  Continuous aerosol monitoring in East Asia is essential due to the massive
aerosol emissions from natural and anthropogenic sources. Geostationary
satellites enable continuous aerosol monitoring; however, the observation is
limited to the daytime. This study proposed machine learning-based models to
estimate daytime and nighttime aerosol optical depth (AOD) in East Asia using a
geostationary satellite, Geo-KOMPSAT-2A (GK-2A). The input variables for the
machine learning models include the brightness temperature (BT) and
top-of-atmosphere (TOA) reflectance from GK-2A, meteorological and geographical
data, and auxiliary variables. The two models that used different combinations
of GK-2A variables were proposed and compared: the all-day BT model, which
estimates AOD during both day and night using BT variables, and the daytime TOA
model, which estimates AOD during the day using TOA reflectance variables as
well. The estimated AODs by the models were validated with ground-based AOD
data from the Aerosol Robotic Network (AERONET) by 10-fold cross-validation and
hold-out validation methods. The performance of the daytime TOA model was
slightly higher than the all-day BT model during the day (R2 = 0.80-0.82, root
mean square error (RMSE) = 0.107-0.116 for the all-day BT model, R2 = 0.83,
RMSE = 0.098 for the daytime TOA model). The SHapley Additive exPlanations
(SHAP) analysis showed that total precipitable water content and seasonality
contributed the most for both proposed models. BT differences and TOA
reflectance variables were identified as the next most contributing variables
for the all-day BT and daytime TOA models. The spatiotemporal distributions of
estimated AODs from the proposed models show similar patterns compared with
other AOD products. A time series comparison at a test station demonstrated
that the estimated AOD of the proposed models was consistent with the AERONET
AOD.
