Summary and
conclusions
The
Landsat series of satellites provides Earth observations dating back to
the 1970s with a high spatial resolution of 30 m every 16 days, making
them highly valuable for a variety of applications, such as land
use/cover classification and change detection, resource assessment, and
environmental monitoring.
However, a significant challenge
faced is the interference caused by atmospheric aerosols, largely
affecting the accuracy of extraction and retrieval of surface
parameters. To tackle this issue,
this study first identifies the critical input features for aerosol
retrieval based on the fundamental principles of an atmospheric
radiative transfer (ART) model. Subsequently, we introduce the
Transformers model, for the first time, to address numerous intractable
nonlinear problems inherent in the complex processes of decoupling the
Earth-atmosphere system.
Additionally, multidimensional
spatial, temporal (time series), and altitude information are
incorporated into the Transformers model (GeoChronoTransformers, or GCT)
to improve the model performance.
Finally, all Landsat data
preprocessing, spatiotemporal matching, and aerosol retrieval tasks are
efficiently executed using the Google Earth Engine (GEE) cloud platform.
In this effort, the study
gathered all available images (~20,755 cloud-screened
scenes) from Landsat 8 and 9, spanning from their launch to 2022,
matching approximately 470 ground monitoring stations.
They then were used to develop a
robust model capable of conducting global aerosol retrieval tasks for
Landsat imagery in an automatic and operational manner.
We utilized the XAI (Explainable
Artificial Intelligence)-SHAP (SHapley Additive exPlanations) method to
elucidate the internal mechanisms of our developed ART-GCT-GEE model,
revealing the significance of multi-band spectral channels, observation
geometry, and multi-dimensional spatiotemporal information in the
aerosol retrieval, contributing 58%, 19%, and 19%, respectively.
The 10-fold cross-validations
demonstrated a high level of agreement between our retrievals and
observations, with sample-based, station-based, and month-based
correlation coefficients (R) [root-mean-square error, RMSE] of 0.902
(0.086), 0.826 (0.111), and 0.871 (0.097), respectively.
Furthermore, independent
spatiotemporal validations demonstrated the effectiveness of our model
in predicting aerosol optical depth (AOD) levels in regions
(with-continent hold) and periods (with-year hold) where observations
are unavailable, as well as historical and future AOD levels (e.g., R =
0.863, RMSE = 0.096) over land.
Moreover, the model exhibits a
remarkable level of robustness, with minimal sensitivity to changes in
surface conditions like surface reflectance, elevation, and land-use
cover. Importantly, it
outperforms most widely used traditional machine- and deep-learning
models. Aerosol retrieval
experiments conducted in four representative regions worldwide have
confirmed the model’s capacity to accurately capture variations in AOD
concentrations over surfaces ranging from dark to bright.
The model excels in providing
high-resolution and highly detailed spatial distributions of AOD,
particularly in urban areas characterized by high levels of
anthropogenic emissions. It also
performs effectively in scenarios involving elevated AOD pollution
levels, like in situations where smoke and dust are present.
In summary, our developed
ART-GCT-GEE model demonstrates that it can provide precise aerosol
retrievals from Landsat imagery over land, offering valuable insights
into environmental and air quality assessments.
Particularly noteworthy is that
our model can be used with data from instruments on other satellites
(e.g., MODIS and VIIRS) as long as their data becomes available on the
GEE platform in the future.
Data availability
The ART-GCT-GEE aerosol retrieval cloud model for Landsat imagery is
free to all users and available
at https://weijing-rs.github.io/product.html.
This online framework will be made publicly available once the paper is
accepted. The authors greatly thank Kaitao Li and Li Li from the Chinese
Academy of Sciences for their help in data collection and processing.