Full-coverage daily 1-km MAIAC Aerosol Optical Depth (AOD) data, 2003-2019

Satellite-derived aerosol optical depth (AOD) provides an effective way to investigate global and regional variations in atmospheric aerosols. However, due to cloud cover and surface reflectance, AOD datasets derived from satellite instruments generally have non-random missing values, which introduces additional uncertainty into AOD data and limits its downstream. To remedy this problem, this study used a two-stage approach based on spatial interpolation and a random forest model to fill the gaps in data generated by the Multiangle Implementation of Atmospheric Correction (MAIAC) aerosol retrieval algorithm, which provides the best-available AOD product to the global public. The relationship between ground-level fine particulate matter concentrations and satellite AOD was considered in the modeling. Using Taiwan island as an example, the two-stage model achieved comparable accuracy (coefficient of determination = 0.52, root-mean-square error = 0.22) against ground-level AERONET AOD measurements to the accuracy that has been achieved by previous studies. Furthermore, it improved daily high-spatial-resolution AOD estimates to 100% of spatial coverage. This study has been published on APR (https://doi.org/10.1016/j.apr.2022.101579).

卫星反演气溶胶光学厚度 (Aerosol Optical Depth, AOD) 是探究大气气溶胶全球与区域变化的有效手段。然而，受云覆盖与地表反射率影响，卫星仪器反演得到的AOD数据集普遍存在非随机缺失值，这会为AOD数据引入额外不确定性，并限制其下游应用。为解决该问题，本研究采用基于空间插值与随机森林模型的两阶段方法，对多角度大气校正（Multiangle Implementation of Atmospheric Correction, MAIAC）气溶胶反演算法生成的数据集进行缺失值补全——该算法可为全球用户提供当前最优的AOD产品。建模过程中纳入了地面细颗粒物浓度与卫星AOD之间的关联关系。以中国台湾岛为研究案例，该两阶段模型在地面气溶胶机器人观测网络（Aerosol Robotic Network, AERONET）AOD观测值上的验证精度（决定系数=0.52，均方根误差=0.22）与既往研究的最优精度相当。此外，该方法将每日高空间分辨率AOD估算结果的空间覆盖率提升至100%。本研究已发表于《大气污染研究》（Atmospheric Pollution Research, APR，https://doi.org/10.1016/j.apr.2022.101579）。