N: 90 S: -90 E: 180 W: -180
Description
The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6.1 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.
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.
The MCD43A4 provides NBAR and simplified mandatory quality layers for MODIS bands 1 through 7. Essential quality information provided in the corresponding MCD43A2 data file should be consulted when using this product.
Known Issues
- 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.
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File Naming Convention
The file name begins with the Product Short Name (MCD43A4) followed by the Julian Date of Acquisition formatted as AYYYYDDD (A2025212), the Tile Identifier which is horizontal tile and vertical tile provided as hXXvYY (h04v10), the Version of the data collection (061), the Julian Date and Time of Production designated as YYYYDDDHHMMSS (2025221032559), and the Data Format (hdf).
Documents
USER'S GUIDE
ALGORITHM THEORETICAL BASIS DOCUMENT (ATBD)
PRODUCT QUALITY ASSESSMENT
SCIENCE DATA PRODUCT VALIDATION
Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Constraining a data-driven CO2 flux model by ecosystem and | Upton, Samuel, Reichstein, Markus, Peters, Wouter, Botia, Santiago, Nelson, Jacob A., Walther, Sophia, Jung, Martin, Gans, Fabian, Haszpra, Laszlo, Bastos, Ana | Carbonaceous Aerosols, Nitrogen Oxides, Particulates, Hydrogen Cyanide, Emissions, Non-methane Hydrocarbons/Volatile Organic Compounds, Particulate Matter, Fire Occurrence, Nitrogen Oxides, Sulfur Dioxide, Carbon And Hydrocarbon Compounds, Reflectance, Albedo, Anisotropy | |
| Unprecedented Amazonian rainforests damage during the 20232024 droughts | Bai, Hao, Liu, Xiangzhuo, Yang, Hui, Chave, Jerome, Ciais, Philippe, Wigneron, Jean-Pierre, Saatchi, Sassan, Xiao, Jingfeng, Le Toan, Thuy, Hu, Xiaomei, Yang, Ziyan, Wang, Lijun, Fan, Lei, Yao, Yitong, Chen, Xiuzhi, Liu, Yanxu, Xue, Baolin, Guo, Qinghua, Tang, Zhiyao, Liu, Hongyan, Fang, Jingyun, Tao, Shengli | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Fire Ecology, Biomass Burning, Wildfires, Fire Occurrence, Burned Area, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Reflectance, Anisotropy | |
| Concurrent climate extremes and biological carryover effects dominate | Luo, Yiting, Yang, Hui, Xu, Hao, Huntingford, Chris, Orth, Rene, Li, Xiangyi, Penuelas, Josep | Reflectance, Anisotropy | |
| Caribou balance winter range fidelity and plasticity in response to weather, pregnancy, and summer range conditions | Fullman, Timothy J, Person, Brian T, Karpovich, Shawna, Prichard, Alexander K, Hepler, Joelle, Zuur, Alain F | Reflectance, Anisotropy, Vegetation Cover, Forests, Alpine/Tundra | |
| Tracing changes in subsurface water storage through a novel | Herrera, David, Belleflamme, Alexandre, Gorgen, Klaus, Rascher, Uwe, Siegmann, Bastian | Reflectance, Anisotropy, Emissivity, Land Surface Temperature | |
| Mapping global leaf inclination angle (LIA) based on field measurement data | Li, Sijia, Fang, Hongliang | Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Leaf Characteristics, Photosynthetically Active Radiation, Reflectance, Anisotropy | |
| Machine learning-based winter wheat yield prediction using multisource | Khosravani Shariati, Seyed Arash, Abbasi, Ali | 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, Reflectance, Anisotropy, Emissivity, Leaf Characteristics, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar) | |
| Modification and Comparison of Two Urban Vegetation Models Over Southern | MadsenColford, Sabrina, Hutyra, Lucy, Smith, Ian, Wu, Dien, Arain, M. Altaf, Staebler, Ralf, Ma, William, RestrepoCoupe, Natalia, Wunch, Debra | Land Use/Land Cover Classification, Plant Phenology, Enhanced Vegetation Index (EVI), Reflectance, Urban Lands, Land Use/Land Cover, Urbanization/Urban Sprawl, Infrastructure, Anisotropy | |
| Mature forest habitat mitigates the decline of an endangered greater | Cally, Justin G., Macak, Phoebe V., Chick, Matt P., Blake, Brad, Wagner, Benjamin, Ramsey, David S.L. | Reflectance, Anisotropy | |
| Learning county from pixels: corn yield prediction with | Wang, Xiaoyu, Ma, Yuchi, Xu, Yijia, Huang, Qunying, Yang, Zhengwei, Zhang, Zhou | Reflectance, Anisotropy | |
| Large-scale diurnal responses of solar-induced chlorophyll fluorescence | Zhao, Dayang, Zhang, Zhaoying, Zhang, Yongguang | Land Use/Land Cover Classification, Reflectance, Anisotropy, Leaf Characteristics, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Plant Phenology, Enhanced Vegetation Index (EVI) | |
| Improving terrestrial solar-induced chlorophyll fluorescence (SIF) reconstruction through multi-satellite-derived SIF integration: a comparative study in regional scale | Pan, Mengting, Lin, Xiaofeng, Xiao, Zhongyong, Wang, Fei, Guo, Lifeng, Wang, Cuiping, Xie, Jinghan, Fang, Jingchun | Atmospheric Carbon Dioxide, Solar Induced Fluorescence, Reflectance, Anisotropy, Primary Production, Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Gross Primary Production (gpp) | |
| Regional-scale soil carbon predictions can be enhanced by transferring global-scale soilenvironment relationships | Zhang, Lei, Yang, Lin, Ma, Yuxin, Zhu, A-Xing, Wei, Ren, Liu, Jie, Greve, Mogens H., Zhou, Chenghu | Reflectance, Anisotropy, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) | |
| PSNet: A deep learning framework following hierarchical yield level | Zhong, Renhai, Xiong, Xingguo, Tian, Qiyu, Huang, Jingfeng, Zhu, Linchao, Yang, Yi, Lin, Tao | Reflectance, Anisotropy | |
| Quantifying shortwave radiative forcing and heating rates of UTLS | Santhosh, V.N., Madhavan, B.L., Ratnam, M. Venkat | Reflectance, Anisotropy | |
| Quantifying the impact of air pollution from coal-fired electricity generation on crop productivity in India | Singh, Kirat, Lobell, David B., Azevedo, Ines M. L. | Reflectance, Anisotropy | |
| Quantifying the impact of urban geometry on the urban albedo: A | Zhou, Hongyan, Chen, Guanwen, Mei, Shuojun, Hang, Jian, Aktas, Yasemin D., Yang, Xinyan, Wang, Kai | Atmospheric Radiation, Longwave Radiation, Shortwave Radiation, Radiative Flux, Radiative Forcing, Surface Radiative Properties, Albedo, Emissivity, Cloud Properties, Cloud Fraction, Cloud Optical Depth/Thickness, Skin Temperature, Skin Temperature, Sea Surface Skin Temperature, Reflectance, Anisotropy | |
| 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 | |
| Seasonality and synchrony of photosynthesis in African forests inferred from spaceborne chlorophyll fluorescence and vegetation indices | Doughty, Russell, Wimberly, Michael C., Wanyama, Dan, Peiro, Helene, Parazoo, Nicholas, Crowell, Sean, Cho, Moses Azong | Atmospheric Carbon Dioxide, Solar Induced Fluorescence, Reflectance, Anisotropy | |
| The Indian Ocean Dipole drives imported-dominated dengue outbreaks in China: Mechanisms and predictions | Ma, Jian, Xu, Lei, Han, Qian, Gao, Jing, Yuan, Huihui, Dong, Kaixing, Huang, Cunrui, Zhou, Cui, Ji, John S, Zhang, Chutian, Zeng, Huatang, Guo, Yongman, Luo, Lexuan, Zhang, Xiangliang, Luo, Yong, Liu, Qiyong, Stenseth, Nils Chr, Liang, Wannian | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Reflectance, Anisotropy | |
| Species diversity advances autumn senescence via enhanced belowground carbon allocation in semi-arid grasslands | Cheng, Huan, Qiao, Yuxin, Zhu, HuaZhong, Zhu, YunQiang, Jia, Qianru, Yang, Yuchuan, Zhong, Huaping, Zohner, Constantin M., Liu, Jianquan | Reflectance, Anisotropy, Biomass, Forests, Grasslands, Alpine/Tundra | |
| Surface Flux Equilibrium Theory-Derived Evapotranspiration Estimate | Thakur, Hitesh, Raghav, Pushpendra, Kumar, Mukesh, Wolkeba, Fitsume | Leaf Characteristics, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Reflectance, Anisotropy | |
| Spatial Soil Moisture Prediction From In Situ Data Upscaled to Landsat | Yu, Yi, Malone, Brendan P., Renzullo, Luigi J., Burton, Chad A., Tian, Siyuan, Searle, Ross D., Bishop, Thomas F. A., Walker, Jeffrey P. | Reflectance, Anisotropy | |
| Vegetation structure and phenology primarily shape the spatiotemporal pattern of ecosystem respiration | Zhao, Cenliang, Zhu, Wenquan | Precipitation Amount, Alpine/Tundra, Biomass, Forests, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Vegetation Cover, Permafrost, NET ECOSYSTEM CO2 EXCHANGE (NEE), Soil Depth, Soil Moisture/Water Content, Soil Respiration, Gross Primary Production (gpp), Snow Depth, Atmospheric Carbon Dioxide, Surface Temperature, Land Surface Temperature, Emissivity, Leaf Characteristics, Photosynthetically Active Radiation, Reflectance, Anisotropy | |
| Transfer learning for improved crop yield predictions in a cross-scale pathway: a case study for Brazilian national soybean | Zhang, Jiaying, Guan, Kaiyu, Chen, Zhangliang, Huang, Yizhi, Zhao, Kejie, Peng, Bin, Wang, Sheng, Wu, Xiaocui, Wang, Sibo, Banerjee, Arindam, Vergopolan, Noemi, Fu, Rong, Zhao, Siyu, Colussi, Joana | Reflectance, Anisotropy |
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_Band_Mandatory_Quality_Band1 | BRDF Albedo Mandatory Quality for Band 1 | Bit Field | uint8 | 255 | 0 to 254 | N/A | N/A |
| BRDF_Albedo_Band_Mandatory_Quality_Band2 | BRDF Albedo Mandatory Quality for Band 2 | Bit Field | uint8 | 255 | 0 to 254 | N/A | N/A |
| BRDF_Albedo_Band_Mandatory_Quality_Band3 | BRDF Albedo Mandatory Quality for Band 3 | Bit Field | uint8 | 255 | 0 to 254 | N/A | N/A |
| BRDF_Albedo_Band_Mandatory_Quality_Band4 | BRDF Albedo Mandatory Quality for Band 4 | Bit Field | uint8 | 255 | 0 to 254 | N/A | N/A |
| BRDF_Albedo_Band_Mandatory_Quality_Band5 | BRDF Albedo Mandatory Quality for Band 5 | Bit Field | uint8 | 255 | 0 to 254 | N/A | N/A |
| BRDF_Albedo_Band_Mandatory_Quality_Band6 | BRDF Albedo Mandatory Quality for Band 6 | Bit Field | uint8 | 255 | 0 to 254 | N/A | N/A |
| BRDF_Albedo_Band_Mandatory_Quality_Band7 | BRDF Albedo Mandatory Quality for Band 7 | Bit Field | uint8 | 255 | 0 to 254 | N/A | N/A |
| Nadir_Reflectance_Band1 | NBAR at local solar noon for Band 1 | N/A | int16 | 32767 | 0 to 32766 | 0.0001 | N/A |
| Nadir_Reflectance_Band2 | NBAR at local solar noon for Band 2 | N/A | int16 | 32767 | 0 to 32766 | 0.0001 | N/A |
| Nadir_Reflectance_Band3 | NBAR at local solar noon for Band 3 | N/A | int16 | 32767 | 0 to 32766 | 0.0001 | N/A |
| Nadir_Reflectance_Band4 | NBAR at local solar noon for Band 4 | N/A | int16 | 32767 | 0 to 32766 | 0.0001 | N/A |
| Nadir_Reflectance_Band5 | NBAR at local solar noon for Band 5 | N/A | int16 | 32767 | 0 to 32766 | 0.0001 | N/A |
| Nadir_Reflectance_Band6 | NBAR at local solar noon for Band 6 | N/A | int16 | 32767 | 0 to 32766 | 0.0001 | N/A |
| Nadir_Reflectance_Band7 | NBAR at local solar noon for Band 7 | N/A | int16 | 32767 | 0 to 32766 | 0.0001 | N/A |