N: 90 S: -90 E: 180 W: -180
Description
The MCD19A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MCD19A2 Version 6.1 data product.
The MCD19A2 Version 6 data product is a Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua combined Multi-angle Implementation of Atmospheric Correction (MAIAC) Land Aerosol Optical Depth (AOD) gridded Level 2 product produced daily at 1 kilometer (km) pixel resolution. The MCD19A2 product provides the atmospheric properties and view geometry used to calculate the MAIAC Land Surface Bidirectional Reflectance Factor (BRF) or surface reflectance, MCD19A1 product.
The MCD19A2 AOD data product contains the following Science Dataset (SDS) layers: blue band AOD at 0.47 µm, green band AOD at 0.55 µm, AOD uncertainty, fine mode fraction over water, column water vapor over land and clouds (in cm), smoke injection height (m above ground), AOD QA, AOD model at 1km, cosine of solar zenith angle, cosine of view zenith angle, relative azimuth angle, scattering angle, and glint angle at 5km. A low-resolution browse image is also included showing AOD of the blue band at 0.47 µm created using a composite of all available orbits.
Each SDS layer within each MCD19A2 Hierarchical Data Format 4 (HDF4) file contains a third dimension that represents the number of orbit overpasses. This factor could affect the total number of bands for each SDS layer.
Known Issues
- The longname in the internal metadata is provided incorrectly. The correct longname is "MODIS/Terra and Aqua MAIAC Land Aerosol Optical Depth Daily L2G 1 km SIN Grid."
- Additional known issues are described on page 14 of the User Guide.
- For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.
Product Summary
Citation
Citation is critically important for dataset documentation and discovery. This dataset is openly shared, without restriction, in accordance with the EOSDIS Data Use and Citation Guidance.
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File Naming Convention
The file name begins with the Product Short Name (MCD19A2) followed by the Julian Date of Acquisition formatted as AYYYYDDD (A2002281), the Tile Identifier which is horizontal tile and vertical tile provided as hXXvYY (h33v11), the Version of the data collection (006), the Julian Date and Time of Production designated as YYYYDDDHHMMSS (2018017104530), and the Data Format (hdf).
Documents
USER'S GUIDE
ALGORITHM THEORETICAL BASIS DOCUMENT (ATBD)
PRODUCT QUALITY ASSESSMENT
Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Assessment of Satellite AOD during the 2020 Wildfire Season in the | Ye, Xinxin, Deshler, Mina, Lyapustin, Alexi, Wang, Yujie, Kondragunta, Shobha, Saide, Pablo | Aerosol Optical Depth/Thickness | |
| Vegetation activity enhanced in India during the COVID-19 lockdowns: evidence from satellite data | Ranjan, Avinash Kumar, Dash, Jadunandan, Xiao, Jingfeng, Gorai, Amit Kumar | Land Use/Land Cover Classification, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Aerosol Optical Depth/Thickness | |
| Introducing the VIIRS-based Fire Emission Inventory version 0 (VFEIv0) | Ferrada, Gonzalo A., Zhou, Meng, Wang, Jun, Lyapustin, Alexei, Wang, Yujie, Freitas, Saulo R., Carmichael, Gregory R. | Aerosol Optical Depth/Thickness, Land Use/Land Cover Classification | |
| Heterogeneous air pollution controls its correlation to urban heat islandA satellite perspective | Ding, Ying, Feng, Huihui, Zou, Bin, Nie, Yunfeng | Aerosol Optical Depth/Thickness, Land Surface Temperature, Emissivity, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) | |
| High spatiotemporal resolution PM2.5 concentration estimation with machine learning algorithmA case study for wildfire in California | Cui, Qian, Zhang, Feng, Fu, Shaoyun, Wei, Xiaoli, Ma, Yue, Wu, Kun | Aerosol Optical Depth/Thickness | |
| Gap-filling MODIS daily aerosol optical depth products by developing a spatiotemporal fitting algorithm | Zhang, Tao, Zhou, Yuyu, Zhao, Kaiguang, Zhu, Zhengyuan, Asrar, Ghassem R., Zhao, Xia | Aerosol Optical Depth/Thickness | |
| Impact of lockdown and crop stubble burning on air quality of India: a case study from wheat-growing region | Mohite, Jayantrao, Sawant, Suryakant, Pandit, Ankur, Pappula, Srinivasu | Aerosol Optical Depth/Thickness | |
| Hospitalization Due to Fire-Induced Pollution in the Brazilian Legal | Campanharo, Wesley Augusto, Morello, Thiago, Christofoletti, Maria A. M., Anderson, Liana O. | Fire Ecology, Biomass Burning, Wildfires, Fire Occurrence, Burned Area, Aerosol Optical Depth/Thickness, Land Surface Temperature, Emissivity | |
| Hourly mapping of surface air temperature by blending geostationary datasets from the two-satellite system of GOES-R series | Zhang, Zhenwei, Du, Qingyun | Aerosol Optical Depth/Thickness, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) | |
| Development of China's first space-borne aerosol-cloud high-spectral-resolution lidar: retrieval algorithm and airborne demonstration | Ke, Ju, Sun, Yingshan, Dong, Changzhe, Zhang, Xingying, Wang, Zijun, Lyu, Liqing, Zhu, Wei, Ansmann, Albert, Su, Lin, Bu, Lingbing, Xiao, Da, Wang, Shuaibo, Chen, Sijie, Liu, Jiqiao, Chen, Weibiao, Liu, Dong | Land Use/Land Cover Classification, Aerosol Optical Depth/Thickness, Infrared Radiance, REFLECTED INFRARED, Visible Radiance | |
| Development of aerosol optical properties for improving the MESSy photolysis module in the GEM-MACH v2. 4 air quality model and application for ... | Majdzadeh, Mahtab, Stroud, Craig A., Sioris, Christopher, Makar, Paul A., Akingunola, Ayodeji, McLinden, Chris, Zhao, Xiaoyi, Moran, Michael D., Abboud, Ihab, Chen, Jack | Aerosols, Aerosol Extinction, Aerosol Optical Depth/Thickness, Angstrom Exponent, Aerosol Particle Properties, Carbonaceous Aerosols, Dust/Ash/Smoke, Organic Particles, Sulfate Particles, Sulfur Oxides, Sulfur Compounds, Sulfate, Sulfur Dioxide, Sulfur Oxides, Particulate Matter, Dimethyl Sulfide, Black Carbon, Sea Salt, PARTICULATE MATTER (PM 2.5), PARTICULATE MATTER (PM 10), PARTICULATE MATTER (PM 1.0) | |
| Estimating high-resolution PM2.5 concentrations by fusing satellite AOD and smartphone photographs using a convolutional neural network and ensemble learning | Wang, Fei, Yao, Shiqi, Luo, Haowen, Huang, Bo | RADAR IMAGERY, Terrain Elevation, Topographical Relief Maps, Digital Elevation/Terrain Model (DEM), Aerosol Optical Depth/Thickness, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) | |
| Evaluation of trophic state for inland waters through combining | Liu, Yongxin, Wu, Huan, Wang, Shenglei, Chen, Xiuwan, Kimball, John S., Zhang, Chenlu, Gao, Han, Guo, Peng | Aerosol Optical Depth/Thickness | |
| Dust radiative effect characteristics during a typical springtime dust storm with persistent floating dust in the Tarim Basin, northwest China | Meng, Lu, Zhao, Tianliang, He, Qing, Yang, Xinghua, Mamtimin, Ali, Wang, Minzhong, Pan, Honglin, Huo, Wen, Yang, Fan, Zhou, Chenglong | Aerosol Optical Depth/Thickness | |
| Estimation of Suspended Sediment Concentration in the Yangtze Main | Zhang, Chenlu, Liu, Yongxin, Chen, Xiuwan, Gao, Yu | Aerosol Optical Depth/Thickness | |
| Estimation of aerosol optical depth at 30 m resolution using Landsat imagery and machine learning | Liang, Tianchen, Liang, Shunlin, Zou, Linqing, Sun, Lin, Li, Bing, Lin, Hao, He, Tao, Tian, Feng | Aerosol Optical Depth/Thickness, Terrain Elevation, RADAR IMAGERY, Topographical Relief Maps | |
| Potential assessment of photovoltaic power generation in China | Qiu, Tianzhi, Wang, Lunche, Lu, Yunbo, Zhang, Ming, Qin, Wenmin, Wang, Shaoqiang, Wang, Lizhe | Aerosol Optical Depth/Thickness | |
| Multi-Criteria Assessment for City-Wide Rooftop Solar PV Deployment: A Case Study of Bandung, Indonesia | Sakti, Anjar Dimara, Ihsan, Kalingga Titon Nur, Anggraini, Tania Septi, Shabrina, Zahratu, Sasongko, Nugroho Adi, Fachrizal, Reza, Aziz, Muhammad, Aryal, Jagannath, Yuliarto, Brian, Hadi, Pradita Octoviandiningrum, Wikantika, Ketut | Aerosol Optical Depth/Thickness, Land Surface Temperature, Emissivity | |
| Machine learning driven by environmental covariates to estimate high-resolution PM2.5 in data-poor regions | Jin, XiaoYe, Ding, Jianli, Ge, Xiangyu, Liu, Jie, Xie, Boqiang, Zhao, Shuang, Zhao, Qiaozhen | Land Surface Temperature, Emissivity, Reflectance, Aerosol Optical Depth/Thickness | |
| LGHAPThe Long-term Gap-free High-resolution Air Pollutant concentration dataset, derived via tensor-flow-based multimodal data fusion | Bai, Kaixu, Li, Ke, Ma, Mingliang, Li, Kaitao, Li, Zhengqiang, Guo, Jianping, Chang, Ni-Bin, Tan, Zhuo, Han, Di | Aerosol Optical Depth/Thickness, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) | |
| Characterizing Aerosols during Forest Fires over Uttarakhand Region in India using Multi-Satellite Remote Sensing Data | Verma, Sunita, Soni, Manish, Kumar, Harshbardhan, Payra, Swagata, Mishra, Manoj K, Bhawar, Rohini | Aerosol Optical Depth/Thickness, Land Surface Temperature, Fire Occurrence, Surface Thermal Properties, THERMAL ANOMALIES | |
| Cleaner air would enhance India's annual solar energy production by 6-28 | Ghosh, Sushovan, Dey, Sagnik, Ganguly, Dilip, Baidya Roy, Somnath, Bali, Kunal | Aerosol Optical Depth/Thickness | |
| Climatological characteristics and aerosol loading trends from 2001 to 2020 based on MODIS MAIAC data for Tianjin, North China Plain | Wu, Zhenling, Zhao, Hujia, Hao, Jian, Wu, Guoliang | Aerosol Optical Depth/Thickness | |
| Wildfire-induced pollution and its short-term impact on COVID-19 cases and mortality in California | Naqvi, Hasan Raja, Mutreja, Guneet, Shakeel, Adnan, Singh, Karan, Abbas, Kumail, Naqvi, Darakhsha Fatma, Chaudhary, Anis Ahmad, Siddiqui, Masood Ahsan, Gautam, Alok Sagar, Gautam, Sneha, Naqvi, Afsar Raza | Aerosol Optical Depth/Thickness | |
| SmokeDriven Changes in Photosynthetically Active Radiation During the US Agricultural Growing Season | Corwin, Kimberley A., Corr, Chelsea A., Burkhardt, Jesse, Fischer, Emily V. | Aerosol Optical Depth/Thickness, Aerosol Backscatter, Aerosol Extinction, Angstrom Exponent, Aerosol Particle Properties, Aerosol Radiance, Carbonaceous Aerosols, Cloud Condensation Nuclei, Dust/Ash/Smoke, Nitrate Particles, Organic Particles, Particulate Matter, Sulfate Particles, Trace Gases/Trace Species, Atmospheric Emitted Radiation, Emissivity, Optical Depth/Thickness, Radiative Flux, Reflectance, Transmittance, Atmospheric Stability, Humidity, Total Precipitable Water, Water Vapor Profiles, Cloud Condensation Nuclei, Cloud Droplet Concentration/Size, Cloud Liquid Water/Ice, Cloud Optical Depth/Thickness, Cloud Asymmetry, Cloud Ceiling, Cloud Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Cloud Vertical Distribution, Cloud Emissivity, Cloud Radiative Forcing, Cloud Reflectance, Rain Storms, Atmospheric Ozone |