Spectral variability represented by the first two principal components (PCs) of the Visible and Near-Infrared (VisNIR) spectra colored by different original datasets imported into the OSSL.

Spectral variability represented by the first two principal components (PCs) of the Visible and Near-Infrared (VisNIR) spectra colored by different original datasets imported into the OSSL.

<p>Spectral variability represented by the first two principal components (PCs) of the Visible and Near-Infrared (VisNIR) spectra colored by different original datasets imported into the OSSL.</p>

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Bibliographic Details
Main Author: José L. Safanelli (20552654) (author)
Other Authors: Tomislav Hengl (623477) (author), Leandro L. Parente (20552657) (author), Robert Minarik (20552660) (author), Dellena E. Bloom (9227405) (author), Katherine Todd-Brown (20552663) (author), Asa Gholizadeh (693351) (author), Wanderson de Sousa Mendes (20552666) (author), Jonathan Sanderman (784692) (author)
Published: 2025
Subjects:
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
Chemical Sciences not elsewhere classified
widely used method
training machine learning
independent model evaluation
global good initiative
estimating soil properties
study also presents
centralized open resource
representation flag ).
soil spectroscopy community
affordable soil data
performing ml algorithm
two spectral regions
harmonizing several ssls
soil spectroscopy
spectral regions
soil types
opening data
discusses several
also subject
spectral variations
open resources
open development
ssls ).
widespread adoption
well predicted
total sulfur
scientific discovery
reliable outputs
refitting show
predictive modeling
prediction uncertainty
prediction capacity
new samples
help bridge
future development
fold cross
extractable sodium
extractable nutrients
exploratory analysis
enable hundreds
cubist comes
conditions represented
agricultural monitoring
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