DEA Surface Reflectance NBAR (Landsat 9)

Version:: 3.0.0
Type:: Baseline, Raster
Resolution:: 15-30 m
Coverage:: 31 Oct 2021 to Present
Data updates:: Daily frequency, Ongoing

../../../_images/surface_reflectance_2_NBARTa_1.png

Overview

About

DEA Surface Reflectance NBAR (Landsat 9) 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.

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Data sources

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Code examples

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Web services

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Web services

Key specifications

For more specifications, see the Specifications tab.

Technical name	Geoscience Australia Landsat 9 OLI-TIRS NBAR Collection 3
Bands	22 bands of data (nbar_coastal_aerosol, nbar_blue, and more)
DOI	10.26186/148693
Currency	See currency and the latest update date
Collection	Geoscience Australia Landsat Collection 3
Tags	geoscience_australia_landsat_collection_3, analysis_ready_data, satellite_images, earth_observation, landsat, baseline_satellite_data
Licence	Creative Commons Attribution 4.0 International Licence

Cite this product

Data citation

Alam, I., Chopra, A., Gray, D., Hooke, J., Li, F., Ly, L., Mettes, J., Miller, J., Norton, A., OHehir, A. 2023. Geoscience Australia Landsat 9 OLI TIRS Analysis Ready Data Collection 3. Geoscience Australia, Canberra. https://dx.doi.org/10.26186/148693

Publications

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. https://doi.org/10.1109/JSTARS.2010.2042281

Description

Background

Sub-product

This is a sub-product of DEA Surface Reflectance (Landsat 9). See the parent product for more information.

Radiance data collected by Landsat 9 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 9 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 2 archive, or a 15 m grid for the panchromatic band.

This product does not apply terrain illumination correction. See the sibling product DEA Surface Reflectance NBART (Landsat 9).

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 9 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 NASA MODIS BRDF/Albedo parameter and MCD43A1 BRDF/Albedo Model Parameters Product.

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.

Bands Included in the Analysis Ready Data

The product contains 8 bands

Band number	Band name	Wave length	Resolution
Band 1	Visible	0.43 - 0.45 µm	30 m
Band 2	Visible	0.45 - 0.51 µm	30 m
Band 3	Visible	0.53 - 0.59 µm	30 m
Band 4	Red	0.64 - 0.67 µm	30 m
Band 5	Near-Infrared	0.85 - 0.88 µm	30 m
Band 6	SWIR 1	1.57 - 1.65 µm	30 m
Band 7	SWIR 2	2.11 - 2.29 µm	30 m
Band 8	Panchromatic (PAN)	0.50 - 0.68 µm	15 m

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

band08


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

thumbnail


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) PHOTOMETRIC colour model: YCBCR
Contrast stretch	Linear Input minimum: 10 Input maximum: 3500 Output minimum: 0 Output maximum: 255
Output CRS	Geographics (Latitude/Longitude) WGS84

Lineage

This product is derived from the USGS Landsat Level 1 Collection 2 archive.

The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A1 Version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters dataset was provided by the National Aeronautics and Space Administration (NASA). It was produced daily using 16 days of Terra and Aqua MODIS data at 500 m resolution. See USGS: MCD43A1, NASA: MODIS BRDF / Albedo Parameter, Schaaf et al. (2002)
The ozone data was provided by Environment Canada. See Environment Canada: Global Ozone Maps
The Aerosol Optical Thickness data was provided by the Commonwealth Scientific and Industrial Research Organisation (CSIRO). See Qin et al. (2015)
The Precipitable Water for Entire Atmosphere data was provided by the National Oceanic and Atmospheric Administration (NOAA) / Earth System Research Laboratory (ESRL) / Physical Sciences Division (PSD). See Kalnay et al. (1996)
The baseline Digital Surface Model (DSM) data produced from the Shuttle Radar Topography Mission (SRTM) was provided by the National Geospatial-Intelligence Agency (NGA). See NGA: SRTM, NASA: SRTM
Level 1 Collection 2 data was provided by the United States Geological Survey (USGS)’s Earth Resources Observation and Science (EROS) Center. See USGS: EROS, USGS: Landsat Collection 2

Processing steps

References

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. https://doi.org/10.1109/JSTARS.2010.2042281
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. https://doi.org/10.1016/j.rse.2012.06.018

Quality

Accuracy

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 9 OLI-TIRS. BRDF correction is applied to each whole Landsat 9 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

Specifications

Bands

Bands are distinct layers of data within a product that can be loaded using the Open Data Cube (on the DEA Sandbox or NCI) or DEA’s STAC API. Here are the bands of the product: ga_ls9c_ard_3.

	Aliases	Resolution	No-data	Units	Type	Description
nbar_coastal_aerosol	nbar_band01 coastal_aerosol	30	-999	-	int16	-
nbar_blue	nbar_band02 blue	30	-999	-	int16	-
nbar_green	nbar_band03 green	30	-999	-	int16	-
nbar_red	nbar_band04 red	30	-999	-	int16	-
nbar_nir	nbar_band05 nir nbar_common_nir	30	-999	-	int16	-
nbar_swir_1	nbar_band06 swir_1 nbar_common_swir_1 swir1	30	-999	-	int16	-
nbar_swir_2	nbar_band07 swir_2 nbar_common_swir_2 swir2	30	-999	-	int16	-
nbar_panchromatic	nbar_band08 panchromatic	15	-999	-	int16	-
oa_fmask	fmask	30	0	-	uint8	-
oa_nbar_contiguity	nbar_contiguity	30	255	-	uint8	-
oa_azimuthal_exiting	azimuthal_exiting	30	NaN	-	float32	-
oa_azimuthal_incident	azimuthal_incident	30	NaN	-	float32	-
oa_combined_terrain_shadow	combined_terrain_shadow	30	255	-	uint8	-
oa_exiting_angle	exiting_angle	30	NaN	-	float32	-
oa_incident_angle	incident_angle	30	NaN	-	float32	-
oa_relative_azimuth	relative_azimuth	30	NaN	-	float32	-
oa_relative_slope	relative_slope	30	NaN	-	float32	-
oa_satellite_azimuth	satellite_azimuth	30	NaN	-	float32	-
oa_satellite_view	satellite_view	30	NaN	-	float32	-
oa_solar_azimuth	solar_azimuth	30	NaN	-	float32	-
oa_solar_zenith	solar_zenith	30	NaN	-	float32	-
oa_time_delta	time_delta	30	NaN	-	float32	-

For all ‘nbar_’ bands, Surface Reflectance is scaled between 0 and 10,000.

Product information

This metadata provides general information about the product.

Product ID	ga_ls9c_ard_3	Used to load data from the Open Data Cube.
Short name	DEA Surface Reflectance NBAR (Landsat 9)	The name that is commonly used to refer to the product.
Technical name	Geoscience Australia Landsat 9 OLI-TIRS NBAR Collection 3	The full technical name that refers to the product and its specific provider, sensors, and collection.
Version	3.0.0	The version number of the product.
Lineage type	Baseline	Baseline products are produced directly from satellite data.
Spatial type	Raster	Raster data consists of a grid of pixels.
Spatial resolution	15-30 m	The size of the pixels in the raster.
Temporal coverage	31 Oct 2021 to Present	The time span for which data is available.
Update frequency	Daily	The expected frequency of data updates. Also called ‘Temporal resolution’.
Update activity	Ongoing	The activity status of data updates.
Currency	See the Currency Report	Currency is a measure based on data publishing and update frequency.
Latest update date	Currency Report	See Table A of the report.
DOI	10.26186/148693	The Digital Object Identifier.
Catalogue ID	148693	The Data and Publications catalogue (eCat) ID.
Licence	Creative Commons Attribution 4.0 International Licence	See the Credits tab.

Product categorisation

This metadata describes how the product relates to other DEA products.

Collection	Geoscience Australia Landsat Collection 3
Tags	geoscience_australia_landsat_collection_3, analysis_ready_data, satellite_images, earth_observation, landsat, baseline_satellite_data

Access

Access the data

DEA Maps	See it on a map	Learn how to use DEA Maps.
DEA Explorer	Explore data availability	Learn how to use the DEA Explorer.
Data sources	Access the data on AWS Access the data on NCI	Learn how to access the data via AWS.
Code examples	View code examples	Learn how to use the DEA Sandbox.
Web services	Web Map Service (WMS) Web Coverage Service (WCS)	Learn how to use DEA’s web services.

History

Version history

No previous versions are available.

FAQs

Frequently asked questions

Credits

Acknowledgments

This research was undertaken with the assistance of resources from the National Computational Infrastructure (NCI), which is supported by the Australian Government.

Landsat level 0 and level 1 data courtesy of the U.S. Geological Survey.

The authors would like to thank the following organisations:

National Aeronautics and Space Administration (NASA)
Environment Canada
The Commonwealth Scientific and Industrial Research Organisation (CSIRO)
National Oceanic and Atmospheric Administration (NOAA) / Earth System Research Laboratories (ESRL) / Physical Sciences Laboratory (PSD)
The National Geospatial-Intelligence Agency (NGA)
The United States Geological Survey (USGS) / Earth Resources Observation and Science (EROS) Center
Spectral Sciences Inc.

License and copyright

Released under Creative Commons Attribution 4.0 International Licence.