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摘要： 基于MODIS地表温度产品和Landsat ETM+影像，提出采用将TsHARP（Thermal sHARPening）模型和STITFM（Spatio\|Temporal Integrated Temperature Fusion Model）算法相结合的方法CTsSTITFM进行地表温度数据的融合。先利用TsHARP方法对相邻t1和t2时刻的1 km MODIS地表温度数据降尺度为250 m空间分辨率地表温度，再将降尺度结果输入STITFM模型进行影像融合，最终生成t2时刻30 m空间分辨率的地表温度数据。结果表明:该方法比与单独采用STITFM算法的模拟结果精度有所提高，在默认参数设置下，融合影像的地表温度与Landsat ETM+数据反演地表温度值相比，均方根误差（RMSE）小于1.33 K。通过对CTsSTITFM融合方法的参数中窗口大小的调节发现，随窗口不断增大，在所选区域融合效果表现出一定的规律性，合理的窗口大小设置有助于提升融合效果。  关键词： 地表温度(LST);  TsHARP;  时空融合;  STITFM;  CTsSTITFM     Abstract: Land Surface Temperature (LST) is an important parameter that describes energy balance of substance and energy exchange between the surface and the atmosphere，and LST has widely used in the fields of urban heat island effect，soil moisture and surface radiative flux.Currently，no satellite sensor can deliver thermal infrared data at both high temporal resolution and spatial resolution，which strongly limits the wide application of thermal infrared data.Based on the MODIS land surface temperature product and Landsat ETM+image，a temporal and spatial fusion method is proposed by combining the TsHARP (Thermal sHARPening) model with the STITFM (Spatio\|Temporal Integrated Temperature Fusion Model) algorithm，defined as CTsSTITFM model in this study.The TsHARP method is used to downscale the 1 km MODIS land surface temperature image to LST data at spatial resolution of 250 m.Then the accuracy is verified by the retrieval LST from Landsat ETM+ image at the same time.Land surface temperature image at 30 m spatial scale is predicted by fusing Landsat ETM+ and downscaling MODIS data using STITFM model.The fusion LST image is validated by the estimated LST from Landsat ETM+ data for the same predicted.The results show that the proposed method has a better precision comparing to the STITFM algorithm.Under the default parameter setting，the predicted LST values using CTsSTITFM fusion method have a root mean square error (RMSE) less than 1.33 K.By adjusting the window size of CTsSTITFM fusion method，the fusion results in the selected areas show some regularity with the increasing of the window.In general，a reasonable window size set may slightly improve the effects of LST fusion.The CTsSTITFM fusion method can solve the problem of mixed pixels caused by coarse\|scale MODIS surface temperature images to some degree.

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