Articles | Volume 10, issue 2
https://doi.org/10.5194/soil-10-619-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-10-619-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
| 
10 Sep 2024
Original research article |
| 10 Sep 2024
| 10 Sep 2024
An ensemble estimate of Australian soil organic carbon using machine learning and process-based modelling
Lingfei Wang
CORRESPONDING AUTHOR
ARC Centre of Excellence for Climate Extremes, Sydney, NSW 2052, Australia
Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
Gab Abramowitz
ARC Centre of Excellence for Climate Extremes, Sydney, NSW 2052, Australia
Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
Ying-Ping Wang
CSIRO Environment, Clayton South, Melbourne, VIC 3169, Australia
Andy Pitman
ARC Centre of Excellence for Climate Extremes, Sydney, NSW 2052, Australia
Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
Raphael A. Viscarra Rossel
Soil and Landscape Science, School of Molecular and Life Sciences, Faculty of Science and Engineering, Curtin University, Perth, WA 6845, Australia
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Cited
15 citations as recorded by crossref.
- Biochar and moisture variability shape soil carbon pools via microbial carbon-degrading genes Y. Pan et al. https://doi.org/10.1016/j.jenvman.2025.128157
- Versioned soil organic carbon maps for climate-resilient and sustainable cities: A ridge-penalised linear mixed-effects ensemble of MIR-based estimates X. Tian et al. https://doi.org/10.1016/j.pce.2026.104390
- A simple pedotransfer function to estimate fine fraction organic carbon contents of surface horizons in French soils E. Rabot et al. https://doi.org/10.1016/j.geoderma.2025.117366
- Drivers of organic carbon dynamics in surface and subsurface agricultural soils of New South Wales, Australia S. Mia et al. https://doi.org/10.1071/SR25136
- Calibration and Validation of an Embedded AD5933 Impedimetric Sensor System for Soil Organic Carbon Measurement W. Afridi et al. https://doi.org/10.1109/JSEN.2026.3680522
- Using explainable AI to diagnose the representation of environmental drivers in process-based soil organic carbon models L. Wang et al. https://doi.org/10.5194/bg-22-7845-2025
- Parameter optimization of CENTURY model based on fine vegetation types improves the simulation accuracy of SOCD in Southwest China L. Xu et al. https://doi.org/10.1016/j.ecolmodel.2026.111759
- Pathways towards Net Zero emissions in grain cropping farms Z. Tan et al. https://doi.org/10.1016/j.agsy.2025.104401
- Bridging Pedology and Data Science: Machine Learning Applications for Soil Organic Matter and Carbon Analysis A. Dolatabadian & K. Kariman https://doi.org/10.3390/app16115412
- ЦИФРОВОЕ СРЕДНЕМАСШТАБНОЕ КАРТОГРАФИРОВАНИЕ СОДЕРЖАНИЯ ОРГАНИЧЕСКОГО УГЛЕРОДА В ПОЧВАХ ОСТРОВНОЙ КУНГУРСКОЙ ЛЕСОСТЕПИ С ПРИМЕНЕНИЕМ АЛГОРИТМА «СЛУЧАЙНЫЙ ЛЕС» . Чащин & . Алёшин https://doi.org/10.25695/AGRPH.2026.02.06
- Modeling soil organic carbon in the Brazilian amazon with geostatistical and machine learning techniques G. Tiruneh et al. https://doi.org/10.1016/j.tfp.2026.101150
- Spatial prediction of soil organic carbon stocks across contrasting Andean basins, Peru C. Carbajal et al. https://doi.org/10.1016/j.geodrs.2025.e01026
- Enhanced predictions of measurable soil carbon pools in the Australian rangelands with the Millennial v2 model M. Zhang & R. Viscarra Rossel https://doi.org/10.1016/j.geoderma.2026.117811
- Shaping smarter prediction: A literature review of GIS, remote sensing and machine learning applications in digital soil organic carbon mapping E. Khaddi et al. https://doi.org/10.51489/tuzal.1808033
- Mapping soil organic carbon research in conservation agriculture: A systematic review Q. Liu et al. https://doi.org/10.1016/j.farsys.2026.100241
15 citations as recorded by crossref.
- Biochar and moisture variability shape soil carbon pools via microbial carbon-degrading genes Y. Pan et al. https://doi.org/10.1016/j.jenvman.2025.128157
- Versioned soil organic carbon maps for climate-resilient and sustainable cities: A ridge-penalised linear mixed-effects ensemble of MIR-based estimates X. Tian et al. https://doi.org/10.1016/j.pce.2026.104390
- A simple pedotransfer function to estimate fine fraction organic carbon contents of surface horizons in French soils E. Rabot et al. https://doi.org/10.1016/j.geoderma.2025.117366
- Drivers of organic carbon dynamics in surface and subsurface agricultural soils of New South Wales, Australia S. Mia et al. https://doi.org/10.1071/SR25136
- Calibration and Validation of an Embedded AD5933 Impedimetric Sensor System for Soil Organic Carbon Measurement W. Afridi et al. https://doi.org/10.1109/JSEN.2026.3680522
- Using explainable AI to diagnose the representation of environmental drivers in process-based soil organic carbon models L. Wang et al. https://doi.org/10.5194/bg-22-7845-2025
- Parameter optimization of CENTURY model based on fine vegetation types improves the simulation accuracy of SOCD in Southwest China L. Xu et al. https://doi.org/10.1016/j.ecolmodel.2026.111759
- Pathways towards Net Zero emissions in grain cropping farms Z. Tan et al. https://doi.org/10.1016/j.agsy.2025.104401
- Bridging Pedology and Data Science: Machine Learning Applications for Soil Organic Matter and Carbon Analysis A. Dolatabadian & K. Kariman https://doi.org/10.3390/app16115412
- ЦИФРОВОЕ СРЕДНЕМАСШТАБНОЕ КАРТОГРАФИРОВАНИЕ СОДЕРЖАНИЯ ОРГАНИЧЕСКОГО УГЛЕРОДА В ПОЧВАХ ОСТРОВНОЙ КУНГУРСКОЙ ЛЕСОСТЕПИ С ПРИМЕНЕНИЕМ АЛГОРИТМА «СЛУЧАЙНЫЙ ЛЕС» . Чащин & . Алёшин https://doi.org/10.25695/AGRPH.2026.02.06
- Modeling soil organic carbon in the Brazilian amazon with geostatistical and machine learning techniques G. Tiruneh et al. https://doi.org/10.1016/j.tfp.2026.101150
- Spatial prediction of soil organic carbon stocks across contrasting Andean basins, Peru C. Carbajal et al. https://doi.org/10.1016/j.geodrs.2025.e01026
- Enhanced predictions of measurable soil carbon pools in the Australian rangelands with the Millennial v2 model M. Zhang & R. Viscarra Rossel https://doi.org/10.1016/j.geoderma.2026.117811
- Shaping smarter prediction: A literature review of GIS, remote sensing and machine learning applications in digital soil organic carbon mapping E. Khaddi et al. https://doi.org/10.51489/tuzal.1808033
- Mapping soil organic carbon research in conservation agriculture: A systematic review Q. Liu et al. https://doi.org/10.1016/j.farsys.2026.100241
Saved (final revised paper)
Latest update: 14 Aug 2026
Short summary
Effective management of soil organic carbon (SOC) requires accurate knowledge of its distribution and factors influencing its dynamics. We identify the importance of variables in spatial SOC variation and estimate SOC stocks in Australia using various models. We find there are significant disparities in SOC estimates when different models are used, highlighting the need for a critical re-evaluation of land management strategies that rely on the SOC distribution derived from a single approach.
Effective management of soil organic carbon (SOC) requires accurate knowledge of its...
