N: 90 S: -90 E: 180 W: -180
1000 Meters x 1000 Meters
The MOD09GA Version 6.1 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 500 meter (m) surface reflectance, observation, and quality bands are a set of ten 1 kilometer (km) observation bands and geolocation flags. The reflectance layers from the MOD09GA are used as the source data for many of the MODIS land products.
Known Issues
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.
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| Ice-resistant breakwater rock sizing at Elim, Alaska | Engel, Chandler, Morriss, Blaine | Reflectance | |
| Runoff from Greenland's firn areawhy do MODIS, RCMs and a firn model disagree? | Machguth, Horst, Tedstone, Andrew, Kuipers Munneke, Peter, Brils, Max, Noel, Brice, Clerx, Nicole, Jullien, Nicolas, Fettweis, Xavier, van den Broeke, Michiel | Ice Velocity, Reflectance | |
| Monitoring Flood Inundation Dynamics From Space | Campo, C., Tamagnone, P., Choy, S., Tran, T. D., Schumann, G. J.P., Kuleshov, Y. | Atmospheric Water Vapor, Precipitation, Brightness Temperature, Surface Soil Moisture, Terrain Elevation, Vegetation Height, Reflectance, Reflectance | |
| The Accuracy, Spatial Consistency, and Impact Factors of Global Cropland Products in Karst Landscapes: A Case Study of the YunnanGuizhou Plateau | Xia, Yi, Bao, Li, Xia, Yunsheng, Liu, Guangjie | Terrain Elevation, RADAR IMAGERY, Topographical Relief Maps, Reflectance | |
| Amazon forest nutrient limitation is mitigated by distant fire emissions | Descals, Adria, Janssens, Ivan A., Penuelas, Josep | Solar Induced Fluorescence, Chlorophyll, Primary Production, Leaf Characteristics, Reflectance, Fire Ecology, Biomass Burning, Wildfires, Fire Occurrence, Burned Area, Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar) | |
| 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 | |
| An End-to-End Foundation Model-Based Framework for Robust LAI Retrieval | Gu, Xiangfeng, Li, Wenyuan, Guan, Shikang | Reflectance | |
| A Costefficient and robust approach to monitor ecosystem photosynthesis using nearinfrared enabled cameras | Syahid, Luri Nurlaila, Luo, Xiangzhong, Zhao, Ruiying, Yu, Liyao, Tan, Li Ming, Detto, Matteo, Sonnentag, Oliver | Reflectance, Land Use/Land Cover Classification, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Vegetation Cover, Plant Phenology, Plant Characteristics | |
| A harmonized 20002024 dataset of daily river ice concentration and annual phenology for major Arctic rivers | Qiu, Jiahui, Luojus, Kari, Kaartinen, Harri, Qiu, Yubao, Silander, Jari, Patro, Epari Ritesh, Klove, Bjorn, Haghighi, Ali Torabi | Reflectance | |
| Combined effects of site and model parameterization for soil respiration components in a Canadian wildfire chronosequence | Zobitz, John, Zhou, Xuan, Aaltonen, Heidi, Koster, Egle, Berninger, Frank, Pumpanen, Jukka, Koster, Kajar | Photosynthesis, Primary Production, Vegetation Productivity, Reflectance, Land Surface Temperature, Emissivity | |
| Comparative machine learning and deep learning approaches for | Azizi, Mahan, Abbasi, Ali, Asli Charandabi, Mohammad Reza | Precipitation, Precipitation Amount, Precipitation Rate, Snow, Rain, Surface Pressure, Heat Flux, Longwave Radiation, Shortwave Radiation, Air Temperature, Specific Humidity, Evapotranspiration, Wind Speed, Soil Moisture/Water Content, Soil Temperature, Land Surface Temperature, Snow Cover, Snow Depth, Snow Water Equivalent, Runoff, Reflectance, Emissivity | |
| Comparison of Sentinel-2 and MODIS for estimating GPP along an ecosystem gradient in eastern Germany | Sayeed, Mostafa, Ahmadpour, Somayeh, Trachte, Katja | Reflectance, Albedo, Anisotropy, Land Surface Temperature, Emissivity | |
| Automated Machine Learning for High-Resolution Daily and Hourly Methane Emission Mapping for Rice Paddies over South Korea: Integrating MODIS, ERA5-Land, and Soil Data | Jang, Jiah, Kim, Seung Hee, Kafatos, Menas, Cho, Jaeil, Yoo, Gayoung, Jeong, Sujong, Lee, Yangwon | Reflectance | |
| Identifying predictors of tropical cyclone impacts on coastal agriculture to assess coastal wetland buffering signals | Rowland, Phebe I., Wartman, Melissa, Nuyts, Siegmund, Duarte de Paula Costa, Micheli | Reflectance | |
| Multi-Sensor Spatiotemporal Fusion for 30-m Daily Gapless Snow Cover | Wu, Jinhang, Zhang, Xueliang, Xiao, Pengfeng, Jia, Yumeng, Tang, Bo, Liu, Yan | Reflectance, Terrain Elevation, RADAR IMAGERY, Topographical Relief Maps, Albedo, Snow Cover | |
| Vegetation Water Stress Response-Driven Approach for Verifying Vegetation Drought Monitoring Results: A Case Study of the Yangtze River Basin, China | Wu, Lixin, Zhang, Zhimei, Haseeb, Muhammad, Jiao, Zhijun | Land Use/Land Cover Classification, Reflectance | |
| Magnitude, drivers, and patterns of gross primary productivity of rice | Mahbub, Riasad Bin, Reba, Michele L., Runkle, Benjamin R.K. | Leaf Area Index (LAI), Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Leaf Characteristics, Photosynthetically Active Radiation, Reflectance | |
| Migratory Birds Advance Spring Arrival and EggLaying in the Arctic, Mostly by Travelling Faster | Lameris, Thomas K., Boom, Michiel P., Nuijten, Rascha J. M., Buitendijk, Nelleke H., Eichhorn, Gotz, Ens, Bruno J., Exo, KlausMichael, Glazov, Petr M., Hanssen, Sveinn Are, Hunke, Philip, van der Jeugd, Henk P., de Jong, Margje E., Kolzsch, Andrea, Kondratyev, Alexander, Kruckenberg, Helmut, Kulikova, Olga, Linssen, Hans, Loonen, Maarten J. J. E., Loshchagina, Julia A., Madsen, Jesper, Moe, Brge, Moonen, Sander, Muskens, Gerhard J. D. M., Nolet, Bart A., Pokrovsky, Ivan, Reneerkens, Jeroen, Scheiber, Isabella B. R., Schekkerman, Hans, Schreven, Kees H. T., Tal, Tohar, Tulp, Ingrid, Verhoeven, Mo A., Versluijs, Tom S. L., Volkov, Sergey, Wikelski, Martin, van Bemmelen, Rob S. A. | Reflectance, Total Surface Water | |
| Inspecting gaseous pollutants and their dynamics in Kathmandu using satellite derived data | Magar, Keshab | Reflectance, Land Surface Temperature, Emissivity | |
| Impacts of land surface temperature and ambient factors on near-surface | Li, Songyang, Wong, Man Sing, Zhu, Rui, Shi, Guoqiang, Yang, Jinxin | Reflectance, Land Surface Temperature, Emissivity | |
| Prediction of Net Longwave Radiation and Turbulent Fluxes Using Remote-Sensing-Derived Net Shortwave Radiation for Different Land Cover Types | Wang, Jingwen, Lu, Lei, Zhou, Xiaoming, Huang, Guanghui, Chen, Zihan | RADAR IMAGERY, Terrain Elevation, Topographical Relief Maps, Digital Elevation/Terrain Model (DEM), Reflectance, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) | |
| pyVPRM: a next-generation vegetation photosynthesis and | Glauch, Theo, Marshall, Julia, Gerbig, Christoph, Botia, Santiago, Gakowski, Micha, Vardag, Sanam N., Butz, Andre | Reflectance, Photosynthesis, Primary Production, Vegetation Productivity | |
| Near real-time satellite soil moisture estimation via residual learning | Sengupta, Soumita, Chu, Hone-Jay | Reflectance | |
| The Process-Cognizant Vegetation Drought Model (PCVDM): Analyzing | Zhang, Zhimei, Jiao, Zhijun, Wu, Lixin | Land Use/Land Cover Classification, Reflectance, Emissivity, Land Surface Temperature | |
| Spatial and temporal assessment of soil degradation risk in Europe | Afshar, Mehdi H., Hassani, Amirhossein, Aminzadeh, Milad, Borrelli, Pasquale, Panagos, Panos, Robinson, David A., Or, Dani, Shokri, Nima | Reflectance, Land Surface Temperature, Emissivity, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) |
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 |
|---|---|---|---|---|---|---|---|
| gflags_1 | Geolocation flags | Bit Field | uint8 | 255 | 0 to 248 | N/A | N/A |
| granule_pnt _1 | Granule pointer | N/A | uint8 | 255 | 0 to 254 | N/A | N/A |
| iobs_res_1 | Observation number | N/A | uint8 | 255 | 0 to 254 | N/A | N/A |
| num_observations_1km | Number of observations within a pixel | N/A | int8 | -1 | 0 to 127 | N/A | N/A |
| num_observations_500m | Number of observations per 500m pixel | N/A | int8 | -1 | 0 to 127 | N/A | N/A |
| obscov_500m_1 | Observation coverage | Percent | int8 | -1 | 0 to 100 | 0.01 | N/A |
| orbit_pnt_1 | Orbit pointer | N/A | int8 | -1 | 0 to 15 | N/A | N/A |
| QC_500m_1 | Surface Reflectance 500m Quality Assurance | Bit Field | uint32 | 787410671 | 0 to 4294966019 | N/A | N/A |
| q_scan_1 | 250m scan value information | N/A | uint8 | 255 | 0 to 254 | N/A | N/A |
| Range_1 | Distance to sensor | Meters | uint16 | 0 | 27000 to 65535 | 25 | N/A |
| SensorAzimuth_1 | Azimuth angle to sensor | Degree | int16 | -32767 | -18000 to 18000 | 0.01 | N/A |
| SensorZenith_1 | Zenith angle to sensor | Degree | int16 | -32767 | 0 to 18000 | 0.01 | N/A |
| SolarAzimuth_1 | Azimuth angle to sun | Degree | int16 | -32767 | -18000 to 18000 | 0.01 | N/A |
| SolarZenith_1 | Zenith angle to sun | Degree | int16 | -32767 | 0 to 18000 | 0.01 | N/A |
| state_1km_1 | 1km Reflectance Data State QA | Bit Field | uint16 | 65535 | 0 to 57335 | N/A | N/A |
| sur_refl_b01_1 | Surface Reflectance Band 1 | N/A | int16 | -28672 | -100 to 16000 | 0.0001 | N/A |
| sur_refl_b02_1 | Surface Reflectance Band 2 | N/A | int16 | -28672 | -100 to 16000 | 0.0001 | N/A |
| sur_refl_b03_1 | Surface Reflectance Band 3 | N/A | int16 | -28672 | -100 to 16000 | 0.0001 | N/A |
| sur_refl_b04_1 | Surface Reflectance Band 4 | N/A | int16 | -28672 | -100 to 16000 | 0.0001 | N/A |
| sur_refl_b05_1 | Surface Reflectance Band 5 | N/A | int16 | -28672 | -100 to 16000 | 0.0001 | N/A |
| sur_refl_b06_1 | Surface Reflectance Band 6 | N/A | int16 | -28672 | -100 to 16000 | 0.0001 | N/A |
| sur_refl_b07_1 | Surface Reflectance Band 7 | N/A | int16 | -28672 | -100 to 16000 | 0.0001 | N/A |