http://www.cnr.it/ontology/cnr/individuo/prodotto/ID43049

Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. (Articolo in rivista)

Type

Articolo in rivista (Classe)
Prodotto della ricerca (Classe)

Label

Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. (Articolo in rivista) (literal)

Anno

2009-01-01T00:00:00+01:00 (literal)

Alternative label

Stroppiana D, Boschetti M, Brivio PA, Bocchi S (2009)
Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry.
in Field crops research
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

Stroppiana D, Boschetti M, Brivio PA, Bocchi S (literal)

Pagina inizio

119 (literal)

Pagina fine

129 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

111 (literal)

Rivista

Field crops research (Rivista)

Note

ISI Web of Science (WOS) (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

S. Bocchi: DI.PRO.VE., Department of Crop Science, University of Milano, Via Celoria 2, 20133 Milano, Italy (literal)

Titolo

Plant nitrogen concentration in paddy rice from field canopy hyperspectral radiometry. (literal)

Abstract

In the context of precision farming, quantitative information on plant nitrogen concentration (PNC) is necessary to apply variable rate technologies of top-dressing fertilization. Radiometric measurements are useful for monitoring crop conditions and, in particular, for nitrogen/chlorophyll assessment. This work aims to quantify PNC from canopy spectra collected in the field with a hand-held spectroradiometer. We propose a vegetation index that is able to predict PNC in rice crops through a regressive model that was calibrated and validated with data from two field campaigns carried out in Northern Italy in 2004 and 2006. The index exploits availability of hyperspectral data (numerous very narrow bands, <10 nm) that can guide the choice of spectral band combinations for PNC estimation. The most suitable bands were selected in the visible (blue/green) region of the electromagnetic spectrum where nitrogen/chlorophyll compounds play a key role in radiation absorption. The index was also shown to be least affected by leaf area index and aboveground biomass variability thus assuring the highest sensitivity to PNC (R2 = 0.65, ***p < 0.001). The regressive model was applied, via a spatial interpolation algorithm, to field data in order to derive, maps of PNC; these showed a high correlation with experimental design and crop conditions through the rice growing cycle. Model precision and accuracy appear suitable for detecting spatial and temporal variations in rice crops and for supporting decisions for application of variable rate technology. (literal)

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