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

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

Year
2021
Citations
13

Abstract

This letter reports uncertainties in the Aqua-Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 dark target (DT), deep blue (DB), and multiangle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD) during the COVID-19 lockdown period (February–May 2020) compared to the pre-COVID-19 period (February–May 2019). Validation of AOD retrievals was conducted against AErosol RObotic NETwork (AERONET) Version 3 Level 1.5 AOD data obtained from three sites located in urban (Beijing_CAMS and Beijing_RADI) and suburban (XiangHe) areas of China. The results show the poor performance of the DT and DB algorithms compared to the MAIAC algorithm, which performed better during the lockdown period. Overall, all MODIS algorithms overestimated the AOD and showed higher positive bias under high aerosol loading conditions during lockdown than during prelockdown. This is mainly attributed to the overestimation of the aerosol single-scattering albedo (SSA), which was found higher during lockdown than during the same period in 2019.

Keywords

AERONETAerosolBeijingModerate-resolution imaging spectroradiometerEnvironmental scienceAlbedo (alchemy)Remote sensingAtmospheric correctionMeteorologyCoronavirus disease 2019 (COVID-19)

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