| 光谱学与光谱分析 |
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| Hyperspectral Estimation of Leaf Water Content for Winter Wheat Based on Grey Relational Analysis(GRA) |
| JIN Xiu-liang1, 2, XU Xin-gang2, WANG Ji-hua2, LI Xin-chuan2, WANG Yan1, TAN Chang-wei1, ZHU Xin-kai1, GUO Wen-shan1* |
1. Key Laboratory of Crop Genetics and Physiology of Jiangsu Province/Key Laboratory of Crop Physiology, Ecology and Cultivation in Middle and Lower Reaches of Yangtse River of Ministry of Agriculture, Yangzhou University, Yangzhou 225009, China
2. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China |
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Abstract The objective of the present study was to compare two methods for the precision of estimating leaf water content (LWC) in winter wheat by combining stepwise regression method and partial least squares (SRM-PLS) or PLS based on the relational degree of grey relational analysis (GRA) between water vegetation indexes (WVIs) and LWC. Firstly, data utilized to analyze the grey relationships between LWC and the selected typical WVIs were used to determine the sensitivity of different WVIs to LWC. Secondly, the two methods of estimating LWC in winter wheat were compared, one was to directly use PLS and the other was to combine SRM and PLS, and then the method with the highest determination coefficient (R2) and lowest root mean square error (RMSE) was selected to estimate LWC in winter wheat. The results showed that the relationships between the first five WVI and LWC were stable by using GRA, and then LWC was estimated by using PLS and SRM-PLS at the whole stages with the R2 and RMSEs being 0.605 and 0.575, 4.75% and 7.35%, respectively. The results indicated that the estimation accuracy of LWC could be improved by using GRA firstly and then by using PLS and SRM-PLS.
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Received: 2012-05-04
Accepted: 2012-08-20
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Corresponding Authors:
GUO Wen-shan
E-mail: guows@yzu.edu.cn
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