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Other publication in scientific journal, 2022

Diffuse reflectance spectroscopy for estimating soil properties: A technology for the 21st century

Rossel, Raphael A. Viscarra; Behrens, Thorsten; Ben-Dor, Eyal; Chabrillat, Sabine; Dematte, Jose Alexandre Melo; Ge, Yufeng; Gomez, Cecile; Guerrero, Cesar; Peng, Yi; Ramirez-Lopez, Leonardo; Shi, Zhou; Stenberg, Bo; Webster, Richard; Winowiecki, Leigh; Shen, Zefang;

Abstract

Spectroscopic measurements of soil samples are reliable because they are highly repeatable and reproducible. They characterise the samples' mineral-organic composition. Estimates of concentrations of soil constituents are inevitably less precise than estimates obtained conventionally by chemical analysis. But the cost of each spectroscopic estimate is at most one-tenth of the cost of a chemical determination. Spectroscopy is cost-effective when we need many data, despite the costs and errors of calibration. Soil spectroscopists understand the risks of over-fitting models to highly dimensional multivariate spectra and have command of the mathematical and statistical methods to avoid them. Machine learning has fast become an algorithmic alternative to statistical analysis for estimating concentrations of soil constituents from reflectance spectra. As with any modelling, we need judicious implementation of machine learning as it also carries the risk of over-fitting predictions to irrelevant elements of the spectra. To use the methods confidently, we need to validate the outcomes with appropriately sampled, independent data sets. Not all machine learning should be considered 'black boxes'. Their interpretability depends on the algorithm, and some are highly interpretable and explainable. Some are difficult to interpret because of complex transformations or their huge and complicated network of parameters. But there is rapidly advancing research on explainable machine learning, and these methods are finding applications in soil science and spectroscopy. In many parts of the world, soil and environmental scientists recognise the merits of soil spectroscopy. They are building spectral libraries on which they can draw to localise the modelling and derive soil information for new projects within their domains. We hope our article gives readers a more balanced and optimistic perspective of soil spectroscopy and its future. Highlights Spectroscopy is reliable because it is a highly repeatable and reproducible analytical technique. Spectra are calibrated to estimate concentrations of soil properties with known error. Spectroscopy is cost-effective for estimating soil properties. Machine learning is becoming ever more powerful for extracting accurate information from spectra, and methods for interpreting the models exist. Large libraries of soil spectra provide information that can be used locally to aid estimates from new samples.

Keywords

calibration; machine learning; model localization; reflectance spectroscopy; regression; soil constituents; spectral libraries; validation

Published in

European Journal of Soil Science

2022, volume: 73, number: 4, article number: e13271
Publisher: WILEY

Authors' information

Rossel, Raphael A. Viscarra
Curtin University
Behrens, Thorsten
Swiss Competence Ctr Soils KOBO
Ben-Dor, Eyal
Tel Aviv University
Chabrillat, Sabine
Helmholtz-Center Potsdam GFZ German Research Center for Geosciences
Chabrillat, Sabine
University of Hannover
Dematte, Jose Alexandre Melo
Universidade de Sao Paulo
Ge, Yufeng
University of Nebraska Lincoln
Gomez, Cecile
INRAE
Gomez, Cecile
Indian Institute of Science (IISC) - Bangalore
Guerrero, Cesar
Universidad Miguel Hernandez de Elche
Peng, Yi
Food and Agriculture Organization of the United Nations (FAO)
Ramirez-Lopez, Leonardo
BUCHI Labortechn AG
Shi, Zhou
Zhejiang University
Swedish University of Agricultural Sciences, Department of Soil and Environment
Webster, Richard
Rothamsted Research
Winowiecki, Leigh
World Agroforestry (ICRAF)
Shen, Zefang
Curtin University

UKÄ Subject classification

Soil Science

Publication Identifiers

DOI: https://doi.org/10.1111/ejss.13271

URI (permanent link to this page)

https://res.slu.se/id/publ/118354