Reply on RC3

Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> 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&ndash;10 cm depth) and bottom (on average 60&ndash;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: <a href="http://dx.doi.org/10.11636/Record.2011.020" target="_blank" rel="noopener">http://dx.doi.org/10.11636/Record.2011.020</a> and <a href="http://dx.doi.org/10.11636/Record.2019.002" target="_blank" rel="noopener">http://dx.doi.org/10.11636/Record.2019.002</a> respectively. The covariates data used for this study was sourced from Terrestrial Ecosystem Research Network (TERN) infrastructure, which is enabled by the Australian Government&rsquo;s National Collaborative Research Infrastructure Strategy (NCRIS) <a href="https://esoil.io/TERNLandscapes/Public/Products/TERN/Covariates/Mosaics/90m/" target="_blank" rel="noopener">https://esoil.io/TERNLandscapes/Public/Products/TERN/Covariates/Mosaics/90m/</a> (TERN, 2019).