Estimation of soil moisture using Sentinel -1 and Sentinel -2 satellite data
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Soil moisture is one of the important parameters in meteorology, hydrology as well as agricultural, which also affect the operation of the mechanism such as global water cycle and plant evapotranspiration. Changes in soil moisture have a severe impact on agricultural productivity, forestry and ecosystem health. So, regular measurements of soil moisture are essential for effective water resources management and understanding ecological processes. However, the measurement of soil moisture is often limited by the spatial limitations of the observation instrument. If remote sensing data can be used to estimate soil moisture, it can have a large area and time series monitoring of soil moisture and that can increase the efficiency of environmental management and monitoring. In this research, Sentinel-1 C band SAR & Sentinel-2 multi-spectrum satellite data which come from Copernicus Programme of ESA (European Space Agency) will be used to estimate soil moisture. The imaging principle of SAR is backscatter, which is directly related to soil moisture. Therefore, this research will use the point of view of their relationship to estimate their situation in Kakira Sugar Estates. Supervised classification was carried out on the optical dataset and the classified raster was reclassified converted to polygon in order to obtain the shape file for sugarcane plantations at the time of satellite data acquisition. Water-cloud model is used to reduce the vegetation cover affect. In water-cloud model, there are two parameters: LAI and canopy water content cannot be obtained directly from Sentinel-1 satellite. That’s why Sentinel-2 satellite reflectance data are used to calculate LAI and canopy water content. The water cloud model can produce good results in vegetated areas if its parameters are well determined. Comparison between the field observed soil moisture and the WCM predicted soil moisture in this research produced very good results with the correlation coefficient R-square of 0.51. Results also show that coupling Sentinel-1 and Sentinel-2 data can be used to predict soil moisture over in vegetated areas using the WCM since the C band radar backscatter coefficient and soil moisture at a depth of 15cm have a good correlation, the correlation coefficient R-square is 0.35. Therefore, their relationship can be applied to estimate soil moisture.