High-Resolution Aerosol Retrieval Algorithm via Convolutional Kolmogorov–Arnold Network

Accurately obtaining the optical/microphysical characteristics of aerosols from satellite data is important for environmental protection and air quality monitoring. In this study, using Sentinel-2 satellite data, we propose a high-resolution satellite aerosol retrieval algorithm [convolutional Kolmogorov–Arnold network (CKAN)] via a convolutional neural network (CNN) and a Kolmogorov–Arnold network (KAN). Unlike traditional retrieval algorithms that require the construction of physical models, the CKAN algorithm relies entirely on deep learning. This algorithm focuses on extracting high-dimensional information from the data through the CNN and learning the potential nonlinear relationships between the data through the powerful fitting ability of the KAN. Compared with the existing algorithms, the CKAN algorithm is characterized by simplicity and accuracy and does not require a large amount of auxiliary meteorological data (e.g., relative humidity and ground air pressure), enabling the retrieval of various aerosol parameters, including aerosol optical depth (AOD), fine-mode AOD (FAOD), coarse-mode AOD (CAOD), and single scattering albedo (SSA). To demonstrate the effectiveness of the algorithm, we retrieved aerosol optical/microphysical characteristics from Sentinel-2 imagery for four study areas. Results indicate that both the AOD and FAOD retrieved by the CKAN algorithm exhibit a high degree of correlation ( ${R} \gt 0.90$ ) with AERONET products. For CAOD and SSA, although a few poor retrievals resulted in a low overall correlation, CKAN’s retrievals show good agreement with AERONET products. The CKAN algorithm has good accuracy in high-resolution satellite aerosol retrievals and is expected to be widely used in urban-scale aerosol monitoring.