河流地下水排泄（River Groundwater Discharge, RGD）是干旱区水文循环的关键组成部分，影响水资源分配与生态系统稳定性。然而，受限于空间分辨率及检测方法的稳定性，利用卫星热红外遥感在区域尺度可靠识别RGD区域仍具挑战。本研究提出一种河流地下水排泄检测（Detection of River Groundwater Discharge, DRGD）方法，利用SDGSAT-1卫星30米高分辨率热红外数据识别潜在RGD区域。DRGD方法包含三个模块：第一，地表温度（Land Surface Temperature, LST）反演模块，采用劈窗算法从SDGSAT-1数据中获取LST；第二，热异常稳健性指数（Thermal Anomaly Robustness Index, R）计算模块，利用受土地覆盖约束的滑动窗口技术量化局部热异常；第三，RGD区域检测模块，通过Dempster–Shafer证据融合技术整合LST、R、地形湿度指数和归一化差异水体指数。该方法在黑河流域中游地区进行测试。结果显示，SDGSAT-1 LST与MODIS产品一致，决定系数（R²）大于0.81，均方根误差（RMSE）小于1.75 K。与Landsat 8 LST相比，SDGSAT-1保留了更精细的河流尺度热变异性和更局部的低温信号。R能有效突出与RGD相关的冷却效应。多源证据融合模块在研究区内识别出207个RGD像素，空间一致性率达94.1%，指示了RGD区域的潜在位置。本研究展示了热红外遥感在干旱区检测RGD的潜力。所提出的方法为水文建模、地下水评估及流域生态管理提供了可靠的框架支持。

River Groundwater Discharge (RGD) is a key component of the hydrological cycle in arid regions, influencing water allocation and ecosystem stability. However, reliably identifying RGD zones at regional scales with satellite thermal infrared remote sensing remains challenging due to limited spatial resolution and unstable detection methods. This study develops a Detection of River Groundwater Discharge (DRGD) approach for identifying potential RGD zones using 30 m high-resolution thermal infrared data from the SDGSAT-1 satellite. The DRGD approach consists of three modules. The first is the Land Surface Temperature (LST) retrieval module, in which LST is derived from SDGSAT-1 data using a split-window algorithm. The second module calculates the Thermal Anomaly Robustness Index (R) using a sliding-window technique constrained by land cover to quantify local thermal anomalies. The third module detects RGD zones by integrating LST, R, topographic wetness index, and normalized difference water index using the Dempster–Shafer evidence fusion technique. The DRGD approach is tested in the middle reaches of the Heihe River Basin. Results show that SDGSAT-1 LST is consistent with MODIS products, with R2>0.81 and RMSE <1.75 K. A comparison with Landsat 8 LST indicates that SDGSAT-1 preserves finer river-scale thermal variability and more localized low temperature signals. R effectively highlights cooling effects associated with RGD. The multi-source evidence fusion module identified 207 RGD pixels in the study area, achieving a spatial agreement rate of 94.1%, indicating the potential locations of the RGD zones. This study demonstrates the potential of thermal infrared remote sensing for detecting RGD in arid regions. The proposed approach offers a reliable framework supporting hydrological modeling, groundwater assessment, and watershed ecological management.