The Barest Earth Sentinel-2 Map Index web map service 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.

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 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.

This compilation data release is a selection of remotely sensed imagery used in the Exploring for the Future (EFTF) East Kimberley Groundwater Project. Datasets include: • Mosaic 5 m digital elevation model (DEM) with shaded relief • Normalised Difference Vegetation Index (NDVI) percentiles • Tasselled Cap exceedance summaries • Normalised Difference Moisture Index (NDMI) • Normalised Difference Wetness Index (NDWI) The 5m spatial resolution digital elevation model with associated shaded relief image were derived from the East Kimberley 2017 LiDAR survey (Geoscience Australia, 2019b). The Normalised Difference Vegetation Index (NDVI) percentiles include 20th, 50th, and 80th for dry seasons (April to October) 1987 to 2018 and were derived from the Landsat 5,7 and 8 data stored in Digital Earth Australia (see Geoscience Australia, 2019a). Tasselled Cap Exceedance Summary include brightness, greenness and wetness as a composite image and were also derived from the Landsat data. These surface reflectance products can be used to highlight vegetation characteristics such as wetness and greenness, and land cover. The Normalised Difference Moisture Index (NDMI) and Normalised Difference Water Index (NDWI) were derived from the Sentinel-2 satellite imagery. These datasets have been classified and visually enhanced to detect vegetation moisture stress or water-logging and show distribution of moisture. For example, positive NDWI values indicate waterlogged areas while waterbodies typically correspond with values greater than 0.2. Waterlogged areas also correspond to NDMI values of 0.2 to 0.4. Geoscience Australia, 2019a. Earth Observation Archive. Geoscience Australia, Canberra. http://dx.doi.org/10.4225/25/57D9DCA3910CD Geoscience Australia, 2019b. Kimberley East - LiDAR data. Geoscience Australia, Canberra. C7FDA017-80B2-4F98-8147-4D3E4DF595A2 https://pid.geoscience.gov.au/dataset/ga/129985

<div>The Kimberley Region and WA Reefs Bathymetry was derived by EOMAP form multispectral satellite data from the European Space Agency’s Sentinel-2 satellite sensor. EOMAP was contracted by Geoscience Australia (GA) to provide high-resolution (10m) Satellite-Derived Bathymetry (SDB) for the Priority Australian Seabed Mapping Sites. The survey area encompasses an area within Kimberley Region in Western Australia which includes Ashmore Reef, Browse Island, Cartier Island, Clerke Reef, Cunningham Island, Mermaid Reef, Scott Reef and Seringapatam Reef. These critical geospatial data layers provide the essential environmental baseline information for the long-term monitoring and management of these Marine Parks. Mapping the shallow water zone is of importance both from an environmental and socioeconomic perspective. Having access to digital, georeferenced, high-resolution maps of bathymetry and benthic habitats of shallow water areas, is of fundamental use in the areas of navigation, ecological research, environmental modelling, management and conservation, and monitoring the impacts from climate change. Bathymetry data was processed using the physics-based inversion method to derive quantitative information of the shallow water bathymetry using the reflected sunlight energy in different wavelengths of the visible and near infrared region. A detailed delivery report is provided in: Delivery Report: Satellite-Derived Bathymetry, Priority Australian Seabed Mapping Sites. Reference: 20220304.0888. EOMAP Australia Pty Ltd. This dataset is not to be used for navigational purposes. This dataset is published with the permission of the CEO, Geoscience Australia.</div>

The Bonaparte and Browse Basins 3D seismic derived bathymetry compilation (20220002C) was produced by the University of Western Australia, Norwegian Geotechnical Institute and UniLasalle in collaboration with Geoscience Australia through the AusSeabed initiative. The compilation integrates 127 bathymetry grids derived from available and workable 3D seismic datasets into a 30 m resolution 32-bit geotiff. A detailed workflow is described in: Lebrec, U., Paumard, V., O'Leary, M. J., and Lang, S. C., 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: Earth System Science Data, v. 13, no. 11, p. 5191-5212 https://doi.org/10.5194/essd-13-5191-2021, 2021. This dataset is not to be used for navigational purposes.

The Barest Earth Sentinel-2 Map Index web map service 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.

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

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.