摘要
Analyses of the correlation between hyperspectral reflectance and pigment content including chlorophyll a, chlorophyll b and carotenoid of leaves in different sites of rice were reported in this paper. The hyperspectral reflectance of late rice during the whole growing season was measured using a Spectroradiometer with spectral range of 350-1050 nm and resolution of 3 nm. The chlorophyll a, chlorophyll b and carotenoid contents in rice leaves in rice fields to which different levels of nitrogen were applied were measured. The chlorophyll a content of upper leaves was well correlated with the spectral variables. However, the correlation between both chlorophyll b and caroteniod and the spectral variables was far from that of chlorophyll a. The potential of hyperspectral reflectance measurement for estimating chlorophyll a of upper leaves was evaluated using univariate correlation and multivariate regression analysis methods with different types of predictors. This study showed that the most suitable estimated model of chlorophyll a of upper leaves was obtained by using some hyperspectral variables such as SD r, SD b and their integration.
Analyses of the correlation between hyperspectral reflectance and pigment content including chlorephyll-a, chlorephyll-b and carotenoid of leaves in different sites of rice were reported in this paper. The hyperspectral reflectance of late rice during the whole growing season was measured using a Spectroradiometer with spectral range of 350 - 1050 nm and resolution of 3 nm. The chlorophyll-a, chlorephyll-b and carotenoid contents in rice leaves in rice fields to which different levels of nitrogen were applied were measured. The chlorephyll-a content of upper leaves was well correlated with the spectral variables. However, the correlation between both chlorephyll-b and careteniod and the spectral variables was far from that of chlorophyll-a. The potential of hyperspectral reflectance measurement for estimating chlorophyll-a of upper leaves was evaluated using univariate correlation and multivariate regression analysis methods with different types of predictors. This study showed that the most suitable estimated model of chlorephyll-a of upper leaves was obtained by using some hyperspectral variables such as SDr , SDb and their integration.
