A comparison of three methods for estimating leaf area index of paddy rice from optimal hyperspectral bands |
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| Authors: | Fu-min Wang Jing-feng Huang Zhang-hua Lou |
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| Institution: | (1) Institute of Hydrology and Water Resources, Zhejiang University, Zijingang Campus, Hangzhou, 310058, China;(2) Research Center of Agricultural Information Science & Technology, Zhejiang University, Huajiachi Campus, 310029 Hangzhou, China |
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| Abstract: | This paper follows previous research that identified 15 hyperspectral wavebands that were suitable to estimate paddy rice
leaf area index (LAI). The objectives of the study were to: (1) test the efficiency of the wavebands selected in the previous
study, (2) to evaluate the potential of least squares support vector machines (LS-SVM) to estimate paddy rice LAI from canopy
hyperspectral reflectance and (3) to compare multiple linear regression-MLR, partial least squares-PLS regression and LS-SVM
to determine paddy rice LAI using the selected wavebands. In the study, measurements of hyperspectral reflectance (350–2500 nm)
and corresponding LAI were made for a paddy rice canopy throughout the growing seasons. On the basis of the wavebands selected
previously, models based on MLR, PLS and LS-SVM to estimate rice LAI were compared using the data from 123 observations, which
were split randomly for model calibration (2/3) and validation (1/3). Root mean square errors (RMSEs) and the correlation
coefficients (r) between measured and predicted LAI values from model calibration and validation were calculated to evaluate the quality
of the models. The results showed that the LS-SVM model using the 15 selected wavebands produced more accurate estimates of
paddy rice LAI than the PLS and MLR models. We concluded that the LS-SVM approach may provide a useful exploratory and predictive
tool for estimating paddy rice LAI when applied to reflectance data using the 15 selected wavebands. |
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