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 |
|---|---|---|---|
| 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 | |
| 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) | |
| Regional Shift in the Peak Time of Maximum Indian Summer Monsoon | Subrahmanyam, Kandula V., Kumar, Prashant, Nelli, Narendra Reddy, Anoop, Sampelli, Ramana, M. V., Rajeevan, M., Chauhan, Prakash | Aerosol Optical Depth/Thickness | |
| Spatiotemporal prediction of soil organic carbon density in Europe (20002022) using earth observation and machine learning | Tian, Xuemeng, de Bruin, Sytze, Simoes, Rolf, Isik, Mustafa Serkan, Minarik, Robert, Ho, Yu-Feng, Sahin, Murat, Herold, Martin, Consoli, Davide, Hengl, Tomislav | Aerosol Optical Depth/Thickness | |
| Recent Widespread Deceleration of Global Surface Urban Heat Islands | Zhan, Wenfeng, Li, Long, Chakraborty, T. C., Hu, Leiqiu, Wang, Dazhong, Liao, Weilin, Wang, Shasha, Du, Huilin, Huang, Fan, Wang, Chunli, Liu, Zihan, Li, Manchun | Albedo, Anisotropy, Land Use/Land Cover Classification, Land Surface Temperature, Emissivity, Aerosol Optical Depth/Thickness, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) | |
| Radiative forcing due to shifting southern African fire regimes | Eames, Tom, Schutgens, Nick, Ioannidis, Eleftherios, van der Velde, Ivar R., van Gerrevink, Max J., Vernooij, Roland, van der Werf, Guido R. | Fire Ecology, Biomass Burning, Wildfires, Fire Occurrence, Burned Area, Aerosol Optical Depth/Thickness | |
| Quantifying the cooling benefits driven by global urban tree cover restoration | Liu, Feng, Wang, Lunche, Cao, Qian, Gao, Jun, Zhang, Zixin, Sun, Jia | Aerosol Optical Depth/Thickness, Canopy Characteristics, Evergreen Vegetation, Crown, Deciduous Vegetation, Leaf Characteristics, Vegetation Cover, Land Use/Land Cover Classification, Land Surface Temperature, Emissivity | |
| 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 | |
| The Downscaling Prediction Algorithm of Traffic Source Carbon Emissions Based on Multi-Source Remote Sensing Data and Deep Learning | Zheng, Liang, Li, Shenshen, Hu, Xuefei, Cai, Kun, Liu, Yang | Aerosol Optical Depth/Thickness | |
| Suitability analysis for an agricultural complex development in Huangma township, Nanchang County, Jiangxi province, China | Cai, Ling, Mohd Yusoff, Zaharah | Aerosol Optical Depth/Thickness | |
| An analysis of roadside particulate matter pollution and population | Wu, Y, Lee, H F, Deng, R R, Yim, S H L | Aerosol Optical Depth/Thickness | |
| A Novel Flexible Geographically Weighted Neural Network for | Wang, Dongchao, Cao, Jianfei, Zhang, Baolei, Zhang, Ye, Xie, Lei | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Aerosol Optical Depth/Thickness | |
| A Multivariate Geostatistical Framework to Assess the Spatio-Temporal Dynamics of Air Pollution and Land Surface Temperature in Bangladesh | Rahaman, Sk Nafiz, Nelson, Jake, Ali, Al Artat Bin, Shermin, Nishat, Pricope, Narcisa G., Al Kafy, Abdulla, Sabuj, Md Shahaduzzaman, Toa, Sharmin Sultana | Aerosol Optical Depth/Thickness, Land Surface Temperature, Emissivity | |
| Annual 30-m maps of global grassland class and extent (20002022) based on spatiotemporal Machine Learning | Parente, Leandro, Sloat, Lindsey, Mesquita, Vinicius, Consoli, Davide, Stanimirova, Radost, Hengl, Tomislav, Bonannella, Carmelo, Teles, Nathalia, Wheeler, Ichsani, Hunter, Maria, Ehrmann, Steffen, Ferreira, Laerte, Mattos, Ana Paula, Oliveira, Bernard, Meyer, Carsten, Sahin, Murat, Witjes, Martijn, Fritz, Steffen, Malek, Ziga, Stolle, Fred | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Aerosol Optical Depth/Thickness, Land Surface Temperature, Emissivity | |
| Assessment of smoke plume height products derived from multisource satellite observations using lidar-derived height metrics for wildfires in the western US | Huang, Jingting, Loria-Salazar, S. Marcela, Deng, Min, Lee, Jaehwa, Holmes, Heather A. | Aerosol Optical Depth/Thickness, Angstrom Exponent | |
| Wavelet local multiple correlation analysis of long-term AOD, LST, and NDVI time-series over different climatic zones of India | Kadaverugu, Rakesh, Nandeshwar, Sukeshini, Biniwale, Rajesh | Reflectance, Aerosol Optical Depth/Thickness, Land Surface Temperature, Emissivity | |
| Is the smoke aloft? Caveats regarding the use of the Hazard Mapping System (HMS) smoke product as a proxy for surface smoke presence across the United States | Liu, Tianjia, Panday, Frances Marie, Caine, Miah C., Kelp, Makoto, Pendergrass, Drew C., Mickley, Loretta J., Ellicott, Evan A., Marlier, Miriam E., Ahmadov, Ravan, James, Eric P. | Aerosol Optical Depth/Thickness | |
| High-Resolution Daily PM2.5 Exposure Concentrations in South Korea Using | Kang, Jin-Goo, Lee, Ju-Yong, Lee, Jeong-Beom, Lim, Jun-Hyun, Yun, Hui-Young, Choi, Dae-Ryun | Aerosol Optical Depth/Thickness | |
| Heatwave vulnerability of large metropolitans in Bangladesh: An evaluation | Adnan, Mohammed Sarfaraz Gani, Kabir, Irfat, Hossain, Md Alamgir, Chakma, Salit, Tasneem, Syeda Nazifa, Saha, Champa Rani, Hassan, Quazi K., Dewan, Ashraf | Aerosol Optical Depth/Thickness | |
| High resolution mapping of nitrogen dioxide and particulate matter in Great Britain (20032021) with multi-stage data reconstruction and ensemble machine learning methods | de la Cruz Libardi, Arturo, Masselot, Pierre, Schneider, Rochelle, Nightingale, Emily, Milojevic, Ai, Vanoli, Jacopo, Mistry, Malcolm N., Gasparrini, Antonio | Nitrogen Dioxide, Aerosol Optical Depth/Thickness, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) | |
| Impact of wildfires on regional ozone and PM2. 5: Considering the light absorption of Brown carbon | Choi, Minsu, Zhang, Jie, Zhang, Yuwei, Fan, Jiwen, Li, Xinghua, Ying, Qi | Aerosol Optical Depth/Thickness, Land Surface Temperature, Fire Occurrence, Surface Thermal Properties, THERMAL ANOMALIES, Carbonaceous Aerosols, Nitrogen Oxides, Particulates, Hydrogen Cyanide, Emissions, Non-methane Hydrocarbons/Volatile Organic Compounds, Particulate Matter, Nitrogen Oxides, Sulfur Dioxide, Carbon And Hydrocarbon Compounds | |
| Improving assessment of population exposure and health impacts to | Zhao, Xia, Zhou, Yuyu, Li, Xi, Zhang, Tao, Wang, Yueying, Zhu, Zhengyuan, Zhang, Kai, Li, Deren | Aerosol Optical Depth/Thickness |