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Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy

Author

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  • Antoine Stevens
  • Marco Nocita
  • Gergely Tóth
  • Luca Montanarella
  • Bas van Wesemael

Abstract

Soil organic carbon is a key soil property related to soil fertility, aggregate stability and the exchange of CO2 with the atmosphere. Existing soil maps and inventories can rarely be used to monitor the state and evolution in soil organic carbon content due to their poor spatial resolution, lack of consistency and high updating costs. Visible and Near Infrared diffuse reflectance spectroscopy is an alternative method to provide cheap and high-density soil data. However, there are still some uncertainties on its capacity to produce reliable predictions for areas characterized by large soil diversity. Using a large-scale EU soil survey of about 20,000 samples and covering 23 countries, we assessed the performance of reflectance spectroscopy for the prediction of soil organic carbon content. The best calibrations achieved a root mean square error ranging from 4 to 15 g C kg−1 for mineral soils and a root mean square error of 50 g C kg−1 for organic soil materials. Model errors are shown to be related to the levels of soil organic carbon and variations in other soil properties such as sand and clay content. Although errors are ∼5 times larger than the reproducibility error of the laboratory method, reflectance spectroscopy provides unbiased predictions of the soil organic carbon content. Such estimates could be used for assessing the mean soil organic carbon content of large geographical entities or countries. This study is a first step towards providing uniform continental-scale spectroscopic estimations of soil organic carbon, meeting an increasing demand for information on the state of the soil that can be used in biogeochemical models and the monitoring of soil degradation.

Suggested Citation

  • Antoine Stevens & Marco Nocita & Gergely Tóth & Luca Montanarella & Bas van Wesemael, 2013. "Prediction of Soil Organic Carbon at the European Scale by Visible and Near InfraRed Reflectance Spectroscopy," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-13, June.
  • Handle: RePEc:plo:pone00:0066409
    DOI: 10.1371/journal.pone.0066409
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    Citations

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    Cited by:

    1. Meyer, Hanna & Reudenbach, Christoph & Wöllauer, Stephan & Nauss, Thomas, 2019. "Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction," Ecological Modelling, Elsevier, vol. 411(C).
    2. Baoyang Liu & Baofeng Guo & Renxiong Zhuo & Fan Dai & Haoyu Chi, 2023. "Prediction of the soil organic carbon in the LUCAS soil database based on spectral clustering," Soil and Water Research, Czech Academy of Agricultural Sciences, vol. 18(1), pages 43-54.
    3. Kuntal M. Hati & Nishant K. Sinha & Monoranjan Mohanty & Pramod Jha & Sunil Londhe & Andrew Sila & Erick Towett & Ranjeet S. Chaudhary & Somasundaram Jayaraman & Mounisamy Vassanda Coumar & Jyoti K. T, 2022. "Mid-Infrared Reflectance Spectroscopy for Estimation of Soil Properties of Alfisols from Eastern India," Sustainability, MDPI, vol. 14(9), pages 1-17, April.
    4. Hobimiarantsoa Rakotonindrina & Kensuke Kawamura & Yasuhiro Tsujimoto & Tomohiro Nishigaki & Herintsitohaina Razakamanarivo & Bruce Haja Andrianary & Andry Andriamananjara, 2020. "Prediction of Soil Oxalate Phosphorus using Visible and Near-Infrared Spectroscopy in Natural and Cultivated System Soils of Madagascar," Agriculture, MDPI, vol. 10(5), pages 1-16, May.
    5. Tao Liu & Huan Zhang & Tiezhu Shi, 2020. "Modeling and Predictive Mapping of Soil Organic Carbon Density in a Small-Scale Area Using Geographically Weighted Regression Kriging Approach," Sustainability, MDPI, vol. 12(22), pages 1-12, November.
    6. Theodora Angelopoulou & Athanasios Balafoutis & George Zalidis & Dionysis Bochtis, 2020. "From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review," Sustainability, MDPI, vol. 12(2), pages 1-24, January.
    7. Giacomo Crucil & Fabio Castaldi & Emilien Aldana-Jague & Bas van Wesemael & Andy Macdonald & Kristof Van Oost, 2019. "Assessing the Performance of UAS-Compatible Multispectral and Hyperspectral Sensors for Soil Organic Carbon Prediction," Sustainability, MDPI, vol. 11(7), pages 1-18, March.
    8. Richter, Franziska & Jan, Pierrick & El Benni, Nadja & Lüscher, Andreas & Buchmann, Nina & Klaus, Valentin H., 2021. "A guide to assess and value ecosystem services of grasslands," Ecosystem Services, Elsevier, vol. 52(C).
    9. Jasiewicz Jarosław & Cierniewski Jerzy, 2021. "SALBEC – A Python Library and GUI Application to Calculate the Diurnal Variation of the Soil Albedo," Quaestiones Geographicae, Sciendo, vol. 40(3), pages 95-107, September.
    10. Konstantinos Karyotis & Theodora Angelopoulou & Nikolaos Tziolas & Evgenia Palaiologou & Nikiforos Samarinas & George Zalidis, 2021. "Evaluation of a Micro-Electro Mechanical Systems Spectral Sensor for Soil Properties Estimation," Land, MDPI, vol. 10(1), pages 1-16, January.

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