2003•Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIERequires access

Relationship between rice LAI, CH.D, and hyperspectral data

Weidong Liu, Yueqin Xiang, Lanfen Zheng, Qingxi Tong

Open publisher page 3 citations

Abstract

The objective of this paper was to determine hyperspectral narrow wavebands that are best suited for estimating rice biophysical characteristics. The paper studied the variational process of leaf area index (LAI), leaf chlorophyll density (CH.D) and hyperspectral data during the period of rice growing season. Correlation between hyperspectral data and LAI, CH.D of rice was analyzed. Spectral derivatives technique was used to suppress the effects of low frequency spectral noises on background. Stepwise regression method was used to create multivariate linear equations for predicting LAI and CH.D of rice with the data of reflectance and the first-order derivatives of reflectance as forecast factors. Results show that: 1) The first order derivatives of reflectance spectrum can enhance the correlation and improve the precision of predicting LAI and CH.D; 2) The first order derivatives of reflectance and CH.D more markedly correlate than LAI at some wavelength, CH.D is more available to express crop canopy spectrum information than leaf area index.

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What this paper is about

The objective of this paper was to determine hyperspectral narrow wavebands that are best suited for estimating rice biophysical characteristics. The paper studied the variational process of leaf area index (LAI), leaf chlorophyll density (CH.D) and hyperspectral data during the period of rice growing season. Correlation between hyperspectral data and LAI, CH.D of rice was analyzed. Spectral derivatives technique was used to suppress the effects of low frequency spectral noises on background. Stepwise regression method was used to create multivariate linear equations for predicting LAI and CH.D of rice with the data of reflectance and the first-order derivatives of reflectance as forecast factors. Results show that: 1) The first order derivatives of reflectance spectrum can enhance the correlation and improve the precision of predicting LAI and CH.D; 2) The first order derivatives of reflectance and CH.D more markedly correlate than LAI at some wavelength, CH.D is more available to express crop canopy spectrum information than leaf area index.

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Available abstract

The objective of this paper was to determine hyperspectral narrow wavebands that are best suited for estimating rice biophysical characteristics. The paper studied the variational process of leaf area index (LAI), leaf chlorophyll density (CH.D) and hyperspectral data during the period of rice growing season. Correlation between hyperspectral data and LAI, CH.D of rice was analyzed. Spectral derivatives technique was used to suppress the effects of low frequency spectral noises on background. Stepwise regression method was used to create multivariate linear equations for predicting LAI and CH.D of rice with the data of reflectance and the first-order derivatives of reflectance as forecast factors. Results show that: 1) The first order derivatives of reflectance spectrum can enhance the correlation and improve the precision of predicting LAI and CH.D; 2) The first order derivatives of reflectance and CH.D more markedly correlate than LAI at some wavelength, CH.D is more available to express crop canopy spectrum information than leaf area index.

Key concepts: Hyperspectral imaging, Leaf area index, Remote sensing, Canopy, Wavelength, Reflectivity, Environmental science, Mathematics

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