地表温度是地表能量收支平衡和水循环过程的重要参数。热红外遥感是当前反演地表温度最重要数据源，劈窗算法是当前使用最广泛的反演算法。本文借鉴高分五号地表温度反演劈窗算法形式，结合高分五号B星(GF5B)传感器波段光谱特性，构建了面向GF5B热红外数据地表温度反演的劈窗算法，并考虑水汽对地表温度反演的影响，进而获取了不同水汽条件下的劈窗算法系数。采用改进的NDVI-NDWI阈值法与水汽劈窗协方差-方差比法分别估算地表发射率和大气水汽含量。在四个不同地表类型的实验区对构建的算法进行地表温度反演应用，并利用HiWATER站点实测地表温度和MODIS地表温度产品进行反演结果验证。结果表明，白天和夜间反演地表温度的地面站点验证误差分别为1.88 K和0.99 K，夜间误差低于白天；与MOD11A1 LST交叉对比结果表明，除河北唐山实验区受云遮挡影响，交叉对比结果较差，其余三个实验区交叉对比的温度差异不超过1.5 K；表明了本文构建的劈窗算法具有较高的地表温度反演精度。

Land surface temperature (LST) is a critical parameter for understanding surface energy balance and water cycle pro-cesses. This study aimed to develop LST retrieval algorithms using thermal infrared data from the Visual and Infrared Multispectral Imager (VIMI) onboard Chinese Gaofen-5B (GF5B) satellite, providing a new data source for global high-spatial-resolution LST product generation. A simulated dataset was first constructed on basis of the conventional thermal infrared radiative transfer model, TIGR-3 atmospheric profiles, and 74 typical surface emissivity samples selected from the ASTER spectral library. Two split-window algorithms (called as SW-1 and SW-2) for GF5B thermal infrared data were developed based on the simulated dataset and sensor characteristics, with both algorithms achieving an RMSE of 1.13 K. Considering the influence of column water vapor (CWV) on LST retrieval, algorithm coefficients were derived under different CWV conditions. Comparison of RMSE under identical conditions demonstrated that SW-2 generally outperformed SW-1 in accuracy. The algorithm"s performance varies with CWV, with RMSE values of SW-1 (SW-2) decreasing from 1.41 K (1.34 K) in high CWV conditions (4.5–6.3 g/cm2) to 0.40 K (0.39 K) in low CWV conditions (0.0–2.5 g/cm2). The SW-2 algorithm was thus recommended as the preferred choice for GF5B-based LST retrieval applications. Key parameters acquisition methods were also proposed, including land surface emissivity (via a modified NDVI-NDWI threshold method) and atmospheric CWV (via a water vapor split-window covariance-variance ratio method). The proposed algorithm was applied to four experimental regions with different land cover types. Combined with false-color composite imagery (green, red, and near-infrared bands) derived from VIMI"s visible and near-infrared data, the spatial distribution of LST retrieval results was evaluated in relation to surface characteristics. Artificial surfaces and bare soil exhibited relatively higher LST values, while water bodies and vegetation showed lower LST values, consistent with thermal radiative characteristics. Validation was conducted using in-situ LST measurements from HiWATER sites and MODIS LST products. Results demonstrated that the LST errors for daytime and nighttime retrievals were 1.88 K and 0.99 K, respectively. Among all validated data, the maximum daytime temperature difference reached 2.86 K, while the nighttime maximum difference was 1.27 K. Additionally, variations in validation accuracy were observed across imagery acquired at different periods for the same site. For cross-comparison with MODIS MOD11A1 LST products, observational discrepancies were minimized by aligning acquisition times and viewing angles, and spatial aggregation was applied to reduce the impact of resolution mismatches. High-resolution Sentinel-2 land cover products and temperature gradients between adjacent pixels were utilized as screening criteria. Except for the Tangshan (Hebei) region, which exhibited significant deviations due to cloud contamination, temperature differences in the remaining three regions remained within 1.5 K. The LST retrieval results across the four experimental regions exhibited reasonable spatial distributions and strong correlations with land cover types. Validation with limited in-situ HiWATER data confirmed higher accuracy for nighttime retrievals compared to daytime. Cross-comparison with MOD11A1 LST products demonstrated acceptable agreement (differences ≤1.5 K) in three regions, indicating that the proposed algorithm achieves satisfactory accuracy.