Mapping Potential Groundwater Discharge Indicators in Urban Rivers: A Thermal Remote Sensing and Machine-Learning Approach for Tangshan City

Groundwater discharge through permeable riverbeds sustains river baseflow and aquatic ecosystems, yet its spatial distribution in urban watersheds remains poorly quantified. To address this, we established a conceptual framework integrating Landsat 8/9 thermal remote sensing with machine learning to screen and map thermally anomalous river reaches consistent with potential groundwater influence in Tangshan City, China. Seasonal Landsat thermal surface temperature (LST) was derived and sampled over river sites from thermal images, and temperature differentials ΔT (summer–winter) were used as a screening metric to flag locations with reduced seasonal thermal amplitude, a pattern commonly associated with groundwater buffering. Random Forest and XGBoost models were used to assess internal consistency and explore environmental controls on the screened locations. Our analysis screened 32 thermally anomalous river locations (5.25% of sites) as potential groundwater-influence indicators based on reduced seasonal thermal variability. These potential locations were cooler in summer and showed smaller seasonal temperature variation, consistent with thermal buffering effects. This research provides a transferable framework for watershed-scale mapping of thermally anomalous river reaches using accessible remote sensing and machine-learning tools, and also offers a transferable baseline for urban watershed screening and monitoring prioritization.