مشخصات پژوهش

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) ¼ 0.92, MAELPRM (2010) ¼ 4.14%, RMSELPRM (2010) ¼ 6.39% and rESA-CCI (2014) ¼ 0.7, MAEESA-CCI (2014) ¼ 2.23%, RMSEESA-CCI (2014) ¼ 2.59%).