Abstract
Abstract. A new multiangle implementation of the atmospheric correction (MAIAC) algorithm has been applied in the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and has recently provided globally high-spatial-resolution aerosol optical depth (AOD) products at 1 km. Moreover, several improvements have been modified in the classical Dark Target (DT) and Deep Blue (DB) aerosol retrieval algorithms in MODIS Collection 6.1 products. Thus, validation and comparison of the MAIAC, DT, and DB algorithms are urgent in China. In this paper, we present a comprehensive assessment and comparison of AOD products at a 550 nm wavelength based on three aerosol retrieval algorithms in the MODIS sensor using ground-truth measurements from AErosol RObotic NETwork (AERONET) sites over China from 2000 to 2017. In general, MAIAC products achieved better accuracy than DT and DB products in the overall validation and accuracy improvement of DB products after the QA filter, demonstrating the highest values among the three products. In addition, the DT algorithms had higher aerosol retrievals in cropland, forest, and ocean land types than the other two products, and the MAIAC algorithms were more accurate in grassland, built-up, unoccupied, and mixed land types among the three products. In the geometry dependency analysis, the solar zenith angle, scattering angle, and relative azimuth angle, excluding the view zenith angle, significantly affected the performance of the three aerosol retrieval algorithms. The three products showed different accuracies with varying regions and seasons. Similar spatial patterns were found for the three products, but the MAIAC retrievals were smaller in the North China Plain and higher in Yunnan Province compared with the DT and DB retrievals before the QA filter. After the QA filter, the DB retrievals were significantly lower than the MAIAC retrievals in south China. Moreover, the spatiotemporal completeness of the MAIAC product was also better than the DT and DB products.
Highlights
Aerosols are a multi-compartment system consisting of suspended solid and liquid particles in the atmosphere, which play an important role in radiative forcing (Rajeev et al, 2001), regional climate (Qian and Giorgi, 1999; Feng et al, 2019), and urban air pollution (Dominici et al, 2014)
multiangle implementation of the atmospheric correction (MAIAC) products have more matchup data than Dark Target (DT) and Deep Blue (DB) products, which indicates the completeness of the MAIAC product may be higher than the DT and DB products
We present the first comprehensive validation and comparison of three Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol retrieval algorithms (i.e., MAIAC, DT, and DB) across China in terms of overall accuracy, land cover dependency, viewed geometry dependency, spatiotemporal retrieval accuracy, spatial distribution difference, and spatiotemporal completeness
Summary
Aerosols are a multi-compartment system consisting of suspended solid and liquid particles in the atmosphere, which play an important role in radiative forcing (Rajeev et al, 2001), regional climate (Qian and Giorgi, 1999; Feng et al, 2019), and urban air pollution (Dominici et al, 2014). High-quality ground measurements are often employed to validate satellite aerosol products (Chu et al, 2002) and to provide a regional aerosol model for the satellite aerosol retrieval algorithm (Levy et al, 2013). A new multiangle implementation of the atmospheric correction (MAIAC) algorithm has been applied in the MODIS sensor, which provides high-spatial-resolution aerosol data at 1 km (Lyapustin et al, 2018). An urgent demand persists for a detailed comparison of the three products to guide user selection of these products In this context, we provide the first comprehensive understanding and comparison of the aerosol retrieval uncertainties for MAIAC, DT, and DB products in China based on spatiotemporal accuracy differentiation patterns, spatiotemporal completeness, land type dependence characteristics, view geometry dependence characteristic aspects, and other features.
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