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The potential of the greenness and radiation (GR) model to interpret 8-day gross primary production of vegetation / Chaoyang Wu in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 88 (February 2014) . - pp 69 - 79 Titre : The potential of the greenness and radiation (GR) model to interpret 8-day gross primary production of vegetation Type de document : Article/Communication Auteurs : Chaoyang Wu, Auteur ; Alemu Gonsamo, Auteur ; Fangmin Zhang, Auteur ; Jing M. Chen, Auteur Année de publication : 2014 Article en page(s) : pp 69 - 79 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] arbre sempervirent [Termes IGN] bilan du carbone [Termes IGN] croissance des arbres [Termes IGN] écosystème forestier [Termes IGN] Enhanced vegetation index [Termes IGN] forêt de feuillus [Termes IGN] indice de végétation [Termes IGN] production primaire brute [Termes IGN] température au sol Résumé : (Auteur) Remote sensing of vegetation gross primary production (GPP) is an important step to analyze terrestrial carbon (C) cycles in response to changing climate. The availability of global networks of C flux measurements provides a valuable opportunity to develop remote sensing based GPP algorithms and test their performances across diverse regions and plant functional types (PFTs). Using 70 global C flux measurements including 24 non-forest (NF), 17 deciduous forest (DF) and 29 evergreen forest (EF), we present the evaluation of an upscaled remote sensing based greenness and radiation (GR) model for GPP estimation. This model is developed using enhanced vegetation index (EVI) and land surface temperature (LST) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and global course resolution radiation data from the National Center for Environmental Prediction (NCEP). Model calibration was achieved using statistical parameters of both EVI and LST fitted for different PFTs. Our results indicate that compared to the standard MODIS GPP product, the calibrated GR model improved the GPP accuracy by reducing the root mean square errors (RMSE) by 16%, 30% and 11% for the NF, DF and EF sites, respectively. The standard MODIS and GR model intercomparisons at individual sites for GPP estimation also showed that GR model performs better in terms of model accuracy and stability. This evaluation demonstrates the potential use of the GR model in capturing short-term GPP variations in areas lacking ground measurements for most of vegetated ecosystems globally. Numéro de notice : A2014-085 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.10.015 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.10.015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32990 [article]

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