DEA Surface Reflectance NBART (Sentinel-2A MSI)

Geoscience Australia Sentinel-2A MSI NBART Collection 3

3.2.1 flat-square
Product ID
Digital Earth Australia
Geoscience Australia Sentinel-2 Collection 3
Resource type
Published Date


DEA Surface Reflectance Nadir corrected Bidirectional reflectance distribution function Adjusted Reflectance Terrain corrected (NBART) Sentinel-2A Multispectral Instrument (MSI) is part of a suite of Digital Earth Australia's (DEA) 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, which have been validated, calibrated, and adjusted for Australian conditions — ready for easy analysis.


This is a sub-product of DEA Surface Reflectance (Sentinel-2A MSI). See the parent product for more information.

Reflectance data at top of atmosphere (TOA) collected by Sentinel-2A MSI sensors can be affected by atmospheric conditions, sun position, sensor view angle, surface slope and surface aspect.

Surfaces with varying terrain can introduce inconsistencies to optical satellite images through irradiance and bidirectional reflectance distribution function (BRDF) effects. For example, slopes facing the sun appear brighter compared with those facing away from the sun. Likewise, many surfaces on Earth are anisotropic in nature, so the signal picked up by a satellite sensor may differ depending on the sensor’s position.

These inconsistencies need to be reduced or removed to ensure the data can be compared over time.

What this product offers

This product takes Sentinel-2A MSI imagery captured over the Australian continent and corrects the inconsistencies across the land and coastal fringe. It achieves this using Nadir corrected Bi-directional reflectance distribution function Adjusted Reflectance (NBAR).

In addition, this product applies terrain illumination correction to correct for varying terrain.

The resolution is a 10/20/60 m grid based on the ESA level 1C archive.

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_s2am_ard_3
Link to data DEA Public Data on AWS
Dataset through time DEA Explorer (AWS)
DEA Explorer (NCI)
DEA Explorer (STAC - collection = ga_s2am_ard_3)
Code examples Jupyter notebook
DEA Sandbox (product = ga_s2am_ard_3)
Link to maps DEA Maps
Web services DEA OGC Web Services (layer name = ga_s2am_ard_3)
eCat record 146571
Open Data Cube product configuration
CMI RESTful node ID 671
Security classification Unclassified
Update frequency asNeeded

Access notes

Dataset Naming Convention

Collection 3 datasets follow a naming convention to enhance accessibility.

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.

DEA Maps

To view and access the data interactively:

  1. Visit DEA Maps
  2. Click "Explore map data"
  3. Select "Baseline satellite data" -> "DEA Surface Reflectance (Sentinel-2)" -> "DEA Surface Reflectance (Sentinel-2A, Collection 3)"
  4. Click "Add to the map"

Technical information

Top of atmosphere reflectance measurements

Sentinel-2 series sensors measure top of atmospheric reflectance, which is a composite of:

  • surface reflectance
  • atmospheric condition
  • interaction between surface land cover, solar radiation and sensor view angle (BRDF effect)
  • land surface orientation relative to the imaging sensor (terrain illumination).

It has been traditionally assumed that satellite 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. 2010, 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 Sentinel-2A MSI 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

Sentinel-2 archive

To improve access to Australia’s archive of Sentinel-2 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 Sentinel-2 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, band08, band8A, band11, band 12

Format GeoTIFF

Resolution 10/20/60m based on Sentinel-2 original pixel resolution

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 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 and total column water vapour available to determine the atmospheric profile at the time of image acquisition (Wang et al., 2009).

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 Sentinel-2A MSI. A single BRDF shape is applied to each Sentinel-2A tile and it does not account for changes in land cover. 

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 accuracy of the terrain correction also depend on the quality, scale and spatial resolution of the DSM data used and the co-registration between the DSM and the satellite image (Li et al., 2013). At present, 30 m resolution SRTM DSM data were used for the correction.

The algorithm 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 DSM 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


1. F. Li, D. L.B. Jupp & M. Thankappan (2015) Issues in the application of Digital Surface Model data to correct the terrain illumination effects in Landsat images, International Journal of Digital Earth, 8:3, 235-257, DOI: 10.1080/17538947.2013.866701

2. L. Wang, F. Li, I. Alam, D. Jupp, S. Oliver and M. Thankappan, "Analysis Ready Data Sensitivity Analyses," IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019, pp. 5642-5645, doi: 10.1109/IGARSS.2019.8898667


Commonwealth of Australia (Geoscience Australia)

Principal contributors

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

Subject matter experts

Fuqin Li, David Jupp


CC BY Attribution 4.0 International License

Rights statement

© Commonwealth of Australia (Geoscience Australia) 2022. Creative Commons Attribution 4.0 International License.