中文摘要：

针对卫星遥感技术监测地表温度（land surface temperature,LST）存在时空分辨率矛盾这一难题,以TsHARP温度降尺度算法为基础,根据地表覆盖类型的不同,分别选择与LST相关性更好的光谱指数（归一化植被指数,NDVI;归一化建造指数,NDBI;改进的归一化水体指数,MNDWI;增强型裸土指数,EBSI）提出了新的转换模型,并从定性和定量两个角度评价了TsHARP法和新模型的降尺度精度。结果表明：两种模型在提高LST空间分辨率的同时又能较好地保持MODIS LST影像热特征的空间分布格局,消除了原始1km影像中的马赛克效应,两种模型均能够达到较好的降尺度效果;全局尺度分析表明,不管是在降尺度结果的空间变异性还是精度方面,本文提出的模型（RMSE：1.635℃）均要优于TsHARP法（RMSE：2.736℃）;TsHARP法在水体、裸地和建筑用地这些低植被覆盖区表现出较差的降尺度结果,尤其对于裸地和建筑用地更为明显（｜MBE｜〉3℃）,新模型提高了低植被覆盖区地物的降尺度精度;不同季节的降尺度结果表明,两种模型都是夏、秋季的降尺度结果优于春、冬季,新模型的降尺度结果四季均好于TsHARP法,其中春、冬季的降尺度精度提升效果要优于夏、秋季。

英文摘要：

Aiming at the problem that the spatial and temporal resolution of land surface temperature (LST) have the contradiction with each other, a new downscaling model was put forward, based on the TsHARP(an algorithm for sharpening thermal imagery) downscaling method, this research makes improvements by selecting the better correlation of spectral index(normalized difference vegetation index, NDVI; normalized difference build-up index, NDBI; modified normalized difference water index, MNDWI; enhanced bare soil index, EBSI) with LST, i.e., replaces the original NDVI with new spectral index according to the different surface land-cover types, to assess the accuracy of each downscaling method based on qualitative and quantitative analysis with synchronous Landsat 8 TIRS LST data. The results show that both models could effectively enhance the spatial resolution while simultaneously preserving the characteristics and spatial distribution of the original 1 km MODIS LST image, and also eliminate the “mosaic” effect in the original 1 km image, both models were proved to be effective and applicable in our study area; global scale analysis shows that the new model (RMSE:1.635) is better than the TsHARP method (RMSE:2.736) in terms of the spatial variability and accuracy of the results; the different land-cover types of downscaling statistical analysis shows that the TsHARP method has poor downscaling results in the low vegetation coverage area, especially for the bare land and building-up area(|MBE|>3), the new model has obvious advantages in the description of the low vegetation coverage area. Seasonal analysis shows that the downscaling results of two models in summer and autumn are superior to those in spring and winter, the new model downscaling results are better than the TsHARP method in the four seasons, in which the spring and winter downscaling improvement is better than summer and autumn. ? 2017, Surveying and Mapping Press. All right reserved.