Abstract
The Visible Infrared Imaging Radiometer Suite (VIIRS) is a next-generation polar-orbiting operational environmental sensor with a capability for global aerosol observations. Identifying land aerosol types is important because aerosol types are a basic input in retrieving aerosol optical properties for VIIRS. The VIIRS algorithm can automatically select the optimal land aerosol model by minimizing the residual between the derived and expected spectral surface reflectance. In this study, these selected VIIRS aerosol types are evaluated using collocated aerosol types obtained from the Aerosol Robotic Network (AERONET) level 1.5 from 23 January 2013 to 28 February 2017. The spatial distribution of VIIRS aerosol types and the aerosol optical depth bias (VIIRS minus AERONET) demonstrate that misidentifying VIIRS aerosol types may lead to VIIRS retrieval being overestimated over the Eastern United States and the developed regions of East Asia, as well as underestimated over Southern Africa, India, and Northeastern China. Approximately 22.33% of VIIRS aerosol types are coincident with that of AERONET. The agreements between VIIRS and AERONET for fine non-absorbing and absorbing aerosol types are approximately 36% and 57%, respectively. However, the agreement between VIIRS and AERONET is extremely low (only 3.51%). The low agreement for coarse absorbing dust may contribute to the poor performance of VIIRS retrieval under the aerosol model (R = 0.61). Results also show that an appropriate aerosol model can improve the retrieval performance of VIIRS over land, particularly for dust type (R increases from 0.61 to 0.72).
Highlights
Atmospheric aerosols significantly influence the radiation budget of the Earth by affecting precipitation rates, the lifetime and microphysical properties of clouds, and tropospheric photochemistry [1,2,3]
This study investigates the selection of Visible Infrared Imaging Radiometer Suite (VIIRS) aerosol types at the Environmental Data Record (EDR) level and compares it with those derived from Aerosol Robotic Network (AERONET) level 1.5 data, which cover the period from 23 January 2013 to 28 February 2017
AERONET retrievals can be classified into four aerosol models, namely, Dust, Mixture, non-absorbing (NA), and black carbon (BC), based on real-time single-scattering albedo (SSA) at 440 nm and fine-mode fraction (FMF) at 550 nm, of which the classification criteria (Table 1) for AERONET aerosol can be found in the study of Lee, et al [23]
Summary
Atmospheric aerosols significantly influence the radiation budget of the Earth by affecting precipitation rates, the lifetime and microphysical properties of clouds, and tropospheric photochemistry [1,2,3]. Satellite remote sensing has long been recognized as an ideal approach for monitoring the spatiotemporal distribution of aerosol optical depth (AOD) at the regional and global scales [4]. Aerosol retrieval algorithms have been developed for the global distribution of AOD using different satellite sensors [5,6,7]. The Visible Infrared Imaging Radiometer Suite (VIIRS), which was launched aboard the Suomi National Polar-orbiting Partnership (NPP) Satellite in October 2011, can be used to measure cloud and aerosol properties, ocean color, sea and land surface temperatures, ice motion and temperature, fires, and Earth’s albedo [8,9].
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