We share the complete aerosol optical depth dataset with high spatial (1x1km2) and temporal (daily) resolution and the Beijing 1954 projection (https://epsg.io/2412) for mainland China (2015-2018).This database contains four datasets: - Daily complete high-resolution AOD image dataset for mainland China from January 1, 2015 to December 31, 2018. The archived resources contain 1461 images stored in 1461 files, and 3 summary Excel files. The table “CHN_AOD_INFO.xlsx” describing the properties of the 1461 images, including projection, training R2 and RMSE, testing R2 and RMSE, minmum, mean, median and maximum AOD that we predicted. - The table “Model_and_Accuracy_of_Meteorological_Elements.xlsx” describing the statistics of performance metrics in interpolation of high-resolution meteorological dataset. - The table “Evaluation_Using_AERONET_AOD.xlsx” showing the evaluation result of AERONET, including R2, RMSE, and monitoring information used in this study.
The original aerosol optical depth images are from Multi-Angle Implementation of Atmospheric Correction Aerosol Optical Depth (MAIAC AOD) (https://lpdaac.usgs.gov/products/mcd19a2v006/) with the similar spatiotemporal resolution and the sinusoidal projection (https://en.wikipedia.org/wiki/Sinusoidal_projection).
After projection conversion, eighteen tiles of MAIAC AOD were merged to obtain a large image of AOD covering the entire area of mainland China. Due to the conditions of clouds and high surface reflectance, each original MAIAC AOD image usually has many missing values, and the average missing percentage of each AOD image may exceed 60%. Such a high percentage of missing values severely limits applicability of the original MAIAC AOD dataset product. We used the sophisticated method of full residual deep networks to impute the daily missing MAIAC AOD, thus obtaining the complete (no missing values) high-resolution AOD data product covering mainland China. The covariates used in imputation included coordinates, elevation, MERRA2 coarse-resolution PBLH and AOD variables, cloud fraction, high-resolution meteorological variables (air pressure, air temperature, relative humidity and wind speed) and/or time index etc. Ground monitoring data were used to generate high-resolution meteorological variables to ensure the reliability of interpolation. Overall, our daily imputation models achieved an average training R2 of 0.90 with a range of 0.75 to 0.97 (average RMSE: 0.075, with a range of 0.026 to 0.32) and an average test R2 of 0.90 with a range of 0.75 to 0.97 (average RMSE: 0.075 with a range of 0.026 to 0.32). With almost no difference between training metrics and test metrics, the high test R2 and low test RMSE show the reliability of AOD imputation. In the evaluation using the ground AOD data from the monitoring stations of the Aerosol Robot Network (AERONET) in mainland China, our method obtained a R2 of 0.78 and RMSE of 0.27, which further illustrated the reliability of the method.
Overall, our daily estimation model has an average training R ^ 2 of 0.90, ranging from 0.75 to 0.97 (average RMSE: 0.075, ranging from 0.026 to 0.32), and an average testing R ^ 2 of 0.90, ranging from 0.75 to 0.97 (average RMSE: 0.075, ranging from 0.026 to 0.32). There is almost no difference between the training and testing metrics, and the high test R ^ 2 and low test RMSE demonstrate the reliability of AOD estimation. In the evaluation using the ground AOD data from AERONET monitoring stations in Chinese Mainland, our method obtained 0.78 R ^ 2 and 0.27 RMSE, which further demonstrated the reliability of the method.
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CSTR:11738.11.NCDC.DVN.DB6522.2024
10.7910/DVN/RNSWRH