Assessing the impact of incorporating soil moisture and roughness as co-variables to improve soil organic carbon content prediction from hyperspectral data
2025
Merlet, Hugues | Fouad, Youssef | Michot, Didier | Gilliot, Jean-Marc | Scriban, Arthur | Vaudour, Emmanuelle | Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS) ; AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Sol Agro et hydrosystème Spatialisation (SAS) ; Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Rennes Angers ; Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro) | Institut Agro Rennes Angers ; Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro) | Ludwig-Maximilians-Universität München | Helmholtz Centre Potsdam | https://enmap.geographie-muenchen.de/ | ANR-22-PEAE-0010,MELICERTES,Modeling the global states and dynamics of ecosystems - Applications to carbon flux and stock in ecosystems modified by agricultural activities(2022)
International audience
Show more [+] Less [-]English. Predicting soil organic carbon (SOC) content is of critical importance for both environmental policies and monitoring issues [Criscuoli et al., 2024]. Traditional field soil sampling methods are tedious and cost-prohibitive, making remote sensing an attractive alternative for simplifying estimates, particularly at large scales. The EnMAP satellite offers great potential, as it captures a broad spectral range and high spectral resolution, both essential for accurately assessing SOC content [Chabrillat et al., 2023].However, remote sensing acquisitions face many challenges, and optimal soil surface conditions are rarely achieved. Soils are often not fully bare; they may be moist and/or rough, which significantly impacts reflectance and, consequently, the ability to predict SOC content. Only few studies accounted for soil roughness [Denis et al., 2014; Piekarczyk et al., 2016] and this in isolation, from soil moisture, which was primarily accounted for under controlled laboratory conditions [see review by Knadel et al., 2023].This study aims to evaluate the benefit of incorporating co-variables related to soil moisture and surface roughness into SOC prediction models based on field spectroscopy and other in situ simultaneous measurements. A joint objective is to assess whether using EnMAP simulations of these spectra can improve model performance compared to other selections of specific wavelengths.
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