Abstract
The knowledge of water surface changes provides invaluable information for water resources management and flood monitoring. However, the accurate identification of water bodies is a long-term challenge due to human activities and climate change. Sentinel-1 synthetic aperture radar (SAR) data have been drawn, increasing attention to water extraction due to the availability of weather conditions, water sensitivity and high spatial and temporal resolutions. This study investigated the abilities of random forest (RF), Extreme Gradient Boosting (XGB) and support vector machine (SVM) methods to identify water bodies using Sentinel-1 imageries in the upper stream of the Yangtze River, China. Three sets of hyper-parameters including default values, optimized by grid searches and genetic algorithms, were examined for each model. Model performances were evaluated using a Sentinel-1 image of the developed site and the transfer site. The results showed that SVM outperformed RF and XGB under the three scenarios on both the validated and transfer sites. Among them, SVM optimized by genetic algorithm obtained the best accuracy with precisions of 0.9917 and 0.985, kappa statistics of 0.9833 and 0.97, F1-scores of 0.9919 and 0.9848 on validated and transfer sites, respectively. The best model was then used to identify the dynamic changes in water surfaces during the 2020 flood season in the study area. Overall, the study further demonstrated that SVM optimized using a genetic algorithm was a suitable method for monitoring water surface changes with a Sentinel-1 dataset.
Highlights
Flooding is a natural phenomenon in which water volume or the water level of rivers and lakes increases rapidly due to rainstorms, melting ice or a storm surge [1]
Machine learning algorithms based on remote sensing data and geographic information systems (GISs) have been successfully applied to river surface monitoring [17,18,19,20,21,22]
Ming et al used genetic algorithms for the parameter optimization of random forest (RF) to classify land cover types with HJ-1B-CCD2 image data. They reported that the optimized model improved the accuracy by 1.02% compared to the original model [40]
Summary
Flooding is a natural phenomenon in which water volume or the water level of rivers and lakes increases rapidly due to rainstorms, melting ice or a storm surge [1]. Machine learning algorithms based on remote sensing data and geographic information systems (GISs) have been successfully applied to river surface monitoring [17,18,19,20,21,22]. Naive Bayes, recursive partitioning and region trees (RPART), natural networks, support vector machines (SVMs), random forest (RF), and gradient promoted machines—for extracting surface water from a Landsat 8 image in Nepal. They found that models performed better in hilly and flat regions.
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