Estimating biophysical parameters of rice with remote sensing data using support vector machines

Publisher:
SpringerOpen
Publication Type:
Journal Article
Citation:
Science China Life Sciences, 2012, 54 (3), pp. 272 - 281
Issue Date:
2012-01
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Hyperspectral reflectance (350â2500 nm) measurements were made over two experimental rice fields containing two cultivars treated with three levels of nitrogen application. Four different transformations of the reflectance data were analyzed for their capability to predict rice biophysical parameters, comprising leaf area index (LAI; m2 green leaf area mâ2 soil) and green leaf chlorophyll density (GLCD; mg chlorophyll mâ2 soil), using stepwise multiple regression (SMR) models and support vector machines (SVMs). Four transformations of the rice canopy data were made, comprising reflectances (R), first-order derivative reflectances (D1), second-order derivative reflectances (D2), and logarithm transformation of reflectances (LOG). The polynomial kernel (POLY) of the SVM using R was the best model to predict rice LAI, with a root mean square error (RMSE) of 1.0496 LAI units. The analysis of variance kernel of SVM using LOG was the best model to predict rice GLCD, with an RMSE of 523.0741 mg mâ2. The SVM approach was not only superior to SMR models for predicting the rice biophysical parameters, but also provided a useful exploratory and predictive tool for analyzing different transformations of reflectance data.
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