Wavelet Transform (WT) and neural network model applied to canopy hyperspectral data for corn Chl-a estimation in Songnen Plain, China
2010
In this study, we present spectral measurements of corn chlorophyll content in Changchun (eight times in 2003) and
Hailun (five time in 2004), both of which lie in the Songnen Plain, China. Corn canopy reflectance and its derivative
reflectance were subsequently used in a linear regression analysis against Chl-a concentration on one by one spectral
reflectance. It was found that determination coefficient for Chl-a concentration was high in blue, red and near infrared
spectral region, and it was low in green and red edge spectral region, however Chl-a concentration obtained its high
determination coefficient in blue, green and red edge spectral region, especially in red edge region with derivative
reflectance. Regression models were established based upon 6 spectral vegetation indices and wavelet coefficient,
reflectance principal components as well. It was found that wavelet transforms is an effective method of hyperspectral
reflectance feature extraction for corn Chl-a estimation, and the best multivariable regressions obtain determination
coefficient (R 2 ) up to 0.87 for Chl-a concentration. Finally, neural network algorithms with both specific band
reflectance and wavelet coefficient as input variables were applied to estimate corn chlorophyll concentration. The
results indicate that estimation accuracy improved with nodes number increasing in the hidden layer, and neural network
performs better with wavelet coefficient than that with specific band reflectance as input variables, determination
coefficient was up to 0.96 for Chl-a concentration. Further studies are still needed to refine the methods for determining
and estimating corn bio-physical/chemical parameters or other vegetation as well in the future.
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