Simultaneous retrieval of geophysical properties and atmospheric parameters from the infrared hyperspectral resolution sounding data using neural network technique

Land surface temperature, land surface emissivity and atmospheric profiles are all of great importance in many applications. As the at-sensor radiances are dependent on both the land surface parameters (temperature and emissivity) and atmospheric conditions, it is difficult to simultaneously retrieve these parameters with a high accuracy from multi-spectral radiances measured at satellite level. However, some studies have recently shown that hyperspectral thermal infrared data could be used to derive these parameters simultaneously from space. This paper tries to explore the possibilities to recover with an acceptable accuracy both the geophysical properties and the atmospheric parameters from the hyperspectral thermal infrared data using the neural network technique. The results show that the land surface temperature can be obtained with a RMSE=0.24 K and the atmospheric profiles can also be retrieved with relatively high accuracy. However, further work has to be performed to improve the retrieval accuracy in the near future.