Enhanced Machine Learning for Reliable Water Body Extraction of Plateau Wetlands Caohai Using Remote Sensing and Big Geospatial Data from Optical Zhuhai-1 and Radar Sat-2 Satellites

In wetland ecological monitoring, accurate acquisition of water bodies is particularly crucial, especially for hydrological monitoring and eutrophication control. Water bodies can be clearly delineated by using optical remote sensors. Optical sensors can clearly delineate water boundaries and features when extracting water bodies via remote sensing. Meanwhile, synthetic aperture radar (SAR), with its unique microwave capabilities, can easily penetrate vegetation and operate regardless of weather conditions, enabling all-weather monitoring. Each sensor type exhibits distinct advantages in water body monitoring and research. This study focuses on Caohai Wetland in Guizhou Province, utilizing data from the optical satellite Zhuhai-1 (launched by China in 2017) and the radar satellite RadarSat-2 (launched by Canada) at identical resolutions during the same period. Five supervised classification methods were applied to extract water bodies using optical imagery within the wetland area, with results evaluated against SAR data. Results indicate that the optimal water body extraction methods based on optical and SAR data are Random Forest Classification and Support Vector Machine classification, respectively, achieving an overall accuracy of 0.896 and 0.940, with Kappa coefficients of 0.791 and 0.879. The water area extracted using SAR was significantly larger than that based on optical data, thereby identifying areas within Caohai Wetland that were not fully submerged in vegetation during this period. This study holds significant implications for accurate water body extraction and analysis benefited an improved monitoring and conserving the wetland environment.