Evaluation of the AVHRR DeepBlue aerosol optical depth dataset over mainland China.
AffiliationUniversity of Derby
Chinese Academy of Sciences
Chinese Academy of Meteorological Sciences
University of Chinese Academy of Sciences
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AbstractAdvanced Very High Resolution Radiometer (AVHRR) on-board NOAA series satellites have been used to observe the Earth’s surface and clouds for almost 40 years. Limited by bands and problematic instrument calibration, aerosol studies using AVHRR data have focused on retrieving data over the ocean. However, continuous developments have made it possible to retrieve aerosol over land as well. The newly developed AVHRR Deep Blue (DB) technique has been applied to process global aerosol datasets over both land and the ocean during 1989–1990, 1995–1999 and 2006–2011. This paper aims to evaluate, in detail, the performance of the AVHRR DB aerosol optical depth (AOD) dataset over mainland China by comparison with both ground-based data and satellite aerosol products. The ground-based validation results show that DB AOD is close to ground-based AOD when AOD is moderate during winter, while DB underestimates AOD when AOD increases over 1.0 during summer over vegetated surfaces. AVHRR DB underestimates dry, urban and transitional surfaces in Western China due to the high uncertainty in low retrievals over bright surfaces. Cross-comparison with the Moderate-resolution imaging spectrometer (MODIS) DB aerosol dataset shows that the disadvantages of the single longer visible channel are greatly increased over bright surfaces. Together with problematic instrument calibration, the differences between the two datasets over most of mainland China are significant. Meanwhile, the differences show strong seasonal variation characteristics.
CitationChe, Y. et al. (2018) ‘Evaluation of the AVHRR DeepBlue aerosol optical depth dataset over mainland China’, ISPRS Journal of Photogrammetry and Remote Sensing, 146, pp.74-90. DOI: 10.1016/j.isprsjprs.2018.09.004
JournalISPRS Journal of Photogrammetry and Remote Sensing