Combining spectral water index with band for surface water area extraction by using Google Earth Engine (GEE) and ArcGIS in the southern low mountain and hilly areas of China

Monitoring and evaluating surface water dynamics is crucial for addressing climate change and fostering growth in various sectors. The spectral water index method is a predominant approach for mapping and monitoring surface water. The study was conducted in the southern low mountain and hilly areas of China. This study presents a combined approach to enhancing extraction accuracy in surface water mapping. Five distinct water and vegetation indices were employed alongside various bands. The modified normalized difference water index (mNDWI), combined with the near-infrared (NIR) band, has demonstrated superior extraction accuracy across different types of water compared to the other combinations. The combination of (mNDWI_NIR) was validated with the Joint Research Center (JRC) product Global Surface Water (GSW) dataset and the available surface runoff data. The accuracy of the combined method was assessed using a confusion matrix, which yielded an overall accuracy of 96.90 % and a kappa value of 0.868. It also shows a strong linear correlation with areal surface runoff distributions, with an R² value of 0.946, compared to GSW and land use and land cover (LULC) values of 0.933 and 0.926, respectively. The method demonstrated a comprehensive approach, stability, and versatility across various environmental conditions over the years in efficiently extracting slender waters. Its usefulness is shown by analyzing spatiotemporal dynamics in the Southern low mountain and hilly areas of China, highlighting its capacity to expand to larger regions, which supports the efforts of the government and water management authorities to recover and restore water resources.