Preprints
https://doi.org/10.5194/essd-2022-418
https://doi.org/10.5194/essd-2022-418
13 Jan 2023
 | 13 Jan 2023
Status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

Digital soil mapping of lithium in Australia

Wartini Ng, Budiman Minasny, Alex McBratney, Patrice de Caritat, and John Wilford

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: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-418', Anonymous Referee #1, 08 Feb 2023
    • AC1: 'Reply on RC1', Wartini Ng, 05 May 2023
  • RC2: 'Comment on essd-2022-418', Anonymous Referee #2, 04 Apr 2023
    • AC2: 'Reply on RC2', Wartini Ng, 05 May 2023
  • RC3: 'Comment on essd-2022-418', Anonymous Referee #3, 27 Apr 2023
    • AC3: 'Reply on RC3', Wartini Ng, 05 May 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-418', Anonymous Referee #1, 08 Feb 2023
    • AC1: 'Reply on RC1', Wartini Ng, 05 May 2023
  • RC2: 'Comment on essd-2022-418', Anonymous Referee #2, 04 Apr 2023
    • AC2: 'Reply on RC2', Wartini Ng, 05 May 2023
  • RC3: 'Comment on essd-2022-418', Anonymous Referee #3, 27 Apr 2023
    • AC3: 'Reply on RC3', Wartini Ng, 05 May 2023

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.