Estimating soil organic carbon using field VNIR-SWIR spectroscopy and existing soil spectral libraries: Mitigating heterogeneity, roughness and moisture effects
Abstract
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 when applied directly to field spectra. With this study we propose a routine to succeed with this desirable approach. We collected field spectra from 178 locations across seven countries under heterogeneous field conditions using different spectrometers. At each site, two surface smoothing intensities were compared. Two SSLs, LUCAS topsoil and GEO-CRADLE, were used to train machine learning models for predicting soil organic carbon (SOC), later applied to the field spectra under different correction scenarios: with or without Internal Soil Standard (ISS) harmonization and External Parameter Orthogonalization (EPO) to mitigate the effects of soil moisture. Combining ISS and EPO enables SSL-based models to reliable predict SOC from field-acquired spectra, particularly when using the LUCAS SSL in combination with a spectrally localized approach to reduce training set size (R² = 0.70; RPD = 1.66). Model performances are consistent with previous laboratory-based studies despite the diverse field conditions. A refined workflow for SOC estimation using hybrid spectral data is proposed, consisting of three steps: i) Spectral acquisition on highly smoothed surfaces; ii) ISS harmonization to align spectra across from different instruments; iii) EPO correction to reduce non-systematic spectral variability due to masking factors such as moisture, enhancing spectral consistency under variable field conditions.
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BibTeX
@article{castaldi2026estimating,
title = {{Estimating soil organic carbon using field VNIR-SWIR spectroscopy and existing soil spectral libraries: Mitigating heterogeneity, roughness and moisture effects}},
author = {F. Castaldi and B. Stenberg and F. Liebisch and K. Metzger and E. Ben-Dor and M. Knadel and T. Koganti and J. Wetterlind and R. Barbetti and G. Debaene and K. Klumpp and M. Lippl and R. Lorenzetti and C. Lozano Fondon and T. Sanden and A. Schaumberger and D. Stajnko},
journal = {Smart Agricultural Technology},
year = {2026},
month = oct,
doi = {10.1016/j.atech.2025.101353},
url = {https://hal.inrae.fr/hal-05845142v1},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Estimating soil organic carbon using field VNIR-SWIR spectroscopy and existing soil spectral libraries: Mitigating heterogeneity, roughness and moisture effects
AU - F. Castaldi
AU - B. Stenberg
AU - F. Liebisch
AU - K. Metzger
AU - E. Ben-Dor
AU - M. Knadel
AU - T. Koganti
AU - J. Wetterlind
AU - R. Barbetti
AU - G. Debaene
AU - K. Klumpp
AU - M. Lippl
AU - R. Lorenzetti
AU - C. Lozano Fondon
AU - T. Sanden
AU - A. Schaumberger
AU - D. Stajnko
PY - 2026
DA - 2026/10/30
JO - Smart Agricultural Technology
DO - 10.1016/j.atech.2025.101353
UR - https://hal.inrae.fr/hal-05845142v1
AB - 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 when applied directly to field spectra. With this study we propose a routine to succeed with this desirable approach. We collected field spectra from 178 locations across seven countries under heterogeneous field conditions using different spectrometers. At each site, two surface smoothing intensities were compared. Two SSLs, LUCAS topsoil and GEO-CRADLE, were used to train machine learning models for predicting soil organic carbon (SOC), later applied to the field spectra under different correction scenarios: with or without Internal Soil Standard (ISS) harmonization and External Parameter Orthogonalization (EPO) to mitigate the effects of soil moisture. Combining ISS and EPO enables SSL-based models to reliable predict SOC from field-acquired spectra, particularly when using the LUCAS SSL in combination with a spectrally localized approach to reduce training set size (R² = 0.70; RPD = 1.66). Model performances are consistent with previous laboratory-based studies despite the diverse field conditions. A refined workflow for SOC estimation using hybrid spectral data is proposed, consisting of three steps: i) Spectral acquisition on highly smoothed surfaces; ii) ISS harmonization to align spectra across from different instruments; iii) EPO correction to reduce non-systematic spectral variability due to masking factors such as moisture, enhancing spectral consistency under variable field conditions.
SN - 2772-3755
ER -