Multiparameter Aerosol Simultaneous Retrieval Combining Satellite Remote Sensing and Atmospheric Simulation Using Space-Time Transformer (STTF) Model

Although multispectral satellite remote sensing (RS) is able to derive relatedly accurate aerosols on a large geographical scale, which is crucial for the study of aerosol-related climate and environment changes, some issues remain in current retrieval algorithms. For example, both radiance transfer and machine learning algorithms fail to consider the aerosol types because it is challenging to use only multispectral information to quantify aerosol sources accurately. This may cause a large uncertainty in RS aerosol retrieval, especially in areas with complex and varying aerosol components. Moreover, there are multiple parameters that can reflect aerosol’s physical and chemical properties, but most developed algorithms only accurately obtain one once, such as aerosol optical depth (AOD), which may lead to a large inconsistency when applying them from different algorithms. To address these issues, we propose a multiparameter aerosol simultaneous retrieval algorithm by combining multispectral satellite and atmospheric simulation using a space-time transformer (STTF) model. Sample-based ten-fold validation suggests that our STTF model can effectively retrieve multiple aerosol parameters in terms of 550-nm AOD with R (root mean square error, RMSE) of 0.89 (0.10), Ångström exponent (AE) with R (RMSE) of 0.89 (0.26), and single scattering albedo (SSA) with R (RMSE) of 0.89 (0.10). The time- and spatial-based validation further underscores the model’s ability to predict different aerosol parameters over areas or periods without ground-based measurements. Moreover, the model also shows better AOD retrievals than the operational aerosol products (i.e., MCD19A2 and MOD04_3K) over the word and has significant improvement in areas with high aerosol loadings.