Digital soil mapping of lithium in Australia
<p>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 that...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2023-06-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/15/2465/2023/essd-15-2465-2023.pdf |
| Summary: | <p>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 that
affect soil formation to predict and map aqua-regia-extractable Li content
across the <span class="inline-formula">7.6×10<sup>6</sup></span> <span class="inline-formula">km<sup>2</sup></span> area of Australia. Catchment outlet sediment
samples (i.e. soils formed on alluvial parent material) were collected by
the National Geochemical Survey of Australia at 1315 sites, with both top (0–10 cm depth) and bottom (on average <span class="inline-formula">∼60</span>–80 cm depth)
catchment outlet sediments sampled. We developed 50 bootstrap models using a
cubist regression tree algorithm for each depth. 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
3.32 <span class="inline-formula">mg kg<sup>−1</sup></span> (which is 44.2 % of the interquartile 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 (red, green, blue) and gamma radiometric
dose have a strong impact on the development of regression-based Li
prediction. The bootstrapped models were then used to generate digital soil
Li prediction maps for both depths, which could identify and delineate areas
with anomalously high Li concentrations in the regolith. The predicted maps
show high Li concentration around existing mines and other potentially
anomalous Li areas that have yet to be verified. The same mapping principles
can potentially be applied to other elements. The Li geochemical data for
calibration and validation are available from de
Caritat and Cooper (2011b; <a href="https://doi.org/10.11636/Record.2011.020">https://doi.org/10.11636/Record.2011.020</a>) and
Main et al. (2019;
<a href="https://doi.org/10.11636/Record.2019.002">https://doi.org/10.11636/Record.2019.002</a>), respectively. The covariate
data used for this study were sourced from the Terrestrial Ecosystem
Research Network (TERN) infrastructure, which is enabled by the Australian
Government's National Collaborative Research Infrastructure Strategy (NCRIS;
<span class="uri">https://esoil.io/TERNLandscapes/Public/Products/TERN/Covariates/Mosaics/90m/</span>, last access: 6 December 2022; TERN, 2019). The final predictive map is available at
<a href="https://doi.org/10.5281/zenodo.7895482">https://doi.org/10.5281/zenodo.7895482</a> (Ng et al., 2023).</p> |
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| ISSN: | 1866-3508 1866-3516 |