The limitation of thermal satellite images at finer spatial resolution (FR) led to increased demand for developing various downscaling techniques for the generation of FR image of land surface temperature (LST) to enhance the information content. Thus, the major concern of the analysis is the thermal sharpening of MODIS-LST in various urban regions by establishing the correlation of LST with various spectral indices (SI). Various regression techniques using combination of SI were applied for the thermal sharpening of LST from MODIS data over four different Indian cities with different climate zones i.e. Bikaner, Vadodara, Hyderabad and Varanasi. The LST image from MODIS sensors at spatial resolution of 930 m was disaggregated to 100 m and accuracy was determined by comparing with the LST image of Landsat-8-TIRS at 100 m. The best combination of indices found to include both vegetation and built-up/soil indices for thermal sharpening of MODIS-LST. Further, the variation of the best combination for different cities indicates its dependence on the present land cover. The correlation coefficients (R) between the downscaled MODIS-LST image and the reference-Landsat image were found to be 0.84, 0.76, 0.83 and 0.92, whereas the RMSE values were found to be 1.27, 1.02, 0.73 and 0.62 for Bikaner, Vadodara, Hyderabad and Varanasi, respectively. The RMSE values were found remarkably below the standard deviation of each reference LST image. Therefore, the downscaling approach adopted in this study showed high potential for accurate LST mapping at FR in various urban areas.

在更精细的空间分辨率（FR）上热卫星图像的局限性导致对开发各种降尺度技术以生成陆面温度（LST）FR图像以增强信息含量的需求增加。因此，分析的主要关注点是通过建立LST与各种光谱指数（SI）的相关性，在各个城市区域对MODIS-LST进行热锐化。在印度的四个不同气候区（比卡内尔，瓦多达拉，海得拉巴和瓦拉纳西）的四个不同城市，采用MO结合SI的各种回归技术对MOST数据中的LST进行热锐化。将来自MODIS传感器的930 m空间分辨率的LST图像分解为100 m，并通过与100 m处的Landsat-8-TIRS的LST图像进行比较来确定精度。发现指数的最佳组合包括植被指数和用于MODIS-LST热锐化的建筑物/土壤指数。此外，针对不同城市的最佳组合的变化表明其依赖于当前的土地覆盖。相关系数（缩小后的MODIS-LST图像和参考Landsat图像之间的R）为0.84、0.76、0.83和0.92，而Bikaner，Vadodara，海得拉巴和瓦拉纳西的RMSE值分别为1.27、1.02、0.73和0.62 ， 分别。发现RMSE值明显低于每个参考LST图像的标准偏差。因此，本研究采用的降尺度方法显示了在各个城市地区进行FR精确LST映射的巨大潜力。