Inversion of Aerosol Optical Depth: Incorporating Multimodel Approach

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.

I. Introduction

Atmospheric aerosols play an important role in the Earth’s climate and environment by directly or indirectly influencing the Earth’s radiation budget through complex interactions with solar radiation, clouds, and the atmosphere [1], [2], [3], [4], [5]. Due to the complexity of aerosol particle types and their diverse sources, the distribution of their quantity and composition on regional and global scales is very heterogeneous, with drastic spatial and temporal variations [6], which brings great uncertainties to related studies. Therefore, high-precision global aerosol inversion is a fundamental requirement for monitoring the impact of aerosols on climate, environment, and air quality.