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hal.structure.identifierDynamiques Forestières dans l'Espace Rural [DYNAFOR]
hal.structure.identifier
dc.contributor.authorRoumiguié, Antoine
hal.structure.identifierAirbus Defence and Space
dc.contributor.authorSigel, Grégoire
hal.structure.identifierAirbus Defence and Space
dc.contributor.authorPoilvé, Hervé
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorBouchard, Bruno
hal.structure.identifierFac Geoinformat Sci & Earth Observat
dc.contributor.authorVrieling, Anton
hal.structure.identifierDynamiques Forestières dans l'Espace Rural [DYNAFOR]
hal.structure.identifier
dc.contributor.authorJacquin, Anne
dc.date.accessioned2019-01-14T10:53:44Z
dc.date.available2019-01-14T10:53:44Z
dc.date.issued2017
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/18374
dc.language.isoenen
dc.subjectgross primary productionen
dc.subjectfcover time-seriesen
dc.subjectbiomass estimationen
dc.subjectcropen
dc.subjectproductionen
dc.subjectuse efficiencyen
dc.subjectfood securityen
dc.subjectmodelen
dc.subjectinsuranceen
dc.subjectecosystemsen
dc.subjectvalidationen
dc.subjectRemote Sensingen
dc.subjectImaging Science & Photographic Technologyen
dc.subject.ddc520en
dc.titleInsuring forage through satellites: testing alternative indices against grassland production estimates for Franceen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenTo mitigate impacts of climate-related reduced productivity of French grasslands, a new insurance scheme bases indemnity payouts to farmers on a Moderate Resolution Imaging Spectroradiometer (MODIS)-derived forage production index (FPI). The objective of this study is to compare several approaches for deriving FPI from satellite data to assess whether better relationships with forage productivity can be attained. The approaches assess pasture productivity using as five input factors estimated from remote sensing and ancillary data, i.e.: (1) fraction of absorbed photosynthetically active radiation (fAPAR); (2) radiation use efficiency estimates; (3) PAR estimates; (4) leaf senescence modelling; and (5) growing season modelling . All the possible combinations from these five factors, including different modalities to estimate some of them, lead to 768 models. Model outputs are compared to reference grassland production estimates provided by a mechanistic model (Information et Suivi Objectif des Prairies – ISOP – system) for a sample of 25 forage regions across France for the years 2003, 2007, 2009, 2011, and 2012 (containing one humid, two normal, and two dry years). Results revealed that: (1) the baseline model based on the fraction of green vegetation cover (fCover) seasonal integral has a reasonable linear relationship to production estimates (standardized root mean square error – SRMSE = 0.57 and coefficient of determination – R2 = 0.68); (2) performance of the baseline model improved with a quadratic function (SRMSE = 0.54 and R2 = 0.71); (3) 34 models outperform the baseline model. We, therefore, suggest to replace the baseline model with the best-performing model (SRMSE = 0.42 and R2 = 0.83) in the insurance product. This model integrates daily fCover with a water stress index and sums these over a variable monitoring period in space and time characterized by the phenological indicators start of season and end of season derived from the fCover annual profile.en
dc.relation.isversionofjnlnameInternational Journal of Remote Sensing
dc.relation.isversionofjnlvol38en
dc.relation.isversionofjnlissue7en
dc.relation.isversionofjnldate2017
dc.relation.isversionofjnlpages1912-1939en
dc.relation.isversionofdoi10.1080/01431161.2016.1230288en
dc.relation.isversionofjnlpublisherTaylor & Francisen
dc.subject.ddclabelSciences connexes (physique, astrophysique)en
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewednonen
dc.relation.Isversionofjnlpeerreviewednonen
dc.date.updated2019-01-14T10:47:25Z
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