Soil moisture is a vital resource that plays a critical role in arid and semi-arid areas. In the present study, a new approach was adopted to estimate surface soil moisture based on multi-index models using reflective and thermal indices as well as surface energy balance system–Iran (SEBS–Iran) in pastures and farmlands in Qom province, Iran in 2016–2017. To select the best model based on remote sensing (RS) indices, 12 models were designed and after analysis, the best ones were selected. Afterward, the results of the SEBS–Iran algorithm and the improved multi-index model [normalized multi-band drought index (NMDI), normalized difference vegetation index (NDVI), land surface temperature (LST) and the temperature vegetation dryness index (TVDI)] were calibrated with field data in the two studied fields (pastures and farmlands). The findings indicated that the multi-index model NMDI–TDVI–LST–NDVI (R = 0.95) and SEBS–Iran (R = 0.93) both had significant correlations with measured soil moisture. Regarding both models in farmlands and pastures, the SEBS–Iran regression model was closer to the line of fit, and R² in the two fields was 0.95 and 0.96, respectively. Compared to SEBS–Iran, the multi-index model showed lower coefficient of determination in pastures (0.71) due to the higher accuracy of SEBS–Iran in areas with lower vegetation density. Generally, both methods were found to be suitable for soil moisture estimation. The multi-index model can be used to estimate soil moisture in densely vegetated areas on a large scale due to its simplicity and good accuracy. Moreover, the highly accurate SEBS–Iran model can be used even in sparsely vegetated areas.

土壤水分是至关重要的资源，在干旱和半干旱地区发挥着至关重要的作用。在本研究中，采用了一种新方法来基于多指标模型估算地表土壤水分，该模型使用了反射和热指数以及伊朗库姆省牧场和农田中的地表能量平衡系统-伊朗（SEBS-伊朗）。 2016–2017。为了基于遥感（RS）指数选择最佳模型，设计了12种模型，经过分析，选择了最佳模型。之后，使用SEBS–Iran算法和改进的多指标模型[归一化多波段干旱指数（NMDI），归一化差异植被指数（NDVI），地表温度（LST）和温度植被干燥指数（TVDI）”的结果）]在两个研究田地（草场和农田）中用田间数据进行校准。R  = 0.95）和SEBS–Iran（R  = 0.93）都与测得的土壤湿度有显着相关性。关于农田和牧场的两种模型，SEBS–Iran回归模型都更接近拟合线，两个领域的R ²分别为0.95和0.96。与SEBS–Iran相比，由于植被密度较低的地区SEBS–Iran的准确性较高，因此多指标模型的草场测定系数较低（0.71）。通常，发现这两种方法都适用于土壤湿度估算。由于其简单性和良好的准确性，该多指标模型可用于大规模估计茂密植被区的土壤水分。此外，即使在植被稀疏的地区，也可以使用高度精确的SEBS–Iran模型。