DOI: 10.57760/sciencedb.25524
title: MODIS MAIAC AOD daily grid data for China (GridMAIACAODChina)
title: 中国区域MODIS MAIAC AOD日合成值数据（GridMAIACAODChina）

description: The GridMAIACAODChina dataset is based on the MODIS Collection 6.1 MCD19A2 product. MCD19A2 is an aerosol product retrieved using the MAIAC algorithm and observation data from two MODIS sensors onboard the Terra and Aqua satellites. It primarily includes parameters such as aerosol optical depth (AOD). MCD19A2 data is stored in HDF format and sinusoidal projection. Data extraction, numerical scaling, coordinate conversion, and conversion to the GeoTIFF format are required. We processed the MAIAC AOD data of the MCD19A2 product from 2000 to 2024 in the China region. The processing steps include: (1) extracting 550 nm AOD data from the HDF file, multiplying it by the scaling factor 0.001 to get the actual AOD value, using the quality assurance band to filter the AOD value of each pixel, retaining only the pixel with the best retrieval quality, and setting the remaining values to an invalid value of -999; (2) calculating the mean of each sin projection “tile” based on the valid value; (3) defining the coordinate of each sin projection “tile”; (4) for each day's data, mosaicking each sin projection “tile” data into one data, and outputting the data; (5) reprojecting the data to the WGS84 coordinate system, and outputting it in GeoTIFF format, using LZW lossless compression to reduce the file size, and writing out metadata at the same time. The temporal resolution of GridMAIACAODChina is 1 day, the spatial resolution is 0.01° (approximately 1 km), the spatial coverage range is 70–140 degrees east longitude and 0–55 degrees north latitude, the scaling factor is 1 (the actual AOD value), the file format is GeoTIFF, the coordinate system is WGS84, the file name is date string, and the “_info.txt” file is the metadata file. Please cite the following data and references when using the dataset:1. Su Xin. Daily composite of MODIS MAIAC AOD in China (GridMAIACAODChina)[DS/OL]. V1. Science Data Bank, 2025[2025-05-26]. https://doi.org/10.57760/sciencedb.25524. DOI:10.57760/sciencedb.25524.2. Su Xin, Cao Mengdan, Wang Lunche, Gui Xuan, Zhang Ming, Huang Yuhang, Zhao Yueji (2023). Validation, inter-comparison, and usage recommendation of six latest VIIRS and MODIS aerosol products over the ocean and land on the global and regional scales. Science of the Total Environment, 884, 163794. https://doi.org/10.1016/j.scitotenv.2023.163794.3. Huang Ge, Su Xin, Wang Lunche, Wang Yi, Cao Mengdan, Wang Lin, Ma Xiaoyu, Zhao Yueji, Yang Leiku (2024). Evaluation and analysis of long-term MODIS MAIAC aerosol products in China. Science of the Total Environment, 948, 174983. https://doi.org/10.1016/j.scitotenv.2024.174983.4. Wang Lunche, Su Xin, Wang Yi, Cao Mengdan, Lang Qin, Li Huaping, Sun Junyao, Zhang Ming, Qin Wenmin, Li Lei, Yang Leiku (2024). Towards long-term, high-accuracy, and continuous satellite total and fine-mode aerosol records: Enhanced Land General Aerosol (e-LaGA) retrieval algorithm for VIIRS. Isprs Journal of Photogrammetry and Remote Sensing, 214, 261-281. https://doi.org/10.1016/j.isprsjprs.2024.06.022.
description: The GridMAIACAODChina dataset is based on the MODIS Collection 6.1 MCD19A2 product. MCD19A2 is an aerosol product retrieved using the MAIAC algorithm and observation data from two MODIS sensors onboard the Terra and Aqua satellites. It primarily includes parameters such as aerosol optical depth (AOD). MCD19A2 data is stored in HDF format and sinusoidal projection. Data extraction, numerical scaling, coordinate conversion, and conversion to the GeoTIFF format are required. We processed the MAIAC AOD data of the MCD19A2 product from 2000 to 2024 in the China region. The processing steps include: (1) extracting 550 nm AOD data from the HDF file, multiplying it by the scaling factor 0.001 to get the actual AOD value, using the quality assurance band to filter the AOD value of each pixel, retaining only the pixel with the best retrieval quality, and setting the remaining values to an invalid value of -999; (2) calculating the mean of each sin projection “tile” based on the valid value; (3) defining the coordinate of each sin projection “tile”; (4) for each day's data, mosaicking each sin projection “tile” data into one data, and outputting the data; (5) reprojecting the data to the WGS84 coordinate system, and outputting it in GeoTIFF format, using LZW lossless compression to reduce the file size, and writing out metadata at the same time. The temporal resolution of GridMAIACAODChina is 1 day, the spatial resolution is 0.01° (approximately 1 km), the spatial coverage range is 70–140 degrees east longitude and 0–55 degrees north latitude, the scaling factor is 1 (the actual AOD value), the file format is GeoTIFF, the coordinate system is WGS84, the file name is date string, and the “_info.txt” file is the metadata file. Please cite the following data and references when using the dataset:1. Su Xin. Daily composite of MODIS MAIAC AOD in China (GridMAIACAODChina)[DS/OL]. V1. Science Data Bank, 2025[2025-05-26]. https://doi.org/10.57760/sciencedb.25524. DOI:10.57760/sciencedb.25524.2. Su Xin, Cao Mengdan, Wang Lunche, Gui Xuan, Zhang Ming, Huang Yuhang, Zhao Yueji (2023). Validation, inter-comparison, and usage recommendation of six latest VIIRS and MODIS aerosol products over the ocean and land on the global and regional scales. Science of the Total Environment, 884, 163794. https://doi.org/10.1016/j.scitotenv.2023.163794.3. Huang Ge, Su Xin, Wang Lunche, Wang Yi, Cao Mengdan, Wang Lin, Ma Xiaoyu, Zhao Yueji, Yang Leiku (2024). Evaluation and analysis of long-term MODIS MAIAC aerosol products in China. Science of the Total Environment, 948, 174983. https://doi.org/10.1016/j.scitotenv.2024.174983.4. Wang Lunche, Su Xin, Wang Yi, Cao Mengdan, Lang Qin, Li Huaping, Sun Junyao, Zhang Ming, Qin Wenmin, Li Lei, Yang Leiku (2024). Towards long-term, high-accuracy, and continuous satellite total and fine-mode aerosol records: Enhanced Land General Aerosol (e-LaGA) retrieval algorithm for VIIRS. Isprs Journal of Photogrammetry and Remote Sensing, 214, 261-281. https://doi.org/10.1016/j.isprsjprs.2024.06.022.