EAA: Eastern Asia; SAA: South Asia; SEA: Southeast Asia; EUR: Europe;
ENAM: Eastern North America; WNAM: Western North America; SAM: South
America; NAME: North Africa and the Middle East; SAF: South Africa; OCE:
Oceania.
Uncertainty analysis
Aerosol retrieval errors can arise from various sources. To assess the
uncertainty of AOD retrievals using the ART-GCT-GEE model, we examined
the model’s performance with respect to variations in surface
reflectance, surface elevation, NDVI, and land-use type (Figure 6).
As surface reflectance increases,
the model’s performance gradually declines, with a widening range of AOD
biases and a decreasing fraction of retrievals falling within the EE
envelope (Figure 6a). This is
primarily because of the increased complexity of handling relationships
caused by the reduced sensitivity of AOD to apparent reflectance.
Nevertheless, the median bias remains relatively close to zero, and the\(f_{\text{EE}}\) consistently exceeds approximately 78%.
This illustrates the model’s
general stability across different surface reflectance conditions,
particularly in the case of bright surfaces.
Traditional physical methods have
historically struggled with such conditions due to the considerable
difficulties in accurately estimating surface reflectance, leading to
substantial estimation uncertainties. In low-altitude (height ≤ 500 m)
areas, the model exhibits stable performance with slight overall
underestimations (median bias = -0.002—0.010) and high proportions
(80–91%) of retrievals falling within the EE envelope (Figure 6b).
In mid- to high-altitude (height
> 500 m) areas, the model’s performance gradually improves
with increasing surface elevation. This improvement is evident in the
reduced variability of estimated biases and the increased fraction of
retrievals within the EE threshold. These findings indicate that our
model is relatively insensitive to variations in terrain.
When using NDVI as an indicator
of surface conditions (Figure 6c), our model demonstrates stability in
sparse and low-vegetation areas (NDVI ≤ 0.3), exhibiting median biases
slightly below zero and (within) EE fractions consistently above 80%.
Moreover, as NDVI values
increase, the model’s overall accuracy gradually improves, with median
biases approaching zero. There is also a continuous enhancement in
(within) EE fractions, reaching approximately 90% in densely vegetated
areas (NDVI ≥ 0.6). Our model
consistently delivers reliable AOD retrievals across various land-use
types (Figure 6d), particularly excelling in grassland (e.g., median
bias = -0.08 and \(f_{\text{EE}}\) = 90%) and forest (e.g., median bias
= -0.03 and \(f_{\text{EE}}\) = 86%).
Importantly, our model also
performs effectively in retrieving AOD over bare land (e.g., median bias
= -0.008, and \(f_{\text{EE}}\) = 84%) and urban areas (e.g., median
bias = -0.005 and \(f_{\text{EE}}\) = 81%), where populations are
densely concentrated despite the presence of complex surface structures
and challenging climate conditions.
Therefore, our model can more
effectively mitigate the challenges posed by complex surface conditions,
providing robust aerosol retrievals across a wide range of terrestrial
surfaces worldwide, a significant ”pathological” issue often encountered
with traditional algorithms.