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>

Geoscience Australia (GA) has acquired Landsat satellite image data over Australia since 1979, from instruments including the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). This data represents raw telemetry which has either been received directly at Geoscience Australia's (GAs) receiving stations (Alice Springs or - formerly - Hobart), or downloaded from the United States Geological Survey Organisation. The data is maintained in raw telemetry format as a baseline to downstream processes. While this data has been used extensively for numerous land and coastal mapping studies, its utility for accurate monitoring of environmental resources has been limited by the processing methods that have been traditionally used to correct for inherent geometric and radiometric distortions in EO imagery. To improve access to Australia's archive of Landsat TM/ETM+/OLI data, several collaborative projects have been undertaken in conjunction with industry, government and academic partners. These projects have enabled implementation of a more integrated approach to image data correction that incorporates normalising models to account for atmospheric effects, BRDF (Bi-directional Reflectance Distribution Function) and topographic shading (Li et al., 2012). The approach has been applied to Landsat TM/ETM+ and OLI imagery to create the surface reflectance products. <b>Value: </b>The Landsat Raw Data Archive is processed and further calibrated to input to development of information products toward an improved understanding of the distribution and status of environmental phenomena. <b>Scope: </b>Data is provided via the US Geological Survey's (USGS) Landsat program, following downlink and recording of the data at Alice Springs Antenna (operated by Geoscience Australia) or downloaded directly from USGS EROS

Data used to generate the National Seismic Hazard Assessments (NSHA). Data includes: original and modified earthquake catalogues, earthquake rate models, probabilistic seismic hazard outputs. The most recent assessment was completed in 2018 and can be viewed on Geoscience Australia's <a href="http://www.ga.gov.au/about/projects/safety/nsha">National Seismic Hazard Assessment (NSHA) Internet Page</a> <b>Value: </b> Data used to generate the NSHA <b>Scope: </b>Continental scale

3D structural and geological models that provide insight and understanding of the continents subsurface. The models capture 3D stratigraphy and architecture, including the depth to bedrock and the locations of different major rock units, faults and geological structures. <b>Value: </b>These models are valuable for exploration and reconstructions of Australia's evolution <b>Scope: </b>Contains a variety of 3D volumetric models and surfaces that were produced for specific projects at regional to continental scale.

The Topographic Position Index measures the topographic slope position of landforms by comparing the mean elevation of a specific neighbourhood area with the elevation value of a central cell. This is done for every cell or pixel in the digital elevation model (DEM) to derive the relative topographic position (e.g. upper, middle and lower landscape elements). Ruggedness informs on the roughness of the surface and is calculated as the standard deviation of elevations. Both these terrain components are used to generate a multi-scale topographic index over the Australian continent using the algorithm developed by Lindsay, J, B., Cockburn, J. M. H. and Russell, H. A. J., 2015. An integral image approach to performing multi-scale topographic position analysis, Geomorphology, 245, 51-61. Topographic position is captured across three spatial scale and display as a ternary image. The ternary image reveals a rich representation of nested landform features with broad application to geomorphological and hydrological process understanding and mapping of regolith and soils. <b>Value: </b>Broad application in understanding geomorphological and hydrological processes and in mapping regolith and soils over the Australian continent. Can be used as inputs into geospatial modelling and machine learning <b>Scope: </b>The dataset is national. The algorithm can be run on any digital elevation gridded dataset.

A `weighted geometric median' approach has been used to estimate the median surface reflectance of the barest state (i.e., least vegetation) observed through Landsat-8 Operational Land Image (OLI) observations from 2013 to September 2018 to generate a six-band Landsat-8 Barest Earth pixel composite mosaic over the Australian continent. The bands include BLUE (0.452 - 0.512), GREEN (0.533 - 0.590), RED, (0.636 - 0.673) NIR (0.851 - 0.879), SWIR1 (1.566 - 1.651) and SWIR2 (2.107 - 2.294) wavelength regions. 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. The product reduces the influence of vegetation and allows for more direct mapping of soil and rock mineralogy. Reference: Dale Roberts, John Wilford, and Omar Ghattas (2018). Revealing the Australian Continent at its Barest, submitted. <b>Value: </b>Has broad application in mapping surface geochemistry and mineralogy of exposed soil and bedrock. Has applications in geological mapping and natural resource management including mapping of soil characteristics. <b>Scope: </b>Two enhanced bare earth products have been generated reflecting different Landsat satellites and acquisition periods. The first only uses Landsat 8 observations from 2013 to 2018. The second incorporates the full 30+ year archive combining Landsat 5, 7, and 8 from 1986 to 2018.

A predictive model of weathering intensity or the degree of weathering has been generate over the Australian continent. The model has been generated using the Random Forest decision tree machine learning algorithm. The algorithm is used to establish predictive relationships between field estimates of the degree of weathering and a comprehensive suite of covariate or predictive datasets. The covariates used to generate the model include satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. The weathering intensity model is an estimate of the degree of surface weathering only. The interpretation of the weathering intensity is different for in-situ or residual landscapes compared with transported materials within depositional landscapes. In residual landscapes, weathering process are operating locally whereas in depositional landscapes the model is reflecting the degree of weathering either prior to erosion and subsequent deposition, or weathering of sediments after being deposited. The degree of surface weathering is particularly important in Australia where variations in weathering intensity correspond to the nature and distribution of regolith (weathered bedrock and sediments) which mantles approximately 90% of the Australian continent. The weathering intensity prediction has been generated using the Random Forest decision tree machine learning algorithm. The algorithm is used to establish predictive relationships between field estimates of the degree of weathering and a comprehensive suite of covariate or predictive datasets. The covariates used to generate the model include satellite imagery, terrain attributes, airborne radiometric imagery and mapped geology. Correlations between the training dataset and the covariates were explored through the generation of 300 random tree models. An r-squared correlation of 0.85 is reported using 5 K-fold cross-validation. The mean of the 300 models is used for predicting the weathering intensity and the uncertainty in the weathering intensity is estimated at each location via the standard deviation in the 300 model values. The predictive weathering intensity model is an estimate of the degree of surface weathering only. The interpretation of the weathering intensity is different for in-situ or residual landscapes compared with transported materials within depositional landscapes. In residual landscapes, weathering process are operating locally whereas in depositional landscapes the model is reflecting the degree of weathering either prior to erosion and subsequent deposition, or weathering of sediments after being deposited. The weathering intensity model has broad utility in assisting mineral exploration in variably weathered geochemical landscapes across the Australian continent, mapping chemical and physical attributes of soils in agricultural landscapes and in understanding the nature and distribution of weathering processes occurring within the upper regolith. <b>Value: </b>Weathering intensity is an important characteristic of the earth's surface that has a significant influence on the chemical and physical properties of surface materials. Weathering intensity largely controls the degree to which primary minerals are altered to secondary components including clay minerals and oxides. In this context the weathering intensity model has broad application in understanding geomorphological and weathering processes, mapping soil/regolith and geology. <b>Scope: </b>National dataset which over time can be improved with additional sites for training and thematic datasets for prediction.

Gravity data measure small changes in gravity due to changes in the density of rocks beneath the Earth's surface. The gravity data collection contains both onshore and offshore data acquired on geophysical surveys conducted by Commonwealth, State & NT Governments and the private sector. <b>Value: </b>Gravity used to infer (model) the presence and position of different rock types in the subsurface. Used in resource assessment <b>Scope: </b>Australia continent and some data from marine surveys in region

This data collection are comprised of magnetic surveys acquired across Australia by Commonwealth, State and Northern Territory governments and the private sector with project management and quality control undertaken by Geoscience Australia. Magnetic surveying is a geophysical method for measuring the intensity (or strength) of the Earth's magnetic field, which includes the fields associated with the Earth's core and the magnetism of rocks in the Earth's crust. Measuring the magnetism of rocks, in particular, provides a means for the direct detection of several different types of mineral deposits and for geological mapping. The magnetism of rocks depends on the volume, orientation and distribution of their constituent magnetic minerals (namely magnetite, monoclinic pyrrhotite, maghaemite and ilmenite). The instrument used in magnetic surveys is a magnetometer, which can measure the intensity of the magnetic field in nanoteslas (nT). Magnetic surveys in this collection have been acquired using aircraft or ship-mounted magnetometers and are a non-invasive method for investigating subsurface geology.

This data collection is comprised of radiometric (gamma-ray spectrometric) surveys acquired across Australia by Commonwealth, State and Northern Territory governments and the private sector with project management and quality control undertaken by Geoscience Australia. The radiometric method measures naturally occurring radioactivity arising from gamma-rays. In particular, the method is able to identify the presence of the radioactive isotopes potassium (K), uranium (U) and thorium (Th). The measured radioactivity is then converted into concentrations of the radioelements K, U and Th in the ground. Radiometric surveys have a limited ability to see into the subsurface with the measured radioactivity originating from top few centimetres of the ground. These surveys are primarily used as a geological mapping tool as changes in rock and soil type are often accompanied by changes in the concentrations of the radioactive isotopes of K, U and Th. The method is also capable of directly detecting mineral deposits. For example, K alteration can be detected using the radiometric method and is often associated with hydrothermal ore deposits. Similarly, the method is also used for U and Th exploration, heat flow studies, and environmental mapping purposes such as characterising surface drainage features. The instrument used in radiometric surveys is a gamma-ray spectrometer. This instrument measures the number of radioactive emissions (measured in counts per second) and their energies (measured in electron volts (eV)). Radiometric data are simultaneously acquired with magnetic data during airborne surveys and are a non-invasive method for investigating near-surface geology and regolith.