被动微波遥感数据反演地表温度研究进展

被动微波遥感具有穿透云层( 甚至雨区) 获取地表辐射信息的能力， 在处理云的问题上， 明 显优于可见光、红外遥感， 可更好地服务于相关领域研究。由于微波信号受多种因素的影响， 目前 被动微波反演地表温度的算法还不成熟。根据建模手段和方法的不同， 从经验反演模型和物理反 演模型两方面入手， 系统回顾国内外被动微波数据反演地表温度的算法并加以评述， 指出今后应 加强尺度转换、物理机理以及多数据源结合方面的研究。

Progr ess in Land Sur face Temper atur e Retr ieval from Passive Microwave Remotely Sensed Data

Land surface temperature(LST) is of considerable importance for many applications,notably global climatic,hydrological,ecological and biogeochemical studies.In the past two decades,a large number of methods and algorithms have been developed to retrieve cloud free LST from thermal infrared data.As for all optical remote sensing,the Earth observation can only be realized for cloud free condition.In order to get LST for all weather conditions,microwave remotely sensed data,instead of thermal infrared data,from space is commonly used due to its capability to penetrate clouds and to some extent rain.Although the passive microwave remote sensing has some advantages compared to the thermal infrared,there are few algorithms developed to retrieve LST from it,mainly because there are various factors affected the microwave signal.In this paper,we review almost all existing algorithms found in literature,which can be roughly categorized into the statistical-based and physics-based retrieval algorithms,to retrieve LST from passive microwave remotely sensed data.The merits and the disadvantages are summarized for each method respectively.Finally further possible improvement and development directions on the scaling transfer,the microwave radiative transfer modeling and the combination of the different remote sensing data are given for LST retrieval from passive microwave data.