地表温度是描述陆表过程和反映地表特征的重要参数。 及时掌握区域和全球尺度上的地表温度时空分布是许多地表过程研究和热红外遥感应用的必需。 热红外遥感是地表温度快捷获取的最佳手段， 但仅局限于天空晴朗无云情形。 云覆盖致使热红外遥感不能直接获取云覆盖像元地表温度， 其反演结果为云顶温度或附加了云辐射强迫效应后的地表温度。 如何精准获取热红外遥感图像中云覆盖像元地表温度信息， 成为热红外遥感地表温度反演和应用亟待完善的难题。 文章系统详细回顾了国内外热红外遥感图像中云覆盖像元地表温度估算方法， 评述了各方法的特性， 并指出今后应加强加深热红外遥感与被动微波遥感融合技术、 数据同化技术、 尺度效应和算法参数化中普适性假定等四方面研究。

Land surface temperature (LST), which reflects surface properties, is one of the key parameters in the physics of land surface processes from local through global scales. LST is very required in time and space for a wide variety of scientific studies and thermal infrared (TIR) remote sensing applications. Satellite TIR channels are very available for LST retrieval but only in clear skies. However, when the surface is obscured by clouds, the actual retrieved LST for the corresponding pixel is, or is contaminated by, the cloud top temperature. Lacking understanding of the complex relationships between clouds and LST, the estimation of LST for cloud-covered pixels poses a big problem and challenge for thermal remote sensing scientists. In the present paper, a review of algorithms and approaches related to LST retrieval for cloud-covered pixels from TIR data is presented, and the characteristics of each method are also discussed. Directions for future research to improve the accuracy of satellite-derived LST for cloud-covered pixels are then suggested.