Digital soil mapping of lithium in Australia

Abstract

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 that affect soil formation to predict and map aqua-regia-extractable Li content across the 7.6×106 km2 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 ∼60–80 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 mg kg−1 (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; https://doi.org/10.11636/Record.2011.020) and Main et al. (2019; https://doi.org/10.11636/Record.2019.002), 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; https://esoil.io/TERNLandscapes/Public/Products/TERN/Covariates/Mosaics/90m/, last access: 6 December 2022; TERN, 2019). The final predictive map is available at https://doi.org/10.5281/zenodo.7895482 (Ng et al., 2023).

Could Airborne Geophysical Data Be Used to Improve Predictive Modeling of Agronomic Soil Properties in Tropical Hillslope Area?

“…DSM often faces several challenges since soil observations are scarce and costly. However, achieving satisfactory results with AGD is still possible, as reported by [7][8][9][75][76][77]. After extensive research over the past years, DSM has made significant progress in producing soil maps, a credible alternative to fulfill the increasing worldwide demand for spatial soil information [2].…”