Landsat Collection 1 Surface Reflectance

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NOTE: Landsat Collection 1 Surface Reflectance products are no longer available to download from the USGS as of December 30, 2022. The information on this webpage is for reference only. 

Landsat Collection 1 Surface Reflectance measures the fraction of incoming solar radiation that is reflected from Earth's surface to the Landsat sensor. 

Sources/Usage: Public Domain. View Media Details

Surface reflectance (SR) improves comparison between multiple images over the same region by accounting for atmospheric effects such as aerosol scattering and thin clouds, which can help in the detection and characterization of Earth surface change. Surface reflectance is the amount of light reflected by the surface of the Earth. It is a ratio of surface radiance to surface irradiance, and as such is unitless, with values between 0 and 1.

Please note that Level-2 Multispectral Scanner (MSS) surface reflectance products are not available for Landsat collection 1 data. They will only become available for later Landsat Collections upon the maturity of an operational atmospheric compensation algorithm which satisfies the USGS Landsat program requirements.

Landsat 8 Operational Land Imager (OLI)

Landsat 8 OLI Collection 1 Surface Reflectance are generated using the Land Surface Reflectance Code (LaSRC) (version 1.4.1), which makes use of the coastal aerosol band to perform aerosol inversion tests, uses auxiliary climate data from MODIS, and uses a unique radiative transfer model.  (Vermote et al., 2016).

LaSRC hardcodes the view zenith angle to “0”, and the solar zenith and view zenith angles are used for calculations as part of the atmospheric correction. 

The Landsat 8 Collection 1 Land Surface Reflectance Code (LaSRC) Product Guide contains details about the LaSRC algorithm and the Landsat 8 Surface Reflectance data products created from it. 

Landsat 4-5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+)

Landsat 4-5 TM and Landsat 7 ETM+ Collection 1 Surface Reflectance are generated using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) algorithm (version 3.4.0), a specialized software originally developed through a National Aeronautics and Space Administration (NASA) Making Earth System Data Records for Use in Research Environments (MEaSUREs) grant by NASA Goddard Space Flight Center (GSFC) and the University of Maryland (Masek et al., 2006).

The software applies Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric correction routines to Level-1 data products. Water vapor, ozone, atmospheric pressure, aerosol optical thickness, and digital elevation are input with Landsat data to the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer models to generate top of atmosphere (TOA) reflectance, surface reflectance, TOA brightness temperature, and masks for clouds, cloud shadows, adjacent clouds, land, and water.

The Landsat 4-7 Collection 1 Surface Reflectance Product Guide contains details about the LEDAPS algorithm and the Surface Reflectance data products created from it. 

Differences in Surface Reflectance Processing Algorithms

While both the LEDAPS and LaSRC algorithms produce similar SR products, the inputs and methods to do so differ between them.  The table below displays each algorithm.   

6S Second Simulation of a Satellite Signal in the Solar Spectrum, AOT Aerosol Optical Thickness, CFmask C Version of Function Of Mask, CMA Climate Modeling Grid - Aerosol, CMG Climate Modeling Grid, DDV Dark Dense Vegetation, DEM Digital Elevation Model, ETM+ Enhanced Thematic Mapper Plus, GSFC Goddard Space Flight Center, INT Integer, MEaSUREs Making Earth Science Data Records for Use in Research Environments, MODIS Moderate Resolution Imaging Spectroradiometer, N/A Not Applicable, NASA National Aeronautics and Space Administration, NCEP National Centers for Environmental Prediction, OLI Operational Land Imager, OMI Ozone Monitoring Instrument, QA Quality Assessment, SR Surface Reflectance, TIRS Thermal Infrared Sensor, TM Thematic Mapper, TOA Top of Atmosphere Reflectance, TOMS Total Ozone Mapping Spectrometer, XML Extensible Markup Language

Constraints and Caveats 

Most day-lit (descending) Collection 1 Landsat 4-8 scenes in the USGS archive can be processed to Surface Reflectance. Please note the following caveats:

1. Due to missing auxiliary input data and/or necessary thermal data, Surface Reflectance processing cannot be applied to data acquired during the dates listed below. 

   Sensor	Dates (DOY)	Reason
   Landsat 7	2016: May 30 (151) to Jun 12 (164) 2017: Mar 12 (071) -Mar 17 (076) 2020: Oct 5 (279)	Missing auxiliary input data Missing auxiliary input data No data collected - Paths 35, 51, 99, 188, 204, 220
   Landsat 8	2019: Dec 20 (354) - Dec 21 (355) 2020: Nov 1 (306) - Nov 3 (308) 2020: Nov 4 (309) - Nov 8 (313) 2020: Nov 9 (314) 2020: Nov 12 (317) - Nov 13 (318) 2020: Nov 14 (319) 2021: Sept 23 (266) - Sept 24 (267)	No thermal data; satellite safehold No thermal data; satellite safehold No thermal data No thermal data - paths 105, 121, 137, 153, 169, 185 No thermal data; satellite safehold No thermal data - paths 108, 124 Missing auxiliary input data

2. The efficacy of land surface reflectance correction is likely to be reduced in hyper-arid or snow-covered regions, areas with low sun angle conditions, coastal regions where land area is small, relative to adjacent water, or areas with extensive cloud contamination.
3. Corrections may not be accurate to data acquired over high latitudes (> 65 degrees North or South). Landsat 7 ETM+ inputs are not gap-filled in the surface reflectance production. Users can refer to the Quality Assessment(QA) band for pixel-level condition and validity flags.
4. Landsat atmospheric correction and surface reflectance retrieval algorithms are not ideal for water bodies due to the inherently low level of water leaving radiance, and the consequential very low signal to noise ratio. Similarly, surface reflectance values greater than 1.0 can be encountered over bright targets such as snow and playas. These are known computational artifacts in the Landsat surface reflectance products.  Quantitative remote sensing retrievals of water column constituents requires different algorithms, which are being considered for integration into future Landsat surface reflectance products.
5. Landsat 8 OLI-only scenes cannot be processed to Surface Reflectance. This issue will be corrected for Collection 2 Surface Reflectance.
6. Thermal and panchromatic bands are not processed to Surface Reflectance. 
7. Landsat 8 Cirrus band is not processed to Surface Reflectance. 

Data Availability 

• Landsat 8 Operational Land Imager (OLI): April 2013 to December 16, 2022
• Landsat 7 Enhanced Thematic Mapper Plus (ETM+): July 1999 to December 16, 2022
• Landsat 5 Thematic Mapper (TM): March 1984 to May 2012
• Landsat 4 Thematic Mapper (TM): July 1982 to December 1993

Data Manipulation Tools

Data manipulation tools that function with Moderate Resolution Imaging Spectroradiometer (MODIS) Land products are likely to work with Landsat surface reflectance data products as well. The public domain tools listed below are suggested for format conversion, science data set extraction, bit extraction (for top of atmosphere reflectance, saturation values only), and reprojection.

HDF-EOS To GeoTIFF Conversion Tool (HEG): Allows a user to reformat, re-project and perform stitching/mosaicking and subsetting operations on HDF-EOS objects. It can also reformats and re-project some SMAP, VIIRS and SRTM products. The output GeoTIFF file is ingestible into commonly used GIS applications. HEG will also write to HDF-EOS Grid & SWATH formats (i.e for Subsetting purposes) and native (or raw) binary. 

Landsat Quality Assessment (QA) Tools: Developed by the MODIS land quality assessment group to work specifically with Landsat Surface Reflectance data. 

Documentation

• Landsat 8 Collection 1 Land Surface Reflectance Code (LaSRC) Product Guide 
• Landsat 4-7 Collection 1 Surface Reflectance (LEDAPS) Product Guide
• Landsat 8 OLI/TIRS Surface Reflectance Digital Object Identifier (DOI): doi.org/10.5066/F78S4MZJ
• Landsat 7 ETM+ Surface Reflectance Digital Object Identifier (DOI): doi.org/10.5066/F7Q52MNK
• Landsat 4-5 TM Surface Reflectance Digital Object Identifier (DOI): doi.org/10.5066/F7KD1VZ9

Citation Information

There are no restrictions on the use of Landsat Science Products. It is not a requirement of data use, but the following citation may be used in publication or presentation materials to acknowledge the USGS as a data source and to credit the original research.

Landsat Level- 2 Surface Reflectance Science Product courtesy of the U.S. Geological Survey.

Masek, J.G., Vermote, E.F., Saleous N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., and Lim, T-K. (2006). A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geoscience and Remote Sensing Letters 3(1):68-72. https://dx.doi.org/10.1109/LGRS.2005.857030.

Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment. https://dx.doi.org/10.1016/j.rse.2016.04.008.

Reprints or citations of papers or oral presentations based on USGS data are welcome to help the USGS stay informed of how data are being used. These can be sent to User Services at custserv@usgs.gov