Articles | Volume 15, issue 6
https://doi.org/10.5194/essd-15-2465-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-2465-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
14 Jun 2023
Data description paper |
| 14 Jun 2023
| 14 Jun 2023
Digital soil mapping of lithium in Australia
Sydney Institute of Agriculture, School of Life and Environmental
Sciences, The University of Sydney, Eveleigh, NSW 2015, Australia
Budiman Minasny
Sydney Institute of Agriculture, School of Life and Environmental
Sciences, The University of Sydney, Eveleigh, NSW 2015, Australia
Alex McBratney
Sydney Institute of Agriculture, School of Life and Environmental
Sciences, The University of Sydney, Eveleigh, NSW 2015, Australia
Patrice de Caritat
Geoscience Australia, Canberra, ACT 2601, Australia
John Wilford
Geoscience Australia, Canberra, ACT 2601, Australia
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The number of samples utilised to create predictive models affected model performance. This research compares the number of samples needed by a deep learning model to outperform the traditional machine learning models using visible near-infrared spectroscopy data for soil properties predictions. The deep learning model was found to outperform machine learning models when the sample size was above 2000.
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This work aims to predict soil water content at a fine spatiotemporal resolution (80 m grids, daily) to support agricultural management in Tasmania. It proves that transfer learning can improve the accuracy of deep learning models to predict multilevel soil moisture. We address the challenge of mapping soil moisture at field-scale resolution and integrate the model into a near-real-time monitoring system.
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This new, extensive dataset from southwestern Australia contributes considerable new data and knowledge to Australia’s strontium isotope coverage. The data are discussed in terms of the lithology and age of the source lithologies. This dataset will reduce Northern Hemisphere bias in future global strontium isotope models. Potential applications of the new data include mineral exploration, hydrogeology, food tracing, dust provenancing, and historic migrations of people and animals.
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Tobias Karl David Weber, Lutz Weihermüller, Attila Nemes, Michel Bechtold, Aurore Degré, Efstathios Diamantopoulos, Simone Fatichi, Vilim Filipović, Surya Gupta, Tobias L. Hohenbrink, Daniel R. Hirmas, Conrad Jackisch, Quirijn de Jong van Lier, John Koestel, Peter Lehmann, Toby R. Marthews, Budiman Minasny, Holger Pagel, Martine van der Ploeg, Shahab Aldin Shojaeezadeh, Simon Fiil Svane, Brigitta Szabó, Harry Vereecken, Anne Verhoef, Michael Young, Yijian Zeng, Yonggen Zhang, and Sara Bonetti
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Candan U. Desem, Patrice de Caritat, Jon Woodhead, Roland Maas, and Graham Carr
Earth Syst. Sci. Data, 16, 1383–1393, https://doi.org/10.5194/essd-16-1383-2024, https://doi.org/10.5194/essd-16-1383-2024, 2024
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Lead (Pb) isotopes form a potent tracer in studies of provenance, mineral exploration and environmental remediation. Previously, however, Pb isotope analysis has rarely been deployed at a continental scale. Here we present a new regolith Pb isotope dataset for Australia, which includes 1119 large catchments encompassing 5.6 × 106 km2 or close to ~75 % of the continent. Isoscape maps have been produced for use in diverse fields of study.
Mercedes Román Dobarco, Alexandre M. J-C. Wadoux, Brendan Malone, Budiman Minasny, Alex B. McBratney, and Ross Searle
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Soil organic carbon (SOC) is of a heterogeneous nature and varies in chemistry, stabilisation mechanisms, and persistence in soil. In this study we mapped the stocks of SOC fractions with different characteristics and turnover rates (presumably PyOC >= MAOC > POC) across Australia, combining spectroscopy and digital soil mapping. The SOC stocks (0–30 cm) were estimated as 13 Pg MAOC, 2 Pg POC, and 5 Pg PyOC.
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This new, extensive (~1.5×106 km2) dataset from northern Australia contributes considerable new information on Australia's strontium (Sr) isotope coverage. The data are discussed in terms of lithology and age of the source areas. This dataset will reduce Northern Hemisphere bias in future global Sr isotope models. Other potential applications of the new data include mineral exploration, hydrology, food tracing, dust provenancing, and examining historic migrations of people and animals.
Patrice de Caritat, Anthony Dosseto, and Florian Dux
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Strontium isotopes are useful in geological, environmental, archaeological, and forensic research to constrain or identify the source of materials such as minerals, artefacts, or foodstuffs. A new dataset, contributing significant new data and knowledge to Australia’s strontium isotope coverage, is presented from an area of over 500 000 km2 of inland southeastern Australia. Various source areas for the sediments are recognized, and both fluvial and aeolian transport processes identified.
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Soil organic carbon sequestration is considered an attractive technology to partially mitigate climate change. Here, we show how the SOC storage potential varies globally. The estimated additional SOC storage potential in the topsoil of global croplands (29–67 Pg C) equates to only 2 to 5 years of emissions offsetting and 32 % of agriculture's 92 Pg historical carbon debt. Since SOC is temperature-dependent, this potential is likely to reduce by 18 % by 2040 due to climate change.
Edward J. Jones, Patrick Filippi, Rémi Wittig, Mario Fajardo, Vanessa Pino, and Alex B. McBratney
SOIL, 7, 33–46, https://doi.org/10.5194/soil-7-33-2021, https://doi.org/10.5194/soil-7-33-2021, 2021
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Soil physical health is integral to maintaining functional agro-ecosystems. A novel method of assessing soil physical condition using a smartphone app has been developed – SLAKES. In this study the SLAKES app was used to investigate aggregate stability in a mixed agricultural landscape. Cropping areas were found to have significantly poorer physical health than similar soils under pasture. Results were mapped across the landscape to identify problem areas and pinpoint remediation efforts.
Wartini Ng, Budiman Minasny, Wanderson de Sousa Mendes, and José Alexandre Melo Demattê
SOIL, 6, 565–578, https://doi.org/10.5194/soil-6-565-2020, https://doi.org/10.5194/soil-6-565-2020, 2020
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Short summary
The number of samples utilised to create predictive models affected model performance. This research compares the number of samples needed by a deep learning model to outperform the traditional machine learning models using visible near-infrared spectroscopy data for soil properties predictions. The deep learning model was found to outperform machine learning models when the sample size was above 2000.
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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.
With a higher demand for lithium (Li), a better understanding of its concentration and spatial...
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