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Evaluation of Surface Soil Moisture Using Remote Sensing and Field Studies
Soil moisture (SM) is an important quantity to examine in terms of agriculture, meteorology, and hydrology to understand the evaporation cycle and drought mechanisms. This study aims to estimate surface soil moisture in arid areas using Sentinel-1A SAR data. In order to collect soil samples from sampling grids that are synchronized with Sentinel-1A passes, study area is divided into 80 grids, each measuring 10 m by 10 m. Six SAR images were collected from Copernicus Open Access Hub website. The vegetation index (NDVI) was calculated using a Sentinel-2A image. The SNAP software was used to process the SAR images, and R studio was used to extract NDVI values and backscattered energy of each sample grid. In this study, an empirical equation was developed to model surface soil moisture using the dielectric constant and backscattering coefficients. The performance of the model was assessed using statistical indicators such as the coefficient of correlation, Nash–Sutcliffe efficiency, and root mean square error, which yielded results of 0.85, 1.46, and 0.75, respectively.
Evaluation of Surface Soil Moisture Using Remote Sensing and Field Studies
Soil moisture (SM) is an important quantity to examine in terms of agriculture, meteorology, and hydrology to understand the evaporation cycle and drought mechanisms. This study aims to estimate surface soil moisture in arid areas using Sentinel-1A SAR data. In order to collect soil samples from sampling grids that are synchronized with Sentinel-1A passes, study area is divided into 80 grids, each measuring 10 m by 10 m. Six SAR images were collected from Copernicus Open Access Hub website. The vegetation index (NDVI) was calculated using a Sentinel-2A image. The SNAP software was used to process the SAR images, and R studio was used to extract NDVI values and backscattered energy of each sample grid. In this study, an empirical equation was developed to model surface soil moisture using the dielectric constant and backscattering coefficients. The performance of the model was assessed using statistical indicators such as the coefficient of correlation, Nash–Sutcliffe efficiency, and root mean square error, which yielded results of 0.85, 1.46, and 0.75, respectively.
Evaluation of Surface Soil Moisture Using Remote Sensing and Field Studies
Lecture Notes in Civil Engineering
Mesapam, Shashi (editor) / Ohri, Anurag (editor) / Sridhar, Venkataramana (editor) / Tripathi, Nitin Kumar (editor) / Kumar, T. N. Santhosh (author) / Pathak, Abhishek A. (author)
International Virtual Conference on Developments and Applications of Geomatics ; 2022
2024-02-27
15 pages
Article/Chapter (Book)
Electronic Resource
English
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