Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land
Abstract. We have developed a Bayesian aerosol retrieval (BAR) algorithm for the
retrieval of aerosol optical depth (AOD) over land from the Moderate Resolution
Imaging Spectroradiometer (MODIS). In the BAR algorithm, we simultaneously
retrieve all dark land pixels in a granule, utilize spatial correlation
models for the unknown aerosol parameters, use a statistical prior model for
the surface reflectance, and take into account the uncertainties due to fixed
aerosol models. The retrieved parameters are total AOD at 0.55  µ m,
fine-mode fraction (FMF), and surface reflectances at four different
wavelengths (0.47, 0.55, 0.64, and 2.1  µ m). The accuracy of the new
algorithm is evaluated by comparing the AOD retrievals to Aerosol Robotic
Network (AERONET) AOD. The results show that the BAR significantly improves
the accuracy of AOD retrievals over the operational Dark Target (DT)
algorithm. A reduction of about 29 % in the AOD root mean square error and
decrease of about 80 % in the median bias of AOD were found globally when
the BAR was used instead of the DT algorithm. Furthermore, the fraction of
AOD retrievals inside the ± ( 0.05 + 15 % ) expected error envelope
increased from 55 to 76 %. In addition to retrieving the values of AOD,
FMF, and surface reflectance, the BAR also gives pixel-level posterior
uncertainty estimates for the retrieved parameters. The BAR algorithm always
results in physical, non-negative AOD values, and the average computation
time for a single granule was less than a minute on a modern personal
computer.