Spatiotemporal high-resolution imputation modeling of aerosol optical depth for investigating its full-coverage variation in China from 2003 to 2020

Investigating spatiotemporal variations of atmospheric aerosols is important for climate change and environ-mental research. Although satellite aerosol optical depth (AOD) retrieved by the MAIAC (Multiangle Imple-mentation of Atmospheric Correct) algorithm provides a unique opportunity to represent global aerosol loading with high spatiotemporal resolution, accurate assessment of long-term aerosol loading countrywide is still challenging due to its non-random missingness. This study 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 in China. 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 ac-curacies at a daily timescale (i.e., overall validation R2 against ground-level 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. Due to the complete coverage, the spatial pattern of monthly/seasonal/yearly mean AOD imputations has better representativeness than that of original MAIAC retrievals. Comparison analysis shows that the monthly/seasonal/yearly aerosol loading over most of China tends to be underestimated by temporal ag-gregates of original satellite-retrieved AODs. Such underestimation is particularly severe in summer and over the North China Plain (the amount of underestimation >0.2). Consequently, our full-coverage AOD imputations can advance scientific research and environmental management by supporting national and local complete pictures of both short-term episodes and long-term trends in atmospheric aerosols.

Automated summary

Investigating the spatiotemporal variations of atmospheric aerosols is important for climate change and environmental research. This study developed an adaptive spatiotemporal imputation model for aerosol optical depth (AOD) using the MAIAC algorithm and multisource data, aiming to support full-coverage aerosol studies in China. The proposed approach effectively captured the massive spatiotemporal variability and delivered high-accuracy full-coverage AODs. The imputed AODs showed better representativeness and revealed underestimation in the original satellite-retrieved AODs, especially during summer and over the North China Plain.