Evaluation of VIIRS, GOCI, and MODIS Collection 6 AOD retrievals against ground sunphotometer measurements over East Asia

Abstract

Abstract. Persistent high aerosol loadings together with extremely high population density have raised serious air quality and public health concerns in many urban centers in East Asia. However, ground based air quality monitoring is relatively limited in this area. Recently, satellite retrieved Aerosol Optical Depth (AOD) at high resolution has become a powerful tool to characterize aerosol patterns in space and time. Using ground AOD measurements from the Aerosol Robotic Network (AERONET) and the Distributed Regional Aerosol Gridded Observation Networks (DRAGON)-Asia Campaign, as well as from handheld sunphotometers, we evaluated emerging aerosol products from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP), the Geostationary Ocean Color Imager (GOCI) aboard the Communication, Ocean, and Meteorology Satellite (COMS), and Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) (Collection 6) in East Asia in 2012 and 2013. In the case study in Beijing, when compared with AOD measured by handheld sunphotometers, 51 % of VIIRS Environmental Data Record (EDR) AOD, 33 % of VIIRS Intermediate Product (IP) AOD, 31 % of GOCI AOD, 26 % of Terra MODIS C6 3 km AOD, and 16 % of Aqua MODIS C6 3 km AOD fell within the reference expected error (EE) envelop (±0.05 ± 0.15 AOD). Comparing against AERONET measurements over the Japan–South Korea region, 64 % of EDR, 37 % of IP, 62 % of GOCI, 39 % of Terra MODIS and 56 % of Aqua MODIS C6 3 km AOD fell within the EE. In general, satellite aerosol products performed better in tracking the day-to-day variability than tracking the spatial variability at high resolutions. The VIIRS EDR and GOCI products provided the most accurate AOD retrievals, while VIIRS IP and MODIS C6 3 km products had positive biases.

Top-Down Estimation of Particulate Matter Emissions from Extreme Tropical Peatland Fires Using Geostationary Satellite Fire Radiative Power Observations

“…For the IP aerosol product, a total of 20 (out of 28) collocated high quality retrievals were available for intercomparison, also shown in Figure A1 . A small bias of 0.12 ± 0.23 is observed between these retrievals and those of AERONET, indicating that for the smoke conditions focused on here with the VIIRS IP product the AOD estimates are generally sound, supporting the findings of more extensive assessments reported elsewhere [ 68 , 69 ]. However, the IP product cannot be used to assess very high AOD values, which is the reason that the ORAC-retrievals of AOD are required for such situations.…”

Evaluation of JAXA Himawari-8-AHI Level-3 Aerosol Products over Eastern China

“…The Cimel sunphotometer is a multichannel, automatic, sun-and-sky scanning radiometer that measures and retrieves aerosol optical properties with a high AOT accuracy around 0.01-0.02 [35,36]. As shown in Figure 2, eleven permanent sunphotometer sites evenly distributed across eastern China were used here, including four Aerosol Robotic Network (AERONET) sites (Beijing, Xianghe, Xuzhou, and Taihu), one site (Wuhan) operated by Wuhan University [37], which is a member of CARSNET (China Meteorological Administration Aerosol Remote Sensing Network) [38], and six Sun-Sky Radiometer Observation Network (SONET) sites [39,40] (Yanqihu, Jiaozuo, Songshan, Hefei, Nanjing, and Shanghai).…”

“…As auxiliary data, the MODIS 3 km AOT products of two satellites, Terra and Aqua (overpass at about 10:30/13:30 CST), which used the same spatial-temporal strategy as H8/AHI, was used for inter-comparison. In order to account for the potential error in the satellite and ground-based datasets, an additional constraint condition of at least 30% effective satellite retrievals within the window and at least two ground-based observations within an hour (possibly four or five AERONET observations in the past 1 hour) were considered [4,36].…”

Supplementary material to "LGHAP: a Long-term Gap-free High-resolution Air Pollutants concentration dataset derived via tensor flow based multimodal data fusion"

“…A specific aerosol retrieval algorithm was designed to infer AOD from VIIRS observations, with changes not only in theoretical basis and retrieval limitations but also data quality flagging (Jackson et al, 2013). Ground-based validation results indicated a generally good accuracy of VIIRS AOD (R 2 =0.73) when compared to AERONET AOD observations (Xiao et al, 2016).…”