N: 90 S: -90 E: 180 W: -180
Description
The MOD09GA Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MOD09GA Version 6.1 data product.
The MOD09GA Version 6 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
- Striping due to a dead detector is noticeable for bands 5, 6, and 7 in scenes acquired February 24 through October 31, 2000. Corrections were implemented to reduce the striping in data acquired after November 1, 2000. Users should always check the band quality for dead detectors even though reflectance values may be in the valid range.
- The Collection 6 MODIS Land Surface Reflectance product (MOD09) may incorrectly flag retrievals as ‘High Aerosol’ over brighter surfaces and at higher view angles. This will impact the downstream MODIS BRDF/Albedo (MCD43) and Vegetation Index (MOD13 and MYD13) data products which use the aerosol quantity flag to screen out high aerosol values.
- Corrections were implemented in Collection 6.1 reprocessing.
- 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 (MOD09GA) followed by the Julian Date of Acquisition formatted as AYYYYDDD (A2002281), the Tile Identifier which is horizontal tile and vertical tile provided as hXXvYY (h33v11), the Version of the data collection (006), the Julian Date and Time of Production designated as YYYYDDDHHMMSS (2015151071841), and the Data Format (hdf).
Documents
USER'S GUIDE
ALGORITHM THEORETICAL BASIS DOCUMENT (ATBD)
DATA PRODUCT SPECIFICATION
PRODUCT QUALITY ASSESSMENT
Dataset Resources
Publications Citing This Dataset
| Title | Year Sort ascending | Author | Topic |
|---|---|---|---|
| An End-to-End Foundation Model-Based Framework for Robust LAI Retrieval | Gu, Xiangfeng, Li, Wenyuan, Guan, Shikang | Reflectance | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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, Albedo, Snow Cover | |
| 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 | |
| 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 | |
| How well does MODIS GPP capture alpine productivity of a Tibetan | Pillai, Nithin D, Wille, Christian, Helbig, Manuel, Sachs, Torsten | Photosynthesis, Primary Production, Vegetation Productivity, Reflectance | |
| An Evaluation of Machine Learning Methods for Leaf Area Index Retrieval | Wang, Dong, Miao, Lijuan, Lu, Yutian, Jiang, Hanyang, Liu, Qiang | Reflectance | |
| Can the MODIS EVI formula be directly applied to other sensor data? A case study with Landsat data | Bai, Yafen, Xu, Hanqiu, Su, Guifen, Shi, Tingting, Yang, Lijuan | Reflectance | |
| Divergent mechanisms regulating freezing rain-induced spring phenology | Zhang, Yating, Chen, Jinghua, Wang, Shaoqiang, Wang, Miaomiao, Zhao, Ziqi, Zhou, Zehan, Deng, Zhuoying, Peng, Haoyu, Ma, Jiageng, Wang, Xueqing, Xiao, Yuhan | Land Use/Land Cover Classification, Reflectance | |
| 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 | |
| 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) | |
| Ice-resistant breakwater rock sizing at Elim, Alaska | Engel, Chandler, Morriss, Blaine | 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 | |
| PIXAL: a physics-inspired explainable machine learning architecture for Greenland ice albedo modeling | Antwerpen, Raf, Tedesco, Marco, Gentine, Pierre, van de Berg, Willem Jan, Fettweis, Xavier | Reflectance, Albedo, Snow Cover | |
| The role of the onset of spring vegetation greening in moisture recycling process of the Eastern Tibetan Plateau | Hou, Miao, Yurova, Alla | Atmospheric Water Vapor, Precipitation, Fraction Of Absorbed Photosynthetically Active Radiation (fapar), Leaf Area Index (LAI), Reflectance | |
| The shrinking Caspian Sea: Ecohydrological responses to human and climate pressures | Duku, Jesse, Tourian, Mohammad J., Azarderakhsh, Marzi, Abbasov, Rovshan, Mehran, Ali, Haghighi, Ali Torabi, Xenarios, Stefanos, Boschee, Azara, Babagiray, Salih, Sadegh, Mojtaba, Shokri, Nima, Nazemi, Ali, Farahmand, Alireza, Ashraf, Samaneh, Khujanazarov, Temur, Hassanzadeh, Elmira, Najib, Dalal, Placht, Daniel, Mashtayeva, Shamshagul, Rets, Ekaterina, Norouzi, Hamid, Madani, Kaveh, Shirzaei, Manoochehr, ShafizadehMoghadam, Hossein, Miao, Chiyuan, Mirchi, Ali, Wang, Shuo, AghaKouchak, Amir | Reflectance | |
| Geo-OLM: Enabling Sustainable Earth Observation Studies with | Stamoulis, Dimitrios, Marculescu, Diana | Reflectance, Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Aerosol Optical Depth/Thickness, Land Surface Temperature, Emissivity | |
| MULTI-AGENT GEOSPATIAL COPILOTS FOR REMOTE SENSING WORKFLOWS | Lee, Chaehong, Paramanayakam, Varatheepan, Karatzas, Andreas, Jian, Yanan, Fore, Michael, Liao, Heming, Yu, Fuxun, Li, Ruopu, Anagnostopoulos, Iraklis, Stamoulis, Dimitrios | Vegetation Index, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Aerosol Optical Depth/Thickness, Land Surface Temperature, Emissivity, 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 |
|---|---|---|---|---|---|---|---|
| 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 for Band 1 | N/A | int16 | -28672 | -100 to 16000 | 0.0001 | N/A |
| sur_refl_b02_1 | Surface Reflectance for Band 2 | N/A | int16 | -28672 | -100 to 16000 | 0.0001 | N/A |
| sur_refl_b03_1 | Surface Reflectance for Band 3 | N/A | int16 | -28672 | -100 to 16000 | 0.0001 | N/A |
| sur_refl_b04_1 | Surface Reflectance for Band 4 | N/A | int16 | -28672 | -100 to 16000 | 0.0001 | N/A |
| sur_refl_b05_1 | Surface Reflectance for Band 5 | N/A | int16 | -28672 | -100 to 16000 | 0.0001 | N/A |
| sur_refl_b06_1 | Surface Reflectance for Band 6 | N/A | int16 | -28672 | -100 to 16000 | 0.0001 | N/A |
| sur_refl_b07_1 | Surface Reflectance for Band 7 | N/A | int16 | -28672 | -100 to 16000 | 0.0001 | N/A |