Soil moisture estimating with NDVI and land surface temperature and normalized moisture index using MODIS images

Soil surface water content is a key variable of hydrologic cycle which plays a significant role in global water and energy balance by affecting several hydrological, ecological and meteorological processes. Soil moisture varies significantly in space and time due to spatial variability of soil properties, topography, vegetation characteristics and atmospheric dynamics. Soil moisture is either measured directly by in situ methods, e.g., Neutron probe, time domain reflectrometry (TDR) or estimated indirectly through pedotransfer functions (PTFs) or remote sensing (RS). Since in situ measurements in large scales are mainly expensive and time consuming, the RS-GIS methods can be used for this purpose. The objective of this study was to estimate surface soil moisture using NDVI, NDMI and LST indices. For this purpose, by using images of MODIS 1B, the indicators of soil moisture were obtained. By using the field soil moisture data, the required analyses were performed to calibrate and validate the model output. The results indicated that there is a reasonable correlation (0.66) between the soil surface moisture and some indices such as NDVI, NDMI and LST. The model validation further indicated that having a mean error of less than about 0.018, the proposed method can predict soil surface moisture reasonably.