Articles | Volume 5, issue 2
https://doi.org/10.5194/soil-5-275-2019
© Author(s) 2019. 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-5-275-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
| 
25 Sep 2019
Original research article |
| 25 Sep 2019
| 25 Sep 2019
Error propagation in spectrometric functions of soil organic carbon
Monja Ellinger
Department of Soil System Science, Helmholtz Centre for Environmental
Research – UFZ, Halle (Saale), Germany
Ines Merbach
Department of Community Ecology, Helmholtz Centre for Environmental
Research – UFZ, Bad Lauchstädt, Germany
Ulrike Werban
Department of Monitoring and Exploration Technologies, Helmholtz
Centre for Environmental Research – UFZ, Leipzig, Germany
Department of Soil System Science, Helmholtz Centre for Environmental
Research – UFZ, Halle (Saale), Germany
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Cited
16 citations as recorded by crossref.
- Soil organic matter and clay predictions by laboratory spectroscopy: Data spatial correlation D. Silva-Sangoi et al. 10.1016/j.geodrs.2022.e00486
- Application of generalized linear geostatistical model for regional soil organic matter mapping: The effect of sampling density M. Zhang et al. 10.1016/j.geoderma.2023.116446
- Do model choice and sample ratios separately or simultaneously influence soil organic matter prediction? K. John et al. 10.1016/j.iswcr.2021.11.003
- A comparison of importance of modelling method and sample size for mapping soil organic matter in Guangdong, China Y. Lai et al. 10.1016/j.ecolind.2021.107618
- Spectral Data Processing for Field-Scale Soil Organic Carbon Monitoring J. Reyes & M. Ließ 10.3390/s24030849
- Explainable Machine Learning to Unveil Detection Mechanisms with Au Nanoisland-Based Surface-Enhanced Raman Scattering for SARS-CoV-2 Antigen Detection W. Pazin et al. 10.1021/acsanm.3c05848
- Errors induced by spectral measurement positions and instrument noise in soil organic carbon prediction using vis-NIR on intact soil X. Sun 10.1016/j.geoderma.2020.114731
- Variability of soil mapping accuracy with sample sizes, modelling methods and landform types in a regional case study X. Sun et al. 10.1016/j.catena.2022.106217
- Quantification techniques of soil organic carbon: an appraisal A. Kanagaraj et al. 10.1007/s44211-025-00746-4
- Using machine learning and an electronic tongue for discriminating saliva samples from oral cavity cancer patients and healthy individuals D. Braz et al. 10.1016/j.talanta.2022.123327
- On-the-Go Vis-NIR Spectroscopy for Field-Scale Spatial-Temporal Monitoring of Soil Organic Carbon J. Reyes & M. Ließ 10.3390/agriculture13081611
- Best performances of visible–near-infrared models in soils with little carbonate – a field study in Switzerland S. Oberholzer et al. 10.5194/soil-10-231-2024
- Predictive monitoring of soil organic carbon using multispectral UAV imagery: a case study on a long-term experimental field J. Reyes et al. 10.1007/s41324-024-00589-7
- A loss function to evaluate agricultural decision-making under uncertainty: a case study of soil spectroscopy T. Breure et al. 10.1007/s11119-022-09887-2
- Accuracy and Reproducibility of Laboratory Diffuse Reflectance Measurements with Portable VNIR and MIR Spectrometers for Predictive Soil Organic Carbon Modeling S. Semella et al. 10.3390/s22072749
- Soil Organic Carbon Assessment for Carbon Farming: A Review T. Petropoulos et al. 10.3390/agriculture15050567
16 citations as recorded by crossref.
- Soil organic matter and clay predictions by laboratory spectroscopy: Data spatial correlation D. Silva-Sangoi et al. 10.1016/j.geodrs.2022.e00486
- Application of generalized linear geostatistical model for regional soil organic matter mapping: The effect of sampling density M. Zhang et al. 10.1016/j.geoderma.2023.116446
- Do model choice and sample ratios separately or simultaneously influence soil organic matter prediction? K. John et al. 10.1016/j.iswcr.2021.11.003
- A comparison of importance of modelling method and sample size for mapping soil organic matter in Guangdong, China Y. Lai et al. 10.1016/j.ecolind.2021.107618
- Spectral Data Processing for Field-Scale Soil Organic Carbon Monitoring J. Reyes & M. Ließ 10.3390/s24030849
- Explainable Machine Learning to Unveil Detection Mechanisms with Au Nanoisland-Based Surface-Enhanced Raman Scattering for SARS-CoV-2 Antigen Detection W. Pazin et al. 10.1021/acsanm.3c05848
- Errors induced by spectral measurement positions and instrument noise in soil organic carbon prediction using vis-NIR on intact soil X. Sun 10.1016/j.geoderma.2020.114731
- Variability of soil mapping accuracy with sample sizes, modelling methods and landform types in a regional case study X. Sun et al. 10.1016/j.catena.2022.106217
- Quantification techniques of soil organic carbon: an appraisal A. Kanagaraj et al. 10.1007/s44211-025-00746-4
- Using machine learning and an electronic tongue for discriminating saliva samples from oral cavity cancer patients and healthy individuals D. Braz et al. 10.1016/j.talanta.2022.123327
- On-the-Go Vis-NIR Spectroscopy for Field-Scale Spatial-Temporal Monitoring of Soil Organic Carbon J. Reyes & M. Ließ 10.3390/agriculture13081611
- Best performances of visible–near-infrared models in soils with little carbonate – a field study in Switzerland S. Oberholzer et al. 10.5194/soil-10-231-2024
- Predictive monitoring of soil organic carbon using multispectral UAV imagery: a case study on a long-term experimental field J. Reyes et al. 10.1007/s41324-024-00589-7
- A loss function to evaluate agricultural decision-making under uncertainty: a case study of soil spectroscopy T. Breure et al. 10.1007/s11119-022-09887-2
- Accuracy and Reproducibility of Laboratory Diffuse Reflectance Measurements with Portable VNIR and MIR Spectrometers for Predictive Soil Organic Carbon Modeling S. Semella et al. 10.3390/s22072749
- Soil Organic Carbon Assessment for Carbon Farming: A Review T. Petropoulos et al. 10.3390/agriculture15050567
Latest update: 23 Apr 2025
Short summary
Vis–NIR spectrometry is often applied to capture soil organic carbon (SOC). This study addresses the impact of the involved data and modelling aspects on SOC precision with a focus on the propagation of input data uncertainties. It emphasizes the necessity of transparent documentation of the measurement protocol and the model building and validation procedure. Particularly, when Vis–NIR spectrometry is used for soil monitoring, the aspect of uncertainty propagation becomes essential.
Vis–NIR spectrometry is often applied to capture soil organic carbon (SOC). This study addresses...
