An improved meteorological variables-based aerosol optical depth estimation method by combining a physical mechanism model with a two-stage model

A two-stage model integrating a spatiotemporal linear mixed effect (STLME) and a geographic
weight regression (GWR) model is proposed to improve the meteorological variables-based
aerosol optical depth (AOD) retrieval method (Elterman retrieval model-ERM). The proposed
model is referred to as the STG-ERM model. The STG-ERM model is applied over the
Beijing-Tianjin-Hebei (BTH) region in China for the years 2019 and 2020. The results show that
data coverage increased by 39.0% in 2019 and 40.5% in 2020. Cross-validation of the retrieval
results versus multi-angle implementation of atmospheric correction (MAIAC) AOD shows the
substantial improvement of the STG-ERM model over earlier meteorological models for AOD
estimation, with a determination coefficient (R2) of daily AOD of 0.86, root mean squared
prediction error (RMSE) and the relative prediction error (RPE) of 0.10 and 36.14% in 2019 and
R2 of 0.86, RMSE of 0.12 and RPE of 37.86% in 2020. The fused annual mean AOD indicates
strong spatial variation with high value in south plain and low value in northwestern mountainous
areas of the BTH region. The overall spatial seasonal mean AOD ranges from 0.441 to 0.586,
demonstrating strongly seasonal variation. The coverage of STG-ERM retrieved AOD, as
determined in this exercise by leaving out part of the meteorological data, affects the accuracy of
fused AOD. The coverage of the meteorological data has smaller impact on the fused AOD in the
districts with low annual mean AOD of less than 0.35 than that in the districts with high annual
mean AOD of greater than 0.6. If available, continuous daily meteorological data with high
spatiotemporal resolution can improve the model performance and the accuracy of fused AOD.
The STG-ERM model may serve as a valuable approach to provide data to fill gaps in
satellite-retrieved AOD products