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Proceedings Paper

Analysis of hyperspectral field radiometric data for monitoring nitrogen concentration in rice crops
Author(s): D. Stroppiana; M. Boschetti; R. Confalonieri; S. Bocchi; P.A. Brivio
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Paper Abstract

Monitoring crop conditions and assessing nutrition requirements is fundamental for implementing sustainable agriculture. Rational nitrogen fertilization is of particular importance in rice crops in order to guarantee high production levels while minimising the impact on the environment. In fact, the typical flooded condition of rice fields can be a significant source of greenhouse gasses. Information on plant nitrogen concentration can be used, coupled with information about the phenological stage, to plan strategies for a rational and spatially differentiated fertilization schedule. A field experiment was carried out in a rice field Northern Italy, in order to evaluate the potential of field radiometric measurements for the prediction of rice nitrogen concentration. The results indicate that rice reflectance is influenced by nitrogen supply at certain wavelengths although N concentration cannot be accurately predicted based on the reflectance measured at a given wavelength. Regression analysis highlighted that the visible region of the spectrum is most sensitive to plant nitrogen concentration when reflectance measures are combined into a spectral index. An automated procedure allowed the analysis of all the possible combinations into a Normalized Difference Index (NDI) of the narrow spectral bands derived by spectral resampling of field measurements. The derived index appeared to be least influenced by plant biomass and Leaf Area Index (LAI) providing a useful approach to detect rice nutritional status. The validation of the regressive model showed that the model is able to predict rice N concentration (R2=0.55 [p<0.01]; RRMSE=29.4; modelling efficiency close to the optimum value).

Paper Details

Date Published: 18 October 2005
PDF: 9 pages
Proc. SPIE 5976, Remote Sensing for Agriculture, Ecosystems, and Hydrology VII, 59760R (18 October 2005); doi: 10.1117/12.629422
Show Author Affiliations
D. Stroppiana, CNR-IREA (Italy)
M. Boschetti, CNR-IREA (Italy)
R. Confalonieri, Joint Research Ctr. of the European Commission (Italy)
S. Bocchi, Univ. of Milano (Italy)
P.A. Brivio, CNR-IREA (Italy)

Published in SPIE Proceedings Vol. 5976:
Remote Sensing for Agriculture, Ecosystems, and Hydrology VII
Manfred Owe; Guido D'Urso, Editor(s)

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