Leaf water content estimation by functional linear regression of field spectroscopy data

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Leaf water content estimation by functional linear regression of field spectroscopy data

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dc.contributor Escuela Superior y Tecnica de Ingenieria Agraria es_ES
dc.contributor.author Rodríguez Pérez, José Ramón, 1968-
dc.contributor.author Ordóñez, Celestino
dc.contributor.author González Fernández, Ana Belén
dc.contributor.author Sanz Ablanedo, Enoc
dc.contributor.author Valenciano Montenegro, José Benito
dc.contributor.author Marcelo Gabella, Victoriano
dc.contributor.other Ingeniería Cartografica, Geodesica y Fotogrametria es_ES
dc.date 2017-06-01
dc.date.accessioned 2017-10-17T12:12:07Z
dc.date.available 2017-10-17T12:12:07Z
dc.date.issued 2017-10-17
dc.identifier.citation GeoFocus (Informes y comentarios), 2017, vol. XXX, n. I-II es_ES
dc.identifier.uri http://hdl.handle.net/10612/6864
dc.description 11 p. es_ES
dc.description.abstract Grapevine 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.language eng es_ES
dc.publisher Elsevier es_ES
dc.subject Ingeniería agrícola es_ES
dc.subject.other Functional linear regression es_ES
dc.subject.other Field spectral reflectance es_ES
dc.subject.other Plant water stress es_ES
dc.subject.other Equivalent water thickness es_ES
dc.subject.other Vitis vinifera L. es_ES
dc.title Leaf water content estimation by functional linear regression of field spectroscopy data es_ES
dc.type info:eu-repo/semantics/article es_ES
dc.description.peerreviewed SI es_ES

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