Articles | Volume 12, issue 1
https://doi.org/10.5194/soil-12-227-2026
© Author(s) 2026. 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-12-227-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article
| 
16 Mar 2026
Review article |
| 16 Mar 2026
| 16 Mar 2026
On soil health and the pivotal role of sensing
Yang Hu
CORRESPONDING AUTHOR
Soil & Landscape Science, School of Molecular & Life Sciences, Faculty of Science & Engineering, Curtin University, GPO Box U1987, Perth WA 6845, Australia
Adam Cross
School of Molecular & Life Sciences, Curtin University, GPO Box U1987, Perth WA 6845, Australia
EcoHealth Network, 1330 Beacon St, Suite 355a, Brookline, MA 02446, United States
Zefang Shen
Soil & Landscape Science, School of Molecular & Life Sciences, Faculty of Science & Engineering, Curtin University, GPO Box U1987, Perth WA 6845, Australia
Johan Bouma
Soil Science, Wageningen University, the Netherlands
Raphael A. Viscarra Rossel
CORRESPONDING AUTHOR
Soil & Landscape Science, School of Molecular & Life Sciences, Faculty of Science & Engineering, Curtin University, GPO Box U1987, Perth WA 6845, Australia
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Yang Hu and Raphael A. Viscarra Rossel
EGUsphere, https://doi.org/10.5194/egusphere-2025-4828, https://doi.org/10.5194/egusphere-2025-4828, 2025
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We analysed 482 Australian topsoils to estimate mineral-associated organic carbon (MAOC) and the carbon storage deficit (Cdef). Using mid-infrared spectra with explainable machine learning, we predicted MAOC (R2=0.86) and Cdef (R2=0.89). Model interpretation revealed signals from organic matter and clay minerals were most significant in predicting MAOC and Cdef. Our work provides an accurate, cost-effective means to assess and better understand the drivers of soil carbon sequestration potential.
Zefang Shen, Haylee D'Agui, Lewis Walden, Mingxi Zhang, Tsoek Man Yiu, Kingsley Dixon, Paul Nevill, Adam Cross, Mohana Matangulu, Yang Hu, and Raphael A. Viscarra Rossel
SOIL, 8, 467–486, https://doi.org/10.5194/soil-8-467-2022, https://doi.org/10.5194/soil-8-467-2022, 2022
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We compared miniaturised visible and near-infrared spectrometers to a portable visible–near-infrared instrument, which is more expensive. Statistical and machine learning algorithms were used to model 29 key soil health indicators. Accuracy of the miniaturised spectrometers was comparable to the portable system. Soil spectroscopy with these tiny sensors is cost-effective and could diagnose soil health, help monitor soil rehabilitation, and deliver positive environmental and economic outcomes.
Yang Hu and Raphael A. Viscarra Rossel
EGUsphere, https://doi.org/10.5194/egusphere-2025-4828, https://doi.org/10.5194/egusphere-2025-4828, 2025
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We analysed 482 Australian topsoils to estimate mineral-associated organic carbon (MAOC) and the carbon storage deficit (Cdef). Using mid-infrared spectra with explainable machine learning, we predicted MAOC (R2=0.86) and Cdef (R2=0.89). Model interpretation revealed signals from organic matter and clay minerals were most significant in predicting MAOC and Cdef. Our work provides an accurate, cost-effective means to assess and better understand the drivers of soil carbon sequestration potential.
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EGUsphere, https://doi.org/10.5194/egusphere-2024-2810, https://doi.org/10.5194/egusphere-2024-2810, 2024
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We integrate various methods to create soil property maps for soil surveyors, which they can utilize as a reference before beginning their fieldwork. A new sampling design based on a geographical stratification is proposed focussing on local feature space variability. It allows for a systematic analysis of predictive accuracy for varying densities. The spectral and spatial models yielded high accuracies. Our study highlights the value of integrating pedometric technologies in soil surveys.
Lingfei Wang, Gab Abramowitz, Ying-Ping Wang, Andy Pitman, and Raphael A. Viscarra Rossel
SOIL, 10, 619–636, https://doi.org/10.5194/soil-10-619-2024, https://doi.org/10.5194/soil-10-619-2024, 2024
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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.
Lewis Walden, Farid Sepanta, and Raphael Viscarra Rossel
EGUsphere, https://doi.org/10.5194/egusphere-2023-2464, https://doi.org/10.5194/egusphere-2023-2464, 2023
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We characterised the chemical and mineral composition of soil organic carbon fractions with mid-infrared spectroscopy. We identified unique and shared features of the spectra of carbon fractions, and the interactions between their organic and mineral components. These interactions are key to the persistence of C in soils, and we propose that mid-infrared spectroscopy could help to infer stability of soil C.
Johan Bouma
SOIL, 8, 751–759, https://doi.org/10.5194/soil-8-751-2022, https://doi.org/10.5194/soil-8-751-2022, 2022
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In the new
Soil Deal for Europe, land users, mostly farmers, and scientists are required to work jointly in
living labsto develop sustainable land use systems. We propose that threshold values for different ecosystem services in line with the UN Sustainable Development Goals (SDGs) and the EU Green Deal (GD) have to be met to define
lighthousesthat demonstrate successful sustainable land use systems, functioning as inspiring examples. A case study illustrates the important role of soils.
Zefang Shen, Haylee D'Agui, Lewis Walden, Mingxi Zhang, Tsoek Man Yiu, Kingsley Dixon, Paul Nevill, Adam Cross, Mohana Matangulu, Yang Hu, and Raphael A. Viscarra Rossel
SOIL, 8, 467–486, https://doi.org/10.5194/soil-8-467-2022, https://doi.org/10.5194/soil-8-467-2022, 2022
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We compared miniaturised visible and near-infrared spectrometers to a portable visible–near-infrared instrument, which is more expensive. Statistical and machine learning algorithms were used to model 29 key soil health indicators. Accuracy of the miniaturised spectrometers was comparable to the portable system. Soil spectroscopy with these tiny sensors is cost-effective and could diagnose soil health, help monitor soil rehabilitation, and deliver positive environmental and economic outcomes.
Yuanyuan Yang, Zefang Shen, Andrew Bissett, and Raphael A. Viscarra Rossel
SOIL, 8, 223–235, https://doi.org/10.5194/soil-8-223-2022, https://doi.org/10.5194/soil-8-223-2022, 2022
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We present a new method to estimate the relative abundance of the dominant phyla and diversity of fungi in Australian soil. It uses state-of-the-art machine learning with publicly available data on soil and environmental proxies for edaphic, climatic, biotic and topographic factors, and visible–near infrared wavelengths. The estimates could serve to supplement the more expensive molecular approaches towards a better understanding of soil fungal abundance and diversity in agronomy and ecology.
Juhwan Lee, Raphael A. Viscarra Rossel, Mingxi Zhang, Zhongkui Luo, and Ying-Ping Wang
Biogeosciences, 18, 5185–5202, https://doi.org/10.5194/bg-18-5185-2021, https://doi.org/10.5194/bg-18-5185-2021, 2021
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We performed Roth C simulations across Australia and assessed the response of soil carbon to changing inputs and future climate change using a consistent modelling framework. Site-specific initialisation of the C pools with measurements of the C fractions is essential for accurate simulations of soil organic C stocks and composition at a large scale. With further warming, Australian soils will become more vulnerable to C loss: natural environments > native grazing > cropping > modified grazing.
Philipp Baumann, Anatol Helfenstein, Andreas Gubler, Armin Keller, Reto Giulio Meuli, Daniel Wächter, Juhwan Lee, Raphael Viscarra Rossel, and Johan Six
SOIL, 7, 525–546, https://doi.org/10.5194/soil-7-525-2021, https://doi.org/10.5194/soil-7-525-2021, 2021
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We developed the Swiss mid-infrared spectral library and a statistical model collection across 4374 soil samples with reference measurements of 16 properties. Our library incorporates soil from 1094 grid locations and 71 long-term monitoring sites. This work confirms once again that nationwide spectral libraries with diverse soils can reliably feed information to a fast chemical diagnosis. Our data-driven reduction of the library has the potential to accurately monitor carbon at the plot scale.
Anatol Helfenstein, Philipp Baumann, Raphael Viscarra Rossel, Andreas Gubler, Stefan Oechslin, and Johan Six
SOIL, 7, 193–215, https://doi.org/10.5194/soil-7-193-2021, https://doi.org/10.5194/soil-7-193-2021, 2021
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In this study, we show that a soil spectral library (SSL) can be used to predict soil carbon at new and very different locations. The importance of this finding is that it requires less time-consuming lab work than calibrating a new model for every local application, while still remaining similar to or more accurate than local models. Furthermore, we show that this method even works for predicting (drained) peat soils, using a SSL with mostly mineral soils containing much less soil carbon.
Zhongkui Luo, Raphael A. Viscarra-Rossel, and Tian Qian
Biogeosciences, 18, 2063–2073, https://doi.org/10.5194/bg-18-2063-2021, https://doi.org/10.5194/bg-18-2063-2021, 2021
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Using the data from 141 584 whole-soil profiles across the globe, we disentangled the relative importance of biotic, climatic and edaphic variables in controlling global SOC stocks. The results suggested that soil properties and climate contributed similarly to the explained global variance of SOC in four sequential soil layers down to 2 m. However, the most important individual controls are consistently soil-related, challenging current climate-driven framework of SOC dynamics.
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Short summary
Effective management of soil health is essential to managing widespread soil degradation. Current frameworks for defining and assessing soil health are limited, focusing on agricultural contexts and relying on expensive, impractical lab analysis. Our socio-ecological framework offers a way forward, grounding soil health in ecological perspective and using modern sensing and data-driven technologies for rapid, scalable, policy-relevant assessment.
Effective management of soil health is essential to managing widespread soil degradation....
