of remote sensing products using LST/VI space derived from MODIS products

Abstract

Abstract Soil moisture (SM) has an important role in the earth's water cycle and is a key variable in water resources management. Considering the critical state of water resources in the Urmia Lake basin, northwest Iran, this study examined the potential for utilizing a variety of remote sensing data and products, in conjunction with a promising downscaling method, to monitor soil moisture with a reasonable spatial and temporal resolution, as a novel and effective tool for agricultural and water resource management. Accordingly, remote sensing products of surface soil moisture were scaled to MODIS's image scale (∼1 km) using the UCLA downscaling method and Temperature, Vegetation, Drought Index (TVDI) values obtained from the scattering space method. Results showed that the LPRM, ESA-CCI, and GLDAS downscaled images had the highest inverse correlation with the TVDI (best results) accordingly equal to −0.600, −0.787, and −0.630. Also, based on the evaluation of the obtained results with the ground stations data, the LPRM and the ESA-CCI downscaled images had the best statistical indices values accordingly in 2010 and 2014 that confirm the promising application of remote sensing soil moisture data (rLPRM (2010) = 0.92, MAELPRM (2010) = 4.14%, RMSELPRM (2010) = 6.39% and rESA-CCI (2014) = 0.7, MAEESA-CCI (2014) = 2.23%, RMSEESA-CCI (2014) = 2.59%).