N: 90 S: -90 E: 180 W: -180
Description
The MCD43A3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MCD43A3 Version 6.1 data product.
The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A3 Version 6 Albedo Model dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the 16 day which is reflected in the Julian date in the file name.
The MCD43A3 provides black-sky albedo (directional hemispherical reflectance) and white-sky albedo (bihemispherical reflectance) data at local solar noon for MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. Along with the albedo layers are the simplified mandatory quality layers for each of the 10 bands. Essential quality information provided in the corresponding MCD43A2 data file should be consulted when using this product.
Users are also 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.
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 MCD43A3 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.
Copy Citation
File Naming Convention
The file name begins with the Product Short Name (MCD43A3) followed by the Julian Date of Acquisition formatted as AYYYYDDD (A2002272), the Tile Identifier which is horizontal tile and vertical tile provided as hXXvYY (h23v15), the Version of the data collection (006), the Julian Date and Time of Production designated as YYYYDDDHHMMSS (2016132202442), 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 |
|---|---|---|---|
| A spatialtemporal seamless reconstruction method for high-resolution 16 m daily land surface albedo products | Guo, Zhaotong, Wen, Jianguang, You, Dongqin, Tang, Yong, Piao, Sen, Liu, Qinhuo, Xiao, Qing | Albedo, Anisotropy | |
| Big data fusion-driven geospatial knowledge graph construction method for sustainable smart cities | Duan, Yuxi, Liang, Maohan, Li, Yan, Gao, Ruobin, Chen, Jin, Chen, Zhong Shuo, Wang, Hua | Albedo, Anisotropy | |
| Incorporating varying vegetation characteristics driven by | Sebastian, Dawn Emil, Ghosh, Subimal | Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Land Use/Land Cover Classification, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Albedo, Anisotropy | |
| Estimating the Near-Surface Air Temperature Field Using Satellite-Based Remote Sensing of Land Surface Temperatures | Frat Ors, Pelin, Mahdavi, Ardeshir | Albedo, Anisotropy, Land Surface Temperature, Emissivity, Reflectance | |
| Commodity-driven deforestation doubles local warming from tropical forest loss | Smith, Callum, Baker, Jessica C A, Doggart, Nike H, Argles, Arthur P K, Robertson, Eddy, Chadwick, Robin, Adami, Marcos, Coelho, Caio A S, Spracklen, Dominick V | Land Use/Land Cover Classification, Albedo, Anisotropy, Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Leaf Area Index (LAI), Evapotranspiration, Photosynthesis, Primary Production, Latent Heat Flux, Land Surface Temperature, Emissivity | |
| Revisiting the Urban Heat and Pollution Islands over the Kolkata Metropolitan Area, India | Pandey, Pragya, Tyagi, Bhishma, Kumar, Pradeep, Sahu, Saroj Kumar, Sharma, Kuldeep | Land Use/Land Cover Classification, Albedo, Anisotropy, Land Surface Temperature, Emissivity, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) | |
| Response of Vegetation Phenology to Hydrothermal Variables on the QTP Using EVI and MSAVI | Zhao, Zhijian, Lin, Hui, Wang, Li, Huang, Min, Wu, Lei, Tang, Linling, Yang, Tao, Xiao, Xin | Albedo, Anisotropy, Evapotranspiration, Latent Heat Flux, Land Use/Land Cover Classification, Land Surface Temperature, Emissivity, Reflectance | |
| AI-Based Downscaling of MODIS LST Using SRDA-Net Model for High-Resolution Data Generation | Ma, Hongxia, Mao, Kebiao, Yuan, Zijin, Xu, Longhao, Shi, Jiancheng, Guo, Zhonghua, Qin, Zhihao | Reflectance, Land Surface Temperature, Emissivity, Albedo, Anisotropy, Land Use/Land Cover Classification | |
| A strategy to estimate daily shortwave downward radiation in rugged regions from a few satellite observations | Xian, Yuyang, Wang, Tianxing, Du, Yihan, Yu, Pei, Letu, Husi | Albedo, Anisotropy | |
| A Spatial Hybrid Model for Crop Yield Prediction in Western Australia | Ibrahim, Muhammad, Singh, Balwinder, Pires, Rodrigo, Easton, Julia, Tareque, Hasnein | Albedo, Anisotropy | |
| Assessing midsummer snow-free land surface albedo variability across multiple Arctic sites using the Harmonized Landsat and Sentinel-2 product | Gottuk, Jannika, Stuenzi, Simone M., Runge, Alexandra, Boike, Julia | Reflectance, Albedo, Anisotropy | |
| Brief communication: Improving lake ice modeling in ORCHIDEE-FLake model using MODIS albedo data | Titus, Zacharie, Cuynet, Amelie, Salmon, Elodie, Ottle, Catherine | Albedo, Anisotropy | |
| Improved global estimates of terrestrial evapotranspiration using the MODIS and VIIRS sensors | Endsley, K. Arthur, Zhao, Maosheng, Kimball, John S., Albrethsen, Tyler, Devadiga, Sadashiva | Albedo, Anisotropy, Land Use/Land Cover Classification, RADAR IMAGERY, Terrain Elevation, Digital Elevation/Terrain Model (DEM), Reflectance, Topographical Relief Maps, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Leaf Characteristics, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar) | |
| Impacts of Topography on Daily Mean Albedo Estimation Over Snow-free Rugged Terrain | Han, Yuan, Wen, Jianguang, You, Dongqin, Xiao, Qing, Liu, Guokai, Tang, Yong, Piao, Sen, Zhao, Na, Liu, Qinhuo | Albedo, Anisotropy, Radiative Forcing, Forests, Alpine/Tundra | |
| Influence of initial soil organic carbon in grassland on the sensitivity of carbon changes to climate after grasslandtocropland conversion | Dou, Pengpeng, Wang, Jie, Cai, Tianyu, Miao, Zhengzhou, Wang, Xu, Liang, Junyi, Li, Ping, Fan, Jiangwen, Tang, Shiming, Xiao, Xiangming, Guo, Lizhu, Huang, Jing, Gao, Qian, Chen, Chao, Liu, Kesi, Wang, Kun | Albedo, Anisotropy | |
| Global non-uniformity in biophysical surface temperature responses to cropland expansion over non-forest vegetation | Si, Menglin, Li, Zhao-Liang, Liu, Xiangyang, Li, Yitao, Leng, Pei, Tang, Bo-Hui, Tang, Ronglin, Duan, Si-Bo, Liu, Meng, Zhou, Chenghu | Evapotranspiration, Latent Heat Flux, Albedo, Anisotropy | |
| Estimating GPP in China using different site-level datasets, vegetation classification and vegetation indices | Xu, Jiahui, Chen, Tiexi, Chen, Xin, Zhou, Shengjie, Gu, Zhe, Li, Wenhui, Cui, Yingying, Wang, Shengzhen, Liu, Shuci | Photosynthetically Active Radiation, Leaf Area Index (LAI), Leaf Characteristics, Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Reflectance, Albedo, Anisotropy, Land Use/Land Cover Classification | |
| Enhancing prediction of wildfire occurrence and behavior in Alaska using | Ahajjam, A., Allgaier, M., Chance, R., Chukwuemeka, E., Putkonen, J., Pasch, T. | Land Use/Land Cover Classification, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Land Surface Temperature, Emissivity, Albedo, Anisotropy | |
| Evaluation of the performance of multiple reanalysis forcing data in | Xie, Yuxuan, Kong, Dongdong, Zhang, Yongqiang, Zhong, Yulong, Ma, Ning, Gong, Rouyan, Ci, Hui, Xiao, Mingzhong, Gu, Xihui | Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Land Surface Temperature, Emissivity, Albedo, Anisotropy, Land Use/Land Cover Classification | |
| Elevation Correction of Forest Biogeophysical Cooling Effect in China | Bai, Tingting, Song, Yongze, Li, Tong, Zheng, Jinxiu, Zhu, Kai | Evapotranspiration, Photosynthesis, Primary Production, Latent Heat Flux, Land Surface Temperature, Emissivity, Leaf Characteristics, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Albedo, Anisotropy | |
| Divergent Seasonal Biophysical Effects Induced by the Three Gorges | Li, Hongbin, Wang, Weiguang, Liu, Guoshuai, Castelli, Fabio, Forzieri, Giovanni | Land Use/Land Cover Classification, Land Surface Temperature, Emissivity, Evapotranspiration, Latent Heat Flux, Albedo, Anisotropy | |
| Pervasive but biome-dependent relationship between fragmentation and resilience in forests | Su, Yongxian, Zhang, Chaoqun, Cescatti, Alessandro, Yu, Kailiang, Ciais, Philippe, Smith, Taylor, Shang, Jiali, Carnicer, Jofre, Liu, Jane, Chen, Jing Ming, Green, Julia K., Wu, Jianping, Ponce-Campos, Guillermo E., Zhang, Yongguang, Zuo, Zhiyan, Liao, Jinbao, Wu, Jianping, Lafortezza, Raffaele, Yan, Kai, Yang, Xueqin, Liu, Liyang, Ren, Jiashun, Yuan, Wenping, Chen, Xiuzhi, Wu, Chaoyang, Zhou, Weiqi | Albedo, Anisotropy, Forest Management, Emissivity, Land Surface Temperature, Leaf Characteristics, Photosynthetically Active Radiation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Land Use/Land Cover Classification, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) | |
| 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 | |
| Mature riparian alder forest acts as a strong and consistent carbon sink | Krasnova, Alisa, Soosaar, Kaido, Rogozin, Svyatoslav, Krasnov, Dmitrii, Mander, Ulo | Albedo, Anisotropy | |
| Machine learning approaches for clear-sky Land Surface Albedo (LSA) retrieval using OCM-3 data over diverse Indian landscapes | KURESHI, ALIYA M., PATHAK, VISHAL N., KARDANI, DISHA B., DAVE, JALPESH A., SHAH, DHIRAJ B., TURAKHIA, TEJAS P., GUJRATI, ASHWIN, PANDYA, MEHUL R., TRIVEDI, HIMANSHU J. | Aerosol Optical Depth/Thickness, Albedo, 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 |
|---|---|---|---|---|---|---|---|
| Albedo_BSA_Band1 | Black-Sky Albedo for Band 1 | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Albedo_BSA_Band2 | Black-Sky Albedo for Band 2 | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Albedo_BSA_Band3 | Black-Sky Albedo for Band 3 | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Albedo_BSA_Band4 | Black-Sky Albedo for Band 4 | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Albedo_BSA_Band5 | Black-Sky Albedo for Band 5 | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Albedo_BSA_Band6 | Black-Sky Albedo for Band 6 | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Albedo_BSA_Band7 | Black-Sky Albedo for Band 6 | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Albedo_BSA_nir | Black-Sky Albedo for NIR broadband | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Albedo_BSA_shortwave | Black-Sky Albedo for shortwave broadband | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Albedo_BSA_vis | Black-Sky Albedo for vis broadband | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Albedo_WSA_Band1 | White-Sky Albedo for Band 1 | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Albedo_WSA_Band2 | White-Sky Albedo for Band 2 | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Albedo_WSA_Band3 | White-Sky Albedo for Band 3 | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Albedo_WSA_Band4 | White-Sky Albedo for Band 4 | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Albedo_WSA_Band5 | White-Sky Albedo for Band 5 | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Albedo_WSA_Band6 | White-Sky Albedo for Band 6 | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Albedo_WSA_Band7 | White-Sky Albedo for Band 7 | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Albedo_WSA_nir | White-Sky Albedo for NIR broadband | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Albedo_WSA_shortwave | White-Sky Albedo for shortwave broadband | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| Albedo_WSA_vis | White-Sky Albedo for vis broadband | N/A | int16 | 32767 | 0 to 32766 | 0.001 | N/A |
| 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 |
| BRDF_Albedo_Band_Mandatory_Quality_nir | BRDF Albedo Mandatory Quality for NIR broadband | Bit Field | uint8 | 255 | 0 to 254 | N/A | N/A |
| BRDF_Albedo_Band_Mandatory_Quality_shortwave | BRDF Albedo Mandatory Quality for shortwave broadband | Bit Field | uint8 | 255 | 0 to 254 | N/A | N/A |
| BRDF_Albedo_Band_Mandatory_Quality_vis | BRDF Albedo Mandatory Quality for vis broadband | Bit Field | uint8 | 255 | 0 to 254 | N/A | N/A |