  Satellite-based retrieval has become a popular PM2.5 monitoring method
currently. To improve the retrieval performance, multiple variables are usually
introduced as auxiliary variable in addition to aerosol optical depth (AOD).
Different kinds of variables are usually at different resolutions varying from
sub-kilometers to dozens of kilometers. Generally, when doing the retrieval,
variables at different resolutions are resampled to the same resolution as the
AOD product to keep the scale consistency. A deficiency of doing this is that
the information contained in the scale difference is discarded. To fully
utilize the information contained at different scales, a dual-scale retrieval
method is proposed in this study. At the first stage, variables which influence
PM2.5 concentration at large scale were used for PM2.5 retrieval at coarse
resolution. Then at the second stage, variables which affect PM2.5 distribution
in finer scale, were used for the further PM2.5 retrieval at high resolution
(sub-km level resolution) with the retrieved PM2.5 at the first stage at
coarser resolution also as input. In this study, four different retrieval
models including multiple linear regression (MLR), geographically weighted
regression (GWR), random forest (RF) and generalized regression neural network
(GRNN) are adopted to test the performance of the dual-scale retrieval method.
Compared with the traditional retrieval method, the proposed dual-scale
retrieval method can achieve PM2.5 mapping at finer resolution and with higher
accuracy. Dual-scale retrieval can fully utilize the information contained at
different scales, thus achieving a higher resolution and accuracy. It can be
used for the generation of quantitative remote sensing products in various
fields, and promote the improvement of the quality of quantitative remote
sensing products.
