Remote sensing retrieval of aerosol types in China using geostationary satellite

Chen, Xingfeng; Ding, Haonan; Li, Jiaguo; Wang, Lili; Li, Lei; Xi, Meng; Zhao, Limin; Shi, Zhicheng; Liu, Ziyan

doi:10.1016/j.atmosres.2023.107150

Cited by 3 publications

(1 citation statement)

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“…Zheng, Che, Xia, Wang, Yang, Chen, Wang, Zhao, Li, Zhang, Gui, Yang, Liang and Zhang [15] identified clean continent aerosols with AOD < 0.25 based on MODIS data. Chen, et al [21] used an AOD threshold value of 0.2 to identify clean aerosols over China based on Himawari-8 observations, and the clean aerosol is identical to the background aerosol type. Whether for ground or satellite aerosoltype classification, the background AOD threshold value varies substantially from 0.1 to 0.3 and has not yet been properly defined.…”

Section: Introductionmentioning

confidence: 99%

Satellite-Based Background Aerosol Optical Depth Determination via Global Statistical Analysis of Multiple Lognormal Distribution

Chen,

Huang,

Dong

et al. 2024

Remote Sensing

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Determining background aerosol optical depth threshold value (BAOD) is critical to aerosol type identification and air pollution control. This study presents a statistical method to select the best BAOD threshold value using the VIIRS DB AOD products at 1 × 1 degree resolution from 2012 to 2019 as a major testbed. A series of multiple lognormal distributions with 1 to 5 peaks are firstly applied to fit the AOD histogram at each grid point, and the distribution with the highest correlation coefficient (R) gives preliminary estimations of BAOD, which is defined as either the intersection point of the first two normal distribution curves when having multiple peaks, or the midpoint between the peak AOD and the first AOD with non-zero probability when the mono peak is the best fit. Then, the lowest 1st to 100th percentile AOD distributions are compared with the preliminary BAOD distribution on a global scale. The final BAOD is obtained from the best cutoff percentile AOD distributions with the lowest bias compared with preliminary BAOD. Results show that the lowest 30th percentile AOD is the best estimation of BAOD for different AOD datasets and different seasons. Analysis of aerosol chemical information from MERRA-2 further supports this selection. Based on the BAOD, we updated the VIIRS aerosol type classification scheme, and the results show that the updated scheme is able to achieve reliable detection of aerosol type change in low aerosol loading conditions.

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Section: Introductionmentioning

confidence: 99%