Eucalyptus Leaf area index estimation from Sentinel 2 images: importance of genotype and sun and view acquisition geometry
2023
Barbosa Ferreira, Vitoria | Le Maire, Guerric | Feret, Jean Baptiste | Guillemot, Joannès | Campoe, Otavio | Ecologie fonctionnelle et biogéochimie des sols et des agro-écosystèmes (UMR Eco&Sols) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier ; 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 National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Universidade Federal de Lavras = Federal University of Lavras (UFLA) | Centre National d'Etudes Spatiales;CNES;FRA;http://dx.doi.org/10.13039/501100002830 | Centre de Coopération Internationale en Recherche Agronomique pour le Développement;CIRAD;FRA;http://dx.doi.org/10.13039/501100007204 | Programme National de Télédétection Spatiale;PNTS;FRA; | IPEF
Source Agritrop Cirad (https://agritrop.cirad.fr/612033/)
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Show more [+] Less [-]English. Brazil is a major producer of wood, with Eucalyptus plantations covering over 7 million hectares. Leaf area index (LAI) is a crucial parameter for determining stand carbon and water balance, and ultimately assessing the crop growth status. However, LAI is highly variable in space, within and between stands, and temporally, across rotations. This study aims to investigate the relationship between LAI and various vegetation indices derived from Sentinel 2 images, and develop empirical equations calibrated at the Eucflux site with Root Mean Squared Error values as low as 0.49 m2/m2. The incorporation of variables related to satellite and sun acquisition geometry significantly improves the accuracy of the prediction model. In addition, separating the model by genotypes greatly improves its performance, but may affect transferability.
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