Retrieval of Regional Aerosol Optical Depth Using Deep Learning

To solve the problem that there exist low precision and spatial resolution in the retrieval algorithm of land aerosol optical depth (AOD), a deep learning-based deep belief network (DBN) is proposed to realize the retrieval of land AOD with a spatial resolution of 30 m. The training samples for the algorithm include the AERONET site data with global long time series as well as the observation geometric data and apparent reflectivity data from Landsat 8 OLI which are corresponding to the former in space and time. To ensure the estimation accuracy and stability of retrieval, the process method for the AERONET site data, the spatial-temporal matching method for satellite and site data, and the setting of the DBN structure are investigated. The AERONET site data, independent of the training samples, are used to test the AOD estimation results at 550 nm for different surface types as a whole. In addition, the small-scale accuracy verification is conducted in the study area. The results demonstrate that the root mean square error and the mean absolute error of the proposed method are 0.11 and 0.072, respectively. The proposed method can break the situation in which the retrieval of AOD based on the existing methods relies excessively on other remote sensing products or time-phase data, and it effectively improves the efficiency and spatial resolution in the retrieval of AOD.