A Critical Evaluation of Deep Blue Algorithm Derived AVHRR Aerosol Product Over China

Abstract

The Deep Blue (DB) aerosol retrieval algorithm has recently been applied to Advanced Very High Resolution Radiometer (AVHRR) data to produce a first version (V001) of a global aerosol optical thickness (AOT) data set. In this paper, we critically evaluate these AVHRR AOT data over China by comparison with ground-based reference data from China Aerosol Remote Sensing Network for the period 2006–2011. The evaluation considers the impact of the surface (type and reflectance) and the aerosol properties (aerosol loading, aerosol absorption) on the quality of the retrieved AOT. We also compare the AVHRR-retrieved AOT with that from Moderate Resolution Imaging Spectroradiometer over major aerosol source regions in China. We further consider seasonal variations and find, in general, a good agreement between AVHRR AOT and the reference data sets. The AVHRR retrieval algorithm performs well over dark vegetated surfaces, but over bright surfaces (e.g., desert regions) the results are less good. The AVHRR algorithm underestimates the AOT, with 32.1% of the values lower than the estimated error envelope of ±0.05 ± 0.25τ. In particular over the desert, the AVHRR-retrieved AOT is frequently underestimated and for AOT ≤ 0.6 the values are on average 0.05 too low due to the pixel filtering, and dust storms are missed. The comparison of the AVHRR AOT with MODIS collection 6 and CARSNET data indicates that improvements are needed for, for example, AVHRR calibration and cloud/aerosol flagging. The analysis presented in this paper contributes to a better understanding of the AVHRR AOT product over China.