This web service contains a selection of remotely sensed raster products used in the Exploring for the Future (EFTF) East Kimberley Groundwater Project. Selected products were derived from LiDAR, Landsat (5, 7, and 8), and Sentinel-2 data. Datasets include: 1) mosaic 5 m digital elevation model (DEM) with shaded relief; 2) vegetation structure stratum and substratum classes; 3) Normalised Difference Vegetation Index (NDVI) 20th, 50th, and 80th percentiles; 4) Tasselled Cap exceedance summaries; 5) Normalised Difference Moisture Index (NDMI) and Normalised Difference Wetness Index (NDWI). Landsat spectral reflectance products can be used to highlight land cover characteristics such as brightness, greenness and wetness, and vegetation condition; Sentinel-2 datasets help to detect vegetation moisture stress or waterlogging; LiDAR datasets providing a five meter DEM and vegetation structure stratum classes for detailed analysis of vegetation and relief.

<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.

This web service contains a selection of remotely sensed raster products used in the Exploring for the Future (EFTF) East Kimberley Groundwater Project. Selected products were derived from LiDAR, Landsat (5, 7, and 8), and Sentinel-2 data. Datasets include: 1) mosaic 5 m digital elevation model (DEM) with shaded relief; 2) vegetation structure stratum and substratum classes; 3) Normalised Difference Vegetation Index (NDVI) 20th, 50th, and 80th percentiles; 4) Tasselled Cap exceedance summaries; 5) Normalised Difference Moisture Index (NDMI) and Normalised Difference Wetness Index (NDWI). Landsat spectral reflectance products can be used to highlight land cover characteristics such as brightness, greenness and wetness, and vegetation condition; Sentinel-2 datasets help to detect vegetation moisture stress or waterlogging; LiDAR datasets providing a five meter DEM and vegetation structure stratum classes for detailed analysis of vegetation and relief.

This web service contains a selection of remotely sensed raster products used in the Exploring for the Future (EFTF) East Kimberley Groundwater Project. Selected products were derived from LiDAR, Landsat (5, 7, and 8), and Sentinel-2 data. Datasets include: 1) mosaic 5 m digital elevation model (DEM) with shaded relief; 2) vegetation structure stratum and substratum classes; 3) Normalised Difference Vegetation Index (NDVI) 20th, 50th, and 80th percentiles; 4) Tasselled Cap exceedance summaries; 5) Normalised Difference Moisture Index (NDMI) and Normalised Difference Wetness Index (NDWI). Landsat spectral reflectance products can be used to highlight land cover characteristics such as brightness, greenness and wetness, and vegetation condition; Sentinel-2 datasets help to detect vegetation moisture stress or waterlogging; LiDAR datasets providing a five meter DEM and vegetation structure stratum classes for detailed analysis of vegetation and relief.

The Sentinel-2 Bare Earth thematic product provides the first national scale mosaic of the Australian continent to support improved mapping of soil and geology. The bare earth algorithm using all available Sentinel-2 A and Sentinel-2 B observations up to September 2020 preferentially weights bare pixels through time to significantly reduce the effect of seasonal vegetation in the imagery. The result are image pixels that are more likely to reflect the mineralogy and/or geochemistry of soil and bedrock. The algorithm uses a high-dimensional weighted geometric median approach that maintains the spectral relationships across all Sentinel-2 bands. A similar bare earth algorithm has been applied to Geoscience Australia’s deeper Landsat time series archive (please search for "Landsat barest Earth". Both bare earth products have spectral bands in the visible near infrared and shortwave infrared region of the electromagnetic spectrum. However, the main visible and near-infrared Sentinel-2 bands have a spatial resolution of 10 meters compared to 30m for the Landsat TM equivalents. The weighted median approach is robust to outliers (such as cloud, shadows, saturation, corrupted pixels) and also maintains the relationship between all the spectral wavelengths in the spectra observed through time. Not all the sentinel-2 bands have been processed - we have excluded atmospheric bands including 1, 9 and 10. The remaining bands have been re-number 1-10 and these bands correlate to the original bands in brackets below: 1 = blue (2) , 2 = green (3) , 3 = red (4), 4 = vegetation red edge (5), 5 = vegetation red edge (6), 6= vegetation red edge (7), 7 = NIR(8), 8 = Narrow NIR (8a), 9 = SWIR1 (11) and 10 = SWIR2(12). All 10 bands have been resampled to 10 meters to facilitate band integration and use in machine learning.

Background: The European Space Agency (ESA) has operated 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 the 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 over time, such as agricultural production and mining activities. 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, 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-2A 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-2A. 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: - Geoscience Australia Sentinel-2A MSI NBART Collection 3 - Geoscience Australia Sentinel-2A 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

The Barest Earth Sentinel-2 Map Index dataset depicts the 1 to 250 000 maps sheet tile frames that have been used to generate individual tile downloads of the Barest Earth Sentinel-2 product. This web service is designed to be used in conjunction with the Barest Earth Sentinel-2 web service to provide users with direct links for imagery download.

This resource contains a bathymetry compilation prepared by the University of Western Australia for the North West Shelf of Australia, between the Cape Range and the Dampier Peninsula. The compilation includes, by decreasing resolution: - Publicly available MBES datasets, made available by Geoscience Australia by December 2019. - Satellite derived bathymetry produced using 1000+ images acquired between January 2017 and December 2019. - Seismic derived bathymetry extracted from 100+ surveys acquired between 1981 and 2015. - SRTM topography, reprocessed by Galant et al, 2011: https://pid.geoscience.gov.au/dataset/ga/72759 - 2009 Australian Bathymetry and Topography grid: https://pid.geoscience.gov.au/dataset/ga/67703 The Seismic and Satellite derived bathymetry grids are also available as individual layers. The vertical and spatial accuracy of the datasets have been thoroughly assessed using high-resolution datasets including publicly available MBES and LADS surveys. The assessment indicates that the seismic derived bathymetry has a depth accuracy better than 1 m + 2% of the absolute water depths while the satellites derived bathymetry has a depth accuracy better than 1 m + 5% of the absolute water depths. A detailed methodology is provided in: Lebrec et al, 2021. Towards a regional high-resolution bathymetry of the North West Shelf of Australia based on Sentinel-2 satellite images, 3D seismic surveys and historical datasets. (in prep.) This dataset is published with the permission of the CEO, Geoscience Australia. AUTHOR’S NOTICE: This dataset should not be used, under any circumstances, for navigation. When used, the dataset should be referenced as follow: Lebrec, U., Paumard, V., O'Leary, M. J., and Lang, S. C.: Towards a regional high-resolution bathymetry of the North West Shelf of Australia based on Sentinel-2 satellite images, 3D seismic surveys and historical datasets, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2021-128, in review, 2021.

<div>A package of deliverables for the Australian Research Data Commons (ARDC), Bushfire History Data Project, Work Package 5. If you require further information or access, please contact earth.observation@ga.gov.au</div><div><br></div><div>Outputs generated for this Project are interim and represent a snapshot of work to date, as of September 2023. Deliverables are developmental in nature and are under further advancement. Datasets or visualisations should not be treated as endorsed, authoritative, or quality assured; and should not be used for anything other than a minimal viable product, especially not for safety of life decisions. The eventual purpose of this information is for strategic decisions, rather than tactical decisions. For local data, updates and alerts, please refer to your State or Territory emergency or fire service.</div><div><br></div><div>The purpose of this Project (WP5) was to generate fire history products from Earth observation (EO) data available from the Landsat and Sentinel-2 satellites. WP5 aimed to implement a suite of automated EO-based algorithms currently in use by State and Territory agencies, to produce National-scale data products describing the timing, location, and extent of bushfires across Australia. WP5 outputs are published here as a “deliverable package”, listed as documents, datasets and Jupyter notebooks.&nbsp;</div><div><br></div><div>Burnt area data demonstrators were produced to a Minimum Viable Product level. Four burnt area detection methods were investigated: </div><div>* BurnCube (Geoscience Australia, ANU, (Renzullo et al. 2019)),</div><div>* Burnt Area Characteristics (Geoscience Australia, unpublished methodology),</div><div>* A version of the Victoria’s Random Forest (Victorian, Tasmanian and New South Wales Governments). Based on method as described in Collins et al. (2018), and</div><div>* Queensland’s RapidFire (Queensland Government, (Van den Berg et al. 2021). Please note that demonstrator burnt area data from the Queensland method was only investigated for the Queensland location. Data were sourced from Terrestrial Ecosystem Research Network (TERN) infrastructure, which is enabled by the Australian Government National Collaborative Research Infrastructure Strategy (NCRIS). </div><div><br></div><div>In addition demonstrator products that examine the use of Near Real Time satellite data to map burnt area, data quality and data uncertainty were delivered. </div><div><br></div><div>The algorithms were tested on several study sites:</div><div>* Eastern Victoria,</div><div>* Cooktown QLD,</div><div>* Kangaroo Island SA,</div><div>* Port Hedland WA, and</div><div>* Esperance WA.</div><div><br></div><div>The BurnCube (Renzullo et al. 2019) method was implemented at a national-scale using the Historic Burnt Area Processing Pipeline documented below “GA-ARDC-DataProcessingPipeline.pdf”. Continental-scale interim summary results were generated for both 2020 Calendar Year and 2020 Financial Year. Results were based upon both Landsat 8 and Sentinel-2 (combined 2a and 2b) satellite outputs, producing four separate interim products:&nbsp;</div><div>* Landsat 8, 2020 Calendar Year, BurnCube Summary (ga_ls8c_nbart_bc_cyear_3),</div><div>* Landsat 8, 2020 Financial Year, BurnCube Summary (ga_ls8c_nbart_bc_fyear_3),</div><div>* Sentinel 2a and 2b, 2020 Calendar Year, BurnCube Summary (ga_s2_ard_bc_cyear_3),</div><div>* Sentinel 2a and 2b, 2020 Financial Year, BurnCube Summary (ga_s2_ard_bc_fyear_3).</div><div>&nbsp;</div><div>The other methods have sample products for the study sites, as discussed in the "lineage" section. </div><div><br></div><div>The Earth observation approach has several limitations, leading to errors of omission and commission (ie under estimation and over estimation of the burnt area). Omission errors can result from: lack of visibility due to clouds; small or patchy fires; rapid vegetation regrowth between fire and satellite observation; cool understorey burns being hidden by the vegetation canopy. Commission errors can result from: incorrect cloud or cloud-shadow masking; high-intensity land-use changes (such as cropping); areas of inundation. In addition cloud and shadow masking lead to differences in elapsed time between reference imagery and observations of change resulting in differences in burn area detection. For more information on data caveats please see Section 7.6 of DRAFT-ARDC-WP5-HistoricBurntArea.</div><div><br></div><div>The official Project title is: The Australian Research Data Commons (ARDC), Bushfire Data Challenges Program; Project Stream 1: the ARDC Bushfire History Data Project; Work Package 5 (WP5): National burnt area products analysed from Landsat and Sentinel 2 satellite imagery.</div><div><br></div><div>We thank the Mindaroo Foundation and ARDC for their support in this work.</div>

Analysis Ready Data (ARD) takes medium resolution satellite 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. This product is a single, cohesive ARD package, which allows you to analyse surface reflectance data as is, without the need to apply additional corrections. ARD consists of sub products, including : 1) NBAR Surface Reflectance which 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). 2) NBART Surface Reflectance which performs the same function as NBAR Surface Reflectance, but also applies terrain illumination correction. 3) OA Observation Attributes product which provides accurate and reliable contextual information about the 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. ARD enables generation of Derivative Data and information products that represent biophysical parameters, either summarised as statistics, or as observations, which underpin an understanding of environmental dynamics. 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 Derivative products include: - Water Observations from Space (WOfS) - National Intertidal Digital Elevation Model (NIDEM) - Fractional Cover (FC) - Geomedian ARD and Derivative products are reproduced through a period collection upgrade process for each sensor platform. This process applied improvements to the algorithms and techniques and benefits from improvements applied to the baseline data that feeds into the ARD production processes. <b>Value: </b>These data are used to understand distributions of and changes in surface character, environmental systems, land use. <b>Scope: </b>Australian mainland and some part of adjacent nations. Access data via the DEA web page - <a href="https://www.dea.ga.gov.au/products/baseline-data">https://www.dea.ga.gov.au/products/baseline-data</a>