Title: Analysis of Hyperspectral Field Radiometric Data for Monitoring Nitrogen Concentration in Rice Crops
Citation: Remote Sensing for Agriculture, Ecosystems, and Hydrology VII - Proceedings of SPIE - ISBN 9780819459961 vol. 5976 p. 189-187
Publisher: 11th SPIE International Symposium
Publication Year: 2005
JRC N°: JRC31922
URI: http://publications.jrc.ec.europa.eu/repository/handle/JRC31922
DOI: 10.1117/12.629422
Type: Articles in periodicals and books
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 green house gasses because of the extremely low efficiency in the use of N fertilizers and, in general, because of the reduced conditions. Information on plant nitrogen concentration can be used, coupled with information about the phenological stage, to plan strategies allowing a rational and spatially differentiated fertilization schedule. A field experiment was carried out in a rice field in Lombardia, 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).
JRC Directorate:Space, Security and Migration

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