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Proceedings Paper

Study on hyperspectral remote sensing estimation models about aboveground fresh biomass of rice
Author(s): Xiuzhen Wang; Jingfeng Huang; Yunmei Li; Renchao Wang
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Paper Abstract

The data for this study was collected from two-year (1999 and 2000) field experiments that based on different artificial nitrogen treatments. Linear, non-linear and stepwise multiple regression analysis were adopted for modeling The data in 1999 was utilized as training sample for modeling hyperspectra remote sensing estimation of rice aboveground fresh biomass, and the data in 2000 was evaluated and tested the models' predictive accuracy. Results of fitness analysis between hyperspectral variables and rice aboveground fresh biomass indicate that some hyperspectral characteristic variables and their combinations are closely correlated to aboveground biomass, such as red edge wavelength (λr),maximum reflectivity in green region, minimum reflectivity in red region, and the vegetation index based on the sum of first derivative spectral reflectance in blue region and that in red region. Determining the highest correlated wavebands and the best-fitting variables for raw spectra, first derivative spectra and hyperspectral characteristic variables through stepwise multiple regressions, and the results reveal that the relationship between the first derivative spectra and rice aboveground biomass is much clearer and simpler when compared with the rest. The best model for rice aboveground biomass estimation is based on the ration vegetation indices that calculated with the sum of the first derivative spectra reflectance in blue region and that in red region.

Paper Details

Date Published: 9 November 2004
PDF: 10 pages
Proc. SPIE 5544, Remote Sensing and Modeling of Ecosystems for Sustainability, (9 November 2004); doi: 10.1117/12.557403
Show Author Affiliations
Xiuzhen Wang, Zhejiang Univ. (China)
Zhejiang Meteorological Institute (China)
Jingfeng Huang, Zhejiang Univ. (China)
Yunmei Li, Nanjing Normal Univ. (China)
Renchao Wang, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 5544:
Remote Sensing and Modeling of Ecosystems for Sustainability
Wei Gao; David R. Shaw, Editor(s)

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