Figure 8. Same as Figure
7 but for two typical bright-surface regions: (a-c) Beijing, China and
(d-f) the Sahara Desert in northern Africa.
Comparison
with other AI models
Finally, we compare the
performance of our ART-GCT-GEE model with ten popular AI models through
spatiotemporally independent validation, i.e., training these models on
data from 2015–2020 and validating them on data from the remaining
years (Table 3). All machine-learning models show good performance in
predicting global AOD levels, with strong correlations with surface
observations (R = 0.72–0.84), MAE and RMSE values generally below 0.08
and 0.014, and a relatively high proportion of retrievals meet the EE
(64–77%) and GCOS (27–44%) criteria.
Moreover, boosting-based models
(i.e., XGBoost, CatBoost, and LightDBM) outperform bagging-based
ensemble-learning methods (Extra Trees and Random Forest) in aerosol
retrievals due to their continuous optimization functions, which correct
the residual more effectively.
However, DL, with its enhanced
data-mining capabilities and improved ability to tackle nonlinear
problems, exhibits improved predictive accuracy (e.g., R = 0.82–0.85,\(f_{\text{EE}}\) = 74–80%), especially the latest models like MLP and
ResNet. The Transformers model
represents a recent powerful state-of-the-art DL approach, incorporating
spatial information and, notably, time series data, surpassing all other
machine and DL methods. This
superiority is evident in its highest correlation, near-zero estimation
bias, lowest MAE and RMSE values, and the highest proportions of
retrieval samples meeting the EE and GCOS criteria.
The 10-CV results performed at
sample, spatial, and temporal scales yield similar conclusions (Table
S8), providing further evidence of the Transformers model’s
effectiveness. This study
presents the first attempt to apply the Transformers model to the
complex problem of aerosol retrieval, effectively decoupling the
Earth-atmosphere nonlinear problem.
Table 3. Comparison in
performance across various machine- and deep-learning models for
retrieving AOD from Landsat imagery.