Skip to main content

Description

The MOD09A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MOD09A1 Version 6.1 data product.

The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra MOD09A1 Version 6 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

  • 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

The following changes and improvements were implemented for inputs to MODIS Version 6 terra surface reflectance data products: (1) Improvements to the aerosol retrieval and correction algorithm along with new aerosol retrieval look-up tables, (2) Refinements to the internal snow, cloud, and cloud shadow detection algorithms. Uses Bidirectional Reflectance Distribution Function (BRDF) database to better constrain the different threshold used, (3) Processes ocean bands to create a new Surface Reflectance Ocean product and provides Quality Assurance (QA) datasets for these bands, and (4) Improved discrimination of salt pans from cloud and snow, along with the inclusion of a salt pan flag in the QA band.

Product Summary

Platforms
Terra
Instruments
MODIS
Projects
TERRA
Spatial Extent

N: 90 S: -90 E: 180 W: -180

Spatial Resolution
500 Meters x 500 Meters
Spatial Reference System(s)
MODIS Sinusoidal Tiling System
Location
GLOBAL
Coordinate System
CARTESIAN
Granule Spatial Representation
GEODETIC
Temporal Extent
2000-02-24 to 2023-02-17
Temporal Resolution
8 Day
Data Partner
Land Processes Distributed Active Archive Center (LP DAAC) ,
MODIS Adaptive Processing System, Terrestrial Information Systems Laboratory, Earth Sciences Division, Science and Exploration Directorate, Goddard Space Flight Center, NASA (NASA/GSFC/SED/ESD/TISL/MODAPS)
Concept ID
C2763297170-LPCLOUD
Data State
DEPRECATED
Number of Files/Granules
0
Processing Level
3
Published
Updated
Science Keywords
Reflectance

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

Vermote, E. (2015). MOD09A1 MODIS/Terra Surface Reflectance 8-Day L3 Global 500m SIN Grid V006 [Data set]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/MODIS/MOD09A1.006 Date Accessed: 2026-06-05

File Naming Convention

MOD09A1.A2002273.h29v03.006.2015151111637.hdf

The file name begins with the Product Short Name (MOD09A1) followed by the Julian Date of Acquisition formatted as AYYYYDDD (A2002273), the Tile Identifier which is horizontal tile and vertical tile provided as hXXvYY (h29v03), the Version of the data collection (006), the Julian Date and Time of Production designated as YYYYDDDHHMMSS (2015151111637), and the Data Format (hdf).

Documents

USER'S GUIDE

ALGORITHM THEORETICAL BASIS DOCUMENT (ATBD)

DATA PRODUCT SPECIFICATION

PRODUCT QUALITY ASSESSMENT

SCIENCE DATA PRODUCT VALIDATION

SHOWING 3 OF 3

Publications Citing This Dataset

Filters
Title Year Author Topic
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
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
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
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
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)
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)
Predicting Postfire Forest Mortality Using Remote Sensing Data and Machine Learning Shvetsov, E. G. Reflectance
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
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 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
Advanced vegetation green-up onset in regions with cooling air Jiang, Nan, Shen, Miaogen, Yang, Zhiyong Reflectance, Land Use/Land Cover Classification
An end-to-end pipeline based on a Two-Dimensional Convolutional Neural Network for monitoring desertification in Africa using remote sensing images Chouikhi, Farah, Abbes, Ali Ben, Farah, Imed Riadh Reflectance
Assessing environmental and anthropogenic drivers for the occurrence and extent of fires in high Andean Grasslands Gutierrez-Flores, Ivon, Mercado, Angela, Zubieta, Ricardo, Beltran, Pablo, Oyague, Eduardo Reflectance
Mapping heartwater risk in Guadeloupe using a combination of spatial modelling approaches Dufleit, Victor, Etter, Eric, Guerrini, Laure Reflectance
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
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
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)
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)
Investigation of Urban Heat Islands and modeling of Land Surface Mandal, Nirup Sundar, Chanda, Kironmala Reflectance
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
Elevation-Dependent Trends in Himalayan Snow Cover (20042024) Based on MODIS Terra Observations Tauqir, Ghania, Zhao, Wei, Xu, Mengjiao, Fu, Dongjie RADAR IMAGERY, Terrain Elevation, Topographical Relief Maps, Digital Elevation/Terrain Model (DEM), Reflectance, Snow Cover
You're viewing 25 of 642

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 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
You're viewing 13 of 13