Estimating Subpixel Surface Temperature Coupling Retrieval Land Surface Parameters with GA-SOFM Neural Network

During the simulation of thermal infrared remote sensing,the high spatial resolution scene of land surface temperature can be estimated by moderate and lower resolution thermal infrared data.The GA-SOFM(Genetic Algorithms Self-Organizing Feature Maps)-Artificial Neural Network(ANN)can be used to construct the relation between the inverted land surface parameters based on VNIR data and lower resolution data,which is also considered the unmixing process of mixed pixel.Finally,the high resolution land surface scene can be generated by this method.In this paper,the discussion and analysis the accuracy,applicability and prospect about this method are carried out.It is easy to put into operation with higher accuracy.Utilizing the ASTER data to test it,conclusions show that subpixel land surface temperature under different land cover types can be retrieved based on a pair of remote sensing data if we don't directly invert high resolution land surface temperature or run short of experienced knowledge about land surface.Also,it is a new approach to quickly estimate and simulate high resolution land surface temperature.