Retrieval of Component Temperature of Continuous Vegetation Using Genetic Algorithm
用遗传算法反演连续植被的组分温度

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