Inversion of Land Surface Temperature and Prediction of Geothermal Anomalies in the Gonghe Basin, Qinghai Province, Based on the Normalized Shade Vegetation Index

Against the backdrop of global energy transition, geothermal energy has emerged as a critical renewable resource, yet its exploration remains challenging due to uneven subsurface distribution and complex surface conditions. This study pioneers a novel framework integrating the Normalized Shaded Vegetation Index (NSVI) with radiative transfer-based land surface temperature inversion to detect geothermal anomalies in the Gonghe Basin, Qinghai Province. Using multi-source remote sensing data (GF5 B AHSI, ZY1–02D/E AHSI, and Landsat 9 TIRS), we first constructed NSVI, achieving 97.74% classification accuracy for shadowed vegetation/water bodies (Kappa = 0.9656). This effectively resolved spectral mixing issues in oblique terrain, enhancing emissivity calculations for land surface temperature retrieval. The radiative transfer equation method combined with NSVI-derived parameters yielded high-precision land surface temperature estimates (RMSE = 2.91 °C; R2 = 0.963 against Landsat 9 products), revealing distinct thermal stratification: bright vegetation (41.31 °C) > shadowed vegetation (38.43 °C) > water (33.56 °C). Geothermal anomalies were identified by integrating temperature thresholds (>45.80 °C), 7 km fault buffers, and concealed Triassic granite constraints, pinpointing high-potential zones covering 0.12% of the basin. These zones are concentrated in central Gonghe, northern Guinan, and central-northern Guide counties. The framework provides a replicable solution for geothermal prospecting in topographically complex regions, with implications for optimizing exploration across the Gonghe Basin.