N: 90 S: -90 E: 180 W: -180
Description
The MCD43C4 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MCD43C4 Version 6.1 data product.
The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43C4 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Nadir BRDF-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data in a 0.05 degree (5,600 meters at the equator) Climate Modeling Grid (CMG). Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. This CMG product covers the entire globe for use in climate simulation models.
Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide.
MCD43C4 removes view angle effects from directional reflectances to model the values as if they were collected from a nadir view at local solar noon. These NBAR values are calculated from MCD43C1. The product includes separate NBAR layers for MODIS spectral bands 1 through 7 as well as ancillary layers for quality, local solar noon, percent finer resolution inputs, snow cover, and uncertainty.
Known Issues
- The incorrect representation of the aerosol quantities (low average high) in the C6 MYD09 and MOD09 surface reflectance products may have impacted downstream products particularly over arid bright surfaces. This (and a few other issues) have been corrected for C6.1. Therefore users should avoid substantive use of the C6 MCD43 products and wait for the C6.1 products. In any event, users are always strongly encouraged to download and use the extensive QA data provided in MCD43A2, in addition to the briefer mandatory QAs provided as part of the MCD43A1, 3, and 4 products.
- Corrections were implemented in Collection 6.1 reprocessing.
- For complete information about the MCD43C4 known issues refer to the MODIS 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.
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File Naming Convention
The file name begins with the Product Short Name (MCD43C4) followed by the Julian Date of Acquisition formatted as AYYYYDDD (A2002272), the Version of the data collection (006), the Julian Date and Time of Production designated as YYYYDDDHHMMSS (2016223173626), 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 |
|---|---|---|---|
| Improvement of the consistency among long-term global land surface | Ye, Yongchang, Zhang, Xiaoyang, Shen, Yu, Tran, Khuong H., Gao, Shuai, Liu, Yuxia, An, Shuai | Albedo, Anisotropy, Reflectance | |
| 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 | |
| Reduced Vegetation Uptake During the Extreme 2023 Drought Turns the Amazon Into a Weak Carbon Source | Botia, S., DiasJunior, C. Q., Komiya, S., van der Woude, A. M., Terristi, M., de Kok, R. J., Koren, G., van Asperen, H., Jones, S. P., D'Oliveira, F. A. F., Weber, U., MarquesFilho, E. P., Cely, I. M., Araujo, A., Lavric, J. V., Walter, D., Li, X., Wigneron, J. P., Stocker, B. D., Goncalves de Souza, J., O'Sullivan, M., Sitch, S., Ciais, P., Chevallier, F., Li, W., Luijkx, I., Peters, W., Quesada, C. A., Zaehle, S., Trumbore, S., Bastos, A. | Terrestrial Ecosystems, Biomass, LIDAR WAVEFORM, Albedo, Anisotropy, Reflectance | |
| QeRSIF: a quality-enhanced red solar-induced chlorophyll fluorescence dataset for TROPOMI satellite | Du, Yulu, Du, Shanshan, Zhao, Dianrun, Liu, Xinjie, Liu, Liangyun | Albedo, Anisotropy, Reflectance | |
| The influence of plant soil moisture stress on solar-induced chlorophyll fluorescence efficiency across Africa | Onkaew, Khomkrit, Quaife, Tristan, Black, Emily, Maidment, Ross I. | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Land Use/Land Cover Classification, Reflectance, Anisotropy, Albedo | |
| CEDAR-GPP: spatiotemporally upscaled estimates of gross primary productivity incorporating CO2 fertilization | Kang, Yanghui, Bassiouni, Maoya, Gaber, Max, Lu, Xinchen, Keenan, Trevor F. | Photosynthesis, Primary Production, VEGETATION PRODUCTIVITY, Leaf Characteristics, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Land Use/Land Cover Classification, Land Surface Temperature, Emissivity, Albedo, Anisotropy, Reflectance, Vegetation Index, Vegetation Cover, Plant Characteristics, Canopy Characteristics, Albedo | |
| An improved spatially downscaled solar-induced chlorophyll fluorescence dataset from the TROPOMI product | Chen, Siyuan, Liu, Liangyun, Sui, Lichun, Liu, Xinjie, Ma, Yan | Land Use/Land Cover Classification, Albedo, Anisotropy, Reflectance | |
| On the added value of sequential deep learning for the upscaling of evapotranspiration | Kraft, Basil, Nelson, Jacob A., Walther, Sophia, Gans, Fabian, Weber, Ulrich, Duveiller, Gregory, Reichstein, Markus, Zhang, Weijie, Ruwurm, Marc, Tuia, Devis, Korner, Marco, Hamdi, Zayd, Jung, Martin | Reflectance, Anisotropy, Land Surface Temperature, Emissivity, Albedo | |
| Simultaneous Reductions in Forest Resilience and Greening Trends in Southwest China | Wu, Huiying, Cui, Tianxiang, Cao, Lin | Land Surface Temperature, Emissivity, Albedo, Anisotropy, Reflectance | |
| Regional Land Surface Conditions Developed Using the High-Resolution | Vinodhkumar, Buri, Osuri, Krishna Kishore, Dimri, A. P., Mukherjee, Sandipan, AlGhamdi, Sami G., Niyogi, Dev | Albedo, Anisotropy, Reflectance | |
| Two Decades of FireInduced Albedo Change and Associated ShortWave Radiative Effect Over SubSaharan Africa | Flegrova, Michaela, Brindley, Helen | Land Use/Land Cover Classification, Fire Ecology, Biomass Burning, Wildfires, Fire Occurrence, Burned Area, Albedo, Anisotropy, Reflectance | |
| The surface mass balance and near-surface climate of the Antarctic ice | van Dalum, Christiaan T., van de Berg, Willem Jan, van den Broeke, Michiel R., van Tiggelen, Maurice | Albedo, Anisotropy, Reflectance | |
| A 20012022 global gross primary productivity dataset using an ensemble model based on the random forest method | Chen, Xin, Chen, Tiexi, Li, Xiaodong, Chai, Yuanfang, Zhou, Shengjie, Guo, Renjie, Dai, Jie | Albedo, Anisotropy, Reflectance, Leaf Characteristics, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Land Use/Land Cover Classification | |
| A satellite-derived dataset on vegetation phenology across Central Asia from 2001 to 2023 | Ding, Chao | Land Use/Land Cover Classification, Albedo, Anisotropy, Reflectance | |
| Analysis of environmental variables and deforestation in the amazon using logistical regression models | da Silva, Helder J. F., Goncalves, Weber A., Bezerra, Bergson G., Santos e Silva, Claudio M., de Oliveira, Cristiano P., Junior, Jorio B. Cabral, Rodrigues, Daniele T., Silva, Fabricio D. S. | Land Surface Temperature, Emissivity, Evapotranspiration, Photosynthesis, Primary Production, Latent Heat Flux, VEGETATION PRODUCTIVITY, Albedo, Anisotropy, Reflectance, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) | |
| Vegetation IndexBased Models Without Meteorological Constraints Underestimate the Impact of Drought on Gross Primary Productivity | Chen, Xin, Chen, Tiexi, Liu, Shuci, Chai, Yuanfang, Guo, Renjie, Dai, Jie, Wang, Shengzhen, Zhang, Lele, Wei, Xueqiong | Land Use/Land Cover Classification, Albedo, Anisotropy, Reflectance | |
| Impacts of forest cover change on local temperature in Yangtze River Delta and Pearl River Delta urban agglomerations of China | Liu, Qing, Shen, Wenjuan, Wang, Tongyu, He, Jiaying, Cao, Pingting, Sun, Tianyi, Zhang, Ying, Ye, Wenjing, Huang, Chengquan | Albedo, Anisotropy, Evapotranspiration, Latent Heat Flux, Land Surface Temperature, Emissivity, Reflectance | |
| Increased crossing of thermal stress thresholds of vegetation under global warming | Li, Xiangyi, Huntingford, Chris, Wang, Kai, Cui, Jiangpeng, Xu, Hao, Kan, Fei, Anniwaer, Nazhakaiti, Yang, Hui, Penuelas, Josep, Piao, Shilong | Albedo, Anisotropy, Reflectance | |
| Global expansion of wildland-urban interface (WUI) and WUI fires: insights from a multiyear worldwide unified database (WUWUI) | Tang, Wenfu, He, Cenlin, Emmons, Louisa, Zhang, Junzhe | Land Use/Land Cover Classification, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Albedo, Anisotropy, Reflectance, Fire Ecology, Biomass Burning, Wildfires, Fire Occurrence, Canopy Characteristics, Evergreen Vegetation, Crown, Deciduous Vegetation, Leaf Characteristics, Vegetation Cover, Burned Area | |
| Critical slowing down of the Amazon forest after increased drought occurrence | Van Passel, Johanna, Bernardino, Paulo N., Lhermitte, Stef, Rius, Bianca F., Hirota, Marina, Conradi, Timo, de Keersmaecker, Wanda, Van Meerbeek, Koenraad, Somers, Ben | RADAR IMAGERY, Terrain Elevation, Digital Elevation/Terrain Model (DEM), Canopy Characteristics, Evergreen Vegetation, Crown, Deciduous Vegetation, Leaf Characteristics, Vegetation Cover, Land Use/Land Cover Classification, Fire Ecology, Biomass Burning, Wildfires, Fire Occurrence, Reflectance, Anisotropy, Albedo, Burned Area | |
| Estimation of Vegetation Parameters of the VIC Model Using Remotely Sensed Data | Gomez, Edna Lucia Espinosa, Rodriguez, Leticia, Zimmermann, Erik | Albedo, Anisotropy, Reflectance, Aerosol Backscatter, Aerosol Extinction, Aerosol Optical Depth/Thickness, Angstrom Exponent, Aerosol Particle Properties, Aerosol Radiance, Carbonaceous Aerosols, Cloud Condensation Nuclei, Dust/Ash/Smoke, Nitrate Particles, Organic Particles, Particulate Matter, Sulfate Particles, Trace Gases/Trace Species, Atmospheric Emitted Radiation, Emissivity, Optical Depth/Thickness, Radiative Flux, Reflectance, Transmittance, Atmospheric Stability, Humidity, Total Precipitable Water, Water Vapor Profiles, Cloud Condensation Nuclei, Cloud Droplet Concentration/Size, Cloud Liquid Water/Ice, Cloud Optical Depth/Thickness, Cloud Asymmetry, Cloud Ceiling, Cloud Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Cloud Vertical Distribution, Cloud Emissivity, Cloud Radiative Forcing, Cloud Reflectance, Rain Storms, Atmospheric Ozone, Surface Pressure, Heat Flux, Longwave Radiation, Shortwave Radiation, Surface Temperature, Evapotranspiration, Surface Winds, Rain, Precipitation Rate, Snow, Soil Moisture/Water Content, Soil Temperature, Land Surface Temperature, Snow Water Equivalent, Runoff, Leaf Characteristics, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar) | |
| Natural forest regeneration is projected to reduce local temperatures | Alibakhshi, Sara, Cook-Patton, Susan C., Davin, Edouard, Maeda, Eduardo Eiji, Araujo, Miguel Bastos, Heinlein, Daniel, Heiskanen, Janne, Pellikka, Petri, Crowther, Thomas W. | Land Surface Temperature, Emissivity, Albedo, Anisotropy, Reflectance, Evapotranspiration, Latent Heat Flux | |
| Megagrazer loss drives complex landscape-scale biophysical cascades | Hyvarinen, Olli, te Beest, Mariska, le Roux, Elizabeth, Kerley, Graham I H, Buitenwerf, Robert, Druce, Dave J, Chen, Jiquan, Rapp, Linda, Fernandes, Joana, Cromsigt, Joris P G M | Canopy Characteristics, Evergreen Vegetation, Crown, Deciduous Vegetation, Leaf Characteristics, Vegetation Cover, Land Use/Land Cover Classification, Albedo, Anisotropy, Reflectance | |
| Reducing Resolution Dependency of Dust Emission Modeling Using | Chappell, Adrian, Hennen, Mark, Schepanski, Kerstin, Dhital, Saroj, Tong, Daniel | Land Use/Land Cover Classification, Reflectance, Albedo, Anisotropy | |
| Spatio-temporal heterogeneity and driving mechanism of ecosystem water | Wang, Feiyu, Xia, Jun, Zou, Lei, Zhang, Liping, Li, Xiaoyang, Yu, Jiarui | Albedo, Anisotropy, Reflectance, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Photosynthesis, Primary Production, VEGETATION PRODUCTIVITY, 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, 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 |
|---|---|---|---|---|---|---|---|
| BRDF_Albedo_Uncertainty | BRDF inversion information | N/A | uint16 | 32767 | 0 to 32766 | 0.001 | N/A |
| BRDF_Quality | Global albedo quality | N/A | uint8 | 255 | 0 to 254 | N/A | N/A |
| Local_Solar_Noon | Local solar noon zenith angle | Degree | uint8 | 255 | 0 to 90 | N/A | N/A |
| Nadir_Reflectance_Band1 | Nadir reflectance for MODIS band 1 | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Nadir_Reflectance_Band2 | Nadir reflectance for MODIS band 2 | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Nadir_Reflectance_Band3 | Nadir reflectance for MODIS band 3 | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Nadir_Reflectance_Band4 | Nadir reflectance for MODIS band 4 | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Nadir_Reflectance_Band5 | Nadir reflectance for MODIS band 5 | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Nadir_Reflectance_Band6 | Nadir reflectance for MODIS band 6 | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Nadir_Reflectance_Band7 | Nadir reflectance for MODIS band 7 | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Percent_Inputs | Processed finer resolution data that contributed to this CMG pixel | Percent | uint8 | 255 | 0 to 100 | N/A | N/A |
| Percent_Snow | Underlying data flagged as snow | Percent | uint8 | 255 | 0 to 100 | N/A | N/A |