Due to high correlation coefficients among multi-channel thermal infrared data and mixed pixels widely existed

it is difficult to improve the accuracy of retrieved land surface temperature; further more

component temperature can not be retrieved from multi-channel thermal infrared data. In this paper

taken erectophile type continuous vegetation as an example

we did many Monte-Carlo simulations

and established empirical analytic expressions of component effective emissivities with soil emissivity and leaf area index. Empirical analytic expressions were used to construct objective function

and genetic algorithm was employed to synchronously retrieve component temperature

soil emissivity and LAI from thermal infrared multi-angle data. Many experiments of genetic algorithm inversion from simulated data were conducted

results show that it is very robust to retrieve component temperature using genetic algorithm

and genetic algorithm can cope with uncertainty inversion problem pretty well if we take full advantage of priori knowledge. Comparison between inversion results and ground-truth data were made. This paper offers a new method to retrieve component temperature from multiangle thermal infrared data based on the model of directionality of thermal radiance