Estimation of Soil Moisture using Optical, Thermal and Radar Remote Sensing )Case Study: South of Tehran(

Soil moisture is one of the important environmental parameters. Traditional methods of field measurement of soil moisture cannot adequately reflect spatial variability of soil moisture. Remote sensing has a widespread role in this. Landsat 8 satellite data and Sentinel-1 radar satellite from Tehran were provided. 72 soil samples were taken at the same time by satellite passing from the area and used in the validation phase with the necessary processing on satellite images and utilizes four different groups of indicators: 1) SAVI, NDVI, MI, NDWI 2) bands of Landsat 8 3) filters Radar 4) LST to modeling soil moisture. The investigation of the accuracy of functions and the introduction of the most accurate models was done by calculating the regression relations between these criteria and the ground points. The results of comparison of relations in the final step introduced two multivariate regression models to estimate the moisture content of the proposed area. The results showed that the proposed models have a good correlation coefficient of R2 = 62 and R2 = 73. Also, among the indicators in the four groups, SAVI, Band 1, Band 11, Li and Least filters have the highest correlation for estimating soil moisture content.