Land Use/Land Cover Classification of the Qinghai Lake Basin Using Multitemporal Sentinel-1/2 Imagery

The Qinghai Lake Basin (QLB) serves as a crucial ecological barrier on the Qinghai‒Tibet Plateau, making high-precision mapping of land use/land cover (LULC) essential for eco-hydrological research within the basin. In this study, multitemporal Sentinel-1 radar and Sentinel-2 optical imagery from 2024, DEM-derived terrain information, and features derived from these sources were used to produce a 10-m resolution LULC map for the QLB using a support vector machine classifier. The Level-1 and Level-2 LULC datasets of QLB (QLBLC-10) achieved sample-based apparent overall accuracies (OAs) of 91.95% and 91.24%, respectively, and kappa coefficients of 0.90 for both. In contrast, the area-weighted apparent overall accuracy (OAw) decreased to 81.50 ± 2.09% (95% confidence interval), indicating that class-area imbalance and small-area classes affect map-level performance. The ablation study confirms the contribution of multisource temporal information and terrain constraints to alpine LULC classification. The OA increased from 77.07% with single-temporal Sentinel-2 to 91.24% when multitemporal Sentinel-1/2 data and DEM-derived features were added, while the kappa coefficient increased from 0.75 to 0.90. The comparison with existing products shows that QLBLC-10 outperforms existing global and regional LULC datasets in representing alpine land cover patterns in the QLB. The LULC system proposed in this study is tailored to the QLB, and the presented LULC classification strategy enhances discrimination among major alpine vegetation types, including temperate and alpine steppes, alpine meadows, and alpine shrublands. It provides an up-to-date (2024) LULC dataset for ecosystem monitoring and land management across the QLB.