Using Sentinel images for analyzing water and land separability in an agricultural river basin.

The presence or absence of water can result in floods or droughts, potentially impacting agricultural productivity to a great extent. With advancements in remote sensing technology, the reliability of identifying water bodies has significantly improved, particularly in terms of distinguishing between water and land. This study introduced remote sensing methods to improve the accuracy of differentiating water within the Dawenhe River basin. Various water body scenarios were examined, and the performance of these methods was evaluated to determine the proper approach for water-land separation. In applying water body indices to Sentinel-2 images, it was found that the normalized difference water index (NDWI) outperformed the modified normalized difference water index (MNDWI) in identifying water bodies. Consequently, histograms of frequency distribution for Sentinel-1 were generated, revealing that water and land were more distinguishable in VV polarization than in VH polarization. Using histogram thresholding on VV polarized images in Dongping Lake resulted in an overall classification accuracy of 97.58%, surpassing that of Otsu's method at 97.36%. To address the persisting misclassifications, this study identified three leading causes and proposed corresponding solutions. These solutions included (1) employing the morphological dilation algorithm to expand the water area, mitigating pixel mixing issues at the water-land boundary that caused the water bodies to appear smaller; (2) utilizing incidence angles and digital elevation model (DEM) to locate and remove shadows; and (3) slightly lowering the thresholds and manually correcting misclassifications. As a result, the average accuracy of the four areas increased from 95.56 to 96.94%.