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 |
|---|---|---|---|
| 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 | |
| Annually resolved atmospheric CO2 growth rate over the past nine centuries | Zhang, Xu, Li, Jinbao, Liu, Laibao | 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 | |
| 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 | |
| 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 | |
| Capturing Spatiotemporal and Subgrid Variability in Global Land Surface | Ralhan, Akarsh, Liang, XinZhong | Land Use/Land Cover Classification, Albedo, Anisotropy, Reflectance | |
| Intensified seasonal droughts and carryover effects amplify negative growth anomalies in Eurasian grasslands during the past four decades | Anniwaer, Nazhakaiti, Chen, Jiana, Zhang, Yanan, Zhao, Weiqing, He, Yue, Wang, Kai, Cao, Sen, Zhu, Zaichun | Land Use/Land Cover Classification | |
| Interannual variability of spring dust column mass concentration over | Zhang, Hailiang, He, Qing, Aihaiti, Ailiyaer, Li, Yongkang, Zeng, Kang, Jiang, Hong, Liao, Qimei | Land Use/Land Cover Classification, 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 10), PARTICULATE MATTER (PM 1.0), Emissions, UV Aerosol Index, Gas/Aerosol Composition, Deposition | |
| 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 | |
| Increasing control of vapor pressure deficit on the end of the growing season in boreal forest | Wang, Meiyu, Zhao, Jianjun, Zhou, Yuyu, Zhang, Hongyan, Zhang, Zhengxiang, Xiong, Tao, Wang, Yeqiao | Land Use/Land Cover Classification | |
| Improving photosynthetic phenology detection by incorporating vegetation index with meteorological factors | Kuang, Yaning, Li, Zifan, Wei, Lixue, Tang, Dong, Mai, Yongjian, Yuan, Huanhuan, Zheng, Lei, Deng, Jianming, Peng, Jie | 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 | |
| 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 | |
| Enhancing Estimation of Fine Particulate Matter Chemical Composition across North America by Including Geophysical A Priori Information in Deep Learning with ... | Shen, Siyuan, van Donkelaar, Aaron, Jacobs, Nathan, Li, Chi, Martin, Randall V. | Land Use/Land Cover Classification | |
| 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 | |
| Development of the long-term harmonized multi-satellite SIF (LHSIF) dataset at 0.05 resolution (19952024) | Zou, Chu, Du, Shanshan, Liu, Xinjie, Liu, Liangyun | Atmospheric Carbon Dioxide, Solar Induced Fluorescence, Land Use/Land Cover Classification, Reflectance, Primary Production, Chlorophyll, Photosynthesis, Leaf Characteristics, Albedo, Anisotropy | |
| 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 | |
| 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) | |
| 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) | |
| Dynamic Modeling of Ammonia Emissions and Nitrogen Deposition via Online Coupling of WRFChem and NoahMPCN | Cao, Yeer, Ren, Chuanhua, Zhang, Han, Wei, Zhongwang, Guo, Yixin, Cai, Xitian | Land Use/Land Cover Classification | |
| Decadal changes in summer and autumn soil moisture drive dual shifts in | Anniwaer, Nazhakaiti, Li, Xiangyi, Xu, Hao, Wang, Kai, Zhu, Zaichun | Land Use/Land Cover Classification, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) | |
| Constraining orientation statistics of ice crystals in clouds with observations from deep space | Kostinski, Alexander, Marshak, Alexander, Varnai, Tamas | 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 |