Zhou Yi, Qin Zhi-Hao, Bao Gang
Guang Pu Xue Yu Guang Pu Fen Xi. 2014 Feb;34(2):364-9.
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.
地表温度（LST）反映了地表特性，是从局部到全球尺度的陆地表面过程物理学中的关键参数之一。在各种科学研究和热红外（TIR）遥感应用中，对地表温度在时间和空间上的需求都非常大。卫星热红外通道可用于反演地表温度，但仅适用于晴空条件。然而，当表面被云层遮挡时，对应像素实际反演得到的地表温度就是云顶温度，或者受到云顶温度的影响。由于缺乏对云和地表温度之间复杂关系的了解，对于云层覆盖像素的地表温度估计给热遥感科学家带来了很大的问题和挑战。本文对利用热红外数据反演云层覆盖像素地表温度的算法和方法进行了综述，并讨论了每种方法的特点。随后提出了未来研究方向，以提高卫星反演云层覆盖像素地表温度的准确性。
