After carefully studying the results of retrieval of land surface temperature (LST) acquired by multi-channel thermal infrared remote sensing data

the authors point out that the accuracy and significance for applications are seriously damaged by high correlation coefficient among multi-channel information and its disablement of direct retrieval of component temperature. Based on the model of directional radiation of non-isothermal mixed pixel

we point out that the mufti-angle thermal infrared remote sensing can offer the possibility to directly retrieve component temperature. But it is difficult to synchronously retrieve all parameters using traditional inversion methods because the model is a numerical conception model based on Monte Carlo simulation. In order to effectively derive the parameters

we use neural network model. The parameters to be retrieved

such as component temperatures

soil emissivity and LAI

are all nonlinear function of mufti-angle radiation

and when the classical back-propagation (BP) algorithm was employed to retrieve these parameters

it was easily entrapped at local optimal regions. Therefore

we first employ genetic algorithm (GA) to train the neural network

and obtain the weights between the layers of neural network

which were used as the initial weights of the BP algorithm

then continue to train the network until the results are satisfied. Thus

the speediness of BP algorithm was developed and the optimal configuration of network weights are obtained. Based on the model of iadiant directionality of non-isothermal mixed pixel

simulated results show that retrieved mufti-dimensional parameters are superior when using GA to optimize the neural network weights.