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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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081-2014021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Spectral response function comparability among 21 satellite sensors for vegetation monitoring / Alemu Gonsamo in IEEE Transactions on geoscience and remote sensing, vol 51 n° 3 Tome 1 (March 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 3 Tome 1 (March 2013) . - pp 1319 - 1335 Titre : Spectral response function comparability among 21 satellite sensors for vegetation monitoring Type de document : Article/Communication Auteurs : Alemu Gonsamo, Auteur ; Jing M. Chen, Auteur Année de publication : 2013 Article en page(s) : pp 1319 - 1335 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] capteur spatial [Termes IGN] étalonnage radiométrique [Termes IGN] modèle de transfert radiatif [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] réponse spectrale [Termes IGN] surveillance de la végétation Résumé : (Auteur) Global and regional vegetation assessment strategies often rely on the combined use of multisensor satellite data. Variations in spectral response function (SRF) which characterizes the sensitivity of each spectral band have been recognized as one of the most important sources of uncertainty for the use of multisensor data. This paper presents the SRF differences among 21 Earth observation satellite sensors and their cross-sensor corrections for red, near infrared (NIR), and shortwave infrared (SWIR) reflectances, and normalized difference vegetation index (NDVI) aimed at global vegetation monitoring. The training data set to derive the SRF cross-sensor correction coefficients were generated from the state-of-the-art radiative transfer models. The results indicate that reflectances and NDVI from different satellite sensors cannot be regarded as directly equivalent. Our approach includes a polynomial regression and spectral curve information generated from a training data set representing a wide dynamics of vegetation distributions to minimize land cover specific SRF cross-sensor correction coefficient variations. The absolute mean SRF caused differences were reduced from 33.9% (20.1%) to 9.4 % (6%) for red, from 3.2 % (8.9%) to 1% (1.1% ) for NIR, from 2.9% (3.6 %) to 1.9% (1.6%) for SWIR, and from 7.1 % (9%) to 1.8% (1.7% ) for NDVI, after applying the SRF cross-sensor correction coefficients on independent top of canopy (top of atmosphere) data for all-embraced-sensor comparisons. Variations in processing strategies, non spectral differences, and algorithm preferences among sensor systems and data streams hinder cross-sensor spectra and NDVI comparability and continuity. The SRF cross-sensor correction approach provided here, however, can be used for studies aiming at large-scale vegetation monitoring with acceptable accuracy. Numéro de notice : A2013-125 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2198828 En ligne : https://doi.org/10.1109/TGRS.2012.2198828 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32263 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2013031A	RAB	Revue	Centre de documentation	En réserve L003	Disponible