Preprints
https://doi.org/10.5194/essd-2022-418
https://doi.org/10.5194/essd-2022-418
 
13 Jan 2023
13 Jan 2023
Status: this preprint is currently under review for the journal ESSD.

Digital soil mapping of lithium in Australia

Wartini Ng1, Budiman Minasny1, Alex McBratney1, Patrice de Caritat2, and John Wilford2 Wartini Ng et al.
  • 1Sydney Institute of Agriculture, School of Life and Environmental Sciences, The University of Sydney, NSW 2015, Australia
  • 2Geoscience Australia, Canberra, ACT 2601, Australia

Abstract. With a higher demand for lithium (Li), a better understanding of its concentration and spatial distribution is important to delineate potential anomalous areas. This study uses a digital soil mapping framework to combine data from recent geochemical surveys and environmental covariates to predict and map Li content across the 7.6 million km2 area of Australia. Soil samples were collected by the National Geochemical Survey of Australia at a total of 1315 sites, with both top (0–10 cm depth) and bottom (on average 60–80 cm depth) catchment outlet sediments sampled. We developed 50 bootstrap models using a Cubist regression tree algorithm for both depths. The spatial prediction models were validated on an independent Northern Australia Geochemical Survey dataset, showing a good prediction with a root mean square error of 3.82 mg kg-1 (which is 50.9 % of the inter-quartile range) for the top depth. The model for the bottom depth has yet to be validated. The variables of importance for the models indicated that the first three Landsat 30+ Barest Earth bands (blue, green, red) and gamma radiometric dose have a strong impact on Li prediction. The bootstrapped models were then used to generate digital soil Li prediction maps for both depths, which could select and delineate areas with anomalously high Li concentrations in the regolith. The map shows high Li concentration around existing mines and other potentially anomalous Li areas. This is the first study that produces soil Li using remote sensing data at a high resolution over a continent. The same mapping principles can potentially be applied to other elements. The Li geochemical data for calibration and validation are available at: http://dx.doi.org/10.11636/Record.2011.020 and http://dx.doi.org/10.11636/Record.2019.002 respectively. The covariates data used for this study was sourced from Terrestrial Ecosystem Research Network (TERN) infrastructure, which is enabled by the Australian Government’s National Collaborative Research Infrastructure Strategy (NCRIS) https://esoil.io/TERNLandscapes/Public/Products/TERN/Covariates/Mosaics/90m/ (TERN, 2019).

Wartini Ng et al.

Status: open (until 10 Mar 2023)

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Wartini Ng et al.

Data sets

National Geochemical Survey of Australia: The Geochemical Atlas of Australia de Caritat, P., Cooper, M. http://dx.doi.org/10.11636/Record.2011.020

Northern Australia Geochemical Survey: Data Release 2 – Total (coarse fraction), Aqua Regia (coarse and fine fraction), and Fire Assay (coarse and fine fraction) element contents Main, P. T., Bastrakov, E. N., Wygralak, A. S., Khan, M. http://dx.doi.org/10.11636/Record.2019.002

Wartini Ng et al.

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Short summary
With a higher demand for lithium (Li), a better understanding of its concentration and spatial distribution is important to delineate potential anomalous areas. This study uses a framework that combines data from recent geochemical surveys and relevant environmental factors to predict and map Li content across Australia. The map shows high Li concentration around existing mines and other potentially anomalous Li areas. The same mapping principles can potentially be applied to other elements.