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 |
|---|---|---|---|
| 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 | |
| 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 | |
| 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 | |
| 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) | |
| Annual irrigated cropland mapping reveals uneven expansion and rising | Tolera, Abera Misgana, Zhang, Chao, You, Nanshan, Dong, Jinwei | Crop/Plant Yields, Landscape Patterns, Cropland, Reflectance, Vegetation Cover | |
| A High-Resolution Forest Soil Organic Carbon Dataset for China Derived from an Enhanced Quantile Regression Forest Model | Chen, Jizhen, Ou, Yuxing, Fan, Zihao, Zhang, Xin, Xiao, Wenfa, Sun, Qiwu, Sun, Xiangyang, Huang, Zhilin | Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Reflectance, Photosynthesis, Primary Production, Vegetation Productivity | |
| Landsat observations reveal increasing trend in lake clarity on the | Wang, Shenglei, Zhang, Wenzhi, Tan, Zhangru, Spyrakos, Evangelos, Shi, Kun, Somasundaram, Deepakrishna, Wu, Zijun, Zhang, Fangfang, Li, Junsheng, Zhang, Bing | Reflectance | |
| Local drivers of Rift Valley fever outbreaks in Mauritania: A one health approach combining ecological, vector, host and livestock movement data | Barry, Yahya, Metz, Markus, Krisztian, Lina, Haas, Julia, Brunn, Victoria-Leandra, Beyit, Abdellahi Diambar, El Bara, Ahmed, El Mamy Beyat, Ahmed Bezeid, Habiboulah, Habiboulah, Neteler, Markus, Cetre-Sossah, Catherine, Arsevska, Elena | Reflectance, Land Surface Temperature, Emissivity, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) | |
| Mapping heartwater risk in Guadeloupe using a combination of spatial modelling approaches | Dufleit, Victor, Etter, Eric, Guerrini, Laure | Reflectance | |
| Monitoring and modeling seasonally varying anthropogenic and biogenic CO2 over a large tropical metropolitan area | Alberti, Rafaela Cruz Alves, Lauvaux, Thomas, Vara-Vela, Angel Liduvino, Barrero, Ricard Segura, Karoff, Christoffer, Andrade, Maria de Fatima, Marques, Marcia Talita Amorim, Benavente, Noelia Rojas, Cabral, Osvaldo Machado Rodrigues, da Rocha, Humberto Ribeiro, Ynoue, Rita Yuri | Reflectance | |
| 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 | |
| Long time-series and high-frequency ecological evaluation of Henan section of the Yellow River | Guo, Jianzhong, Xu, Daozhu, Xu, Jian, Zhu, Ruoxin, Li, Ning | Reflectance | |
| Investigation of Urban Heat Islands and modeling of Land Surface | Mandal, Nirup Sundar, Chanda, Kironmala | Reflectance | |
| 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 | |
| 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 | |
| Satellite Remote Sensing Reveals More Beneficial Effect of Forest Plant Diversity on Drought Resistance in More Arid Areas of Yunnan, China | Ma, Guotao, Sun, Hao, Hu, Keke, Zhou, Hong | Reflectance | |
| Satellite Monitoring of Postfire Normalized Burn Ratio Dynamics in | Shvetsov, E. G. | Reflectance, Fire Ecology, Biomass Burning, Wildfires, Fire Occurrence, Burned Area | |
| Revealing European-wide ecosystem strategies to drought from space | Chen, Qi, Timmermans, Joris, van Bodegom, Peter M. | Reflectance, Evapotranspiration, Latent Heat Flux | |
| Retrieval of 1 km Resolution Mid-Infrared Land Surface Emissivity | Liu, Weihan, Cheng, Jie | Reflectance, Emissivity, Land Surface Temperature, Clouds, Cloud Frequency, Cloud Height, Albedo, Snow Cover |
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 |