K-Means Clustering for Improved Data-Driven Satellite Aerosol Retrieval

Accurate retrieval of the spatiotemporal distribution of atmospheric aerosols is essential for studying aerosolradiationcloud interactions, air-quality forecasting, and climate-change assessment. Although data-driven methods have significantly advanced aerosol retrieval, the existing models often neglect the influence of aerosol type on retrieval accuracy. To address this gap, this study presents an improved data-driven aerosol retrieval framework that explicitly incorporates aerosol type information into model training. Aerosol classification is performed using the K -means unsupervised clustering algorithm to optimize training samples, thereby enhancing model adaptability and retrieval accuracy. The refined samples are then used to train an extremely randomized trees (ERTs) model, achieving an optimal balance between accuracy and computational efficiency. Validation results demonstrate strong performance, with a correlation coefficient of 0.93, a root mean square error (RMSE) of 0.072, and over 89% of results falling within the expected error range [(EE: ± (0.05+20% × in situ observations)], better than that of the traditional model. The findings demonstrate that integrating aerosol-type information into data-driven retrievals substantially improves accuracy and applicability for aerosol remote sensing. Future research should focus on refining aerosol classification techniques and integrating multisource remote sensing data to enhance model robustness and global applicability further.