Estimating soil organic carbon using field VNIR-SWIR spectroscopy and existing soil spectral libraries: Mitigating heterogeneity, roughness and moisture effects

VNIR-SWIR spectra acquired in the field are inherently affected by uncontrolled conditions, such as variable illumination, surface roughness, and soil moisture. As a result, models trained on soil spectral libraries (SSLs), typically composed of dry, sieved samples analyzed in the lab, often fail wh...

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Detalhes bibliográficos

Principais autores:	F. Castaldi, B. Stenberg, F. Liebisch, K. Metzger, E. Ben-Dor, M. Knadel, T. Koganti, J. Wetterlind, R. Barbetti, G. Debaene, K. Klumpp, M. Lippl, R. Lorenzetti, C. Lozano Fondon, T. Sanden, A. Schaumberger, D. Stajnko
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Elsevier 2025-12-01
coleção:	Smart Agricultural Technology
Assuntos:	Field Spectroscopy SOC LUCAS EPO Soil Machine learning
Acesso em linha:	http://www.sciencedirect.com/science/article/pii/S2772375525005842
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