To obtain high-spatial-resolution land surface temperature (LST) in karst areas, it is necessary to select a downscale regression model with a better simulation effect and the scale factors that can best represent the topographic characteristics of karst mountainous areas. In Guiyang, a typical karst mountain city, two areas are selected as the study area, which is dominated by natural surface and construction land. Based on the data of Landsat-8 Thermal Infrared Sensor (TIRS), Sentinel-2, Advanced Land Observing Satellite Digital Elevation Model (ALOS DEM), and meteorological stations, the scale factors representing bare land: bare soil index and topographic relief: mountain shadow (hillshade), relief degree of land surface (RDLS), solar incident angle, and sky view factor (SVF) are added on the basis of the conventional factors. At the same time, random forest (RF) and extreme gradient boosting (XGBoost) models are used to construct an LST downscaling method that is more suitable for karst mountain cities. After the above steps, the LST product with a spatial resolution of 10 m is finally estimated. The results show that, due to the characteristics of large elevation variation, fragmentation, and high heterogeneity of surface landscape in karst areas, digital elevation model (DEM), RDLS, and SVF factors need to be considered in the downscaling of surface temperature, and the contribution rates of these factors are all more than 6% in the model. In terms of accuracy evaluation of ground temperature, XGBoost model has the highest accuracy with an average absolute error of 1.67K, RF model has an average error of 1.90K, and thermal image sharpening has the worst accuracy with an average error of 2.41K. In terms of accuracy evaluation of ascending scale, the XGBoost model also shows higher accuracy and richer texture details. The research results can provide basic data for the acquisition of high-resolution LST and its intermediate parameters in this area and also provide a method reference for the reduction of high-resolution LST in similar areas.

中文翻译：

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考虑地形特征的喀斯特山地城区地表温度降尺度

为了获得喀斯特地区高空间分辨率的地表温度（LST），需要选择模拟效果较好的降尺度回归模型和最能代表喀斯特山区地形特征的尺度因子。在典型的喀斯特山地城市贵阳，选择两个区域作为研究区，以自然地表和建设用地为主。基于Landsat-8热红外传感器（TIRS）、Sentinel-2、高级陆地观测卫星数字高程模型（ALOS DEM）和气象站的数据，代表裸地的尺度因子：裸土指数和地形起伏：山在常规因素的基础上增加了阴影（hillshade）、地表起伏度（RDLS）、太阳入射角和天空视角因子（SVF）。同时，随机森林（RF）和极端梯度提升（XGBoost）模型用于构建更适合喀斯特山区城市的LST降尺度方法。经过以上步骤，最终估计出空间分辨率为 10 m 的 LST 乘积。结果表明，由于喀斯特地区地表景观具有高程变化大、碎片化、异质性高等特点，地表温度降尺度需要考虑数字高程模型（DEM）、RDLS和SVF等因素，这些因素在模型中的贡献率都在6%以上。在地温精度评价方面，XGBoost模型精度最高，平均绝对误差为1.67K，RF模型平均误差为1.90K，热图像锐化的精度最差，平均误差为 2.41K。在升尺度精度评价方面，XGBoost模型也表现出更高的精度和更丰富的纹理细节。研究成果可为该地区高分辨率地表温度及其中间参数的获取提供基础数据，也为类似地区高分辨率地表温度的降低提供方法参考。