Evaluation and Comparison of Long-Term MODIS C5.1 and C6 Products against AERONET Observations over China

Abstract

MODIS (MODerate Resolution Imaging Spectroradiometer) aerosol products are the most widely used satellite retrieved aerosol optic depth (AOD) products, which compensate for the spatial lack of ground-based sun photometer observations. The newly released Collection 6 (C6) aerosol products have some improvements compared to the Collection 5.1 (C5.1) products with optimized algorithms and newly revised upstream products. Additionally, a three-kilometer resolution AOD product was added in the C6 product. In this study, the accuracies and regional applicability of long-term (2001-2015) different MODIS C5.1 and C6 aerosol products in China were evaluated against the 16 AERONET (Aerosol Robotic Network) observations with observations over more than three years. The overall analysis indicates that the C6 DT (Dark Target) 10 km products slightly improved the retrieval accuracies, with about 3% more data falling within the Expected Error (EE) envelope. However, for Deep Blue (DB) products, the C6 algorithm significantly improved the accuracy over all of China, and increased the successful retrieval number by extending retrieval coverages. Regional analysis demonstrated that the C6 DT 10 km product did not perform well in East China, with only 33.5% of retrievals falling within the EE envelope. For the DB product, the C6 algorithm significantly increased the number successfully retrieved, and was more accurate in all four regions in China. The validation of the DT 3 km product suggests large differences existed between the Terra and Aqua results. The accuracy of the Aqua DT 3 km product is obviously higher than that of the Terra DT 3 km product. The results of the study suggest that proper AOD products need to be considered when evaluating aerosol loading situations in different regions in China.

Modulation of the ENSO on Winter Aerosol Pollution in the Eastern Region of China

“…For the spatial distribution of correlation coefficients (R), we found that highly correlated areas ( R > 0.8) are largely located in high AOD regions, implying that MERRA‐2 AOD may well capture the spatial pattern of the aerosol pollution, especially over regions with heavy aerosol pollution over China (Figure c). In addition, many studies related to MODIS AOD estimation suggest that AOD over China is substantially overestimated by MODIS (Fan et al, ; Tao et al, , ; Wang et al, ; Xie et al, ), especially over urban areas of eastern China (Xie et al, ). For example, one result from Tao et al (Tao et al, ) indicates that under high AOD (>0.4) conditions the overestimation of the AOD in DT retrieval is prevalent throughout the whole year, whereas the DB retrieval has a positive bias in winter (in the range of 0.13–0.24) and slight overestimation in fall.…”

Large contribution of meteorological factors to inter-decadal changes in regional aerosol optical depth

“…This deviation may be related to the use of different versions of MODIS data: in the MERRA-2 AOD observing system, MERRA-2 assimilated the biascorrected AOD derived from MODIS radiances, Collection 5 (C5; Buchard et al, 2017), and the MODIS data used in this study was the latest collection (Collection 6.1, C6). Different versions mean differences in algorithms (Fan et al, 2017), which may affect the statistical error.…”

“…However, as a consequence of clean-air actions, anthropogenic emissions in China have declined significantly in recent years (Zheng et al, 2018). It has been proven that these changes in local pollutant emissions or aerosol precursors over the above regions can, to a certain extent, explain the regional AOD variability, as observed in long-term satellite aerosol data records (De Meij et al, 2012;Itahashi et al, 2012;Feng et al, 2018). On the other hand, various studies have shown that meteorological changes play a major role in determining the inter-decadal trend of AOD over mineraldust-dominant regions, particularly in the Sahara desert (SD) and the ME (Pozzer et al, 2015;Klingmüller et al, 2016).…”

A Climatological Satellite Assessment of Absorbing Carbonaceous Aerosols on a Global Scale

“…The aerosol data used as input to the algorithm, are part of MODIS Aqua spatiotemporally aggregated Level-3 daily gridded atmospheric data product (MYD08_D3). More specifically, Collection 006 (C006) MODIS Aqua aerosol data are used, which are generated by reprocessing MODIS data archives using calibration enhancements, algorithm refinements, and upstream product improvements [55,56] and have replaced the previous MODIS C005 data providing a better performance against AERONET data [57][58][59]. The following MODIS aerosol data describing the load and size of aerosols are used (as explained in Section 2.2) in the algorithm: (i) Aerosol Optical Depth (AOD), (ii) Ångström Exponent (a), and (iii) Fine Mode Fraction (FF) of AOD (the aerosol fine-mode fraction is defined as [60]: FF=AOD f /(AOD f + AOD c ), where AOD f is the aerosol fine-mode optical depth and AOD c is the aerosol coarse-mode optical depth).…”