Figure 6. Uncertainty
analysis with box plots of bias and the fraction falling within the EE
envelope (curves) of global AOD from Landsat imagery retrievals against
ground-based measurements as a function of (a) surface reflectance, (b)
elevation (m), (c) NDVI, and (d) land-use type. Black horizontal solid
lines represent zero biases. In each box, the red dots, middle, lower,
and upper horizontal lines represent the AOD bias mean, median, and 25th
and 75th percentiles, respectively.
Aerosol retrieval over
typical regions
Given the extensive volume of
Landsat imagery on a global scale, this study selected four distinct
regions: Denver, USA; Madrid, Spain; Beijing, China; and the Sahara
Desert to perform aerosol retrieval experiments (Figure S6). These
regions encompass diverse land-use types and serve as representatives of
varying surface conditions, climates, and levels of human activity.
All available Landsat 8/9 OLI
images from these regions from 2013 to 2022 were collected to conduct
aerosol retrievals using the trained ART-GCT-GEE model on the GEE cloud
platform. Figure 7 presents
true-color images and corresponding AOD retrievals (550 nm) in Denver,
USA, and Madrid, Spain on different dates, both illustrative examples of
dark surfaces. These regions
share similar land surface characteristics, primarily comprising plains
and mountains, dense vegetation (forests, grasslands, croplands), low
population densities, and consistently low aerosol levels throughout the
year (Figure S6a-b). Our model
exhibits high spatial continuity and provides detailed pollution
information at a high spatial resolution (30 m), capturing subtle
spatial variations within the overall low aerosol background throughout
the months. Importantly, the
model is capable of capturing exceptionally high smoke AOD values
generated by sudden wildfires on specific days (e.g., 15 September 2018
and 4 September 2020) during fire seasons in Denver, USA, even across
the entire image at relatively low pollution levels (pointed by yellow
arrows in Figure 7b-c).
Furthermore, besides very clean
conditions, our model also works well in retrieving the AOD spatial
distribution under infrequent highly polluted conditions (Figure 7f).
We also conducted a validation of
aerosol retrievals using data from 12 available AERONET stations within
the two study areas (Table S7). A high level of agreement between
observations and retrievals is revealed, with estimated biases less than
± 1% and close RMSEs equal to 0.06.
Approximately 91% and 85% of
the retrievals met the EE criteria, with proportions falling within the
GCOS range of 67% and 55%, respectively.