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.
Copy Citation
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 |
|---|---|---|---|
| Quantification of Urban Forest and Grassland Carbon Fluxes Using Field Measurements and a SatelliteBased Model in Washington DC/Baltimore Area | Winbourne, J. B., Smith, I. A., Stoynova, H., Kohler, C., Gately, C. K., Logan, B. A., Reblin, J., Reinmann, A., Allen, D. W., Hutyra, L. R. | Reflectance, Anisotropy | |
| Multi-modal temporal CNNs for live fuel moisture content estimation | Miller, Lynn, Zhu, Liujun, Yebra, Marta, Rudiger, Christoph, Webb, Geoffrey I. | RADAR IMAGERY, Terrain Elevation, Topographical Relief Maps, Digital Elevation/Terrain Model (DEM), Reflectance, Anisotropy, Total Surface Water | |
| NIRv and SIF better estimate phenology than NDVI and EVIEffects of spring and autumn phenology on ecosystem production of planted forests | Zhang, Jingru, Xiao, Jingfeng, Tong, Xiaojuan, Zhang, Jinsong, Meng, Ping, Li, Jun, Liu, Peirong, Yu, Peiyang | Reflectance, Anisotropy | |
| Spatiotemporal fusion modelling using STARFMExamples of Landsat 8 and Sentinel-2 NDVI in Bavaria | Dhillon, Maninder Singh, Dahms, Thorsten, Kubert-Flock, Carina, Steffan-Dewenter, Ingolf, Zhang, Jie, Ullmann, Tobias | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Reflectance, Anisotropy | |
| The restoration potential of the grasslands on the Tibetan Plateau | Wang, Ruijing, Feng, Qisheng, Jin, Zheren, Liang, Tiangang | Reflectance, Anisotropy, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) | |
| Spatiotemporal remote sensing image fusion using multiscale two-stream convolutional neural networks | Chen, Yuehong, Shi, Kaixin, Ge, Yong, Zhou, Ya'nan | Reflectance, Anisotropy | |
| Spatial and Temporal Evolution of Sowing and the Onset of the Rainy | Fonseca, Humberto Paiva, Pires, Gabrielle Ferreira, Brumatti, Livia Maria | Reflectance, Anisotropy | |
| Scorched earth tactics of the ``Islamic State'' after its loss of | Jaafar, Hadi, Sujud, Lara, Woertz, Eckart | Reflectance, Anisotropy | |
| Spatial difference between temperature and snowfall driven spring phenology of alpine grassland land surface based on process-based modeling on the Qinghai-Tibet Plateau | An, Shuai, Zhang, Xiaoyang, Ren, Shilong | Reflectance, Anisotropy, Albedo | |
| What explains the year-to-year variation in growing season timing of boreal black spruce forests? | El-Amine, Mariam, Roy, Alexandre, Koebsch, Franziska, Baltzer, Jennifer L., Barr, Alan, Black, Andrew, Ikawa, Hiroki, Iwata, Hiroki, Kobayashi, Hideki, Ueyama, Masahito, Sonnentag, Oliver | Reflectance, Anisotropy | |
| Thirty-eight years of CO2 fertilization has outpaced growing aridity to drive greening of Australian woody ecosystems | Rifai, Sami W., De Kauwe, Martin G., Ukkola, Anna M., Cernusak, Lucas A., Meir, Patrick, Medlyn, Belinda E., Pitman, Andy J. | Reflectance, Anisotropy, Canopy Characteristics, Evergreen Vegetation, Crown, Deciduous Vegetation, Leaf Characteristics, Vegetation Cover, Land Use/Land Cover Classification, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar) | |
| A dynamic hierarchical Bayesian approach for forecasting vegetation condition | Salakpi, Edward E., Hurley, Peter D., Muthoka, James M., Bowell, Andrew, Oliver, Seb, Rowhani, Pedram | Reflectance, Anisotropy | |
| A convolutional neural network for spatial downscaling of satellite-based solar-induced chlorophyll fluorescence (SIFnet) | Gensheimer, Johannes, Turner, Alexander J., Kohler, Philipp, Frankenberg, Christian, Chen, Jia | Atmospheric Carbon Dioxide, Solar Induced Fluorescence, Reflectance, Anisotropy | |
| DSWEmodThe Production of HighFrequency Surface Water Map Composites from Daily MODIS Images | Soulard, Christopher E., Waller, Eric K., Walker, Jessica J., Petrakis, Roy E., Smith, Britt W. | Reflectance, Anisotropy | |
| Ecosystem Gross Primary Productivity After Autumn Snowfall and Melt | Stoy, P. C., Khan, A. M., Van Dorsten, K., Sauer, P., Weaver, T., Brookshire, E. N. J. | Reflectance, Anisotropy, Plant Characteristics, Plant Phenology, Vegetation Cover, Vegetation Index | |
| Contrasting 20-year trends in NDVI at two Siberian larch forests with and without multiyear waterlogging-induced disturbances | Nagano, Hirohiko, Kotani, Ayumi, Mizuochi, Hiroki, Ichii, Kazuhito, Kanamori, Hironari, Hiyama, Tetsuya | Reflectance, Anisotropy | |
| Developing and evaluating the feasibility of a new spatiotemporal fusion framework to improve remote sensing reflectance and dynamic LAI monitoring | Li, Yan, Gao, Wanlin, Jia, Jingdun, Tao, Sha, Ren, Yanzhao | Reflectance, Anisotropy | |
| Deep learning models to map an agricultural expansion area with MODIS | Luo, Dong, Caldas, Marcellus M., Yang, Huichen | Reflectance, Anisotropy | |
| Environment-sensitivity functions for gross primary productivity in | Bao, Shanning, Wutzler, Thomas, Koirala, Sujan, Cuntz, Matthias, Ibrom, Andreas, Besnard, Simon, Walther, Sophia, Sigut, Ladislav, Moreno, Alvaro, Weber, Ulrich, Wohlfahrt, Georg, Cleverly, Jamie, Migliavacca, Mirco, Woodgate, William, Merbold, Lutz, Veenendaal, Elmar, Carvalhais, Nuno | Reflectance, Anisotropy | |
| A view from space on global flux towers by MODIS and Landsat: the FluxnetEO data set | Walther, Sophia, Besnard, Simon, Nelson, Jacob Allen, El-Madany, Tarek Sebastian, Migliavacca, Mirco, Weber, Ulrich, Carvalhais, Nuno, Ermida, Sofia Lorena, Brummer, Christian, Schrader, Frederik, Prokushkin, Anatoly Stanislavovich, Panov, Alexey Vasilevich, Jung, Martin | Land Surface Temperature, Emissivity, Reflectance, Anisotropy, Albedo | |
| A model framework to investigate the role of anomalous land surface processes in the amplification of summer drought across Ireland during 2018 | Ishola, Kazeem A., Mills, Gerald, Fealy, Reamonn M., Fealy, Rowan | Reflectance, Anisotropy, Land Surface Temperature, Emissivity | |
| A Reconstructed Global Daily Seamless SIF Product at 0.05 Degree | Hu, Jiaochan, Jia, Jia, Ma, Yan, Liu, Liangyun, Yu, Haoyang | Reflectance, Anisotropy, Albedo | |
| A new spatialtemporal depthwise separable convolutional fusion network for generating Landsat 8-day surface reflectance time series over forest regions | Zhang, Yuzhen, Liu, Jindong, Liang, Shunlin, Li, Manyao | Reflectance, Anisotropy | |
| A hierarchical category structure based convolutional recurrent neural network (HCS-ConvRNN) for Land-Cover classification using dense MODIS Time-Series data | Li, Jiayi, Zhang, Ben, Huang, Xin | Land Use/Land Cover Classification, Reflectance, Anisotropy | |
| A Bayesian Domain Adversarial Neural Network for Corn Yield Prediction | Ma, Yuchi, Zhang, Zhou | Reflectance, Anisotropy |