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Neural network estimation of LAI, fAPAR, fCover and LAI*Cab, from top of canopy MERIS reflectance data: principles and validation / Cédric Bacour in Remote sensing of environment, vol 105 n° 4 (30/12/2006)  

Public [article]  inRemote sensing of environment > vol 105 n° 4 (30/12/2006) . - pp 313 - 325 Titre : Neural network estimation of LAI, fAPAR, fCover and LAI*Cab, from top of canopy MERIS reflectance data: principles and validation Type de document : Article/Communication Auteurs : Cédric Bacour, Auteur ; F. Baret, Auteur ; D. Beal, Auteur ; M. Weiss, Auteur ; K. Pavageau, Auteur Année de publication : 2006 Article en page(s) : pp 313 - 325 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse comparative [Termes IGN] canopée [Termes IGN] chlorophylle [Termes IGN] classification par réseau neuronal [Termes IGN] image Envisat-MERIS [Termes IGN] Leaf Area Index [Termes IGN] modèle de transfert radiatif [Termes IGN] réflectance végétale Résumé : (Auteur) A neural network is developed to operationally estimate biophysical variables over land surfaces from the observations of the ENVISAT-MERIS instrument: the leaf area index (LAI), the fraction of absorbed photosynthetically active radiation (fAPAR), the fraction of vegetation cover (fCover), and the canopy chlorophyll content (LAI*Cab). The neural network requires as input the geometry of observation and the top of canopy reflectances, corrected from the atmospheric effects, in eleven spectral bands. It is trained on a reflectance database made of radiative transfer model simulations. The principles underlying the generation of the database and the design of the network are first presented. The estimated variables are then compared to other existing products, LAI- and fAPAR-MODIS and MGVI-MERIS, and validated against ground measurements performed in the framework of the VALERI project. Results show remarkable consistency of the temporal dynamics between the several products with however some differences in the range of variation. When compared to actual VALERI ground measurements, the proposed algorithm shows the best performances for LAI (RMSE = 0.47) and fAPAR (RMSE = 0.09). Copyright Elsevier Numéro de notice : A2006-562 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.07.014 En ligne : https://doi.org/10.1016/j.rse.2006.07.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28285 [article]