Technical note: Intercomparison of three AATSR Level 2 (L2) AOD products over China

Abstract

Abstract. One of four main focus areas of the PEEX initiative is to establish and sustain long-term, continuous, and comprehensive ground-based, airborne, and seaborne observation infrastructure together with satellite data. The Advanced Along-Track Scanning Radiometer (AATSR) aboard ENVISAT is used to observe the Earth in dual view. The AATSR data can be used to retrieve aerosol optical depth (AOD) over both land and ocean, which is an important parameter in the characterization of aerosol properties. In recent years, aerosol retrieval algorithms have been developed both over land and ocean, taking advantage of the features of dual view, which can help eliminate the contribution of Earth's surface to top-of-atmosphere (TOA) reflectance. The Aerosol_cci project, as a part of the Climate Change Initiative (CCI), provides users with three AOD retrieval algorithms for AATSR data, including the Swansea algorithm (SU), the ATSR-2ATSR dual-view aerosol retrieval algorithm (ADV), and the Oxford-RAL Retrieval of Aerosol and Cloud algorithm (ORAC). The validation team of the Aerosol-CCI project has validated AOD (both Level 2 and Level 3 products) and AE (Ångström Exponent) (Level 2 product only) against the AERONET data in a roundrobin evaluation using the validation tool of the AeroCOM (Aerosol Comparison between Observations and Models) project. For the purpose of evaluating different performances of these three algorithms in calculating AODs over mainland China, we introduce ground-based data from CARSNET (China Aerosol Remote Sensing Network), which was designed for aerosol observations in China. Because China is vast in territory and has great differences in terms of land surfaces, the combination of the AERONET and CARSNET data can validate the L2 AOD products more comprehensively. The validation results show different performances of these products in 2007, 2008, and 2010. The SU algorithm performs very well over sites with different surface conditions in mainland China from March to October, but it slightly underestimates AOD over barren or sparsely vegetated surfaces in western China, with mean bias error (MBE) ranging from 0.05 to 0.10. The ADV product has the same precision with a low root mean square error (RMSE) smaller than 0.2 over most sites and the same error distribution as the SU product. The main limits of the ADV algorithm arePublished by Copernicus Publications on behalf of the European Geosciences Union. 9656Y. Che et al.: Intercomparison of three AATSR Level 2 (L2) AOD products over China underestimation and applicability; underestimation is particularly obvious over the sites of Datong, Lanzhou, and Urumchi, where the dominant land cover is grassland, with an MBE larger than 0.2, and the main aerosol sources are coal combustion and dust. The ORAC algorithm has the ability to retrieve AOD at different ranges, including high AOD (larger than 1.0); however, the stability deceases significantly with increasing AOD, especially when AOD > 1.0. In addition, the ORAC product is consistent with the CARSN...

Two decades of satellite observations of AOD over mainland China using ATSR-2, AATSR and MODIS/Terra: data set evaluation and large-scale patterns

“…Figure 7 shows a density scatterplot of the ATSR-retrieved AOD vs. AERONET AOD for the sites listed in Table S1 in the Supplement. Comparison of the AOD scatterplots in Che et al (2016) (their Fig. 2a) with those in Fig.…”

“…The results show that the AATSR-retrieved AOD is underestimated by a factor which increases with increasing AOD. However, Che et al (2016) likely used an older version of the ADV-retrieved data set than the one produced by v2.31 which is used in the current work. The v2.31 AOD is substantially different from the earlier version as shown in Sogacheva et al (2017), especially for high AOD regions.…”

“…The v2.31 AOD is substantially different from the earlier version as shown in Sogacheva et al (2017), especially for high AOD regions. (Che et al (2016) do not mention which ADV version was used, but at their submission date the latest updates described in Sogacheva et al, 2017 had not been implemented). Sogacheva et al (2017) present a validation of the full-mission AATSR AOD over China vs. AERONET AOD.…”

“…AATSR AOD data sets over China produced by three different algorithms, including ADV, have been validated by Che et al (2016) using as reference the AOD data from selected AERONET and CARSNET sites for the years 2007, 2008 and 2010. The results show that the AATSR-retrieved AOD is underestimated by a factor which increases with increasing AOD.…”

“…Furthermore, the most recent MODIS C6 data have been selected because of updates described by Levy et al (2013), with further specification for DB updates by Hsu et al (2013) and expanded description and evaluation of DTDB by Sayer et al (2014). To use these data sets together requires an evaluation of their similarities and differences across the study area, where it is possible to include an independent reference data set like the one provided by the Aerosol Robotic Network (AERONET; Holben et al, 1998), CARSNET Che. et al, 2016) or SONET .…”