Multi-Feature Fusion and Cloud Restoration-Based Approach for Remote Sensing Extraction of Lake and Reservoir Water Bodies in Bijie City

Abstract

Featured Application: The multi-feature fusion and cloud restoration-based approach proposed in this study is particularly suitable for the remote sensing extraction and large-scale dynamic monitoring of lake and reservoir water bodies in karst landscapes (e.g., Bijie City, Guizhou Province). It effectively overcomes two core challenges of traditional algorithms: poor cross-regional adaptability to complex geomorphology and information loss caused by cloud occlusion, achieving an overall extraction accuracy of over 96%. Practically, this method can provide reliable technical support for regional water resource management (e.g., real-time tracking of lake/reservoir area and storage changes), ecological restoration (e.g., assisting rocky desertification control by analyzing the interaction between water body dynamics and groundwater recharge), flood risk prediction, in-depth research on hydrological cycles, and assessment of climate change impacts on surface freshwater systems. Moreover, its universal technical framework enables potential extension to lake and reservoir monitoring in other complex geomorphic regions (beyond karst areas), offering a scalable solution for global large-scale surface water body dynamic monitoring. Current lake and reservoir water body extraction algorithms are confronted with two critical challenges: (1) design dependency on specific geographical features, leading to constrained cross-regional adaptability (e.g., the JRC Global Water Body Dataset achieves ~90% overall accuracy globally, while the ESA WorldCover 2020 reaches ~92% for water body classification, both showing degraded performance in complex karst terrains); (2) information loss due to cloud occlusion, compromising dynamic monitoring accuracy. To address these limitations, this study presents a multi-feature fusion and multi-level hierarchical extraction algorithm for lake and reservoir water bodies, leveraging the Google Earth Engine (GEE) cloud platform and Sentinel-2 multispectral imagery in the karst landscape of Bijie City. The proposed method integrates the Automated Water Extraction Index (AWEIsh) and Modified Normalized Difference Water Index (MNDWI) for initial water body extraction, followed by a comprehensive fusion of multi-source data—including Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Red-Edge Index (NDREI), Sentinel-2 B8/B9 spectral bands, and Digital Elevation Model (DEM). This strategy hierarchically mitigates vegetation shadows, topographic shadows, and artificial feature non-water targets. A temporal flood frequency algorithm is employed to restore cloud-occluded water bodies, complemented by morphological filtering to exclude non-target water features (e.g., rivers and canals). Experimental validation using high-resolution reference data demonstrates that the algorithm achieves an overall extraction accuracy exceeding 96% in Bijie City, effectively suppressing dark object interference (e.g., false positives due to topographic and anthropogenic features) while preserving water body boundary integrity. Compared with single-index methods (e.g., MNDWI), this method reduces false positive rates caused by building shadows and terrain shadows by 15–20%, and improves the IoU (Intersection over Union) by 6–13% in typical karst sub-regions. This research provides a universal technical framework for large-scale dynamic monitoring of lakes and reservoirs, particularly addressing the challenges of regional adaptability and cloud compositing in karst environments.