| 光谱学与光谱分析 |
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| Quantitative Models between Canopy Hyperspectrum and Its Component Features at Apple Tree Prosperous Fruit Stage |
| WANG Ling1, ZHAO Geng-xing1*, ZHU Xi-cun1, LEI Tong1, DONG Fang2 |
1. College of Resource and Environment, Shandong Agricultural University, Taian 271018, China
2. College of City Development, University of Jinan, Ji’nan 250022, China |
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Abstract Hyperspectral technique has become the basis of quantitative remote sensing. Hyperspectrum of apple tree canopy at prosperous fruit stage consists of the complex information of fruits, leaves, stocks, soil and reflecting films, which was mostly affected by component features of canopy at this stage. First, the hyperspectrum of 18 sample apple trees with reflecting films was compared with that of 44 trees without reflecting films. It could be seen that the impact of reflecting films on reflectance was obvious, so the sample trees with ground reflecting films should be separated to analyze from those without ground films. Secondly, nine indexes of canopy components were built based on classified digital photos of 44 apple trees without ground films. Thirdly, the correlation between the nine indexes and canopy reflectance including some kinds of conversion data was analyzed. The results showed that the correlation between reflectance and the ratio of fruit to leaf was the best, among which the max coefficient reached 0.815, and the correlation between reflectance and the ratio of leaf was a little better than that between reflectance and the density of fruit. Then models of correlation analysis, linear regression, BP neural network and support vector regression were taken to explain the quantitative relationship between the hyperspectral reflectance and the ratio of fruit to leaf with the softwares of DPS and LIBSVM. It was feasible that all of the four models in 611-680 nm characteristic band are feasible to be used to predict, while the model accuracy of BP neural network and support vector regression was better than one-variable linear regression and multi-variable regression, and the accuracy of support vector regression model was the best. This study will be served as a reliable theoretical reference for the yield estimation of apples based on remote sensing data.
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Received: 2009-12-10
Accepted: 2010-03-20
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Corresponding Authors:
ZHAO Geng-xing
E-mail: zhaogx@sdau.edu.cn
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