ESTIMATION OF SOIL MOISTURE USING SENTINEL-1 AND SENTINEL-2 IMAGES
Keywords: Soil Moisture, Sentinel-1, Sentinel-2, Gaussian, Laplacian, Majority, Morphology, Rank
Abstract. Soil moisture is a vital parameter for environmental research such as agriculture, hydrology, natural resources, environmental hazards, etc. It is essential to have timely soil moisture maps prepared with high accuracy, speed, and low cost. Therefore, in this study, an attempt was made to evaluate the efficiency of Sentinel 1 and 2 sensor images in some cases to prepare a soil moisture map. For this study, soil moisture was sampled at 24 points in the common area of the two images in the south of Malard city, Tehran province (Iran) was obtained by survey. After pre-processing the images, the values of bands 1 to 7, 11, and 12 of the Sentinel-2 and applying filters (Gaussian, Laplacian, Majority, Morphology, and rank) to the Sentinel-1 soil moisture were calculated. Moreover, R, R2, and RMSE were calculated using soil moisture obtained from sample points. Furthermore, Maps of data used by sentinel-1 and sentinel-2 images were obtained. Using maps of data shows the potential of applied filters to sentinel-1 and bands used for sentinel-2 in the estimation of soil moisture. According to the results, the highest coefficient of determination (R2) for the Sentinel-2 is related to band 6 with 84%. The result of Sentinel-1 demonstrated that the highest coefficient of determination was related to the Rank filter (54%). The highest correlation of the Sentinel-2 and the Sentinel-1 is related to band 6 with 74% and the Rank filter with 46%, respectively. The lowest RMSE in Sentinel-2 and Sentinel-1 is related to band three (1.64 %) and rank filter (1.03 %), respectively. According to the obtained results, band 6 in the Sentinel-2 and filter in Sentinel-1 have better performance among the data and methods used. However, it is emphasized that using more samples can be tested for improving results.