GA Landsat 5 TM Analysis Ready Data Collection 3

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 need to 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 need to be reduced or removed to ensure the data is consistent and can be compared over time. &nbsp WHAT THIS PRODUCT OFFERS GA Landsat 5 TM Analysis Ready Data Collection 3 takes Landsat 5 Thematic Mapper (TM) imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. The TM instrument is an advanced, multispectral scanning, Earth resources sensor which is designed to categorise the Earth's surface. It is particularly useful for agricultural applications and identification of land use. 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:      1) GA Landsat 5 TM NBAR Collection 3      2) GA Landsat 5 TM NBART Collection 3      3) GA Landsat 5 TM OA Collection 3 The resolution is a 30 m grid based on the USGS Landsat Collection 1 archive.

GA Landsat 8 OLI/TIRS Analysis Ready Data Collection 3

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 need to 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 need to be reduced or removed to ensure the data is consistent and can be compared over time. &nbsp WHAT THIS PRODUCT OFFERS GA Landsat 8 OLI/TIRS Analysis Ready Data Collection 3 takes Landsat 8 imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. 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 8. 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:      1) GA Landsat 8 OLI/TIRS NBAR Collection 3      2) GA Landsat 8 OLI/TIRS NBART Collection 3      3) GA Landsat 8 OLI/TIRS OA Collection 3 The resolution is a 30 m grid based on the USGS Landsat Collection 1 archive.

Wetlands Insight Tool: Characterising the Surface Water and Vegetation Cover Dynamics of Individual Wetlands Using Multidecadal Landsat Satellite Data

Wetlands around the world provide crucial ecosystem services and are under increasing pressure from multiple sources including climate change, changing flow and flooding regimes, and encroaching human populations. The Landsat satellite imagery archive provides a unique observational record of how wetlands have responded to these impacts during the last three decades. Information stored within this archive has historically been difficult to access due to its petabyte-scale and the challenges in converting Earth observation data into biophysical measurements that can be interpreted by wetland ecologists and catchment managers. This paper introduces the Wetlands Insight Tool (WIT), a workflow that generates WIT plots that present a multidecadal view of the biophysical cover types contained within individual Australian wetlands. The WIT workflow summarises Earth observation data over 35 years at 30 m resolution within a user-defined wetland boundary to produce a time-series plot (WIT plot) of the percentage of the wetland covered by open water, areas of water mixed with vegetation (‘wet’), green vegetation, dry vegetation, and bare soil. We compare these WIT plots with documented changes that have occurred in floodplain shrublands, alpine peat wetlands, and lacustrine and palustrine wetlands, demonstrating the power of satellite observations to supplement ground-based data collection in a diverse range of wetland types. The use of WIT plots to observe and manage wetlands enables improved evidence-based decision making. Citation: Dunn, B., Ai, E., Alger, M.J. et al. Wetlands Insight Tool: Characterising the Surface Water and Vegetation Cover Dynamics of Individual Wetlands Using Multidecadal Landsat Satellite Data. Wetlands 43, 37 (2023). https://doi.org/10.1007/s13157-023-01682-7

Geoscience Australia Sentinel-2A Observation Attributes Collection 3 - DEA Surface Reflectance OA (Sentinel-2A MSI)

Background This is a sub-product of Geoscience Australia Sentinel-2A MSI Analysis Ready Data Collection 3 - DEA Surface Reflectance 3 (Sentinel-2A). See the parent product for more information. The contextual information related to a dataset is just as valuable as the data itself. This information, also known as data provenance or data lineage, includes details such as the data’s origins, derivations, methodology and processes. It allows the data to be replicated and increases the reliability of derivative applications. Data that is well-labelled and rich in spectral, spatial and temporal attribution can allow users to investigate patterns through space and time. Users are able to gain a deeper understanding of the data environment, which could potentially pave the way for future forecasting and early warning systems. The surface reflectance data produced by NBART requires accurate and reliable data provenance. Attribution labels, such as the location of cloud and cloud shadow pixels, can be used to mask out these particular features from the surface reflectance analysis, or used as training data for machine learning algorithms. Additionally, the capacity to automatically exclude or include pre-identified pixels could assist with emerging multi-temporal and machine learning analysis techniques. What this product offers This product contains a range of pixel-level observation attributes (OA) derived from satellite observation, providing rich data provenance: - null pixels - clear pixels - cloud pixels - cloud shadow pixels - snow pixels - water pixels - spectrally contiguous pixels - terrain shaded pixels It also features the following pixel-level information pertaining to satellite, solar and sensing geometries: - solar zenith - solar azimuth - satellite view - incident angle - exiting angle - azimuthal incident - azimuthal exiting - relative azimuth - timedelta

Digital Earth Australia video

60 second video announcing Digital Earth Australia - a world first analysis platform for satellite imagery and other Earth observations.

Presentations from the GEO Week 2019 Targeted Side Event - Earth Observations: Preparing the global resource industry for a sustainable future

On 5 November 2019, Geoscience Australia presented a Targeted Side Event at the GEO Week 2019 Ministerial Summit in Canberra (http://www.earthobservations.org/geoweek19.php?t=home). GEO, the Group on Earth Observations, is a global intergovernmental partnership of 105 Member governments, 127 Participating Organizations and thousands of individuals and businesses that strives to improve the availability, access and use of Earth observations for a more sustainable planet. The theme of the Targeted Side Event was as follows. Strong, resilient and sustainable communities have jobs, homes, clean water, feel safe and are well connected locally, nationally and internationally. Government, business, industry and community decision makers can progress economic, social and cultural development using new, free digital information and mapping tools. Smart, fast and trusted decisions made using digital information and digital mapping can be used for any sized community, remote, rural, city, national. Sustainable development, responsible growth through a reform and transform approach can unlock new resource opportunities and respond to the economic and social challenges faced by many countries. Presented is a new digital mapping decision making tool that integrates resources: minerals, energy and water, within a social, economic and environment frame. Addressing social licence and environmental sustainability is becoming increasingly important to ensuring the future economic development of Earth resources. The challenge for geoscientists is to create tools using data integrated from multiple disciplines to deliver insight into the complex interactions between diverse Earth systems and human society. These tools will enable specialists and non-specialists in communities, government and industry to make informed decisions for a sustainable future.

Geoscience Australia's predictive schedules for public good satellites over Australia

Background Every day more than a dozen foreign-operated, public-good, non-commercial, medium to low resolution satellites fly over Australia and its territories. They cross the continent several times a day and their sensors capture images of the landand coastal waters. Satellite overpass schedules for each spacecraft are predictable and can be calculated with a degree of accuracy. The Daily SatPaths provides information on which satellite sensors have and will potentially acquire data over Australia during a given date and time interval. It is important to note that actual acquisition schedules may differ from those presented in Daily SatPaths due the operational limitations of the satellite.

GA Landsat 7 ETM+ Analysis Ready Data Collection 3

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 need to 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 need to be reduced or removed to ensure the data is consistent and can be compared over time. &nbsp WHAT THIS PRODUCT OFFERS GA Landsat 7 ETM+ Analysis Ready Data Collection 3 takes Landsat 7 Enhanced Thematic Mapper (ETM+) imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. The ETM+ instrument is a fixed ‘whisk broom’, eight-band, multispectral scanning radiometer capable of providing high-resolution imaging information of the Earth’s surface. It is an enhanced version of the Thematic Mapper (TM) sensor. 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:      1) GA Landsat 7 ETM+ NBAR Collection 3      2) GA Landsat 7 ETM+ NBART Collection 3      3) GA Landsat 7 ETM+ OA Collection 3 The resolution is a 30 m grid based on the USGS Landsat Collection 1 archive.

Geoscience Australia Sentinel-2B Observation Attributes Collection 3 - DEA Surface Reflectance OA (Sentinel-2B MSI)

DEA Surface Reflectance OA (Sentinel-2B 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. Background: This is a sub-product of Geoscience Australia Sentinel-2B MSI Analysis Ready Data Collection 3 - DEA Surface Reflectance (Sentinel-2B MSI). See the parent product for more information. The contextual information related to a dataset is just as valuable as the data itself. This information, also known as data provenance or data lineage, includes details such as the data’s origins, derivations, methodology and processes. It allows the data to be replicated and increases the reliability of derivative applications. Data that is well-labelled and rich in spectral, spatial and temporal attribution can allow users to investigate patterns through space and time. Users are able to gain a deeper understanding of the data environment, which could potentially pave the way for future forecasting and early warning systems. The surface reflectance data produced by NBART requires accurate and reliable data provenance. Attribution labels, such as the location of cloud and cloud shadow pixels, can be used to mask out these particular features from the surface reflectance analysis, or used as training data for machine learning algorithms. Additionally, the capacity to automatically exclude or include pre-identified pixels could assist with emerging multi-temporal and machine learning analysis techniques. What this product offers: This product contains a range of pixel-level observation attributes (OA) derived from satellite observation, providing rich data provenance: - null pixels - clear pixels - cloud pixels - cloud shadow pixels - snow pixels - water pixels - spectrally contiguous pixels - terrain shaded pixels It also features the following pixel-level information pertaining to satellite, solar and sensing geometries: - solar zenith - solar azimuth - satellite view - incident angle - exiting angle - azimuthal incident - azimuthal exiting - relative azimuth - timedelta

Geoscience Australia Sentinel-2B MSI Analysis Ready Data Collection 3 - DEA Surface Reflectance (Sentinel-2B MSI)

Background: The European Space Agency (ESA) has operated the medium resolution satellites - Sentinel-2 series (Sentinel-2A and Sentinel-2B) since 2015. The spectral bands and spatial resolution of Sentinel-2 are similar to those of Landsat series, but Sentinel-2 has a higher revisit frequency and spatial coverage. A combination of Sentinel-2 and Landsat data can provide good spatial and temporal coverage of the Earth's surface and provide useful information to monitor environmental resources, such as agricultural production and mining activities, over time. However, the raw remotely sensed data received by these satellites in the solar spectral range do not directly characterise the underlying reflectance of surface objects. The data are modified by the atmosphere and variation of solar and sensor positions as well as surface anisotropic conditions. To make accurate comparisons of imagery acquired at different times, seasons and geographic locations and detect the change of surface, it is necessary to remove/reduce these effects to ensure the data are consistent and can be compared over time. What this product offers: This product takes Sentinel-2B imagery captured over the Australian continent and corrects for inconsistencies across land and coastal fringes. The result is accurate and standardised surface reflectance data, which is instrumental in identifying and quantifying environmental change. The imagery is captured using the Multispectral Instrument (MSI) sensor aboard Sentinel-2B. This product is a single, cohesive Analysis Ready Data (ARD) package, which allows the analysis of surface reflectance data as is, without the need to apply additional corrections. It contains two sub-products that provide corrections or attribution information: - DEA Surface Reflectance NBART(Sentinel-2B MSI) - Geoscience Australia Sentinel-2B MSI NBART Collection 3 - DEA Surface Reflectance OA(Sentinel-2B MSI) - Geoscience Australia Sentinel-2B Observation Attributes Collection 3 The resolution is a 10/20/60 m grid based on the ESA Level 1C archive. 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 This Collection 3 (C3) product and has been created by reprocessing Collection 1 (C1) and making improvements to the processing pipeline and packaging. Packaging updates include: - Open Data Cube (ODC) eo3 metadata - metadata includes STAC fields to enable users to filter by fields such as tile ID or cloud cover percentage in applications such as ODC - additional STAC metadata file in JSON format - directory structure and file names that are consistent with Geoscience Australia’s Landsat C3 products. Additional updates include: - upgrading the spectral response function to result in a more accurate product. These new versions include minor updates, slight changes of the central wavelengths for band B02 of S2A and S2B, and band B01 of S2B, along with slight changes of the Full Width Half Maximum (FMWH) for most of the bands - correction of solar constant errors in the conversion between reflectance and radiance as well as in the atmospheric correction - an additional cloud mask layer (s2cloudless) - removal of NBAR layers - reduced spatial resolution of observation attribute layers to 20m resolution, with the contiguity layer being maintained at 10m - additional of GQA information to dataset metadata - removal of buffering from fmask layer - BRDF ancillary upgraded from MODIS BRDF C5 to MODIS BRDF C6 - Upgrading from MODTRAN 5.2 to MODTRAN 6. The introduction of a maturity concept. The Collection 3 product is 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 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 < 48 hours after image capture. - Interim ARD – If there are extended delays (>18 days) in delivery of inputs to the ARD model, interim production is utilised until the issue is resolved. - Final ARD - As the higher quality ancillary datasets become available, a “Final” version of the Sentinel 2 ARD data is produced, which replaces the NRT or interim product.