N: 90 S: -90 E: 180 W: -180
Description
The MCD19A2 Version 6.1 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
- Known issues are described in Section 6 of the User Guide.
- For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.
- Users should be aware that they may see a dip in AOD values for the first 5 months of 2022. A fix is planned to be implemented in Collection 7.
Version Description
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 (AYYYYDDD), the Tile Identifier which is horizontal tile and vertical tile (h22v02), the Version of the data collection (061), the Julian Date of Production (YYYYDDDHHMMSS), and the Data Format (hdf).
Documents
USER'S GUIDE
ALGORITHM THEORETICAL BASIS DOCUMENT (ATBD)
PRODUCT QUALITY ASSESSMENT
SCIENCE DATA PRODUCT VALIDATION
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| A Bayesian Multisource Fusion Model for Spatiotemporal PM2.5 | Riley, Abi I., Blangiardo, Marta, Piel, Frederic B., Beddows, Andrew, Beevers, Sean, Fuller, Gary W., Agnew, Paul, Pirani, Monica | Aerosol Optical Depth/Thickness | |
| Downscaling aerosol optical depth by fusing satellite retrieval and | Sun, Lin, Zhang, Xiangshuo, Fan, Yulong, Wang, Zhihui, Sun, Xiaohu | 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 1.0), PARTICULATE MATTER (PM 10), Geopotential Height, Altitude, Surface Temperature, Upper Air Temperature, Dew Point Temperature, Air Temperature, Cloud Top Temperature, Atmospheric Winds, Surface Winds, U/V Wind Components, Upper Level Winds, U/V Wind Components, Vertical Wind Velocity/Speed, Atmospheric Pressure, Sea Level Pressure, Cloud Top Pressure, Sea Level Pressure, Surface Pressure, Specific Humidity, Total Precipitable Water, Cloud Liquid Water/Ice, Atmospheric Water Vapor, Atmospheric Ozone, Oxygen Compounds, Boundary Layer Winds, Skin Temperature, Aerosol Backscatter, Aerosol Radiance, Nitrate Particles, Optical Depth/Thickness, Radiative Flux, Reflectance | |
| Fire Spread, Intensity, and Emissions Observations by Multiple | Li, Fangjun, Zhang, Xiaoyang, Cochrane, Mark, Kondragunta, Shobha, An, Shuai | Aerosol Optical Depth/Thickness | |
| Spatiotemporal variations of PM2. 5 concentrations in the FenWei Plain of China between 2015 and 2020 based on MAIAC | Wang, Jingyi, Xu, Jiahui | Aerosol Optical Depth/Thickness | |
| Short-term PM2.5 exposure disproportionately increases pediatric ambulance dispatches among girls and children under age five in India | Kawano, Ayako, Heft-Neal, Sam, Janagama, Srinivasa Rao, Newberry, Jennifer A., Strehlow, Matthew, Bendavid, Eran | Aerosol Optical Depth/Thickness, Population | |
| A coupled machine-learning and sensitivity analysis framework to link | Hosseinipoor, Mahdi, Danesh-Yazdi, Mohammad | Land Use/Land Cover Classification, Evapotranspiration, Photosynthesis, Primary Production, Latent Heat Flux, Aerosol Optical Depth/Thickness | |
| A daily sunshine duration (SD) dataset in China from Himawari AHI imagery (20162023) | Zhang, Zhanhao, Fang, Shibo, Han, Jiahao | Aerosol Optical Depth/Thickness | |
| Conceptualizing dust emission areas and hotspots over the Aeolian landforms via remote-sensing aerosol algorithms (case study: Lake Urmia, a major hypersaline ... | Ahmady-Birgani, Hesam | Aerosol Optical Depth/Thickness | |
| Correlation Analysis of Seasonal Changes on Aerosol Concentration Using Remote Sensing in Java Island | Muhammad, Garda Asa, Amaanah, Annisa, Dewi, Vanya Chathy Kemala | Aerosol Optical Depth/Thickness | |
| An investigation of the impact of Canadian wildfires on US air quality using model, satellite, and ground measurements | Xue, Zhixin, Udaysankar, Nair, Christopher, Sundar A. | Fire Occurrence, Surface Thermal Properties, Land Surface Temperature, THERMAL ANOMALIES, Fire Ecology, Biomass Burning, Wildfires, Burned Area, 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, Optical Depth/Thickness, Radiative Flux, Reflectance | |
| Atmospheric Evolution of Brown Carbon from Wildfires in North America | Chen, Jhao-Hong, Puttu, Uma, Huynh, Han N., Ahern, Adam T., Ball, Katherine, Bates, Kelvin H., Brock, Charles A., Campos, Teresa, Coggon, Matthew M., Crounse, John D., de Gouw, Joost, DiGangi, Joshua P., Diskin, Glenn S., Gkatzelis, Georgios I., Halliday, Hannah S., Hu, Lu, Koss, Abigail R., Li, Yanshun, Lyu, Ming, Michailoudi, Georgia, Murphy, Shane M., Nowak, John B., Palm, Brett B., Peischl, Jeff, Permar, Wade, Perring, Anne E., Pokhrel, Rudra P., Schafer, Nell B., Schwarz, Joshua P., Sekimoto, Kanako, Selimovic, Vanessa, Stockwell, Chelsea E., Sullivan, Amy P., Thornton, Joel A., Wagner, Nicholas L., Wang, Siyuan, Warneke, Carsten, Wennberg, Paul O., Zeng, Linghan, Yokelson, Robert J., Weber, Rodney J., Xu, Lu | Aerosol Optical Depth/Thickness | |
| Assessment of aerosol remote sensing uncertainty in urban centers of Latin America | Urquiza, Josefina, Diez, Sebastian, Tames, Maria Florencia, Puliafito, Salvador Enrique | Aerosol Optical Depth/Thickness | |
| COVID lockdowns significantly affect statewide atmospheric fine aerosols | Etchie, Tunde O., Etchie, Ayotunde T., Pinker, Rachel T., Kumar, Prashant, Swaminathan, Nedunchezhian | Aerosol Optical Depth/Thickness | |
| East African City Centers Show Lower PM2.5 Levels than Their Suburbs | Chua, Samuel De Xun, Oguge, Otienoh, Oliewo, Celestine Atieno, Sserunjogi, Richard, Okure, Deo, Adong, Priscilla, Manyele, Asinta, Hussein, Tareq, Yang, Yuheng, Lu, Xixi, Lehtipalo, Katrianne, Zaidan, Martha Arbayani, Petaja, Tuukka | Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow, Aerosol Optical Depth/Thickness | |
| Economic impacts of capital city relocation in Myanmar | Huang, Xiaochen, Yan, Haosheng, Zhang, Zebang | Aerosol Optical Depth/Thickness | |
| ensembleDownscaleR: R Package for Bayesian Ensemble Averaging of | Madden, Wyatt G., Qi, Meng, Liu, Yang, Chang, Howard H. | Terrain Elevation, Digital Elevation/Terrain Model (DEM), Topographical Relief Maps, Aerosol Optical Depth/Thickness | |
| Evaluating the direct radiative forcing of a giant Saharan dust storm | Rizza, Umberto, Grasso, Fabio Massimo, Morichetti, Mauro, Tiesi, Alessandro, Avolio, Elenio, de Tomasi, Ferdinando, Miglietta, Mario Marcello | Aerosol Optical Depth/Thickness | |
| Exploring environmental and meteorological factors influencing | Ali, Md. Tushar, Bari, Quazi Hamidul, Islam, Abu Reza Md. Towfiqul | Aerosol Optical Depth/Thickness | |
| Impact of climate dataset variability on spatiotemporal modelling of | Calubad, Mark Joseph, Tamamadin, Mamad, Yee, Jurng-Jae | Aerosol Optical Depth/Thickness | |
| Global 30-m annual median vegetation height maps (20002022) based on ICESat-2 data and Machine Learning | Hunter, Maria O., Parente, Leandro, Ho, Yu-feng, Bonannella, Carmelo, Guimaraes Ferreira, Laerte, Morton, Douglas, Consoli, Davide, Sloat, Lindsey | Aerosol Optical Depth/Thickness, Land Surface Temperature, Emissivity | |
| Global air quality index prediction using integrated spatial observation data and geographics machine learning | Anggraini, Tania Septi, Irie, Hitoshi, Sakti, Anjar Dimara, Wikantika, Ketut | Land Use/Land Cover Classification, Aerosol Optical Depth/Thickness, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) | |
| Evolution of aerosol optical depth over China in 2010-2024: increasing | Fan, Cheng, de Leeuw, Gerrit, Yan, Xiaoxi, Dong, Jiantao, Kang, Hanqing, Fang, Chengwei, Li, Zhengqiang, Zhang, Ying | Aerosol Optical Depth/Thickness | |
| Machine learning approaches for clear-sky Land Surface Albedo (LSA) retrieval using OCM-3 data over diverse Indian landscapes | KURESHI, ALIYA M., PATHAK, VISHAL N., KARDANI, DISHA B., DAVE, JALPESH A., SHAH, DHIRAJ B., TURAKHIA, TEJAS P., GUJRATI, ASHWIN, PANDYA, MEHUL R., TRIVEDI, HIMANSHU J. | Aerosol Optical Depth/Thickness, Albedo, Anisotropy | |
| Latest 40-year afforestation efforts sustained ecological restoration in the breadbasket of the Tibetan Plateau | Zhao, Wei, Xie, Xinyao, Liu, Liyang, Shen, Miaogen, Yue, Yuemin, Tarolli, Paolo, Wang, Xiaodan, Li, Zhao-Liang | Aerosol Optical Depth/Thickness, Evapotranspiration, Latent Heat Flux | |
| Improved daily PM2.5 estimates in India reveal inequalities in recent enhancement of air quality | Kawano, Ayako, Kelp, Makoto, Qiu, Minghao, Singh, Kirat, Chaturvedi, Eeshan, Dahiya, Sunil, Azevedo, Ines, Burke, Marshall | Population Size, Land Use/Land Cover Classification, Aerosol Optical Depth/Thickness |
Variables
The table below lists the variables contained within a single granule for this dataset. Variables often contain observed or derived geophysical measurements collected from a variety of sources, including remote sensing instruments on satellite and airborne platforms, field campaigns, in situ measurements, and model outputs. The terms variable, parameter, scientific data set, layer, and band have been used across NASA’s Earth science disciplines; however, variable is the designated nomenclature in NASA’s Common Metadata Repository (CMR). Variable metadata attributes such as Name, Description, Units, Data Type, Fill Value, Valid Range, and Scale Factor allow users to efficiently process and analyze the data. The full range of attributes may not be applicable to all variables. Additional information on variable attributes is typically available in the data, user guide, and/or other product documentation.
For questions on a specific variable, please use the Earthdata Forum.
| Name Sort descending | Description | Units | Data Type | Fill Value | Valid Range | Scale Factor | Offset |
|---|---|---|---|---|---|---|---|
| AngstromExp_470- 780 | Angstrom Exponent 470-780nm over the ocean | N/A | int16 | -28672 | -5000 to 30000 | 0.0001 | N/A |
| AOD_QA | AOD QA | N/A | uint16 | 0 | 1 to 65535 | N/A | N/A |
| AOD_Uncertainty | AOD uncertainty based on blue-band surface brightness (reflectance) | N/A | int16 | -28672 | -100 to 30000 | 0.0001 | N/A |
| Column_WV | Column Water Vapor (cm) over land | cm | int16 | -28672 | 0 to 8000 | 0.001 | N/A |
| cosSZA | Cosine of Solar zenith angle (5 km) | N/A | int16 | -28672 | 0 to 10000 | 0.0001 | N/A |
| cosVZA | Cosine View zenith angle (5 km) | N/A | int16 | -28672 | 0 to 10000 | 0.0001 | N/A |
| FineModeFraction | Fine mode fraction for ocean | N/A | float32 | -99999 | 0 to 1000 | N/A | N/A |
| Glint_Angle | Glint Angle (5 km) | N/A | int16 | -28672 | -18000 to 18000 | 0.01 | N/A |
| Injection_Height | Smoke injection height (m above ground) | Meters | float32 | -99999 | 0 to 10000 | N/A | N/A |
| Optical_Depth_047 | Blue band (0.47 μm) aerosol optical depth over land | N/A | int16 | -28672 | -100 to 6000 | 0.001 | N/A |
| Optical_Depth_055 | Green band (0.55 μm) aerosol optical depth over land | N/A | int16 | -28672 | -100 to 6000 | 0.001 | N/A |
| RelAZ | Relative azimuth angle (5 km) | N/A | int16 | -28672 | -18000 to 18000 | 0.01 | N/A |
| Scattering_Angle | Scattering Angle (5 km) | N/A | int16 | -28672 | -18000 to 18000 | 0.01 | N/A |