Surface water monitoring can provide important references for water resource protection.Using 20132022 remote sensing images from the domestic high-resolution GF-1 constellation,this study developed a pixel-scale method for surface water information extraction based on the DeepLabv3+deep learning model.The experimental results of derived in Miyun District of Beijing indicate that the proposed method can quickly obtain multiple phases of pixel-scale spatiotemporal distributions of surface water,with the extraction results roughly consistent with actual spatial distribution.Compared to conventional classification algorithms such as random forest,support vector machine,and maximum likelihood,this method exhibited extraction precision and recall of 99.22％and 98.01％,respectively,demonstrating high accuracy in water information extraction.The long-term serial monitoring results indicate that the surface water area evolved from a continuous decrease to an increase and then to stabilization from 2013 to 2022.Since the extraction accuracy and efficiency can meet the demand for the monitoring of the spatial changes in regional water bodies,the proposed method enjoys broad prospects for practical application in the fields of remote sensing-based rapid monitoring and ecological assessment of regional surface water resources.
维普
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