DEA Surface Reflectance NBAR (Landsat 8 OLI-TIRS)

Geoscience Australia Landsat 8 OLI-TIRS NBAR Collection 3

3.0.0 flat-square
Product ID
Digital Earth Australia
Geoscience Australia Landsat Collection 3
Resource type
Published Date


DEA Surface Reflectance NBAR (Landsat 8 OLI-TIRS) is part of a suite of Digital Earth Australia (DEA)’s Surface Reflectance datasets that represent the vast archive of images captured by the US Geological Survey (USGS) Landsat and European Space Agency (ESA) Sentinel-2 satellite programs, validated, calibrated, and adjusted for Australian conditions — ready for easy analysis.


This is a sub-product of DEA Surface Reflectance (Landsat 8 OLI-TIRS). See the parent product for more information.

Radiance data collected by Landsat 8 OLI-TIRS sensors can be affected by atmospheric conditions, sun position, sensor view angle, surface slope and surface aspect. These need to be reduced or removed to ensure the data is consistent and can be compared over time.

What this product offers

This product takes Landsat 8 OLI-TIRS imagery captured over the Australian continent and corrects the inconsistencies across land and coastal fringes using Nadir corrected Bi-directional reflectance distribution function Adjusted Reflectance (NBAR). This consistency over time and space is instrumental in identifying and quantifying environmental change.

The resolution is a 30 m grid based on the USGS Landsat Collection 1 archive.

This product does not apply terrain illumination correction. See the sibling product DEA Surface Reflectance NBART (Landsat 8 OLI-TIRS).

Related products


  • Li, F., Jupp, D. L. B., Reddy, S., Lymburner, L., Mueller, N., Tan, P., & Islam, A. (2010). An evaluation of the use of atmospheric and BRDF correction to standardize Landsat data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(3), 257–270.
  • Li, F., Jupp, D. L. B., Thankappan, M., Lymburner, L., Mueller, N., Lewis, A., & Held, A. (2012). A physics-based atmospheric and BRDF correction for Landsat data over mountainous terrain. Remote Sensing of Environment, 124, 756–770.

Data access

Product ID ga_ls8c_ard_3
Link to data NCI - THREDDS
Digital Earth Australia - Public Data
Code examples Jupyter notebook
eCat record 132317
Open Data Cube product configuration
CMI RESTful node ID 402
NCI project code xu18
Security classification Unclassified
Update frequency asNeeded

Access notes

Open Data Cube

This product is contained in the Open Data Cube instance managed by Digital Earth Australia (DEA). This simplified process allows you to query data from its sub-products as part of a single query submitted to the database.

See Analysis Ready Data: example queries

Technical information

Radiance measurements

Landsat’s Earth Observation (EO) sensors measure radiance (brightness of light), which is a composite of:

  • surface reflectance
  • atmospheric condition
  • interaction between surface land cover, solar radiation and sensor view angle
  • land surface orientation relative to the imaging sensor

It has been traditionally assumed that Landsat imagery displays negligible variation in sun and sensor view angles. However, these can vary significantly both within and between scenes, especially in different seasons and geographic regions (Li et al. 2012).

Surface reflectance correction models

This product represents standardised optical surface reflectance using robust physical models to correct for variations and inconsistencies in image radiance values.

It delivers modelled surface reflectance from Landsat 8 OLI-TIRS data using physical rather than empirical models. This ensures that the reflective value differences between imagery acquired at different times by different sensors will be primarily due to on-ground changes in biophysical parameters rather than artefacts of the imaging environment.

This product is created using a physics-based, coupled Bidirectional Reflectance Distribution Function (BRDF) and atmospheric correction model that can be applied to both flat and inclined surfaces (Li et al. 2012). The resulting surface reflectance values are comparable both within individual images and between images acquired at different times.

For more information on the BRDF/Albedo Model Parameters product, see MCD43A1 Collection 6

Landsat archive

To improve access to Australia’s archive of Landsat TM/ETM+/OLI data, several collaborative projects have been undertaken in conjunction with industry, government and academic partners. These projects have enabled implementation of a more integrated approach to image data correction that incorporates normalising models to account for atmospheric effects, BRDF and topographic shading (Li et al. 2012). The approach has been applied to Landsat TM/ETM+ and OLI imagery to create baseline surface reflectance products.

The advanced supercomputing facilities provided by the National Computational Infrastructure (NCI) at the Australian National University (ANU) have been instrumental in handling the considerable data volumes and processing complexities involved with the production of this product.

Image format specifications

band01, band02, band03, band04, band05, band06, band07
Format GeoTIFF
Resolution 30m
Datatype Int16
No data value -999
Valid data range [1,10000]
Tiled with X and Y block sizes 512x512
Compression Deflate, Level 6, Predictor 2
Pyramids Levels: [8,16,32]
Compression: deflate
Resampling: GDAL default (nearest)
Overview X&Y block sizes: 512x512
Contrast stretch None
Output CRS As specified by source dataset; source is UTM with WGS84 as the datum
Format GeoTIFF
Resolution 15m
Datatype Int16
No data value -999
Valid data range [1,10000]
Tiled with X and Y block sizes 512x512
Compression Deflate, Level 6, Predictor 2
Pyramids None
Contrast stretch None
Output CRS As specified by source dataset; source is UTM with WGS84 as the datum
Format JPEG
RGB combination Red: band 4
Green: band 3
Blue: band 2
Resolution X and Y directions each resampled to 10% of the original size
Compression JPEG, Quality 75 (GDAL default)
Contrast stretch Linear
Input minimum: 10
Input maximum: 3500
Output minimum: 0
Output maximum: 255
Output CRS Geographics (Latitude/Longitude) WGS84

Accuracy and limitations

Atmospheric correction accuracy depends on the quality of aerosol data available to determine the atmospheric profile at the time of image acquisition.

BRDF correction is based on low resolution imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS), which is assumed to be relevant to medium resolution imagery such as that captured by Landsat 8 OLI-TIRS. BRDF correction is applied to each whole Landsat 8 OLI-TIRS scene and does not account for changes in land cover. It also excludes effects due to topographic shading and local BRDF.

The algorithm assumes that BRDF effect for inclined surfaces is modelled by the surface slope and does not account for land cover orientation relative to gravity (as occurs for some broadleaf vegetation with vertical leaf orientation).

The algorithm also depends on several auxiliary data sources:

  • Availability of relevant MODIS BRDF data
  • Availability of relevant aerosol data
  • Availability of relevant water vapour data
  • Availability of relevant DEM data
  • Availability of relevant ozone data

Improved or more accurate sources for any of the above listed auxiliary dependencies will also improve the surface reflectance result.

Quality assurance

Results from the DEA Cal/Val workflow over 17 data takes from 9 field sites were created based on both BRDF Collections 5 and 6.

The results for each collection were averaged and then compared. The comparison showed small changes in individual field sites, but overall there was no significant difference in the average results over all field sites to within 1% at most.

The technical report containing the data summary for the Phase 1 DEA Surface Reflectance Validation is available: DEA Analysis Ready Data Phase 1 Validation Project : Data Summary


Commonwealth of Australia (Geoscience Australia)

Principal contributors

Fuqin Li, David Jupp, Joshua Sixsmith, Lan-Wei Wang, Passang Dorji, Alexander Vincent, Imam Alam, Jeremy Hooke, Simon Oliver, Medhavy Thankappan

Subject matter experts

Fuqin Li, David Jupp, Joshua Sixsmith


CC BY Attribution 4.0 International License

Rights statement

© Commonwealth of Australia (Geoscience Australia) 2020. Creative Commons Attribution 4.0 International License.