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 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.

An estimate of the spectra of the barest state (i.e., least vegetation) observed from imagery of the Australian continent collected by the Landsat 5, 7, and 8 satellites over a period of more than 30 years (1983 – 2018). 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 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. This product complements the Landsat-8 Barest Earth which is based on the same algorithm but just uses Landsat8 satellite imagery from 2013-2108. Landsat-8’s OLI sensor provides improved signal-to-noise radiometric (SNR) performance quantised over a 12-bit dynamic range compared to the 8-bit dynamic range of Landsat-5 and Landsat-7 data. However the Landsat 30+ Barest Earth has a greater capacity to find the barest ground due to the greater temporal depth. Reference: Exposed Soil and Mineral Map of the Australian Continent Revealing the Land at its Barest - Dale Roberts, John Wilford and Omar Ghattas Ghattas (2019). Nature Communications, DOI: 10.1038/s41467-019-13276-1. https://www.nature.com/articles/s41467-019-13276-1

<div>Strontium isotopes (87Sr/86Sr) are useful to trace processes in the Earth sciences as well as in forensic, archaeological, palaeontological, and ecological sciences. As very few large-scale Sr isoscapes exist in Australia, we have identified an opportunity to determine 87Sr/86Sr ratios on archived fluvial sediment samples from the low-density National Geochemical Survey of Australia (www.ga.gov.au/ngsa; last access: 15 December 2022). The present study targeted the northern parts of Western Australia, the Northern Territory and Queensland, north of 21.5 °S. The samples were taken mostly from a depth of ~60-80 cm in floodplain deposits at or near the outlet of large catchments (drainage basins). A coarse (< 2 mm) grain-size fraction was air-dried, sieved, milled then digested (hydrofluoric acid + nitric acid followed by aqua regia) to release <em>total</em> Sr. The Sr was then separated by chromatography and the 87Sr/86Sr ratio determined by multicollector-inductively coupled plasma mass spectrometry. Results demonstrate a wide range of Sr isotopic values (0.7048 to 1.0330) over the survey area, reflecting a large diversity of source rock lithologies, geological processes and bedrock ages. Spatial distribution of 87Sr/86Sr shows coherent (multi-point anomalies and smooth gradients), large-scale (> 100 km) patterns that appear to be broadly consistent with surface geology, regolith/soil type, and/or nearby outcropping bedrock. For instance, the extensive black clay soils of the Barkly Tableland define a > 500 km-long northwest-southeast-trending unradiogenic anomaly (87Sr/86Sr < 0.7182). Where sedimentary carbonate or mafic/ultramafic igneous rocks dominate, low to moderate 87Sr/86Sr values are generally recorded (medians of 0.7387 and 0.7422, respectively). In proximity to the outcropping Proterozoic metamorphic basement of the Tennant, McArthur, Murphy and Mount Isa geological regions, conversely, radiogenic 87Sr/86Sr values (> 0.7655) are observed. A potential correlation between mineralisation and elevated 87Sr/86Sr values in these regions needs to be investigated in greater detail. Our results to-date indicate that incorporating soil/regolith Sr isotopes in regional, exploratory geoscience investigations can help identify basement rock types under (shallow) cover, constrain surface processes (e.g. weathering, dispersion), and, potentially, recognise components of mineral systems. Furthermore, the resulting Sr isoscape and future models derived therefrom can also be utilised in forensic, archaeological, paleontological and ecological studies that aim to investigate, e.g., past and modern animal (including humans) dietary habits and migrations. The new spatial Sr isotope dataset for the northern Australia region is publicly available (de Caritat et al., 2022a; https://dx.doi.org/10.26186/147473; last access: 15 December 2022).</div> <b>Citation:</b> de Caritat, P., Dosseto, A., and Dux, F.: A strontium isoscape of northern Australia, <i>Earth Syst. Sci. Data</i>, 15, 1655–1673, https://doi.org/10.5194/essd-15-1655-2023, <b>2023</b>.

<div>Soil is a complex and spatially variable material that has a demonstrated potential to be a useful evidence class in forensic casework and intelligence operations. Here, the capability to spatially constrain searches and prioritise resources by triaging areas as low and high interest is advantageous. Conducted between 2017 and 2021, a forensically relevant topsoil survey (0-5 cm depth; 1 sample per 1 km2) has been carried out over Canberra, Australia, with the aims of documenting the distribution of chemical elements in an urban/suburban environment, and of acting as a testbed for investigating various aspects of forensic soil provenancing. Geochemical data from X-Ray Fluorescence (XRF; for total major oxides) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS; for trace elements) following a total digestion (HF + HNO3) were obtained from the survey’s 685 topsoil samples (plus 138 additional quality control samples and six “Blind” simulated evidentiary samples). Using those “Blind” samples, we document a likelihood ratio approach where for each grid cell the analytical similarity between the grid cell and evidentiary sample is attributed from a measure of overlap between both Cauchy distributions, including appropriate uncertainties. Unlike existing methods that base inclusion/exclusion on an arbitrary threshold (e.g., ± three standard deviations), our approach is free from strict binary or Boolean thresholds, providing an unconstrained gradual transition dictated by the analytical similarity. Using this provenancing model, we present and evaluate a new method for upscaling from a fine (25 m x 25 m) interpolated grid to a more appropriate coarser (500 m x 500 m) grid, in addition to an objective method using Random Match Probabilities for ranking individual variables to be used for provenancing prior to receiving evidentiary material. Our results show this collective procedure generates more consistent and robust provenance maps between two different interpolation algorithms (e.g., inverse distance weighting, and natural neighbour), grid placements (e.g., grid shifts to the north or east) and theoretical users (e.g., different computer systems, or forensic geoscientists).</div> <b>Citation:</b> Michael G. Aberle, Patrice de Caritat, James Robertson, Jurian A. Hoogewerff, A robust interpolation-based method for forensic soil provenancing: A Bayesian likelihood ratio approach,<i> Forensic Science International</i>, Volume 353, 2023, 111883, ISSN 0379-0738. https://doi.org/10.1016/j.forsciint.2023.111883.

Remotely sensed datasets provide fundamental information for understanding the chemical, physical and temporal dynamics of the atmosphere, lithosphere, biosphere and hydrosphere. Satellite remote sensing has been used extensively in mapping the nature and characteristics of the terrestrial land surface, including vegetation, rock, soil and landforms, across global to local-district scales. With the exception of hyper-arid regions, mapping rock and soil from space has been problematic because of vegetation that either masks the underlying substrate or confuses the spectral signatures of geological materials (i.e. diagnostic mineral spectral features), making them difficult to resolve. As part of the Exploring for the Future program, a new barest earth Landsat mosaic of the Australian continent using time-series analysis significantly reduces the influence of vegetation and enhances mapping of soil and exposed rock from space. Here, we provide a brief background on geological remote sensing and describe a suite of enhanced images using the barest earth Landsat mosaic for mapping surface mineralogy and geochemistry. These geological enhanced images provide improved inputs for predictive modelling of soil and rock properties over the Australian continent. In one case study, use of these products instead of existing Landsat TM band data to model chromium and sodium distribution using a random forest machine learning algorithm improved model performance by 28–46%. <b>Citation:</b> Wilford, J. and Roberts, D., 2020. Enhanced barest earth Landsat imagery for soil and lithological modelling. In: Czarnota, K., Roach, I., Abbott, S., Haynes, M., Kositcin, N., Ray, A. and Slatter, E. (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, 1–4.

<div>Strontium isotopes (87Sr/86Sr) are useful in the earth sciences (e.g. recognising geological provinces, studying geological processes) as well in archaeological (e.g. informing on past human migrations), palaeontological/ecological (e.g. investigating extinct and extant taxa’s dietary range and migrations) and forensic (e.g. validating the origin of drinks and foodstuffs) sciences. Recently, Geoscience Australia and the University of Wollongong have teamed up to determine 87Sr/86Sr ratios in fluvial sediments selected mostly from the low-density National Geochemical Survey of Australia (NGSA; www.ga.gov.au/ngsa). The present study targeted the Yilgarn geological region in southwestern Australia. The samples were mostly taken from a depth of ~60-80 cm (Bottom Outlet Sediments, BOS) in floodplain deposits at or near the outlet of large catchments (drainage basins). A small number of surface (0-10 cm) samples (Top Outlet Sediments, TOS) were also included in the study. For all, a coarse grain-size fraction (<2 mm) was air-dried, sieved, milled then digested (hydrofluoric acid + nitric acid followed by aqua regia) to release total strontium. Overall, 107 NGSA BOS < 2 mm and 13 NGSA TOS < 2 mm were analysed for Sr isotopes. Given that there are ~10 % field duplicates in the NGSA, all those samples originate from within 97 NGSA catchments, which together cover 533 000 km2 of southwestern Australia. Preliminary results for the BOS samples demonstrate a wide range of strontium isotopic values (0.7152 < 87Sr/86Sr < 1.0909) over the survey area, reflecting a large diversity of source rock lithologies, geological processes and bedrock ages. Spatial distribution of 87Sr/86Sr shows coherent (multi-point anomalies and smooth gradients), large-scale (>100 km) patterns that appear to be consistent, in many places, with surface geology, regolith/soil type and/or nearby outcropping bedrock. For instance, catchments in the western and central Yilgarn dominated by felsic intrusive basement geology have radiogenic 87Sr/86Sr signatures in the floodplain sediments consistent with published whole-rock data. Similarly, unradiogenic signatures in sediments in the eastern Yilgarn are in agreement with published whole-rock data. Our results to-date indicate that incorporating soil/regolith strontium isotopes in regional, exploratory geoscience investigations can help identify basement rock types under (shallow) cover, constrain surface processes (e.g. weathering, dispersion), and, potentially, recognise components of mineral systems. Furthermore, the resulting strontium isoscape and model derived therefrom can also be utilised in archaeological, paleontological and ecological studies that aim to investigate past and modern animal (including humans) dietary habits and migrations.&nbsp; The new spatial dataset is publicly available through the Geoscience Australia portal https://portal.ga.gov.au/.</div>

<div>Strontium isotopes (87Sr/86Sr) are useful in the earth sciences (e.g. recognising geological provinces, studying geological processes) as well in archaeological (e.g. informing on past human migrations), palaeontological/ecological (e.g. investigating extinct and extant taxa’s dietary range and migrations) and forensic (e.g. validating the origin of drinks and foodstuffs) sciences. Recently, Geoscience Australia and the University of Wollongong have teamed up to determine 87Sr/86Sr ratios in fluvial sediments selected mostly from the low-density National Geochemical Survey of Australia (www.ga.gov.au/ngsa), with a few additional Northern Australia Geochemical Survey infill samples. The present study targeted the northern parts of Western Australia, the Northern Territory and Queensland in Australia, north of 21.5 °S. The samples were taken mostly from a depth of ~60-80 cm depth in floodplain deposits at or near the outlet of large catchments (drainage basins). A coarse grain-size fraction (&lt;2 mm) was air-dried, sieved, milled then digested (hydrofluoric acid + nitric acid followed by aqua regia) to release total strontium. Preliminary results demonstrate a wide range of strontium isotopic values (0.7048 &lt; 87Sr/86Sr &lt; 1.0330) over the survey area, reflecting a large diversity of source rock lithologies, geological processes and bedrock ages. Spatial distribution of 87Sr/86Sr shows coherent (multi-point anomalies and smooth gradients), large-scale (&gt;100 km) patterns that appears to be consistent, in many places, with surface geology, regolith/soil type and/or nearby outcropping bedrock. For instance, the extensive black clay soils of the Barkly Tableland define a &gt;500 km-long northwest-southeast-trending low anomaly (87Sr/86Sr &lt; 0.7182). Where carbonate or mafic igneous rocks dominate, a low to moderate strontium isotope signature is observed. In proximity to the outcropping Proterozoic metamorphic provinces of the Tennant, McArthur, Murphy and Mount Isa geological regions, conversely, high 87Sr/86Sr values (&gt; 0.7655) are observed. A potential link between mineralisation and elevated 87Sr/86Sr values in these regions needs to be investigated in greater detail. Our results to-date indicate that incorporating soil/regolith strontium isotopes in regional, exploratory geoscience investigations can help identify basement rock types under (shallow) cover, constrain surface processes (e.g. weathering, dispersion), and, potentially, recognise components of mineral systems. Furthermore, the resulting strontium isoscape and model derived therefrom can also be utilised in archaeological, paleontological and ecological studies that aim to investigate past and modern animal (including humans) dietary habits and migrations. &nbsp;The new spatial dataset is publicly available through the Geoscience Australia portal https://portal.ga.gov.au/.</div>

During the last 10-20 years, Geological Surveys around the world have undertaken a major effort towards delivering fully harmonized and tightly quality controlled low-density multi-element soil geochemical maps and datasets of vast regions including up to whole continents. Concentrations of between 45 and 60 elements commonly have been determined in a variety of different regolith types (e.g., sediment, soil). The multi-element datasets are published as complete geochemical atlases and made available to the general public. Several other geochemical datasets covering smaller areas but generally at a higher spatial density are also available. These datasets may, however, not be found by superficial internet-based searches because the elements are not mentioned individually either in the title or in the keyword lists of the original references. This publication attempts to increase the visibility and discoverability of these fundamental background datasets covering large areas up to whole continents. <b>Citation:</b> P. de Caritat, C. Reimann, D.B. Smith, X. Wang, Chemical elements in the environment: Multi-element geochemical datasets from continental- to national-scale surveys on four continents, <i>Applied Geochemistry</i>, Volume 89, 2018, Pages 150-159, ISSN 0883-2927, https://doi.org/10.1016/j.apgeochem.2017.11.010

Major oxides provide valuable information about the composition, origin, and properties of rocks and regolith. Analysing major oxides contributes significantly to understanding the nature of geological materials and processes (i.e. physical and chemical weathering) – with potential applications in resource exploration, engineering, environmental assessments, agriculture, and other fields. Traditionally most measurements of oxide concentrations are obtained by laboratory assay, often using X-ray fluorescence, on rock or regolith samples. To expand beyond the point measurements of the geochemical data, we have used a machine learning approach to produce seamless national scale grids for each of the major oxides. This approach builds predictive models by learning relationships between the site measurements of an oxide concentration (sourced from Geoscience Australia’s OZCHEM database and selected sites from state survey databases) and a comprehensive library of covariates (features). These covariates include: terrain derivatives; climate surfaces; geological maps; gamma-ray radiometric, magnetic, and gravity grids; and satellite imagery. This approach is used to derive national predictions for 10 major oxide concentrations at the resolution of the covariates (nominally 80 m). The models include the oxides of silicon (SiO2), aluminium (Al2O3), iron (Fe2O3tot), calcium (CaO), magnesium (MgO), manganese (MnO), potassium (K2O), sodium (Na2O), titanium (TiO2), and phosphorus (P2O5). The grids of oxide concentrations provided include the median of multiple models run as the prediction, and lower and upper (5th and 95th) percentiles as measures of the prediction’s uncertainty. Higher uncertainties correlate with greater spreads of model values. Differences in the features used in the model compared with the full feature space covering the entire continent are captured in the ‘covariate shift’ map. High values in the shift model can indicate higher potential uncertainty or unreliability of the model prediction. Users therefore need to be mindful, when interpreting this dataset, of the uncertainties shown by the 5th-95th percentiles, and high values in the covariate shift map. Details of the modelling approach, model uncertainties and datasets are describe in an attached word document “Model approach uncertainties”. This work is part of Geoscience Australia’s Exploring for the Future program that provides precompetitive information to inform decision-making by government, community and industry on the sustainable development of Australia's mineral, energy and groundwater resources. By gathering, analysing and interpreting new and existing precompetitive geoscience data and knowledge, we are building a national picture of Australia’s geology and resource potential. This leads to a strong economy, resilient society and sustainable environment for the benefit of all Australians. This includes supporting Australia’s transition to net zero emissions, strong, sustainable resources and agriculture sectors, and economic opportunities and social benefits for Australia’s regional and remote communities. The Exploring for the Future program, which commenced in 2016, is an eight year, $225m investment by the Australian Government. These data are published with the permission of the CEO, Geoscience Australia.

The values and distribution patterns of the strontium (Sr) isotope ratio 87Sr/86Sr in Earth surface materials is of use in the geological, environmental and social sciences. Ultimately, the 87Sr/86Sr ratio of any mineral or biological material reflects its value in the rock that is the parent material to the local soil and everything that lives in and on it. In Australia, there are few large-scale surveys of 87Sr/86Sr available, and here we report on a new, low-density dataset using 112 catchment outlet (floodplain) sediment samples covering 529,000 km2 of inland southeastern Australia (South Australia, New South Wales, Victoria). The coarse (<2 mm) fraction of bottom sediment samples (depth ~0.6-0.8 m) from the National Geochemical Survey of Australia were fully digested before Sr separation by chromatography and 87Sr/86Sr determination by multicollector-inductively coupled plasma-mass spectrometry. The results show a wide range of 87Sr/86Sr values from a minimum of 0.7089 to a maximum of 0.7511 (range 0.0422). The median 87Sr/86Sr (± robust standard deviation) is 0.7199 (± 0.0112), and the mean (± standard deviation) is 0.7220 (± 0.0106). The spatial patterns of the Sr isoscape observed are described and attributed to various geological sources and processes. Of note are the elevated (radiogenic) values (≥~0.7270; top quartile) contributed by (1) the Palaeozoic sedimentary country rock and (mostly felsic) igneous intrusions of the Lachlan geological region to the east of the study area; (2) the Palaeoproterozoic metamorphic rocks of the central Broken Hill region; both these sources contribute fluvial sediments into the study area; and (3) the Proterozoic to Palaeozoic rocks of the Kanmantoo, Adelaide, Gawler and Painter geological regions to the west of the area; these sources contribute radiogenic material to the region mostly by aeolian processes. Regions of low 87Sr/86Sr (≤~0.7130; bottom quartile) belong mainly to (1) a few central Murray Basin catchments; (2) some Darling Basin catchments in the northeast; and (3) a few Eromanga geological region-influenced catchments in the northwest of the study area. The new spatial dataset is publicly available through the Geoscience Australia portal (https://portal.ga.gov.au/restore/cd686f2d-c87b-41b8-8c4b-ca8af531ae7e).