Full-coverage daily 1-km MAIAC Aerosol Optical Depth (AOD) data in China, 2000-2020

Aerosols significantly affect climate change, and ambient aerosols are related to various adverse health outcomes. Satellite aerosol optical depth (AOD) retrieved by the MAIAC (Multiangle Implementation of Atmospheric Correct) algorithm provides a unique opportunity to represent worldwide local-scale gradients of aerosol loading. Although the MAIAC AOD product has assisted in examining the spatiotemporal pattern of atmospheric aerosols in China, accurate assessment of long-term aerosol loading countrywide at high spatiotemporal resolution is still challenging due to its non-random missingness. We aimed to develop an adaptive spatiotemporal high-resolution imputation modeling framework for AOD that incorporates random forest models and multisource data (the simulated AOD, meteorological, and surface condition data) to support full-coverage long- and short-term aerosol studies. Aided by the time-stratified approach, the imputation model was constructed for each day, and the MAIAC AOD was used as the target variable. The proposed approach could effectively capture the massive spatiotemporal variability in a large amount of data and deliver full-coverage AODs with high accuracies at a daily timescale (i.e., overall validation R2 against ground-level AERONET AOD measurements of 0.77). We then employed the proposed approach to impute the daily MAIAC retrieved AOD towards complete coverage for China for 2003-2020. This work has been published on Atmospheric Research (https://doi.org/10.1016/j.atmosres.2022.106481) Here we upload the monthly data. If you want daily data, please contact us via email.

气溶胶对气候变化具有显著影响，环境气溶胶还与多种不良健康结局密切相关。通过MAIAC（Multiangle Implementation of Atmospheric Correct）算法反演得到的卫星气溶胶光学厚度（AOD），为表征全球范围内气溶胶负荷的局地尺度梯度提供了独特契机。尽管MAIAC AOD产品已助力我国大气气溶胶时空分布特征的相关研究，但由于其存在非随机缺失问题，在全国范围内以高时空分辨率精准评估长期气溶胶负荷仍颇具挑战。本研究旨在构建一种融合随机森林模型与多源数据（模拟AOD、气象数据及地表状况数据）的自适应高时空分辨率AOD插补建模框架，以支撑全覆盖范围的长短期气溶胶相关研究。借助时间分层方法，我们针对每日数据构建插补模型，并将MAIAC AOD作为目标变量。所提出的方法可有效捕捉海量数据中的复杂时空变异特征，并在日尺度上生成高精度的全覆盖AOD产品——其针对地面全球气溶胶自动观测网络（AERONET）的AOD观测值的整体验证决定系数（R²）可达0.77。随后我们利用该方法对2003-2020年中国区域的日尺度MAIAC反演AOD数据进行插补，实现了全区域覆盖。本研究成果已发表于《Atmospheric Research》（大气研究，https://doi.org/10.1016/j.atmosres.2022.106481）。本次我们上传的为月度数据，若需获取日尺度数据，请通过邮件联系我们。