Surface reflectance auto retrieval model based on hyperspectral remotely sensed images

This method presented in this paper was tested using CASI&SASI data collected from a site at the Huailai County, Hebei Province, China. Various methods for retrieving AOT and CWV specific to this region were assessed. Results show that retrieval of AOT from the remote sensing data required establishing empirical relationships between 465.6nm, 659nm and 2105nm augmented by ground-based reflectance validation data, and minimizing the merit function based on optimization of AOT@550nm. The paper also extends the SODA method using Powell's method to optimize the retrieval of CWV. The resultant CWV image shows relatively less residual surface features compared with the standard methods. Comparison of derived remote sensing surface reflectance with ground spectra of comparable vegetation and soil targets show significant correlations (R²) is 0.942 and 0.786, with RMSE being 0.0387 and 0.0406 respectively. Therefore, the method proposed in this paper is reliable enough for integrated atmospheric correction and surface reflectance retrieval from hyperspectral remote sensing data. This case provides a good reference to the surface reflectance inversion under the condition of lack of synchronized atmospheric parameters.