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Yang, XH, Huang, JF, Wang, XZ, Wang, FM (2008). The estimation model of rice leaf area index using hyperspectral data based on support vector machine. SPECTROSCOPY AND SPECTRAL ANALYSIS, 28(8), 1837-1841.

In order to compare the prediction powers between the best statistical model and SVM technique using each VI for rice LAI, the VIs are as independent variables in statistical models and are as net inputs in SVM, and the rice LAI are as dependent variables in statistical models and are as net outputs in SVM. Hyperspectral reflectance (350 to 2 500 nm) data were recorded in two experiments involving four replicates of two rice cultivars (Xiushui 110 and Xieyou 9308), three nitrogen levels (0, 120, 240 kg . ha(-1) N), and with a plant density of 45 plants . m(-2). The first experiment was seeded on 30 May 2004 and the second experiment on 15 June 2004. Both sets of seedlings were transplanted to the field one month later. Hyperspectral reflectance was ground-based and measured using Analytical Spectral Devices (TM) and 1 meter above the rice canopy. The solar angle compared to nadir was for all measurements less than 45 and no disturbing clouds were observed. Hyperspectral reflectance was transformed to ten different vegetation indices including RVI, NDVI, NDVIgreen, SAVI, OSAVI, MSAVI, MCACI, TCARI OSAVI, RDVI and RVI2, according to the width of TM bands of Ladsat-5. Different statistical models including linearity model, exponent model, power model and logarithm model, were analyzed using all samples' LAI and vegetatign indices. Three good relationships including exponent relationship of NDVIgreen, power relationship of TCARI/OSAVI and power relationship of RV12 were selected based on the R-2 of models. These three relationships were used to predict the LAI of rice through SVM models with different kernel functions including an analysis of variance kernel (ANOVA), a polynomial kernel (POLY) and a radial basic function kernel (RBF), and corresponding statistical models. The results show that all SVM models have lower RMSE values and higher estimation precision than corresponding statistical models; SVM with POLY kernel function using TCARI/OSAVI has the highest estimation precision for rice LAI compared to other models, and it's RMSE value is lower than corresponding statistical model by 11 percent points. Therefore, SVM has a high accuracy for learning and a good robustness for estimation of LAI of rice using hyperspectral data. Consequently, SVM provides a useful explorative tool for improvement of the relationships between VIs and rice LAI.



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