Interference of Heavy Aerosol Loading on the VIIRS Aerosol Optical Depth (AOD) Retrieval Algorithm

Abstract

Aerosol optical depth (AOD) has been widely used in climate research, atmospheric environmental observations, and other applications. However, high AOD retrieval remains challenging over heavily polluted regions, such as the North China Plain (NCP). The Visible Infrared Imaging Radiometer Suite (VIIRS), which was designed as a successor to the Moderate Resolution Imaging Spectroradiometer (MODIS), will undertake the aerosol observations mission in the coming years. Using the VIIRS AOD retrieval algorithm as an example, we analyzed the influence of heavy aerosol loading through the 6SV radiative transfer model (RTM) with a focus on three aspects: cloud masking, ephemeral water body tests, and data quality estimation. First, certain pixels were mistakenly screened out as clouds and ephemeral water bodies because of heavy aerosols, resulting in the loss of AOD retrievals. Second, the greenness of the surface could not be accurately identified by the top of atmosphere (TOA) index, and the quality of the aggregation data may be artificially high. Thus, the AOD retrieval algorithm did not perform satisfactorily, indicated by the low availability of data coverage (at least 37.97% of all data records were missing according to ground-based observations) and overestimation of the data quality (high-quality data increased from 63.42% to 80.97% according to radiative simulations). To resolve these problems, the implementation of a spatial variability cloud mask method and surficial index are suggested in order to improve the algorithm.

Impact of the Dust Aerosol Model on the VIIRS Aerosol Optical Depth (AOD) Product across China

“…However, according to the recent preliminary validations [11,12], the VIIRS retrievals overestimate the AOD when the AOD is low and underestimate the AOD when the AOD is high, and the biases depend on the AOD range. Under the condition of heavy polluted aerosol loadings across China, the VIIRS AOD retrievals are unavailable because of strict cloud mask and ephemeral water test [13].According to radiation transfer theory, the top-of-atmosphere (TOA) radiance is a combination of the atmospheric path radiance and contributions from the Earth's surface, but the radiance information observed by a passive satellite is not sufficient enough to distinguish them. Therefore, retrieval of aerosol properties from this complicated TOA signal is an ill-posed inverse problem, and requires prior knowledge about the surface and aerosol.…”

“…However, according to the recent preliminary validations [11,12], the VIIRS retrievals overestimate the AOD when the AOD is low and underestimate the AOD when the AOD is high, and the biases depend on the AOD range. Under the condition of heavy polluted aerosol loadings across China, the VIIRS AOD retrievals are unavailable because of strict cloud mask and ephemeral water test [13].…”

Evaluation of the Aqua-MODIS C6 and C6.1 Aerosol Optical Depth Products in the Yellow River Basin, China

“…Wang et al [49] evaluated the performance of VIIRS (Visible Infrared Imaging Radiometer Suite) and MODIS C6 AOD products in the Yangtze River basin (YRB). Bilal et al verified the performance of MODIS C6 and C6.1 aerosol products on different vegetation surfaces globally [50].In conclusion, the evaluations of MODIS C6 and C6.1 aerosol products are currently concentrated in developed regions in eastern and southern China, such as Beijing-Tianjin-Hebei [43,46,48,51,52], Wuhan [53,54], and the YRB [49,55,56]. Little research has been conducted for the entire Yellow River basin (YERB) in central-north and north-western China.The Yellow River basin is one of the underdeveloped areas in China [57].…”

Analysis of influential factors for the relationship between PM<sub>2.5</sub> and AOD in Beijing

“…Recent studies have proposed methods to estimate the surface PM 2.5 based on the AOD observations from satellites (van Donkelaar et al, 2006(van Donkelaar et al, , 2010(van Donkelaar et al, , 2013Drury et al, 2008;Wang et al, 2010b;Xin et al, 2016). Although PM 2.5 from AOD has no high temporal resolution and is not available when it is cloudy or very pollutant, these methods provide the spatial distribution of PM 2.5 globally or regionally (Paciorek et al, 2008;Li et al, 2017;Wang et al, 2017).…”