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.