Mostrar registro simples

dc.contributorEscuela Superior y Tecnica de Ingenieria Agrariaes_ES
dc.contributor.authorRodríguez Pérez, José Ramón, 1968- 
dc.contributor.authorOrdóñez, Celestino
dc.contributor.authorGonzález Fernández, Ana Belén
dc.contributor.authorSanz Ablanedo, Enoc 
dc.contributor.authorValenciano Montenegro, José Benito 
dc.contributor.authorMarcelo Gabella, Victoriano 
dc.contributor.otherIngeniería Cartografica, Geodesica y Fotogrametriaes_ES
dc.date2017-06-01
dc.date.accessioned2017-10-17T12:12:07Z
dc.date.available2017-10-17T12:12:07Z
dc.date.issued2017-10-17
dc.identifier.citationGeoFocus (Informes y comentarios), 2017, vol. XXX, n. I-IIes_ES
dc.identifier.urihttp://hdl.handle.net/10612/6864
dc.description11 p.es_ES
dc.description.abstractGrapevine water status is critical as it affects fruit quality and yield. We assessed the po-tential of field hyperspectral data in estimating leaf water content (Cw) (expressed as equivalent water thickness) in four commercial vineyards of Vitis vinifera L. reflecting four grape varieties (Mencı´a, Cabernet Sauvignon, Merlot and Tempranillo). Two regression models were evaluated and compared: ordinary least squares regression (OLSR) and functional linear regression (FLR). OLSR was used to fit Cw and vegetation indices, whereas FLR considered reflectance in four spectral ranges centred at the 960, 1190, 1465 and 2035 nm wavelengths. The best parameters for the FLR model were determined using cross-validation. Both models were compared using the coefficient of determination (R2) and percentage root mean squared error (%RMSE). FLR using continuous stretches of the spectrum as input produced more suitable Cw models than the vegetation indices, considering both the fit and degree of adjustment and the interpretation of the model. The best model was obtained using FLR in the range centred at 1465 nm (R2 ¼ 0.70 and %RMSE ¼ 8.485). The results depended on grape variety but also suggested that leaf Cw can be predicted on the basis of spectral signature.es_ES
dc.languageenges_ES
dc.publisherElsevieres_ES
dc.subjectIngeniería agrícolaes_ES
dc.subject.otherFunctional linear regressiones_ES
dc.subject.otherField spectral reflectancees_ES
dc.subject.otherPlant water stresses_ES
dc.subject.otherEquivalent water thicknesses_ES
dc.subject.otherVitis vinifera L.es_ES
dc.titleLeaf water content estimation by functional linear regression of field spectroscopy dataes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.description.peerreviewedSIes_ES


Arquivos deste item

Thumbnail

Este item aparece na(s) seguinte(s) coleção(s)

Mostrar registro simples