发展了一种迭代算法，能够利用ATSR－2双角观测同时进行大气校正和反演地表的组分（植被和土壤）温度。在算法中，全球通用二次方（QUAD）算法用于进行大气校正，LSF模型用于计算等效方向发射率，通过迭代的方法，同时反演地表组分温度和进行大气校正。结果表明，在可接受的范围内，土壤温度和植被温度可以被分离开来，而且，反演出的两个方向发射率的差和经过大气校正后的两个方向亮温的差有很好的相关性。更进一步的敏感性和不确定性分析表明，如果利用USM进行分阶段反演，可以得到更好的结果。

Precise retrieval of land surface temperature from satellite remotely sensed data need atmospheric correction and a known effective emissivity of the pixel. Various split window algorithms have been used to solve the first problem, but they all need a known surface emissivity. LSF model can calculate the effective emissivity of the nonisothermal and heterogeneous pixel, but the data must be atmospherically corrected when using satellite images. In this paper, we have developed a model based algorithm that can correct the atmosphere effects and retrieve component temperatures using ATSR 2 dual angle observation. In this algorithm, QUAD algorithm is used to perform atmospheric correction, and LSF model is used to calculate the directional effective emissivity , by iteration, atmospheric correction and component temperatures retrieval can be completed synchronously. Good linearity was found between the difference of the directional emissivity and the difference of the directional brightness temperature after atmospheric correction. Although the range of the retrieved component temperatures is large, it is still clear that the component temperatures of vegetation and soil are separated. Further analysis of the uncertainty and sensitivity for the two component temperatures show that if only the most sensitive sample is used in inversion, the results tend to be more robust.