In situ hyperspectral data analysis for pigment content estimation of rice leaves

Institute of Agricultural Remote Sensing & Information Application, Zhejiang University, Hangzhou 310029, China.
Journal of Zhejiang University SCIENCE 11/2003; 4(6):727-33. DOI: 10.1631/jzus.2003.0727
Source: PubMed


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.

8 Reads
  • Source
    • "Log(1/R) (also called pseudo absorbance) is often employed because it provides a curve comparable to an absorption curve, with peaks occurring at the corresponding absorption wavelengths. The relationships between agronomic parameters and some specific narrow spectral bands have promoted the development of various models to estimate the agronomic parameters at leaf to canopy scales (Cheng et al., 2003). However, most of those models were developed using the means of regression analysis based on an assumption of a linear relationship between dependent and independent variables. "
    [Show abstract] [Hide abstract]
    ABSTRACT: AbstractThe radial basis function (RBF) emerged as a variant of artificial neural network. Generalized regression neural network (GRNN) is one type of RBF, and its principal advantages are that it can quickly learn and rapidly converge to the optimal regression surface with large number of data sets. Hyperspectral reflectance (350 to 2 500 nm) data were recorded at two different rice sites in two experiment fields with two cultivars, three nitrogen treatments and one plant density (45 plants m−2). Stepwise multivariable regression model (SMR) and RBF were used to compare their predictability for the leaf area index (LAI) and green leaf chlorophyll density (GLCD) of rice based on reflectance (R) and its three different transformations, the first derivative reflectance (D1), the second derivative reflectance (D2) and the log-transformed reflectance (LOG). GRNN based on D1 was the best model for the prediction of rice LAI and GLCD. The relationships between different transformations of reflectance and rice parameters could be further improved when RBF was employed. Owing to its strong capacity for nonlinear mapping and good robustness, GRNN could maximize the sensitivity to chlorophyll content using D1. It is concluded that RBF may provide a useful exploratory and predictive tool for the estimation of rice biophysical parameters.
    Full-text · Article · Apr 2009 · Pedosphere
  • Source
    • "Different remote sensing applications have proved to be a potential source of reflectance data for estimation of several canopy variables related to biophysical, physiological or biochemical characteristics (Ahlrichs and Bauer, 1983; Serrano et al., 2000; Thenkabail et al., 2000; Cheng et al., 2003; Tang, et al., 2004; Zhang, et al., 2006). Various Vegetation Indices (VIs) have been related to crop variables such as biomass, leaf area, plant cover, leaf gap fraction, nitrogen, and chlorophyll in cereals (Best and Harlan, 1985; Christensen and Goudriaan, 1993; Lukina et al., 1999; Aparicio et al., 2000; Cheng, 2006). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Hyperspectral reflectance (350∼2500 nm) data were recorded at two different sites of rice in two experiment fields including two cultivars, and three levels of nitrogen (N) application. Twenty-five Vegetation Indices (VIs) were used to predict the rice agronomic parameters including Leaf Area Index (LAI, m2 green leaf/m2 soil) and Green Leaf Chlorophyll Density (GLCD, mg chlorophyll/m2 soil) by the traditional regression models and Radial Basis Function Neural Network (RBF). RBF emerged as a variant of Artificial Neural Networks (ANNs) in the late 1980’s. A large variety of training algorithms has been tested for training RBF networks. In this study, Original RBF (ORBF), Gradient Descent RBF (GDRBF), and Generalized Regression Neural Network (GRNN) were employed. Results showed that green waveband Normalized Difference Vegetation Index (NDVIgreen) and TCARI/OSAVI have the best prediction power for LAI by exponent model and ORBF respectively, and that TCARI/OSAVI has the best prediction power for GLCD by exponent model and GDRBF. The best performances of RBF are compared with the traditional models, showing that the relationship between VIs and agronomic variables are further improved when RBF is used. Compared with the best traditional models, ORBF using TCARI/OSAVI improves the prediction power for LAI by lowering the Root Mean Square Error (RMSE) for 0.1119, and GDRBF using TCARI/OSAVI improves the prediction power for GLCD by lowering the RMSE for 26.7853. It is concluded that RBF provides a useful exploratory and predictive tool when applied to the sensitive VIs.
    Full-text · Article · May 2007 · Journal of Zhejiang University - Science A: Applied Physics & Engineering
  • [Show abstract] [Hide abstract]
    ABSTRACT: Sensing of an intracardiac electrogram is critical to pacemaker design and operation. Characterization of an intracardiac electrogram sensing electrode has long been based on the assumption that the electrode is very small and has no effect on the field it senses. In fact, any intracardiac sensing electrode has a significantly large surface area where electrical charges are induced and disturb the sensed potential field. To account for the effect of electrode size and position on electrogram sensing, a surface charge integral equation is formulated, based an the current continuity equation in homogeneous myocardial medium. A numerical method, such as the method of moments, is then applied to obtain the solution of the integral equation, meanwhile the potential sensed by the electrode. As an application of the proposed technique, several electrode configurations have been analyzed and their relative electrogram sensitivities are compared
    No preview · Article · Jan 1995
Show more