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
Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Transported dust modulates aerosol pollution domes over rapidly urbanizing Indian cities | Sethi, Soumya Satyakanta, Vinoj, V. | 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) | |
| Understanding air pollution dynamics of Antalya Manavgat forest fires: a WRF-Chem analysis | Kara, Yigitalp, Yavuz, Veli, Toros, Huseyin | Aerosol Optical Depth/Thickness, Fire Dynamics, Surface Radiative Properties, Land Surface Temperature | |
| The Waters That Do Not Reach the River: Stable Precipitation, Rising Evapotranspiration, and the Flow Decline in BrahmaniBaitarani River Basin, Eastern India | Mohanty, Dibya Jyoti, Rout, Jajnaseni | Evapotranspiration, Photosynthesis, Primary Production, Latent Heat Flux, Aerosol Optical Depth/Thickness, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Vegetation Productivity | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| Exploring environmental and meteorological factors influencing | Ali, Md. Tushar, Bari, Quazi Hamidul, Islam, Abu Reza Md. Towfiqul | 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, Terrain Elevation | |
| 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) | |
| 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 | |
| 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 | |
| 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 | |
| XIS-PM2.5: A daily spatiotemporal machine-learning model for PM2.5 in | Just, Allan C., Arfer, Kodi B., Rush, Johnathan, Lyapustin, Alexei, Kloog, Itai | 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), Population Size, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) | |
| Geo-OLM: Enabling Sustainable Earth Observation Studies with | Stamoulis, Dimitrios, Marculescu, Diana | Reflectance, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Aerosol Optical Depth/Thickness, Land Surface Temperature, Emissivity | |
| Hindcasting fine particulate matter in Alaska, US, during wildfire seasons | Bredder, Allison, Loboda, Tatiana V., Chen, Dong | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Aerosol Optical Depth/Thickness, Maximum/Minimum Temperature, 24 Hour Precipitation Amount, Snow Water Equivalent, Vapor Pressure, Shortwave Radiation | |
| MULTI-AGENT GEOSPATIAL COPILOTS FOR REMOTE SENSING WORKFLOWS | Lee, Chaehong, Paramanayakam, Varatheepan, Karatzas, Andreas, Jian, Yanan, Fore, Michael, Liao, Heming, Yu, Fuxun, Li, Ruopu, Anagnostopoulos, Iraklis, Stamoulis, Dimitrios | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Aerosol Optical Depth/Thickness, Land Surface Temperature, Emissivity, Reflectance | |
| Machine learning based urban sprawl assessment using integrated | Sakti, Anjar Dimara, Deliar, Albertus, Hafidzah, Dyah Rezqy, Chintia, Adria Viola, Anggraini, Tania Septi, Ihsan, Kalingga Titon Nur, Virtriana, Riantini, Suwardhi, Deni, Harto, Agung Budi, Nurmaulia, Sella Lestari, Aritenang, Adiwan Fahlan, Riqqi, Akhmad, Hernandi, Andri, Soeksmantono, Budhy, Wikantika, Ketut | Aerosol Optical Depth/Thickness, Land Surface Temperature, Emissivity | |
| Land potential assessment and trend-analysis using 20002021 FAPAR monthly time-series at 250 m spatial resolution | Hacklander, Julia, Parente, Leandro, Ho, Yu-Feng, Hengl, Tomislav, Simoes, Rolf, Consoli, Davide, Sahin, Murat, Tian, Xuemeng, Jung, Martin, Herold, Martin, Duveiller, Gregory, Weynants, Melanie, Wheeler, Ichsani | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Aerosol Optical Depth/Thickness, Land Surface Temperature, Emissivity |