Refined monitoring of water bodies in built-up areas and analysis and evaluation of water color characteristics in complex urban backgrounds

With the rapid construction and development of cities in China, urban water environment changes and water quality monitoring face increasingly complex challenges. Water body extraction in complex urban settings is susceptible to environmental disturbances, reducing monitoring accuracy and efficiency. Additionally, Traditional water quality detection methods, relying on laboratory sampling and analysis, are hindered by high costs and long cycles. To address these issues, this study constructs a high-resolution satellite imagery dataset for urban contexts and trains four deep learning models (UNet, DeepLab, PSPNet, Segformer) for water body extraction. A method combining the water color index and geographic detector for rapid water quality assessment is also proposed. The research shows that Segformer performs best in handling complex urban background issues (e.g., shadow problems), achieving a water body extraction accuracy of 98.64%. The self-constructed dataset demonstrates good accuracy across all four models, effectively addressing urban environment diversity and interference factors. For water quality assessment, the study combines the water color index and geographic detector, establishing a rapid evaluation method. Using the water color index for preliminary assessment and the geographic detector to analyze the impact of environmental factors on water quality changes, the results for Shenzhen's built-up area show that primary and secondary water colors represent 39.17% and 52.81%, respectively. Comparison with measured data reveals a root mean square error of 3.9° and an average absolute percentage error of 1.5%, validating the proposed method's accuracy. Industrial location, population density, soil erosion, and other factors significantly affect water color, particularly as industrial activities and soil erosion alter water composition and suspended solids, directly impacting water color. The interaction analysis using the geographic detector highlights how the combined influence of factors such as industrial activities, soil erosion affects water color changes, providing a clearer understanding of the spatial factors driving water quality variations in urban environments.