Estimation of Soil Surface Moisture in Agricultural Lands Using Satellite Images and Remote Sensing Indicators (Case Study: Shushtar County)

Document Type : Research Paper


1 MSc in Natural Resources Engineering- Environment, Islamic Azad University, Science and Research Branch of Tehran (Khuzestan), Ahvaz, Iran

2 PhD Student in Geography and Rural Planning, Faculty of Geographical Sciences and Planning, Isfahan University, Isfahan, Iran


Estimation of soil moisture is essential for optimal management of water and soil resources. Landsat images with appropriate spatial and temporal resolution are good tools for these studies. The purpose of this study is to estimate and zoning the soil surface moisture in agricultural lands of Shushtar County in Khuzestan province using remote sensing indicators. To do this, first 25 soil samples of agricultural lands were taken from a depth of 0-15 cm and their moisture was measured. Then, normalized vegetation difference index (NDVI), soil reflection adjustment index (SAVI), surface temperature (LST), normalized moisture difference index (NDMI), normalized agricultural differential index (NDTI) and soil moisture index Infrared short band (SMSWIR) were applied to the Landsat 8 Image. In the next step, the values ​​of these indices were transferred to SPSS software for statistical regression and the soil moisture estimation functions were obtained by multivariate linear regression. The results showed; Due to the high coefficient of determination (0.73) and the low root mean square error (1.31) in the simultaneous method (Enter Method), this model was considered suitable for estimating and zoning the surface moisture of agricultural lands in the region. According to the research results, soil surface moisture was directly related to NDVI, SAVI, NDMI, NDTI and SMSWIR indices and inversely related to LST index. Also, LST index has a better estimate of soil moisture, which indicates a significant effect of this factor on the amount of soil surface moisture.


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