Abstract. Satellite-based aerosol retrievals provide a timely view of atmospheric
aerosol properties, having a crucial role in the subsequent estimation of air
quality indicators, atmospherically corrected satellite data products, and
climate applications. However, current aerosol data products based on
satellite data often have relatively large biases compared to accurate
ground-based measurements and distinct uncertainty levels associated with
them. These biases and uncertainties are often caused by oversimplified
assumptions and approximations used in the retrieval algorithms due to unknown
surface reflectance or fixed aerosol models. Moreover, the retrieval
algorithms do not usually take advantage of all the possible observational
data collected by the satellite instruments and may, for example, leave some
spectral bands unused. The improvement and the re-processing of the past and
current operational satellite data retrieval algorithms would become tedious
and computationally expensive. To overcome this burden, we have developed a
model-enforced post-process correction approach to correct the existing
operational satellite aerosol data products. Our approach combines the
existing satellite aerosol retrievals and a post-processing step carried out
with a machine-learning-based correction model for the approximation error in
the retrieval. The developed approach allows for the utilization of auxiliary
data sources, such as meteorological information, or additional observations
such as spectral bands unused by the original retrieval algorithm. The
post-process correction model can learn to correct for the biases and
uncertainties in the original retrieval algorithms. As the correction is
carried out as a post-processing step, it allows for computationally efficient
re-processing of existing satellite aerosol datasets without fully
re-processing the much larger original radiance data. We demonstrate with
over-land aerosol optical depth (AOD) and Ångström exponent (AE) data from the
Moderate Imaging Spectroradiometer (MODIS) of the Aqua satellite that our approach
can significantly improve the accuracy of the satellite aerosol data products
and reduce the associated uncertainties. For instance, in our evaluation, the
number of AOD samples within the MODIS Dark Target expected error envelope
increased from 63 % to 85 % when the post-process correction
was applied. In addition to method description and accuracy results, we also
give recommendations for validating machine-learning-based satellite data
products.
Quasiconformal Mapping Kernel Machine Learning-Based Intelligent Hyperspectral Data Classification for Internet Information Retrieval
