  One of the techniques for estimating the surface particle concentration with
a diameter of fewer than 2.5 micrometers (PM2.5) is using aerosol optical depth
(AOD) products. Different AOD products are retrieved from various satellite
sensors, like MODIS and VIIRS, by various algorithms, such as Deep Blue and
Dark Target. Therefore, they do not have the same accuracy and spatial
resolution. Additionally, the weakness of algorithms in AOD retrieval reduces
the spatial coverage of products, particularly in cloudy or snowy areas.
Consequently, for the first time, the present study investigated the
possibility of fusing AOD products from observations of MODIS and VIIRS sensors
retrieved by Deep Blue and Dark Target algorithms to estimate PM2.5 more
accurately. For this purpose, AOD products were fused by machine learning
algorithms using different fusion strategies at two levels: the data level and
the decision level. First, the performance of various machine learning
algorithms for estimating PM2.5 using AOD data was evaluated. After that, the
XGBoost algorithm was selected as the base model for the proposed fusion
strategies. Then, AOD products were fused. The fusion results showed that the
estimated PM2.5 accuracy at the data level in all three metrics, RMSE, MAE, and
R2, was improved (R2=0.64). Despite the simplicity and lower computational cost
of the data level fusion method, the spatial coverage did not improve
considerably due to eliminating poor quality data through the fusion process.
Afterward, the fusion of products at the decision level was followed in eleven
scenarios. In this way, the best result was obtained by fusing Deep Blue
products of MODIS and VIIRS sensors (R2=0.81)
