Trans-AODnet for Aerosol Optical Depth Retrieval and Atmospheric Correction of Moderate to High-Spatial-Resolution Satellite Imagery

High accuracy and time synchronous aerosol optical depth (AOD) is essential for atmospheric correction (AC) of medium and high spatial resolution (MHSR) remote sensing data. However, existing high-resolution AOD retrieval methods often rely on sparsely distributed ground-based measurements, which limits their capacity to resolve fine-scale spatial heterogeneity and consequently constrains retrieval performance. To address this limitation, we propose a framework that takes GF-1 top-of-atmosphere (TOA) reflectance as input, where the model is first pre-trained using MCD19A2 as Pseudo-labels, with high-confidence samples weighted according to their spatial consistency and temporal stability, and then fine-tuned using Aerosol Robotic Network (AERONET) observations. This approach enables improved retrieval accuracy while better capturing surface variability. Validation across multiple regions demonstrates strong agreement with AOD measurements, achieving the correlation coefficient (R) of 0.941 and RMSE of 0.113. Compared to models without pretraining, the proportion of AOD retrievals within EE improves by 13%. While applied to AC, the corrected surface reflectance also shows strong consistency with in situ observations (R > 0.93, RMSE < 0.04). The proposed Trans-AODnet significantly enhances the accuracy and reliability of AOD inputs for AC of high-resolution wide-field sensors (e.g., GF-WFV), offering robust support for regional environmental monitoring and exhibiting strong potential for broader remote sensing applications.