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Featured researches published by Jean-Marc Gilliot.


Journal of remote sensing | 2008

Spatial retrieval of soil reflectance from SPOT multispectral data using the empirical line method

Emmanuelle Vaudour; Julien Moeys; Jean-Marc Gilliot; Yves Coquet

The use of empirical relationships to calibrate remotely sensed data has been reported by many authors, but few studies have been devoted to the specific retrieval of soil reflectance and most studies have used two calibration targets only, using few ( = 5) validation targets to assess the error in the empirical relationships. Absolute corrections based on empirical line methods have seldom been applied to satellite data, particularly Satellite pour lObservation de la Terre (SPOT) images. In this study, which deals with bare cultivated soils, the empirical line method was used to retrieve soil reflectance from three programmed SPOT satellite images with 20 m and 10 m resolution based on independent sets of 8 calibration and 15 validation field targets of bare soil. The empirical line method was applied to orthorectified images with digital number (DN) values interpolated both with the cubic convolution and the nearest neighbour interpolation mode. Root mean squared errors (RMSEs) between corrected and measured values at calibration sites were lower than 0.56% for the two visible bands and ranged between 0.61% and 1.97% for the near‐infrared band, whereas at all validation sites they were mostly lower than 2.5% except for most near‐infrared bands. Such accurate results have been facilitated both by the flat topographical conditions of the Beauce region (Western Parisian Basin, France) and the homogeneous sky conditions in a small study area (2500 ha). Differences in illumination conditions in both orbital and field cases did not appear to affect the results dramatically, which suggest that even oblique viewing images can be used for such correction. Errors could also be due to changes in soil moisture and roughness related to soil management practices, or slight vegetation growth over the end of the study period. Due to spatial error, the 20‐m resolution with cubic convolution mode led to a much lower performance than the 10‐m images with either interpolation modes. The achieved results are generalizable to European sedimentary basins with loess deposits, where similar soils and topographic conditions are to be found over thousands of km2.


Proceedings of the 5th Global Workshop on Digital Soil Mapping 2012 | 2012

Spatial stochastic modeling of topsoil organic carbon content over a cultivated peri-urban region, using soil properties, soil types and a digital elevation model

Jonas Hamiache; Liliane Bel; Emmanuelle Vaudour; Jean-Marc Gilliot


10. International Conference | 2008

Biophysical modelling of NO emissions from agricultural soils in northern France for use in regional chemistry-transport modelling

Marie Noelle Rolland; Benoit Gabrielle; Patricia Laville; M. Beekman; Jean-Marc Gilliot; Joël Michelin; Dalila Hadjar; Gabriele Curci; O. Sanchez; Pierre Cellier


15. International Conferences of RAMIRAN (Network on R ecycling of Agricultural, Municipal and Industrial Residues in Agriculture) | 2012

Simulation with the NCSOIL model of carbon and nitrogen dynamics in a loamy soil after various compost applications

Paul Emile Noirot Cosson; Laetitia Brechet; Jean-Marc Gilliot; Marie Eden; Jerome Molina; Jean-Noel Rampon; Benoit Gabrielle; Emmanuelle Vaudour; Sabine Houot


Archive | 2018

Automatic Extraction of Agricultural Parcels from Remote Sensing Images and the RPG Database with QGIS/OTB

Jean-Marc Gilliot; Camille Le Priol; Emmanuelle Vaudour; Philippe Martin


EGU General Assembly 2016 Conference Abstracts, European Geophysical Union | 2016

Retrospective farm scale spatial analysis of viticultural terroir fertility using a 70 y-aerial photograph time series, soil survey and very high resolution Pléiades and EM38 data

Emmanuelle Vaudour; Lea Leclercq; Jean-Marc Gilliot; Benôit Chaignon


EGU General Assembly 2016 Conference Abstracts, European Geophysical Union | 2016

Within-field and regional-scale accuracies of topsoil organic carbon content prediction from an airborne visible near-infrared hyperspectral image combined with synchronous field spectra for temperate croplands

Emmanuelle Vaudour; Jean-Marc Gilliot; Liliane Bel; J. Lefevre; K. Chehdi


Journées scientifiques annuelles BASC 2015 | 2015

Optimization of Exogenous Organic Matter (EOM) use at the territory scale : maximization of Carbon Storage (CS) in soil and synthetic Nitrogen Savings (NS) in cropped soils

Paul Emile Noirot Cosson; Emmanuelle Vaudour; Christine Aubry; Jean-Marc Gilliot; Benoit Gabrielle; Sabine Houot


Sciences Eaux and Territoires : la Revue du IRSTEA | 2013

Évaluer la performance écologique d'un aménagement autoroutier : utilisation couplée d'un diagnostic sol et de la biodiversité végétale

Flavie Mayrand; Damien Marage; Jean-Marc Gilliot; Joël Michelin; Yves Coquet


Journée scientifique Méthodes NIRS et MIRS : applications aux Sciences de l'Environnement " réponse spectrale du sol à la plante, du labo à la mesure de terrain et télédetection, Fédération Île-de-France de Recherche sur l'Environnement (FIRE) | 2012

Réponse spectrale du sol pour prédire les stocks de C sur la plaine de Versailles

Emmanuelle Vaudour; Jean-Marc Gilliot; Liliane Bel; Alexis De Junet; Joël Michelin; Dalila Hadjar; Philippe Cambier; Sabine Houot; Yves Coquet

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Dalila Hadjar

Institut national de la recherche agronomique

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Liliane Bel

Institut national de la recherche agronomique

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Marie Noelle Rolland

Institut national de la recherche agronomique

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Sabine Houot

Institut national de la recherche agronomique

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Paul Emile Noirot Cosson

Institut national de la recherche agronomique

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Julien Moeys

Swedish University of Agricultural Sciences

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Abel Dorigny

Institut national de la recherche agronomique

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Bernard Nicoullaud

Institut national de la recherche agronomique

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