Comparison of soil organic carbon stocks predicted using visible and near infrared reflectance (VNIR) spectra acquired in situ vs. on sieved dried samples: Synthesis of different studies

Cambou Aurélie, Allory Victor, Cardinael Rémi, Carvalho Vieira Lola, Barthès Bernard. 2021. Comparison of soil organic carbon stocks predicted using visible and near infrared reflectance (VNIR) spectra acquired in situ vs. on sieved dried samples: Synthesis of different studies. Soil Security, 5:100024, 15 p.

https://doi.org/10.1016/j.soisec.2021.100024

Article de revue ; Article de recherche ; Article de revue à comité de lecture Revue en libre accès total

Prévisualisation

Version publiée - Anglais
Sous licence

.
Cambou2021 Comparison of soil organic carbon stocks predicted using visible and near.pdf
Télécharger (1MB) | Prévisualisation

Résumé : There is increasing demand for data on soil organic carbon (SOC) stock (SSOC; kgC m−2), but the acquisition of such data, which relies on the determination of volumetric SOC content (SOCv; gC dm−3), is often tedious or complex. Visible and near infrared reflectance spectroscopy (VNIRS) has proven useful for soil characterization, but has rarely been used for direct prediction of SOCv. The objectives of this work were: (i) to compare SOCv predictions using VNIR spectra collected in situ vs. on 2-mm sieved air-dried soil (laboratory conditions), on three sample sets separately (with in situ spectra collected differently for each set); and (ii) to assess SOCv prediction in independent validation using laboratory spectra from all sets. Predictions of SOCv were more accurate using laboratory than in situ spectra for two sets, but not for the third set, where coarse particles content was rather high and variable. Considering the total set of laboratory spectra, predictions in independent validation (leave-one-site-out) yielded accurate SOCv and SSOC predictions (standard errors of prediction were 1.9 gC dm−3 and 0.36 kgC m−2 at 0–30 cm depth, respectively). This result was achieved using local partial least squares regression (PLSR), based on spectral neighbors, which noticeably outperformed global PLSR (which uses all calibration samples equally), as often reported when using large soil spectral libraries for independent validation. Finally, this work demonstrated that SSOC could be quantified accurately using a VNIRS library built on archive soil samples, which offers important perspectives for SSOC accounting.

Mots-clés Agrovoc : spectroscopie infrarouge, agroforesterie

Mots-clés géographiques Agrovoc : France

Mots-clés libres : Spectroscopy, Soil organic carbon, Diffuse reflectance spectroscopy, Global PLS regression, Local PLS regression

Champ stratégique Cirad : CTS 2 (2019-) - Transitions agroécologiques

Agences de financement hors UE : Agence de l'Environnement et de la Maîtrise de l'Energie, Ministère de l'Écologie, du Développement Durable et de l'Énergie, Conseil Régional des Pays de la Loire

Auteurs et affiliations