N: 90 S: -90 E: 180 W: -180
Description
The MCD12C1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MCD12C1 Version 6.1 data product.
The Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Climate Modeling Grid (CMG) (MCD12C1) Version 6 data product provides a spatially aggregated and reprojected version of the tiled MCD12Q1 Version 6 data product. Maps of the International Geosphere-Biosphere Programme (IGBP), University of Maryland (UMD), and Leaf Area Index (LAI) classification schemes are provided at yearly intervals at 0.05 degree (5,600 meter) spatial resolution for the entire globe from 2001 to 2020. Additionally, sub-pixel proportions of each land cover class in each 0.05 degree pixel is provided along with the aggregated quality assessment information for each of the three land classification schemes.
Provided in each MCD12C1 Version 6 Hierarchical Data Format 4 (HDF4) file are layers for Majority Land Cover Type 1-3, Majority Land Cover Type 1-3 Assessment, and Majority Land Cover Type 1-3 Percent.
Known Issues
- Known issues are described on pages 3 and 4 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 (MCD12C1) followed by the Julian Date of Acquisition formatted as AYYYYDDD (A2003001), the Version of the data collection (006), the Julian Date and Time of Production designated as YYYYDDDHHMMSS (2018053185458), and the Data Format (hdf).
Documents
USER'S GUIDE
ALGORITHM THEORETICAL BASIS DOCUMENT (ATBD)
Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Annually resolved atmospheric CO2 growth rate over the past nine centuries | Zhang, Xu, Li, Jinbao, Liu, Laibao | Land Use/Land Cover Classification | |
| Amazon forest loss: An all-sky biophysical top-of-atmosphere cooling feedback | Dror, Tom, Feingold, Graham | Atmospheric Emitted Radiation, Emissivity, Optical Depth/Thickness, Radiative Flux, Reflectance, Transmittance, Clouds, Cloud Condensation Nuclei, Cloud Droplet Concentration/Size, Cloud Liquid Water/Ice, Cloud Optical Depth/Thickness, Cloud Precipitable Water, Cloud Asymmetry, Cloud Ceiling, Cloud Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Cloud Vertical Distribution, Cloud Emissivity, Cloud Radiative Forcing, Cloud Reflectance, Cloud Types, Land Use/Land Cover Classification | |
| An integrated heat and pollution index for sustainable urban planning: Evidence from Delhi | Kuttippurath, Jayanarayanan, Patel, Vikas Kumar | Land Use/Land Cover Classification | |
| A global 0.05 gross primary production dataset from 2001 to 2024 generated using a hybrid LSTM framework | Chen, Shaoyang, Liu, Xinjie, Han, Qizhi, Wu, Yanhong, Liu, Liangyun | Land Use/Land Cover Classification, Leaf Characteristics, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Albedo, Anisotropy, Reflectance, Photosynthesis, Primary Production, Vegetation Productivity | |
| A Model for Dry Deposition of Atmospheric Micro-and Nanoplastic Fibers | Foroutan, Hosein | Land Use/Land Cover Classification | |
| A versatile classification model for assessing tree crown components across Central Amazon forests using RGB drone imagery | Simonetti, Adriana, Marra, Daniel Magnabosco, Goncalves, Nathan Borges, Lopes, Aline P., Higuchi, Niro, Wu, Jin, Nelson, Bruce Walker | Land Use/Land Cover Classification | |
| Interannual variations in terrestrial carbon uptake are dominated by temperature and the vapor pressure deficit rather than water availability | Jiang, Dong, Yu, Zhe, Wang, Jianhua, Hao, Mengmeng, Zhang, Xingxing, Yan, Xiaoxi, Liu, Jinglei, Li, Zhaoxing, Liu, Zhaofei | Land Use/Land Cover Classification, Gross Primary Production (gpp), Vegetation Cover, Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Primary Production | |
| Key Driving Factors of Ecosystem Resilience Under Drought Stress in the Dongjiang River Basin, China | Huang, Qiang, Luo, Xiaoshan, Ouyang, Liao, Yuan, Shuyun, Li, Peng | Land Use/Land Cover Classification | |
| Identifying Surface Degeneracies in Single-Visit Reflected Light Observations of Modern Earth using the Habitable Worlds Observatory | Zelakiewicz, Aiden S., Mullens, Elijah, Kaltenegger, Lisa, Savransky, Dmitry | Land Use/Land Cover Classification | |
| DeepProfile: An inverse fusion framework for root zone soil moisture | Zhu, Liujun, Tan, Yi, Yuan, Shanshui, Jin, Junliang, Tang, Zhengyang, Walker, Jeffrey P. | Land Use/Land Cover Classification, Soil Temperature, Soil Moisture/Water Content | |
| Burning bans have altered burned area changes in China since 2003 | You, Chao, Wang, Jing, Dong, Xiao, Xu, Chao | Land Use/Land Cover Classification, Fire Ecology, Biomass Burning, Wildfires, Fire Occurrence, Burned Area | |
| Co-occurrence patterns of malnutrition indicators among children in sub-Saharan Africa | Seiler, Johannes, Muller, Benjamin, Gunther, Isabel, Wetscher, Mattias, Stauffer, Reto, Umlauf, Nikolaus, Harttgen, Kenneth | Land Use/Land Cover Classification | |
| Capturing Spatiotemporal and Subgrid Variability in Global Land Surface | Ralhan, Akarsh, Liang, XinZhong | Land Use/Land Cover Classification, Albedo, Anisotropy, Reflectance | |
| Differentiable land model reveals global environmental controls on latent ecological functions | Fang, Jianing, Bowman, Kevin, Zhao, Wenli, Lian, Xu, Gentine, Pierre | Atmospheric Carbon Dioxide, Carbon Dioxide, Land Use/Land Cover Classification, Carbon, Cation Exchange Capacity, Organic Matter | |
| Divergent Subtropical Forest Functional and Structural Responses to the | Li, Baoni, Liu, Junguo, Wang, Dashan, Liu, Xiaoye, Liang, Shijing, Zhang, Shuyu, Gong, Guoqing, Zeng, Ling, Guo, Zhilin, Ye, Jianhuai, Wang, Chen, Zeng, Zhenzhong | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Land Use/Land Cover Classification, Surface Soil Moisture | |
| Do urban areas intensify hail? | Pryor, Sara C, Barthelmie, Rebecca J, Zhou, Xin | Land Use/Land Cover Classification | |
| Downscaling MicrowaveBased Evapotranspiration With a FourierSupervised MultiSource Fusion Network in CentralSouthern East Asia | Li, Haoyang, Li, Dong, Wang, Yipu, Liu, Qingyang, Hu, Jiheng, Song, Binbin, Wu, Shengli, Zhang, Peng, Hong, Danfeng, Li, Rui | Land Use/Land Cover Classification, Evapotranspiration, Photosynthesis, Primary Production, Latent Heat Flux, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) | |
| Dramatic increase in ecosystem respiration causes record-breaking atmospheric CO2 growth rate in 2024 | Dong, Guanyu, Jiang, Fei, Ju, Weimin, Penuelas, Josep, Ciais, Philippe, Zhang, Yongguang, Xiao, Jingfeng, Wang, Xuhui, Yuan, Wenping, Huang, Yuanyuan, Yue, Chao, Liu, Liangyun, Li, Xing, Fan, Lei, van der Werf, Guido R., Wu, Mousong, Wang, Jun, Zhou, Yanlian, Tian, Jiaqi, Wang, Hengmao, He, Wei, Zhang, Lingyu, Lv, Guoyuan, Zhang, Yuanyuan, Chen, Jing M. | Land Use/Land Cover Classification, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) | |
| Enhancing climate mitigation: photovoltaic deployment as a complement to afforestation | Liu, Jia, Zhang, Yongguang | Land Use/Land Cover Classification, Land Surface Temperature, Emissivity, Albedo, Anisotropy | |
| Global soil moisture dynamics under future scenarios: evaluation-constrained multi-model ensemble and climatic attribution analysis | Song, Qian, Liu, Yangxiaoyue, Xu, Jie | Land Use/Land Cover Classification | |
| Global UrbanRural Differences in Precipitation: Cities See More Light Rain But Milder Extremes | Ding, Mingze, Zheng, XiaoTong, Li, Dan, Li, Zhe, Chen, Haonan, Sun, Ting | Land Use/Land Cover Classification | |
| Greening and browning of global drylands: climatic constraints from soil moisture and atmospheric water demand | Daramola, Mojolaoluwa Toluwalase, Li, Renqiang, Xu, Ming | Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Land Use/Land Cover Classification, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Characteristics, Photosynthetically Active Radiation | |
| EXtreFormer: a general deep learning framework for forecasting compound extreme events: experience with dry-hot extremes and vegetation response | Subhadarsini, Suchismita, Nagesh Kumar, D., Govindaraju, Rao S. | Land Use/Land Cover Classification | |
| Photosynthetic Recovery Dynamics Reveal Declining Vegetation Functional | Behera, Subhrasita, Diao, Chan, Bathiany, Sebastian, Boers, Niklas, Dutta, Debsunder | Surface Pressure, Heat Flux, Longwave Radiation, Shortwave Radiation, Surface Temperature, Humidity, Evapotranspiration, Surface Winds, Rain, Precipitation Rate, Snow, Soil Moisture/Water Content, Soil Temperature, Land Surface Temperature, Snow Water Equivalent, Runoff, Land Use/Land Cover Classification | |
| Quantifying the impact of vegetation on carbon monoxide reduction using multi-source remote sensing in China | Yao, Jiaqi, Wei, Jing, Liu, Tong, Cao, Yongqiang, Zhai, Haoran, Gong, Haiying, Liu, Zihua, Xu, Nan, Lu, Hui | Heat Flux, Air Temperature, Skin Temperature, Specific Humidity, Water Vapor, Precipitation Rate, Snow/Ice, Evaporation, Latent Heat Flux, Latent Heat Flux, Sensible Heat Flux, Diffusion, Surface Winds, Wind Speed, U/V Wind Components, Wind Stress, Wind Stress, Surface Roughness, Planetary Boundary Layer Height, Ice Fraction, 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 |
|---|---|---|---|---|---|---|---|
| Land_Cover_Type_1_Percent | Percent cover of each IGBP class at each pixel | Percent | uint8 | 255 | 0 to 100 | N/A | N/A |
| Land_Cover_Type_2_Percent | Percent cover of each UMD class at each pixel | Percent | uint8 | 255 | 0 to 100 | N/A | N/A |
| Land_Cover_Type_3_Percent | Percent cover of each LAI class at each pixel | Percent | uint8 | 255 | 0 to 100 | N/A | N/A |
| Majority_Land_Cover_Type_1 | Most likely IGBP class for each 0.05 degree pixel | Class | uint8 | 255 | 0 to 16 | N/A | N/A |
| Majority_Land_Cover_Type_1_Assessment | Majority IGBP class confidence | Percent | uint8 | 255 | 0 to 100 | N/A | N/A |
| Majority_Land_Cover_Type_2 | Most likely UMD class for each 0.05 degree pixel | Class | uint8 | 255 | 0 to 15 | N/A | N/A |
| Majority_Land_Cover_Type_2_Assessment | Majority UMD class confidence (filled with land/water mask) | Percent | uint8 | 255 | 0 to 100 | N/A | N/A |
| Majority_Land_Cover_Type_3 | Most likely LAI class for each 0.05 degree pixel | Class | uint8 | 255 | 0 to 10 | N/A | N/A |
| Majority_Land_Cover_Type_3_Assessment | Majority LAI class confidence (filled with land/water mask) | Percent | uint8 | 255 | 0 to 100 | N/A | N/A |