N: 90 S: -90 E: 180 W: -180
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The submitted value 10 in the Items element is not allowed.Description
The MCD43A4 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MCD43A4 Version 6.1 data product.
The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6 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
- 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 MCD43A4 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 (MCD43A4) followed by the Julian Date of Acquisition formatted as AYYYYDDD (A2002272), the Tile Identifier which is horizontal tile and vertical tile provided as hXXvYY (h16v16), the Version of the data collection (006), the Julian Date and Time of Production designated as YYYYDDDHHMMSS (2016132202805), 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 |
|---|---|---|---|
| Utilization of synthetic minority oversampling technique for improving potato yield prediction using remote sensing data and machine learning algorithms with small ... | Ebrahimy, Hamid, Wang, Yi, Zhang, Zhou | Reflectance, Anisotropy, Land Surface Temperature, Emissivity | |
| Unravelling geological controls on groundwater flow and surface | Marti, Etienne, Leray, Sarah, Villela, Daniela, Maringue, Jose, Yanez, Gonzalo, Salazar, Esteban, Poblete, Fernando, Jimenez, Jose, Reyes, Gabriela, Poblete, Guillermo, Huaman, Zeidy, Figueroa, Ronny, Araya Vargas, Jaime, Sanhueza, Jorge, Munoz, Marjorie, Charrier, Reynaldo, Fernandez, Gabriel | Reflectance, Anisotropy | |
| Photosynthetically Active Radiation and Foliage Clumping Improve Satellite-Based NIRv Estimates of Gross Primary Production | Filella, Iolanda, Descals, Adria, Balzarolo, Manuela, Yin, Gaofei, Verger, Aleixandre, Fang, Hongliang, Penuelas, Josep | Reflectance, Anisotropy | |
| Observations of satellite land surface phenology indicate that maximum leaf greenness is more associated with global vegetation productivity than growing season ... | Gao, Xiaojie, McGregor, Ian R., Gray, Josh M., Friedl, Mark A., Moon, Minkyu | Reflectance, Anisotropy, Plant Phenology, Enhanced Vegetation Index (EVI) | |
| Predictive performance of machine learning model with varying sampling | Bouasria, Abdelkrim, Bouslihim, Yassine, Gupta, Surya, Taghizadeh-Mehrjardi, Ruhollah, Hengl, Tomislav | Reflectance, Anisotropy, Photosynthesis, Primary Production, Vegetation Productivity | |
| Projecting live fuel moisture content via deep learning | 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 | |
| Deep learning models to map an agricultural expansion area with MODIS | Luo, Dong, Caldas, Marcellus M., Yang, Huichen | Reflectance, Anisotropy | |
| 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 | |
| Impact of BRDF spatiotemporal smoothing on land surface albedo estimation | Yang, Jian, Shuai, Yanmin, Duan, Junbo, Xie, Donghui, Zhang, Qingling, Zhao, Ruishan | Reflectance, Albedo, Anisotropy, Land Use/Land Cover Classification | |
| Improved detection of abrupt change in vegetation reveals dominant fractional woody cover decline in Eastern Africa | Abera, Temesgen Alemayehu, Heiskanen, Janne, Maeda, Eduardo Eiji, Hailu, Binyam Tesfaw, Pellikka, Petri K.E. | Reflectance, Anisotropy, Canopy Characteristics, Evergreen Vegetation, Crown, Deciduous Vegetation, Leaf Characteristics, Vegetation Cover, Land Use/Land Cover Classification, Population Density, Fire Ecology, Biomass Burning, Wildfires, Fire Occurrence, Burned Area | |
| Improving GPP estimates by partitioning green APAR from total APAR in | Chen, Siyuan, Liu, Liangyun, Sui, Lichun, Liu, Xinjie | Reflectance, Anisotropy | |
| How long is the memory of forest growth to rainfall in asynchronous | Joshi, Rakesh Chandra, Sheridan, Gary J., Ryu, Dongryeol, Lane, Patrick N.J. | Reflectance, Anisotropy | |
| Impact of Image-Processing Routines on Mapping Glacier Surface Facies | Jawak, Shridhar D., Wankhede, Sagar F., Luis, Alvarinho J., Balakrishna, Keshava | Terrain Elevation, Digital Elevation/Terrain Model (DEM), Topographical Relief Maps, Reflectance, Anisotropy, Albedo | |
| A Bayesian Domain Adversarial Neural Network for Corn Yield Prediction | Ma, Yuchi, Zhang, Zhou | 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 | |
| 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 | |
| Estimation of 1-km Resolution All-Sky Instantaneous Erythemal UV-B with | Zhao, Ruixue, He, Tao | Aerosol Optical Depth/Thickness, Atmospheric Ozone, Reflectance, Reflectance, Anisotropy | |
| Estimation of actual evapotranspiration using TDTM model and MODIS derived variables | Ruiz-Alvarez, Marcos, Gomariz-Castillo, Francisco, Alonso-Sarria, Francisco, Lopez-Ballesteros, Ana | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Albedo, Anisotropy, Land Surface Temperature, Emissivity, Photosynthetically Active Radiation, Leaf Area Index (LAI), Leaf Characteristics, Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Reflectance | |
| 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 | |
| Matching high resolution satellite data and flux tower footprints improves their agreement in photosynthesis estimates | Kong, Juwon, Ryu, Youngryel, Liu, Jiangong, Dechant, Benjamin, Rey-Sanchez, Camilo, Shortt, Robert, Szutu, Daphne, Verfaillie, Joe, Houborg, Rasmus, Baldocchi, Dennis D. | Reflectance, Anisotropy, Albedo | |
| Model-driven estimation of closed and open shrublands live fuel moisture content | Lai, Gengke, Quan, Xingwen, Yebra, Marta, He, Binbin | Land Use/Land Cover Classification, Reflectance, Anisotropy, Canopy Characteristics, Evergreen Vegetation, Crown, Deciduous Vegetation, Leaf Characteristics, Vegetation Cover, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar) | |
| Mapping South Americas drylands through remote sensingA review of the methodological trends and current challenges | Ganem, Khalil Ali, Xue, Yongkang, Rodrigues, Ariane de Almeida, Franca-Rocha, Washington, Oliveira, Marceli Terra de, Carvalho, Nathalia Silva de, Cayo, Efrain Yury Turpo, Rosa, Marcos Reis, Dutra, Andeise Cerqueira, Shimabukuro, Yosio Edemir | Reflectance, Anisotropy, Albedo, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Canopy Characteristics, Evergreen Vegetation, Crown, Deciduous Vegetation, Leaf Characteristics, Vegetation Cover, Land Use/Land Cover Classification, Total Surface Water |