| 光谱学与光谱分析 |
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| Research on Universality of Least Squares Support Vector Machine Method for Estimating Leaf Area Index of Winter Wheat |
| XIE Qiao-yun1, 2, 3, HUANG Wen-jiang1*, LIANG Dong2, 3, PENG Dai-liang1, HUANG Lin-sheng2, 3, SONG Xiao-yu4, ZHANG Dong-yan2, 3, YANG Gui-jun4 |
1. Key Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2. Key Laboratory of Intelligent Computer & Signal Processing, Ministry of Education, Anhui University, Hefei 230039, China
3. School of Electronic and Information Engineering, Anhui University, Hefei 230039, China
4. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China |
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Abstract Leaf area index (LAI) is one of the most important parameters for evaluating winter wheat growth status and forecasting its yield. Hyperspectral remote sensing is a new technical approach that can be used to acquire the instant information of vegetation LAI at large scale. This study aims to explore the capability of least squares support vector machines (LS-SVM) method to winter wheat LAI estimation with hyperspectral data. After the compression of PHI airborne data with principal component analysis (PCA), the sample set based on the measured LAI data and hyperspectral reflectance data was established. Then the method of LS-SVM was developed respectively to estimate winter wheat LAI under four different conditions, to be specific, different plant type cultivars，different periods，different nitrogenous fertilizer and water conditions. Compared with traditional NDVI model estimation results, each experiment of LS-SVM model yielded higher determination coefficient as well as lower RMSE value, which meant that the LS-SVM method performed better than the NDVI method. In addition, NDVI model was unstable for winter wheat under the condition of different plant type cultivars，different nitrogenous fertilizer and different water, while the LS-SVM model showed good stability. Therefore, LS-SVM has high accuracy for learning and considerable universality for estimation of LAI of winter wheat under different conditions using hyperspectral data.
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Received: 2013-05-09
Accepted: 2013-07-16
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Corresponding Authors:
HUANG Wen-jiang
E-mail: huangwenjiang@gmail.com
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