The National Geochemical Survey of Australia (NGSA) is Australia’s first national-scale geochemical survey. It was delivered to the public on 30 June 2011, after almost five years of stakeholder engagement, strategic planning, sample collection, preparation and analysis, quality assurance/quality control, and preliminary data analytics. The project was comprehensively documented in seven initial open-file reports and six data and map sets, followed over the next decade by more than 70 well-cited scientific publications. This review compiles the body of work and knowledge that emanated from the project to-date as an indication of the impact the NGSA had over the decade 2011-2021. The geochemical fabric of Australia as never seen before has been revealed by the NGSA. This has spurred further research and stimulated the mineral exploration industry. This paper also critically looks at operational decisions taken at project time (2007-2011) that were good and perhaps – with the benefit of hindsight – not so good, with the intention of providing experiential advice for any future large-scale geochemical survey of Australia or elsewhere. Strengths of the NGSA included stakeholder engagement, holistic approach to a national survey, involvement of other geoscience agencies, collaboration on quality assurance with international partners, and targeted promotion of results. Weaknesses included gaining successful access to all parts of the nation, and management of sample processing in laboratories. <b>Citation:</b> Patrice de Caritat; The National Geochemical Survey of Australia: review and impact. <i>Geochemistry: Exploration, Environment, Analysis </i>2022;; 22 (4): geochem2022–032. doi: https://doi.org/10.1144/geochem2022-032 This article appears in multiple journals (Lyell Collection & GeoScienceWorld)

<b>Please Note:</b> The data related to this Abstract can be obtained by contacting <a href = "mailto: clientservices@ga.gov.au">Manager Client Services</a> and quoting Catalogue number 144231. The data are arranged by regions, so please download the Data Description document found in the Downloads tab to determine your area of interest. 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%.

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).

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

Plutonium (Pu) interactions in the environment are highly complex. Site-specific variables play an integral role in determining the chemical and physical form of Pu, and its migration, bioavailability, and immobility. This paper aims to identify the key variables that can be used to highlight regions of radioecological sensitivity and guide remediation strategies in Australia. Plutonium is present in the Australian environment as a result of global fallout and the British nuclear testing program of 1952 – 1958 in central and west Australia (Maralinga and Monte Bello islands). We report the first systematic measurements of 239+240Pu and 238Pu activity concentrations in distal (≥1,000 km from test sites) catchment outlet sediments from Queensland, Australia. The average 239+240Pu activity concentration was 0.29 mBq.g -1 (n = 73 samples) with a maximum of 4.88 mBq.g -1. 238Pu/239+240Pu isotope ratios identified a large range (0.02 – 0.29 (RSD: 74%)) which is congruent with the heterogeneous nuclear material used for the British nuclear testing programme at Maralinga and Montebello Islands. The use of a modified PCA relying on non-linear distance correlation (dCorr) provided broader insight into the impact of environmental variables on the transport and migration of Pu in this soil system. Primary key environmental indicators of Pu presence were determined to be actinide/lanthanide/heavier transition metals, elevation, electrical conductivity (EC), CaO, SiO2, SO3, landform, geomorphology, land use, and climate explaining 81.7% of the variance of the system. Overall this highlighted that trace level Pu accumulations are associated with the coarse, refractive components of Australian soils, and are more likely regulated by the climate of the region and overall soil type. <b>Citation:</b> Megan Cook, Patrice de Caritat, Ross Kleinschmidt, Joёl Brugger, Vanessa NL. Wong, Future migration: Key environmental indicators of Pu accumulation in terrestrial sediments of Queensland, Australia,<i> Journal of Environmental Radioactivity</i>, Volumes 223–224, 2020, 106398,ISSN 0265-931X, https://doi.org/10.1016/j.jenvrad.2020.106398

Soil is a ubiquitous material at the Earth's surface with potential to be a useful evidence class in forensic and intelligence applications. Compositional data from a soil survey over North Canberra, Australian Capital Territory, are used to develop and test an empirical soil provenancing method. Mineralogical data from Fourier Transform InfraRed spectroscopy (FTIR) and geochemical data from X-Ray Fluorescence (XRF; for total major oxides) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS; for both total and aqua regia-soluble trace elements) are obtained from the survey's 268 topsoil samples (0–5 cm depth; 1 sample per km2). The simultaneous provenancing approach is underpinned by (i) the calculation of Spearman's correlation coefficients (rS) between an evidentiary sample and all the samples in the database for all variables generated by each analytical method; and (ii) the preparation of an interpolated raster grid of rS for each evidentiary sample and method resulting in a series of provenance rasters (“heat maps”). The simultaneous provenancing method is tested on the North Canberra soil survey with three “blind” samples representing simulated evidentiary samples. Performance metrics of precision and accuracy indicate that the FTIR (mineralogy) and XRF (geochemistry) analytical methods offer the most precise and accurate provenance predictions. Maximizing the number of analytes/analytical techniques is advantageous in soil provenancing. Despite acknowledged limitations, it is concluded that the empirical soil provenancing approach can play an important role in forensic and intelligence applications. <b>Citation:</b> de Caritat, P, Woods, B, Simpson, T, Nichols, C, Hoogenboom, L, Ilheo, A, et al. Forensic soil provenancing in an urban/suburban setting: A simultaneous multivariate approach. <i>J Forensic Sci</i>. 2022; 67: 927–935. https://doi.org/10.1111/1556-4029.14967

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.

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.

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.