DEA Surface Reflectance (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 (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.

Access the data

For help accessing the data, see the Access tab.

See it on a map

DEA Maps

Explore data availability

DEA Explorer

Access the data on AWS

Data sources

Access the data on NCI

Data sources

View code examples

Code examples

Web Map Service (WMS)

Web services

Web Coverage Service (WCS)

Web services

Key specifications

For more specifications, see the Specifications tab.

Technical name	Geoscience Australia Landsat 9 OLI-TIRS Analysis Ready Data Collection 3
Bands	22 bands of data (nbart_coastal_aerosol, nbart_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

The United States Geological Survey’s (USGS) Landsat satellite program has been capturing images of the Australian continent for more than 30 years. This data is highly useful for land and coastal mapping studies.

In particular, the light reflected from the Earth’s surface (surface reflectance) is important for monitoring environmental resources – such as agricultural production and mining activities – over time.

We make accurate comparisons of imagery acquired at different times, seasons and geographic locations. However, inconsistencies can arise due to variations in atmospheric conditions, sun position, sensor view angle, surface slope and surface aspect. These are 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 for inconsistencies caused by atmospheric conditions, sun position, sensor view angle, surface slope and surface aspect. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change.

The imagery is captured using the Operational Land Imager (OLI) and Thermal Infra-Red Scanner (TIRS) sensors aboard Landsat 9. Only the OLI data is included in this product, while the TIRS data is used in processing, particularly in identification of clouds.

This product is a single, cohesive Analysis Ready Data (ARD) package, which allows you to analyse surface reflectance data as is, without the need to apply additional corrections.

It contains three sub-products that provide corrections or attribution information:

Note: DEA produces NBAR as part of the Landsat ARD, this is available in the National Computing Infrastructure environment only. This product is not produced as part of the Sentinel-2 ARD.

The resolution is a 30 m grid based on the USGS Landsat Collection 2 archive, or 15 m for the panchromatic band. This data forms part of the DEA Collection 3 archive.

* Nadir corrected Bi-directional reflectance distribution function Adjusted Reflectance (NBAR)

** Nadir corrected Bi-directional reflectance distribution function Adjusted Reflectance with terrain illumination correction (NBART)

*** Observation Attributes (OA)

The introduction of a maturity concept.

DEA Collection 3 products are comprised of data produced to varying degrees of maturity. The maturity of a dataset is dictated by the quality of the ancillary information, such as Bidirectional Reflectance Distribution Function (BRDF) and atmospheric data, used to generate the product. The maturity levels are Near Real Time (NRT), Interim and Final. The maturity level is designated in the filename and in the metadata.

Near Real Time (NRT) is a rapid ARD product produced within 48 hours after image capture, using existing long term climatology data that is of slightly lower quality, allowing NRT data to be published quickly.
Interim ARD is produced if high quality ancillaries required for the final ARD model don’t become available within 23 days of image capture. Interim maturity data is produced as a stand-in until the full ancillaries are available to produce the final version. This is our fall-back until the issue is resolved.
Final ARD is DEA’s best quality ARD, produced using high quality ancillary datasets derived from observed data. These ancillary datasets are slower to produce but are observational datasets of the conditions at the time of image capture and so provide our most accurate dataset corrections.

You can read more about dataset maturity in this page: DEA dataset maturity explained.

Applications

The development of derivative products to monitor land, inland waterways and coastal features, such as:
- urban growth
- coastal habitats
- mining activities
- agricultural activity (e.g. pastoral, irrigated cropping, rain-fed cropping)
- water extent
The development of refined information products, such as:
- areal units of detected surface water
- areal units of deforestation
- yield predictions of agricultural parcels.
Compliance surveys
Emergency management

Technical information

Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS)

OLI is a push-broom sensor with a four-mirror telescope and 14-bit quantisation. OLI collects data for visible, near infrared, and short wave infrared spectral bands as well as a panchromatic band.

TIRS measures land surface temperature in two thermal bands with new technology that applies quantum physics to detect heat.

The Analysis Ready Data concept

The Analysis Ready Data (ARD) package allows you to get up and running with your analysis as quickly as possible with minimal data preparation and additional input. This makes it simpler for you to develop applications and for the database to execute queries.

The satellite data has been processed to a minimum set of requirements and organised into a form that allows immediate analysis and interoperability through time and with other datasets. It has been adapted from CEOS Analysis Ready Data (CARD4L).

The technical report containing the data summary for the Phase 1 DEA Surface Reflectance Validation is available.

ARD sub-products

DEA Surface Reflectance NBAR (Landsat 9 OLI-TIRS)

This sub-product produces standardised optical surface reflectance data using robust physical models which correct for variations and inconsistencies in image radiance values. Corrections are performed using Nadir corrected Bi-directional reflectance distribution function Adjusted Reflectance (NBAR).

DEA Surface Reflectance NBART (Landsat 9 OLI-TIRS)

This sub-product performs the same function as Surface Reflectance (Landsat 9 OLI-TIRS NBAR), but also applies terrain illumination correction.

DEA Surface Reflectance OA (Landsat 9 OLI-TIRS)

The NBAR and NBART sibling products depend upon the Observation Attributes (OA) product to provide accurate and reliable contextual information about the Landsat data. This ‘data provenance’ provides a chain of information which allows the data to be replicated or utilised by derivative applications. It takes a number of different forms, including satellite, solar and surface geometry and classification attribution labels.

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

Software

References

Berk, A., Conforti, P., Kennett, R., Perkins, T., Hawes, F., & van den Bosch, J. (2014, June 13). MODTRAN6: A major upgrade of the MODTRAN radiative transfer code (M. Velez-Reyes & F. A. Kruse, Eds.). https://doi.org/10.1117/12.2050433

Dymond, J. R., & Shepherd, J. D. (1999). Correction of the topographic effect in remote sensing. IEEE Transactions on Geoscience and Remote Sensing, 37(5), 2618–2619. https://doi.org/10.1109/36.789656

Hudson, S. R., Warren, S. G., Brandt, R. E., Grenfell, T. C., & Six, D. (2006). Spectral bidirectional reflectance of Antarctic snow: Measurements and parameterization. Journal of Geophysical Research, 111(D18), D18106. https://doi.org/10.1029/2006JD007290

Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., & Gandin, L. et al. (1996). The NCEP/NCAR 40-Year Reanalysis Project. Bulletin Of The American Meteorological Society, 77(3), 437-471. https://doi.org/10.1175/1520-0477(1996)077<0437:tnyrp>2.0.co;2

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

Qin, Y., Mitchell, R., & Forgan, B. W. (2015). Characterizing the aerosol and surface reflectance over Australia using AATSR. IEEE Transactions on Geoscience and Remote Sensing, 53(11), 6163–6182. https://doi.org/10.1109/TGRS.2015.2433911

Schaaf, C., Gao, F., Strahler, A., Lucht, W., Li, X., & Tsang, T. et al. (2002). First operational BRDF, albedo nadir reflectance products from MODIS. Remote Sensing Of Environment, 83(1-2), 135-148. https://www.doi.org/10.1016/s0034-4257(02)00091-3

SZA. (2011). Retrieved May 2019, from http://sacs.aeronomie.be/info/sza.php

Zhu, Z., Wang, S., & Woodcock, C. (2015). Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images. Remote Sensing Of Environment, 159, 269-277. https://doi.org/10.1016/j.rse.2014.12.014

Zhu, Z., & Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118, 83–94. https://doi.org/10.1016/j.rse.2011.10.028

Quality

Accuracy

For detailed information on accuracy and limitations, refer to the sub-products’ pages:

Quality assurance

For detailed information on quality assurance, refer to the sub-products’ pages:

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
nbart_coastal_aerosol	nbart_band01 coastal_aerosol	30	-999	-	int16	-
nbart_blue	nbart_band02 blue	30	-999	-	int16	-
nbart_green	nbart_band03 green	30	-999	-	int16	-
nbart_red	nbart_band04 red	30	-999	-	int16	-
nbart_nir	nbart_band05 nir nbart_common_nir	30	-999	-	int16	-
nbart_swir_1	nbart_band06 swir_1 nbart_common_swir_1 swir1	30	-999	-	int16	-
nbart_swir_2	nbart_band07 swir_2 nbart_common_swir_2 swir2	30	-999	-	int16	-
nbart_panchromatic	nbart_band08 panchromatic	15	-999	-	int16	-
oa_fmask	fmask	30	0	-	uint8	-
oa_nbart_contiguity	nbart_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 ‘nbart_’ 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 (Landsat 9)	The name that is commonly used to refer to the product.
Technical name	Geoscience Australia Landsat 9 OLI-TIRS Analysis Ready Data 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.