N: 90 S: -90 E: 180 W: -180
Description
The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) Version 6.1 data product provides global land cover types at yearly intervals. The MCD12Q1 Version 6.1 data product is derived using supervised classifications of MODIS Terra and Aqua reflectance data. Land cover types are derived from the International Geosphere-Biosphere Programme (IGBP), University of Maryland (UMD), Leaf Area Index (LAI), BIOME-Biogeochemical Cycles (BGC), and Plant Functional Types (PFT) classification schemes. The supervised classifications then undergo additional post-processing that incorporate prior knowledge and ancillary information to further refine specific classes. Additional land cover property assessment layers are provided by the Food and Agriculture Organization (FAO) Land Cover Classification System (LCCS) for land cover, land use, and surface hydrology.
Layers for Land Cover Type 1-5, Land Cover Property 1-3, Land Cover Property Assessment 1-3, Land Cover Quality Control (QC), and a Land Water Mask are provided in each MCD12Q1 Version 6.1 Hierarchical Data Format 4 (HDF4) file.
Known Issues
- The "units" field is missing in the metadata, however, this information can be found in Table 1 of the User Guide.
- The MCD12Q1.061 land cover data product is derived using supervised classification of MODIS Terra and Aqua reflectance data. The classification algorithm uses labeled training samples selected globally to represent land cover categories derived under the IGBP, UMD, LAI, BIOME-BGC, and PFT classification schemes. These training samples were selected to represent land classes, best suited over a certain period. However, due to lack of funding, the science team was not able to keep the training database updated over the course of the years, and some of these training sites could well have undergone changes in their land cover characteristics, especially after 2021. Hence users are urged to maintain caution while using the V6.1/MCD12Q1 land cover layers for 2021 and beyond. More information on this known issue can be found in the Land Data Products Operational Products (LDOPE) Quality Assessment.
- Known issues are described in Section 2.2 of the User Guide.
- For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.
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.
Copy Citation
File Naming Convention
The file name begins with the Product Short Name (MCD12Q1) followed by the Julian Date of Acquisition formatted as AYYYYDDD (A2024001), the Tile Identifier which is horizontal tile and vertical tile provided as hXXvYY (h21v07), the Version of the data collection (061), the Julian Date and Time of Production designated as YYYYDDDHHMMSS (2025205222320), and the Data Format (hdf).
Documents
USER'S GUIDE
ALGORITHM THEORETICAL BASIS DOCUMENT (ATBD)
PRODUCT QUALITY ASSESSMENT
SCIENCE DATA PRODUCT VALIDATION
Dataset Resources
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| A hybrid machine learning model for flood prediction with recursive feature elimination informed by training performance | Gong, Liying, Woo, Wai Lok, Wu, Yue Ivan, Zheng, Xiujuan | RADAR IMAGERY, Terrain Elevation, Topographical Relief Maps, Digital Elevation/Terrain Model (DEM), Land Use/Land Cover Classification, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| A localized plant species-specific BVOC emission rate library of China established using a developed statistical approach based on field measurements | Han, Huijuan, Jia, Yanqi, Shi, Rende, Nie, Changliang, Kajii, Yoshizumi, Wu, Yan, Li, Lingyu | Land Use/Land Cover Classification | |
| A high performance assimilation of surface soil moisture based on a | Zhu, Shuang, Zha, Gang, Wang, Qi, Ma, Siyu, Qin, Hui | Land Use/Land Cover Classification | |
| Advancing ecohydrological modelling: coupling LPJ-GUESS with ParFlow for integrated vegetation and surface-subsurface hydrology simulations | Jia, Zitong, Chen, Shouzhi, Fu, Yongshuo H., Martin Belda, David, Warlind, David, Olin, Stefan, Xu, Chongyu, Tang, Jing | Plant Phenology, Enhanced Vegetation Index (EVI), Land Use/Land Cover Classification | |
| Advancing point-to-grid scale ET mapping: comparative assessment of ECOSTRESS and MODIS LST and ET across heterogeneous landscapes | Park, Kijin, Baik, Jongjin, Kim, Kiyoung, Park, Jongmin | Evapotranspiration, Latent Heat Flux, Geolocation, Land Use/Land Cover Classification, Land Surface Temperature, Emissivity, Clouds | |
| Active Fire Dynamics in Venezuela | Correia Filho, Washington Luiz Felix, Freire, Felipe Machado, de Oliveira-Junior, Jose Francisco, Santiago, Dimas de Barros, Paredes-Trejo, Franklin, Pereira, Carlos Rodrigues, Abdo, Hazem Ghassan | Land Use/Land Cover Classification, Land Surface Temperature, Emissivity, Evapotranspiration, Latent Heat Flux, Photosynthesis, Primary Production | |
| A super-resolution framework for downscaling machine learning weather prediction toward 1-km air temperature | Park, Hyebin, Park, Seonyoung, Kang, Daehyun, Kim, Jeong-Hwan | Land Use/Land Cover Classification, Land Surface Temperature, Emissivity | |
| All-sky hourly estimation over East Asia using Himawari-8 AHI and | Bae, Sejeong, Son, Bokyung, Sung, Taejun, Kim, Yejin, Kim, Youngseok, Im, Jungho | Land Use/Land Cover Classification | |
| Alpha, beta and gamma diversity in relatively natural, mixed and transformed landscape scenarios | Deng, Shuyu, Beale, Colin M., Thomas, Chris D. | Land Use/Land Cover Classification | |
| Assessing the Impacts of Long-Term Weather Variability and Urban Development on Crop Production to Analyze Food Security in Iran | Garajeh, Mohammad Kazemi, Kamran, Khalil Valizadeh, Feizizadeh, Bakhtiar, Khorrami, Behnam | Land Use/Land Cover Classification, Evapotranspiration, Photosynthesis, Primary Production, Latent Heat Flux, Precipitation, Rain, Precipitation Amount, Precipitation Rate, Snow | |
| Characterization of irrigation timing using thermal satellite | Jalilvand, Ehsan, Kumar, Sujay V., Truong, Charles, Haacker, Erin, Mahanama, Sarith | Land Use/Land Cover Classification, Land Surface Temperature, Emissivity | |
| Commodity-driven deforestation doubles local warming from tropical forest loss | Smith, Callum, Baker, Jessica C A, Doggart, Nike H, Argles, Arthur P K, Robertson, Eddy, Chadwick, Robin, Adami, Marcos, Coelho, Caio A S, Spracklen, Dominick V | Land Use/Land Cover Classification, Albedo, Anisotropy, Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Leaf Area Index (LAI), Evapotranspiration, Photosynthesis, Primary Production, Latent Heat Flux, Land Surface Temperature, Emissivity | |
| Data gaps and outliers distort critical-slowing-down-based resilience | Liu, Teng, Morr, Andreas, Bathiany, Sebastian, Blaschke, Lana L., Qian, Zhen, Diao, Chan, Smith, Taylor, Boers, Niklas | Plant Phenology, Enhanced Vegetation Index (EVI), Vegetation Index, Normalized Difference Vegetation Index (NDVI), Land Use/Land Cover Classification, Photosynthesis, Primary Production, VEGETATION PRODUCTIVITY | |
| Earth observation for land representation: implementing the Paris Agreement requirements for greenhouse gas reporting | Cima, Valentina, Vitullo, Marina, Boni, Roberta, Fiore, Angela, Tornato, Antonella, Taramelli, Andrea | Land Use/Land Cover Classification | |
| Enhancing 5-day particulate matter (PM10) forecasts in Morocco using U-net: A deep learning approach | Houdou, Anass, Khomsi, Kenza, Delle Monache, Luca, Hu, Weiming, Boutayeb, Saber, Belyamani, Lahcen, Abdulla, Fayez, el Badisy, Imad, K. Al-Delaimy, Wael, Khalis, Mohamed | Land Use/Land Cover Classification | |
| Enhanced effect of warming on the leaf-onset date of boreal deciduous broadleaf forest | Li, Wenyu, Lu, Hui, Chen, Jing M., Piao, Shilong, Keenan, Trevor F., Miao, Guofang, Liu, Qiang, Zang, Zhou, Xu, Nan, Liu, Jane, Cheng, Qu, Wang, Han, Wang, Rong, Ji, Wenjie, Zhu, Peng, Li, Congcong, Xin, Qinchuan, Gong, Peng | Plant Characteristics, Plant Phenology, Vegetation Cover, Vegetation Index, Land Use/Land Cover Classification | |
| Deriving Gridded Soil Moisture Estimates Using Earth Observation Data | Fealy, Rowan, Ishola, Kazeem, McCarthy, Tim, Nair, Ajay, de Andrade Moral, Rafael | Plant Phenology, Enhanced Vegetation Index (EVI), Land Use/Land Cover Classification, Land Surface Temperature, Emissivity, Vegetation Index, Normalized Difference Vegetation Index (NDVI) | |
| Do CMIP6 earth system models outperform their predecessors in simulating | Xu, Ruixuan, Zhao, Weiqing, Cao, Sen, Zhu, Zaichun | Plant Phenology, Enhanced Vegetation Index (EVI), Land Use/Land Cover Classification | |
| Deciphering the causes of lake expansion and shrinkage in India using | Nitish, Kumar, Indu, J. | Land Use/Land Cover Classification | |
| Heat requirement, not warming, governs the urbanrural disparity in spring phenology advancement | He, Can, Wang, Xiaoyue, Penuelas, Josep, Zhang, Xuezhen, Wu, Chaoyang | Plant Phenology, Enhanced Vegetation Index (EVI), Land Use/Land Cover Classification, Reflectance, Albedo, Anisotropy, Shortwave Radiation | |
| Impact of land-use change on ecosystem services in Africa's Great Green | Wang, Yizhuo, Scott, Catherine E., Dallimer, Martin | Land Use/Land Cover Classification, Plant Phenology, Enhanced Vegetation Index (EVI), Vegetation Index, Normalized Difference Vegetation Index (NDVI), Evapotranspiration, Latent Heat Flux, Topographical Relief Maps, Terrain Elevation, Digital Elevation/Terrain Model (DEM) | |
| Hybrid MCDMIsolation Forest approach for spatiotemporal landslide mapping | Jiang, Bingqi, Wang, Shouwu, Han, Chunhua, Meng, Jingkai, Wang, Lida | Terrain Elevation, RADAR IMAGERY, Topographical Relief Maps, Land Use/Land Cover Classification | |
| Geospatially-Aware Multi-Modal Fusion for Satellite Image Manipulation Detection | Chapman, Matthew W, Tewkesbury, Andrew, Boyd, Doreen S, Obara, Boguslaw, Bhowmik, Deepayan | Land Use/Land Cover Classification | |
| Global Mercury Emissions from Open Biomass Burning Estimated Using a Mass-Balance Approach | Shen, Yuzhe, Chen, Long, Wang, Danyu, Chen, Qinzheng, Zhou, Qi, Huang, Ye, Du, Wei, Chen, Yuanchen, Yang, Yi | Biomass, Forests, Grasslands, Alpine/Tundra, Plant Phenology, Enhanced Vegetation Index (EVI), Land Use/Land Cover Classification, Canopy Characteristics, Evergreen Vegetation, Crown, Deciduous Vegetation, Leaf Characteristics, Vegetation Cover | |
| Global near real-time 500 m 10 d FPAR dataset from MODIS and VIIRS for operational agricultural monitoring and crop yield forecasting | Seguini, Lorenzo, Klisch, Anja, Meroni, Michele, Vrieling, Anton, Manfron, Giacinto, Atzberger, Clement, Rembold, Felix | Leaf Characteristics, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Land Use/Land Cover Classification |
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 |
|---|---|---|---|---|---|---|---|
| LC_Prop1 | FAO-Land Cover Classification System 1 (LCCS1) land cover layer | Class | uint8 | 255 | 1 to 43 | N/A | N/A |
| LC_Prop1_Assessment | LCCS1 land cover layer confidence | Percent | uint8 | 255 | 0 to 100 | N/A | N/A |
| LC_Prop2 | FAO-LCCS2 land use layer | Class | uint8 | 255 | 1 to 40 | N/A | N/A |
| LC_Prop2_Assessment | LCCS2 land use layer confidence | Percent | uint8 | 255 | 0 to 100 | N/A | N/A |
| LC_Prop3 | FAO-LCCS3 surface hydrology layer | Class | uint8 | 255 | 1 to 51 | N/A | N/A |
| LC_Prop3_Assessment | LCCS3 surface hydrology layer confidence | Percent | uint8 | 255 | 0 to 100 | N/A | N/A |
| LC_Type1 | Land Cover Type 1: Annual International Geosphere-Biosphere Programme (IGBP) classification | Class | uint8 | 255 | 1 to 17 | N/A | N/A |
| LC_Type2 | Land Cover Type 2: Annual University of Maryland (UMD) classification | Class | uint8 | 255 | 0 to 15 | N/A | N/A |
| LC_Type3 | Land Cover Type 3: Annual Leaf Area Index (LAI) classification | Class | uint8 | 255 | 0 to 10 | N/A | N/A |
| LC_Type4 | Land Cover Type 4: Annual BIOME-Biogeochemical Cycles (BGC) classification | Class | uint8 | 255 | 0 to 8 | N/A | N/A |
| LC_Type5 | Land Cover Type 5: Annual Plant Functional Types classification | Class | uint8 | 255 | 0 to 11 | N/A | N/A |
| LW | Binary land (class 2) / water (class 1) mask derived from MOD44W | Class | uint8 | 255 | 1 to 2 | N/A | N/A |
| QC | Product quality flags | Quality Flag | uint8 | 255 | 0 to 10 | N/A | N/A |