Título : Diffuse reflectance spectroscopy for estimating soil properties: A technology for the 21st century |
Autor : VISCARRA ROSSEL, Raphael Behrens, Thorsten Ben Dor, Eyal Chabrillat, Sabine Melo Demattê, José Alexandre Ge, Yufeng Gomez, Cecile Peng, Yi Ramirez-Lopez, Leonardo Shi, Zhou Stenberg, Bo Webster, Richard Winowiecki, Leigh Ann Shen, Zefang Guerrero, César |
Editor : Wiley |
Departamento: Departamentos de la UMH::Agroquímica y Medio Ambiente |
Fecha de publicación: 2022-06-26 |
URI : https://hdl.handle.net/11000/32250 |
Resumen :
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.
|
Palabras clave/Materias: calibration machine learning model localization reflectance spectroscopy regression |
Área de conocimiento : CDU: Ciencias puras y naturales |
Tipo documento : application/pdf |
Derechos de acceso: info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
DOI : https://doi.org/10.1111/ejss.13271 |
Aparece en las colecciones: Artículos Agroquímica y Medio Ambiente
|