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dc.contributor.authorMERONI MICHELEen_GB
dc.contributor.authorMARINHO Eduardoen_GB
dc.contributor.authorSGHAIER Nabilen_GB
dc.contributor.authorVERSTRAETE Michelen_GB
dc.contributor.authorLEO Olivieren_GB
dc.date.accessioned2014-04-09T00:01:30Z-
dc.date.available2013-03-06en_GB
dc.date.available2014-04-09T00:01:30Z-
dc.date.created2013-01-30en_GB
dc.date.issued2013en_GB
dc.date.submitted2012-11-09en_GB
dc.identifier.citationREMOTE SENSING vol. 5 p. 539-557en_GB
dc.identifier.issn2072-4292en_GB
dc.identifier.urihttp://www.mdpi.com/2072-4292/5/2/539en_GB
dc.identifier.urihttp://publications.jrc.ec.europa.eu/repository/handle/JRC76475-
dc.description.abstractMultitemporal optical remote sensing constitutes a useful, cost efficient method for crop status monitoring over large areas. Modelers interested in yield monitoring can rely on past and recent observations of crop reflectance to estimate aboveground biomass and infer the likely yield. Therefore, in a framework constrained by the information availability, remote sensing data to yield conversion parameters are to be estimated. Statistical models are suitable for this purpose given their ability to deal with statistical errors. This paper explores the performance in yield estimation of various remote sensing indicators based on varying degrees of bio-physical insight, in interaction with statistical methods (linear regressions) that rely on different hypotheses. Jackknifed results (leave one year out) are presented for the case of wheat yield regional estimation in Tunisia using the SPOT-VEGETATION instrument.en_GB
dc.description.sponsorshipJRC.H.4-Monitoring Agricultural Resourcesen_GB
dc.format.mediumOnlineen_GB
dc.languageENGen_GB
dc.publisherMDPIen_GB
dc.relation.ispartofseriesJRC76475en_GB
dc.titleRemote Sensing Based Yield Estimation in a Stochastic Framework – Case Study of Durum Wheat in Tunisiaen_GB
dc.typeArticles in periodicals and booksen_GB
dc.identifier.doi10.3390/rs5020539en_GB
JRC Directorate:Sustainable Resources

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