Articles | Volume 8, issue 2
https://doi.org/10.5194/soil-8-467-2022
© Author(s) 2022. 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-8-467-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
| 
18 Jul 2022
Original research article |
| 18 Jul 2022
| 18 Jul 2022
Miniaturised visible and near-infrared spectrometers for assessing soil health indicators in mine site rehabilitation
Zefang Shen
Soil and Landscape Science, School of Molecular and Life Sciences, Faculty of Science and Engineering, Curtin University, G.P.O. Box U1987, Perth, WA 6845, Australia
Haylee D'Agui
ARC Centre for Mine Site Restoration, School of Molecular and Life Sciences, Faculty of Science and Engineering, Curtin University, G.P.O. Box U1987, Perth, WA 6845, Australia
Lewis Walden
Soil and Landscape Science, School of Molecular and Life Sciences, Faculty of Science and Engineering, Curtin University, G.P.O. Box U1987, Perth, WA 6845, Australia
Mingxi Zhang
Soil and Landscape Science, School of Molecular and Life Sciences, Faculty of Science and Engineering, Curtin University, G.P.O. Box U1987, Perth, WA 6845, Australia
Tsoek Man Yiu
Soil and Landscape Science, School of Molecular and Life Sciences, Faculty of Science and Engineering, Curtin University, G.P.O. Box U1987, Perth, WA 6845, Australia
Kingsley Dixon
ARC Centre for Mine Site Restoration, School of Molecular and Life Sciences, Faculty of Science and Engineering, Curtin University, G.P.O. Box U1987, Perth, WA 6845, Australia
Paul Nevill
ARC Centre for Mine Site Restoration, School of Molecular and Life Sciences, Faculty of Science and Engineering, Curtin University, G.P.O. Box U1987, Perth, WA 6845, Australia
Trace and Environmental DNA Laboratory, School of Molecular and Life Sciences, Faculty of Science and Engineering, Curtin University, G.P.O Box U1987, Perth, WA, 6845, Australia
Adam Cross
ARC Centre for Mine Site Restoration, School of Molecular and Life Sciences, Faculty of Science and Engineering, Curtin University, G.P.O. Box U1987, Perth, WA 6845, Australia
EcoHealth Network, 1330 Beacon St, Suite 355a, Brookline, MA 02446, USA
Mohana Matangulu
Soil and Landscape Science, School of Molecular and Life Sciences, Faculty of Science and Engineering, Curtin University, G.P.O. Box U1987, Perth, WA 6845, Australia
Yang Hu
Soil and Landscape Science, School of Molecular and Life Sciences, Faculty of Science and Engineering, Curtin University, G.P.O. Box U1987, Perth, WA 6845, Australia
Raphael A. Viscarra Rossel
CORRESPONDING AUTHOR
Soil and Landscape Science, School of Molecular and Life Sciences, Faculty of Science and Engineering, Curtin University, G.P.O. Box U1987, Perth, WA 6845, Australia
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Cited
10 citations as recorded by crossref.
- On-Site Soil Monitoring Using Photonics-Based Sensors and Historical Soil Spectral Libraries K. Karyotis et al. 10.3390/rs15061624
- Mid-infrared spectroscopy determines the provenance of coastal marine soils and their organic and inorganic carbon content L. Walden et al. 10.1016/j.scitotenv.2024.174871
- Multi-scale mapping of Australia’s terrestrial and blue carbon stocks and their continental and bioregional drivers L. Walden et al. 10.1038/s43247-023-00838-x
- Research on the chemical oxygen demand spectral inversion model in water based on IPLS-GAN-SVM hybrid algorithm Q. Lu et al. 10.1371/journal.pone.0301902
- On-site soil analysis: A novel approach combining NIR spectroscopy, remote sensing and deep learning M. Kok et al. 10.1016/j.geoderma.2024.116903
- Micro-Near-Infrared (Micro-NIR) sensor for predicting organic carbon and clay contents in agricultural soil J. Liu et al. 10.1016/j.still.2024.106155
- Portable gamma spectrometry for rapid assessment of soil texture, organic carbon and total nitrogen in agricultural soils A. Taylor et al. 10.1007/s11368-023-03488-w
- Review of Miniaturized Computational Spectrometers Q. Guan et al. 10.3390/s23218768
- Soil Organic Matter Detection Based on Pyrolysis and Electronic Nose Combined with Multi-Feature Data Fusion Optimization X. Xia et al. 10.3390/agriculture12101540
- Evaluating performance of miniaturized spectrometers in predicting soil properties using multivariate statistical analysis G. Gowera et al. 10.1139/cjss-2023-0084
10 citations as recorded by crossref.
- On-Site Soil Monitoring Using Photonics-Based Sensors and Historical Soil Spectral Libraries K. Karyotis et al. 10.3390/rs15061624
- Mid-infrared spectroscopy determines the provenance of coastal marine soils and their organic and inorganic carbon content L. Walden et al. 10.1016/j.scitotenv.2024.174871
- Multi-scale mapping of Australia’s terrestrial and blue carbon stocks and their continental and bioregional drivers L. Walden et al. 10.1038/s43247-023-00838-x
- Research on the chemical oxygen demand spectral inversion model in water based on IPLS-GAN-SVM hybrid algorithm Q. Lu et al. 10.1371/journal.pone.0301902
- On-site soil analysis: A novel approach combining NIR spectroscopy, remote sensing and deep learning M. Kok et al. 10.1016/j.geoderma.2024.116903
- Micro-Near-Infrared (Micro-NIR) sensor for predicting organic carbon and clay contents in agricultural soil J. Liu et al. 10.1016/j.still.2024.106155
- Portable gamma spectrometry for rapid assessment of soil texture, organic carbon and total nitrogen in agricultural soils A. Taylor et al. 10.1007/s11368-023-03488-w
- Review of Miniaturized Computational Spectrometers Q. Guan et al. 10.3390/s23218768
- Soil Organic Matter Detection Based on Pyrolysis and Electronic Nose Combined with Multi-Feature Data Fusion Optimization X. Xia et al. 10.3390/agriculture12101540
- Evaluating performance of miniaturized spectrometers in predicting soil properties using multivariate statistical analysis G. Gowera et al. 10.1139/cjss-2023-0084
Latest update: 13 Dec 2024
Short summary
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.
We compared miniaturised visible and near-infrared spectrometers to a portable...
