Uncertainty in Aqua-MODIS Aerosol Retrieval Algorithms During COVID-19 Lockdown

Bilal, Muhammad; Qiu, Zhongfeng; Nichol, Janet Elizabeth; Mhawish, Alaa; Ali, Arfan; Khedher, Khaled Mohamed; Leeuw, Gerrit de; Wang, Yu; Tiwari, Pravash; Nazeer, Majid; Bleiweiss, Max P.

doi:10.1109/lgrs.2021.3077189

Cited by 10 publications

(7 citation statements)

References 32 publications

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“…In this paper, the MODIS/Terra and MODIS/Aqua AOD product MCD19A2, retrieved with the multiangle implementation of atmospheric correction (MAIAC) algorithm [73,74] was used to produce a MODIS AOD time series from 2011 to the end of 2020. The MAIAC MCD19A2 AOD product was used instead of the C6.1 DBDT product used by Sogacheva et al, [23,39] because of its better performance over China [75].…”

Section: Research Approach Satellite Datamentioning

confidence: 99%

Air Quality over China

Leeuw

Bai

et al. 2021

Remote Sensing

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The strong economic growth in China in recent decades, together with meteorological factors, has resulted in serious air pollution problems, in particular over large industrialized areas with high population density. To reduce the concentrations of pollutants, air pollution control policies have been successfully implemented, resulting in the gradual decrease of air pollution in China during the last decade, as evidenced from both satellite and ground-based measurements. The aims of the Dragon 4 project “Air quality over China” were the determination of trends in the concentrations of aerosols and trace gases, quantification of emissions using a top-down approach and gain a better understanding of the sources, transport and underlying processes contributing to air pollution. This was achieved through (a) satellite observations of trace gases and aerosols to study the temporal and spatial variability of air pollutants; (b) derivation of trace gas emissions from satellite observations to study sources of air pollution and improve air quality modeling; and (c) study effects of haze on air quality. In these studies, the satellite observations are complemented with ground-based observations and modeling.

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Section: Research Approach Satellite Datamentioning

confidence: 99%

Air Quality over China

Leeuw

Bai

et al. 2021

Remote Sensing

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“…The EE is commonly used in MODIS validation studies (e.g., [12,16,34,35]). It is designed to account for errors which are normally expected in a satellite retrieval of AOD.…”

Section: Spatio-temporal Co-location Of Aeronet and Modis Aod And Eva...mentioning

confidence: 99%

“…However, as AOD increases, assumptions within the aerosol model employed become much more significant, dominating at high AOD. The form of Equation ( 5) was defined so as to combine these two factors and has been widely used in satellite AOD retrieval validation studies of the type described in this work [12,16,35]. Typically, the assumption is made that if 66% of data points are found to lie within the EE envelope, a dataset can be considered validated or it can be considered that there is a 'good' match [36].…”

Section: Spatio-temporal Co-location Of Aeronet and Modis Aod And Eva...mentioning

confidence: 99%

An Evaluation of Two Decades of Aerosol Optical Depth Retrievals from MODIS over Australia

2022

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We present an evaluation of Aerosol Optical Depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) over Australia covering the period 2001–2020. We focus on retrievals from the Deep Blue (DB) and Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms, showing how these compare to one another in time and space. We further employ speciated AOD estimates from Copernicus Atmospheric Monitoring Service (CAMS) reanalyses to help diagnose aerosol types and hence sources. Considering Australia as a whole, monthly mean AODs show similar temporal behaviour, with a well-defined seasonal peak in the Austral summer. However, excepting periods of intense biomass burning activity, MAIAC values are systematically higher than their DB counterparts by, on average, 50%. Decomposing into seasonal maps, the patterns of behaviour show distinct differences, with DB showing a larger dynamic range in AOD, with markedly higher AODs (ΔAOD∼0.1) in northern and southeastern regions during Austral winter and summer. This is counter-balanced by typically smaller DB values across the Australian interior. Site level comparisons with all available level 2 AOD data from Australian Aerosol Robotic Network (AERONET) sites operational during the study period show that MAIAC tends to marginally outperform DB in terms of correlation (RMAIAC = 0.71, RDB = 0.65) and root-mean-square error (RMSEMAIAC = 0.065, RMSEDB = 0.072). To probe this behaviour further, we classify the sites according to the predominant surface type within a 25 km radius. This analysis shows that MAIAC’s advantage is retained across all surface types for R and all but one for RMSE. For this surface type (Bare, comprising just 1.2% of Australia) the performance of both algorithms is relatively poor, (RMAIAC = 0.403, RDB = 0.332).

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“…Studies using MODIS retrievals during the lockdowns have found anomalously low AOD in India, Bangladesh, China, Europe, and the United States during the early months of 2020 (Lal et al 2020, Ranjan et al 2020, Acharya et al 2021, Bilal et al 2021). Data from CALIOP have provided additional context to such lockdown observations; in sub-Saharan Africa, analysis of CALIOP retrievals showed an increase in smoke, possibly due to diminished enforcement of land management policies (Kganyago andShikwambana 2021, Qiu et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Beyond the lockdowns: satellite observations of aerosol optical depth through 2020, the first year of the COVID-19 pandemic

Smith

Ting

et al. 2022

Environ. Res. Lett.

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Anticipated future reductions in aerosol emissions are expected to accelerate warming and substantially change precipitation characteristics. Therefore, it is vital to identify the existing patterns and possible future pathways of anthropogenic aerosol reductions. The COVID-19 pandemic prompted abrupt, global declines in transportation and industrial activities, providing opportunities to study the aerosol effects of pandemic-driven emissions changes. Here, measurements of aerosol optical depth (AOD) from two satellite instruments were used to characterize aerosol burdens throughout 2020 in four Northern Hemisphere source regions (Eastern & Central China, the United States, India, and Europe). In most regions, record-low measures of AOD persisted beyond the earliest ‘lockdown’ periods of the pandemic. Record-low values were most concentrated during the boreal spring and summer months, when 56% to 72% of sampled months showed record-low AOD values for their respective regions. However, in India and Eastern & Central China, the COVID-19 AOD signature was eclipsed by sources of natural variability (dust) and a multi-year trend, respectively. In the United States and Europe, a likely COVID-19 signal peaks in the summer of 2020, contributing as much as −.01 to −.03 AOD units to observed anomalies.

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Uncertainty in Aqua-MODIS Aerosol Retrieval Algorithms During COVID-19 Lockdown

Cited by 10 publications

References 32 publications

Air Quality over China

Air Quality over China

An Evaluation of Two Decades of Aerosol Optical Depth Retrievals from MODIS over Australia

Beyond the lockdowns: satellite observations of aerosol optical depth through 2020, the first year of the COVID-19 pandemic

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