Petascale archives of Earth observations from space (EOS) have the potential to characterise water resources at continental scales. For this data to be useful, it needs to be organised, converted from individual scenes as acquired by multiple sensors, converted into ‘analysis ready data’ and made available through high performance computing platforms. Moreover, converting this data into insights requires integration of non-EOS datasets that can provide biophysical and climatic context for EOS. Digital Earth Australia has demonstrated its ability to link EOS to rainfall and stream gauge data to provide insight into surface water dynamics during the hydrological extremes of flood and drought. This information is supporting the characterisation of groundwater resources across Australia’s north and could potentially be used to gain an understanding of the vulnerability of transport infrastructure to floods in remote, sparsely gauged regions of northern and central Australia.

Background: This is a sub-product of DEA Surface Reflectance (Sentinel-2A MSI) - Geoscience Australia Sentinel-2A MSI Analysis Ready Data Collection 3. 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 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 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 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

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. <b>Background:</b> 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. <b>What this product offers:</b> 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

<b>BACKGROUND</b> <p> <p>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. <p>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. <p>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. <p>&nbsp</p> <b>WHAT THIS PRODUCT OFFERS</b> <p> <p>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. <p> <p>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. <p> <p>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. <p> <p>It contains three sub-products that provide corrections or attribution information: <p> <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1) GA Landsat 5 TM NBAR Collection 3 <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2) GA Landsat 5 TM NBART Collection 3 <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3) GA Landsat 5 TM OA Collection 3 <p> <p>The resolution is a 30 m grid based on the USGS Landsat Collection 1 archive.

<b>BACKGROUND</b> <p> <p>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. <p>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. <p>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. <p>&nbsp</p> <b>WHAT THIS PRODUCT OFFERS</b> <p> <p>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. <p> <p>The imagery is captured using the Operational Land Imager (OLI) and Thermal Infra-Red Scanner (TIRS) sensors aboard Landsat 8. <p> <p>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. <p> <p>It contains three sub-products that provide corrections or attribution information: <p> <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1) GA Landsat 8 OLI/TIRS NBAR Collection 3 <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2) GA Landsat 8 OLI/TIRS NBART Collection 3 <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3) GA Landsat 8 OLI/TIRS OA Collection 3 <p> <p>The resolution is a 30 m grid based on the USGS Landsat Collection 1 archive.

<b>BACKGROUND</b> <p> <p>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. <p>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. <p>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. <p>&nbsp</p> <b>WHAT THIS PRODUCT OFFERS</b> <p> <p>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. <p> <p>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. <p> <p>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. <p> <p>It contains three sub-products that provide corrections or attribution information: <p> <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1) GA Landsat 7 ETM+ NBAR Collection 3 <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;2) GA Landsat 7 ETM+ NBART Collection 3 <p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;3) GA Landsat 7 ETM+ OA Collection 3 <p> <p>The resolution is a 30 m grid based on the USGS Landsat Collection 1 archive.

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

<div>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.</div><div><br></div><div>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.</div><div><br></div><div>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.</div><div><br></div><div>The Geoscience Australia Landsat 9 OLI TIRS Analysis Ready Data Collection 3 contains three sub-products that provide corrections or attribution information:</div><div>- DEA Surface Reflectance NBAR* (Landsat 9)</div><div>- DEA Surface Reflectance NBART** (Landsat 9)</div><div>- DEA Surface Reflectance OA*** (Landsat 9)</div><div><br></div><div>Note: DEA produces NBAR as part of the Landsat ARD, this is available in the National Computing Infrastructure environment only and is not available in the DEA cloud environments.</div><div><br></div><div>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. </div><div><br></div><div>* Nadir corrected Bi-directional reflectance distribution function Adjusted Reflectance (NBAR)</div><div>** Nadir corrected Bi-directional reflectance distribution function Adjusted Reflectance with terrain illumination correction (NBART)</div><div>*** Observation Attributes (OA)</div>

<b>Background:</b> 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. <b>What this product offers:</b> 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. <b>Applications:,</b> - 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. <b>Packaging updates include: </b> - 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. <b>Additional updates include:</b> - 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. <b>The introduction of a maturity concept.</b> 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.

Up to date information about the extent and location of surface water provides all Australians with a common understanding of this valuable and increasingly scarce resource. Digital Earth Australia (DEA) Waterbodies shows the wet surface area of waterbodies as estimated from satellites. It does not show depth, volume, purpose of the waterbody, nor the source of the water. DEA Waterbodies uses Geoscience Australia’s archive of over 30 years of Landsat satellite imagery to identify where over 300,000 waterbodies are in the Australian landscape and tells us the wet surface area within those waterbodies. It supports users to understand and manage water across Australia. For example, users can gain insights into the severity and spatial distribution of drought or identify potential water sources for aerial firefighting. The tool uses a water classification for every available Landsat satellite image and maps the locations of waterbodies across Australia. It provides a timeseries of wet surface area for waterbodies that are present more than 10% of the time and are larger than 2700m2 (3 Landsat pixels). The tool indicates changes in the wet surface area of waterbodies. This can be used to identify when waterbodies are increasing or decreasing in wet surface area. More information on using this dataset can be accessed on the DEA Knowledge Hub at <a href="https://docs.dea.ga.gov.au/data/product/dea-waterbodies-landsat/?tab=overview">https://docs.dea.ga.gov.au/data/product/dea-waterbodies-landsat/?tab=overview</a>. Refer to the research paper Krause et al. 2021 for additional details: <a href="https://doi.org/10.3390/rs13081437">https://doi.org/10.3390/rs13081437</a> The update from version 2 to version 3.0 of the DEA Waterbodies product and service was created through a collaboration between Geoscience Australia, the National Aerial Firefighting Centre, Natural Hazards Research Australia, and FrontierSI to make the product more useful in hazard applications. Geoscience Australia, the National Aerial Firefighting Centre, Natural Hazards Research Australia, and FrontierSI advise that the information published by this service comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law, FrontierSI, Geoscience Australia, the National Aerial Firefighting Centre and Natural Hazards Research Australia (including its employees and consultants) are excluded from all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it.