2024/01/01 by Xiaohu Sun, Lin Sun, Yu Sun +3
Earth and Planetary Sciences · Environmental Science · #Atmospheric Ozone and Climate #Atmospheric aerosols and clouds #Atmospheric chemistry and aerosols
paper · doi:10.1109/tgrs.2024.3397315
openalex publication_date 2024/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Atmospheric aerosols originate from diverse sources and exert a notable influence on the radiation budget, atmospheric environment, and human health. However, current aerosol inversion models still have limitations in dealing with multiple types of variables and intricate scenarios, for which a two-stage hybrid model named convolutional neural network-random forest (CNNRF) is proposed in this study. Convolution is employed to extract continuous spectral signals. This study takes into account the synergistic impact of spatiotemporal, meteorological, and surface information. Ensemble learning is then applied to adeptly handle diverse-independent input variables. In this article, eight regions were selected globally for modeling and testing based on different scenario types. Additionally, an aerosol hotspot region (India) was chosen for independent experiments. Accuracy was validated at the site scale using a 10-fold cross-validation (10-CV) approach and cross-comparison with the MCD19A2 product, and convolutional neural network (CNN) and random forest (RF) model results. The sample-based CV of the CNNRF model demonstrates high and consistent accuracy, with a Pearson correlation coefficient (R) value of 0.958, mean absolute error (MAE) of 0.048, and a within expected error (EE) envelope of 87.15%. For the Indian region, the MAE and EE are 0.05 and 95.8%, respectively. In summary, the proposed hybrid model demonstrates robust generalization capabilities, enabling accurate and stable aerosols estimation on a global scale.