Jean-Marc Gilliot
ParisTech
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Featured researches published by Jean-Marc Gilliot.
Journal of remote sensing | 2008
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
Jonas Hamiache; Liliane Bel; Emmanuelle Vaudour; Jean-Marc Gilliot
10. International Conference | 2008
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
Paul Emile Noirot Cosson; Laetitia Brechet; Jean-Marc Gilliot; Marie Eden; Jerome Molina; Jean-Noel Rampon; Benoit Gabrielle; Emmanuelle Vaudour; Sabine Houot
Archive | 2018
Jean-Marc Gilliot; Camille Le Priol; Emmanuelle Vaudour; Philippe Martin
EGU General Assembly 2016 Conference Abstracts, European Geophysical Union | 2016
Emmanuelle Vaudour; Lea Leclercq; Jean-Marc Gilliot; Benôit Chaignon
EGU General Assembly 2016 Conference Abstracts, European Geophysical Union | 2016
Emmanuelle Vaudour; Jean-Marc Gilliot; Liliane Bel; J. Lefevre; K. Chehdi
Journées scientifiques annuelles BASC 2015 | 2015
Paul Emile Noirot Cosson; Emmanuelle Vaudour; Christine Aubry; Jean-Marc Gilliot; Benoit Gabrielle; Sabine Houot
Sciences Eaux and Territoires : la Revue du IRSTEA | 2013
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
Emmanuelle Vaudour; Jean-Marc Gilliot; Liliane Bel; Alexis De Junet; Joël Michelin; Dalila Hadjar; Philippe Cambier; Sabine Houot; Yves Coquet