N: 90 S: -90 E: 180 W: -180
Description
The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra MOD09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of Terra MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.
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 (MOD09A1) followed by the Julian Date of Acquisition formatted as AYYYYDDD (A2025209), the Tile Identifier which is horizontal tile and vertical tile provided as hXXvYY (h28v12), the Version of the data collection (061), the Julian Date and Time of Production designated as YYYYDDDHHMMSS (2025218035852), 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 |
|---|---|---|---|
| Declining grassland canopy height in China under asymmetric biomass allocation | Li, Huaqiang, Hu, Xinmiao, Li, Fei, Zhang, Yingjun, Lin, Kejian, Wang, Jie, Wang, Jiating | Plant Phenology, Canopy Characteristics, Vegetation Cover, Lidar, Topography, Vegetation Height, Reflectance, Digital Elevation/Terrain Model (DEM), VIEWING GEOMETRY, Terrain Elevation, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar) | |
| Desert mycobiome of Saudi Arabia is driven by vegetation patterns | Mani, Israel, Mikryukov, Vladimir, Alkahtani, Saad, Tedersoo, Leho | Reflectance, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) | |
| A CNN-Transformer Hybrid Framework for Mapping Annual Wheat Fractional Cover from 2001-2023 using MODIS Satellite Data over Asia | Li, Wenyuan, Liang, Shunlin, Chen, Yongzhe, Ma, Han, Xu, Jianglei, Ma, Yichuan, Chen, Zhongxin, Fang, Husheng, Zhang, Fengjiao | Reflectance | |
| The Arctic Boreal Burned Area (ABBA) Product | Chen, Dong, Hall, Joanne V., HoffmanHall, Amanda, Shevade, Varada, Argueta, Fernanda, Liang, Xiaoyu, Loboda, Tatiana | Forests, Fire Occurrence, Reforestation, Burned Area, Reflectance, Canopy Characteristics, Evergreen Vegetation, Crown, Deciduous Vegetation, Leaf Characteristics, Vegetation Cover, Land Use/Land Cover Classification, Total Surface Water | |
| Satellite-based detection of agricultural flash droughts and associated vegetation responses in southeastern South America | Masaro, Lumila, Lovino, Miguel A, Pierrestegui, M Josefina, Muller, Gabriela V, Dorigo, Wouter | Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Leaf Area Index (LAI), Photosynthesis, Primary Production, Vegetation Productivity, Evapotranspiration, Latent Heat Flux, Reflectance, Root Zone Soil Moisture, Surface Soil Moisture | |
| 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 | |
| Predicting Postfire Forest Mortality Using Remote Sensing Data and Machine Learning | Shvetsov, E. G. | Reflectance | |
| Deconstructing the Effects of Climate and Phenology on Hydrological | Zheng, Lilin, Li, Dahui, Wang, Ling, Chen, Ruishan, Xu, Jianhua | Reflectance | |
| Concurrent Increases of Impervious Surface Area and Vegetation Greenness and Productivity in China's Yangtze River Delta | Yin, Chenglong, Xiao, Xiangming, Pan, Li, Chen, Ruishan, Yin, Yi, Qin, Yuanwei, Shi, Wenjiao, Van de Voorde, Tim, Yin, Shenglai, Yao, Yuan, Pan, Baihong, Jia, Nan, Guo, Xiaona, Meng, Fei | Reflectance, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Land Use/Land Cover Classification, Vegetation Cover, Plant Characteristics, Leaf Characteristics, Canopy Characteristics, Albedo | |
| Conservation effects of transboundary protected areas on mitigating | An, Li, Shen, Lei, Zhong, Shuai, Li, Delong, Zhu, Yidong | Reflectance, Terrain Elevation, RADAR IMAGERY, Topographical Relief Maps | |
| Deteccion del maximo verdor en maiz (Zea mays) mediante series temporales de datos MODIS en la parroquia Colonche de la provincia de Santa Elena | Saenz Flores, Cesar, Villacres, Julio Cesar | Reflectance | |
| Evaluating patterns of plant phenological progression and pronghorn | Proffitt, Kelly M., Terrill Paterson, J., DeVoe, Jesse D., Hansen, Christopher P., Millspaugh, Joshua J. | Reflectance | |
| Evaluating Vegetation Greening and Browning across the Rio Grande Basin | Talchabhadel, Rocky, Rhodes, Edward C., Palmate, Santosh S., Kumar, Saurav | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Reflectance, Maximum/Minimum Temperature, 24 Hour Precipitation Amount, Snow Water Equivalent, Shortwave Radiation, Vapor Pressure | |
| Evaluating the Performance of Satellite-Derived Vegetation Indices in | Cao, Deli, Huang, Xiaojuan, Liu, Gang, Tian, Lingwen, Xin, Qi, Yang, Yuli | Reflectance | |
| Evaluation of the potential effects of forest vegetation cover on surface temperature in different geographical and climatic regions of Shaanxi Province, China | Wang, Minghui, Liu, Jincheng | Terrain Elevation, Digital Elevation/Terrain Model (DEM), Topographical Relief Maps, Reflectance, Land Surface Temperature, Emissivity, Evapotranspiration, Latent Heat Flux | |
| Incorporation of needleleaf traits improves estimation of light | Pan, Baihong, Xiao, Xiangming, Pan, Li, Meng, Cheng, Blanken, Peter D., Burns, Sean P., Celis, Jorge A., Zhang, Chenchen, Qin, Yuanwei | Reflectance | |
| Incorporating environmental stress improves estimation of photosynthesis | Gao, Lun, Guan, Kaiyu, Jiang, Chongya, Lu, Xiaoman, Wang, Sheng, Ainsworth, Elizabeth A., Wu, Xiaocui, Chen, Min | Reflectance, Photosynthetically Active Radiation, Plant Characteristics, REFLECTED INFRARED, Gross Primary Production (gpp), Land Use/Land Cover Classification | |
| Impacts of meteorological drought on peak vegetation productivity of grasslands from perspectives of canopy structure and leaf physiology | Bai, Wenrui, Wang, Huanjiong, Xiao, Jingfeng, Li, Xing, Ge, Quansheng | Land Use/Land Cover Classification, Reflectance, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar) | |
| Integrating remote sensing and deep learning forecasting model: A fluid-environment interface study | Hassanian, Reza, Cavallaro, Gabriele, Riedel, Morris | Reflectance, Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain | |
| Integrating Remote Sensing and machine learning for dynamic burn probability mapping in data-limited contexts | Diaz-Vazquez, Diego, Casillas-Garcia, Luis Fernando, Garcia- Gonzalez, Alejandro, Graf Montero, Sergio Humberto, Marquez Rubio, Jose Isaac, Llamas Llamas, Juan Jose, Gradilla Hernandez, Misael Sebastian | Reflectance, Land Surface Temperature, Emissivity | |
| A 25-year assessment of aerosol dynamics and environmental drivers in Iran's Lakes and wetlands | Ebrahimi-Khusfi, Zohre, Samadi-Todar, Seyed Arman, Okati, Narjes, Kaskaoutis, Dimitris G. | Reflectance | |
| A 30-m annual paddy rice dataset in Northeastern China during period 20002023 | Hou, Dawei, Chen, Jing, Dong, Jinwei, Ji, Chao, Feng, Jingxuan, Du, Guoming, Yang, Lixiao | Reflectance | |
| Dynamic difference between surging and normal glaciers in the context of | Gao, Yongpeng, Liu, Shiyin, Qi, Miaomiao, Xie, Fuming | Reflectance | |
| Dynamic monitoring of vegetation phenology on the Qinghai-Tibetan plateau from 2001 to 2020 via the MSAVI and EVI | Zhao, Zhijian, Lu, Chengfang, Tonooka, Hideyuki, Wu, Lei, Lin, Hui, Jiang, Xunyan | Land Use/Land Cover Classification, Reflectance | |
| Ensemble Machine Learning Models for Rice and Wheat Yield Prediction: A Comparative Study Across Districts in India's Kharif and Rabi Seasons | Zhdanov, Vladimir, Kaplin, Dmitry, Akimov, Mikhail, Antonov, Alexey, Gasanov, Mikhail, Malchenko, Vitaly | Reflectance |
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 |
|---|---|---|---|---|---|---|---|
| sur_refl_b01 | Surface Reflectance Band 1 (620-670 nm) | N/A | int16 | -28672 | -100 to 16000 | 0.0001 | N/A |
| sur_refl_b02 | Surface Reflectance Band 2 (841-876 nm) | N/A | int16 | -28672 | -100 to 16000 | 0.0001 | N/A |
| sur_refl_b03 | Surface Reflectance Band 3 (459-479 nm) | N/A | int16 | -28672 | -100 to 16000 | 0.0001 | N/A |
| sur_refl_b04 | Surface Reflectance Band 4 (545-565 nm) | N/A | int16 | -28672 | -100 to 16000 | 0.0001 | N/A |
| sur_refl_b05 | Surface Reflectance Band 5 (1230-1250 nm) | N/A | int16 | -28672 | -100 to 16000 | 0.0001 | N/A |
| sur_refl_b06 | Surface Reflectance Band 6 (1628-1652 nm) | N/A | int16 | -28672 | -100 to 16000 | 0.0001 | N/A |
| sur_refl_b07 | Surface Reflectance Band 7 (2105-2155 nm) | N/A | int16 | -28672 | -100 to 16000 | 0.0001 | N/A |
| sur_refl_day_of_year | Day of the year for the pixel | Julian day | uint16 | 65535 | 1 to 366 | N/A | N/A |
| sur_refl_qc_500m | Surface reflectance 500m band quality control flags | Bit Field | uint32 | 4294967295 | 0 to 4294966531 | N/A | N/A |
| sur_refl_raz | MODIS relative azimuth angle | Degree | int16 | 0 | -18000 to 18000 | 0.01 | N/A |
| sur_refl_state_500m | Surface reflectance 500m state flags | Bit Field | uint16 | 65535 | 0 to 57343 | N/A | N/A |
| sur_refl_szen | MODIS solar zenith angle | Degree | int16 | 0 | 0 to 18000 | 0.01 | N/A |
| sur_refl_vzen | MODIS view zenith angle | Degree | int16 | 0 | 0 to 18000 | 0.01 | N/A |