%0 Dataset
%T Daily full-coverage, 1-km MAIAC Aerosol Optical Depth (AOD) data in China (2013-2022) - Daily data
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/82bab287-bbbe-4f73-bca4-228253f30e2a
%W NCDC
%R 10.12072/ncdc.atmosphere.db7451.2026
%A he Qing Qing
%K AOD;aerosol optical thickness;gap free
%X Investigating spatiotemporal variations of atmospheric aerosols is important for climate change and environmental research. Although satellite aerosol optical depth (AOD) retrieved by the MAIAC (Multiangle Implementation 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 accuracies at a daily timescale (i.e., overall validation R2 against ground-level AOD measurements of 0.77). Here we share the daily full-coverage AOD data, which combined the random forest estimates over the areas without MAIAC AOD retrievals and MAIAC original AOD retrievals wherever available. This daily dataset is archived in CSV format and each zip file contains one-month data. 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.Please cite the relevant references listed below when using this dataset. For data from 2012 and earlier, please contact the authors via email at qqhe@whut.edu.cn.