Estimating soil moisture using remotely sensed data and surface energy balance system

Soil moisture is one of the key parameters in watershed and water resources studies. Field measurement of this parameter is extremely difficult, time-consuming and costly. Hence, in recent years, numerous satellite-based methods for estimating and modeling soil moisture have been developed and presented. Among proposed methods, surface energy models performed better and have a higher degree of accuracy because of their physical nature. But, due to their particular complexity, they have been used rarely. Therefore, this research was carried out to estimate soil moisture using Landsat 8 Satellite imagery and Surface Energy Balance System (SEBS) near the Shadegan Wetland, located in the south-west of Iran. For this purpose, volumetric soil moisture content was measured at 39 points on 27 June 2016, simultaneous with the overpass of Landsat 8 Satellite over the study area. After necessary image processing, the was calculated using the applying the SEBS on satellite image. Then, the evaporation fraction was used as the main input in an experimental model (saturation ratio model) for estimating the soil moisture. Results showed the good ability of the model for estimating soil moisture with the coefficient of determination of 0.69 and the RMSE error value of 0.03 . It can be concluded that combination of remote sensing data, surface energy balance system and the experimental model of soil moisture can be used for modeling soil moisture in a large scale.