Deep Learning with Pretrained Framework Unleashes the Power of Satellite-Based Global Fine-Mode Aerosol Retrieval

Abstract

Fine-mode aerosol optical depth (fAOD) is a vital proxy for the concentration of anthropogenic aerosols in the atmosphere. Currently, the limited data length and high uncertainty of the satellite-based data diminish the applicability of fAOD for climate research. Here, we propose a novel pretrained deep learning framework that can extract information underlying each satellite pixel and use it to create new latent features that can be employed for improving retrieval accuracy in regions without in situ data. With the proposed model, we developed a new global fAOD (at 0.5 μm) data from 2001 to 2020, resulting in a 10% improvement in the overall correlation coefficient (R) during site-based independent validation and a 15% enhancement in non-AERONET site areas validation. Over the past two decades, there has been a noticeable downward trend in global fAOD (−1.39 × 10 −3 /year). Compared to the general deep-learning model, our method reduces the global trend's previously overestimated magnitude by 7% per year. China has experienced the most significant decline (−5.07 × 10 −3 /year), which is 3 times greater than the global trend. Conversely, India has shown a significant increase (7.86 × 10 −4 / year). This study bridges the gap between sparse in situ observations and abundant satellite measurements, thereby improving predictive models for global patterns of fAOD and other climate factors.