Please use this identifier to cite or link to this item: https://hdl.handle.net/11000/32250
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dc.contributor.authorVISCARRA ROSSEL, Raphael-
dc.contributor.authorBehrens, Thorsten-
dc.contributor.authorBen Dor, Eyal-
dc.contributor.authorChabrillat, Sabine-
dc.contributor.authorMelo Demattê, José Alexandre-
dc.contributor.authorGe, Yufeng-
dc.contributor.authorGomez, Cecile-
dc.contributor.authorPeng, Yi-
dc.contributor.authorRamirez-Lopez, Leonardo-
dc.contributor.authorShi, Zhou-
dc.contributor.authorStenberg, Bo-
dc.contributor.authorWebster, Richard-
dc.contributor.authorWinowiecki, Leigh Ann-
dc.contributor.authorShen, Zefang-
dc.contributor.authorGuerrero, César-
dc.contributor.otherDepartamentos de la UMH::Agroquímica y Medio Ambientees_ES
dc.date.accessioned2024-06-03T07:34:02Z-
dc.date.available2024-06-03T07:34:02Z-
dc.date.created2022-06-26-
dc.identifier.citationEuropean Journal of Soil Science, 2022;73:e13271es_ES
dc.identifier.issn1365-2389-
dc.identifier.issn1351-0754-
dc.identifier.urihttps://hdl.handle.net/11000/32250-
dc.description.abstractSpectroscopic 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.es_ES
dc.formatapplication/pdfes_ES
dc.format.extent9es_ES
dc.language.isoenges_ES
dc.publisherWileyes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectcalibrationes_ES
dc.subjectmachine learninges_ES
dc.subjectmodel localizationes_ES
dc.subjectreflectance spectroscopyes_ES
dc.subjectregressiones_ES
dc.subject.otherCDU::5 - Ciencias puras y naturaleses_ES
dc.titleDiffuse reflectance spectroscopy for estimating soil properties: A technology for the 21st centuryes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://doi.org/10.1111/ejss.13271es_ES
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