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| Hyperspectral Estimation Model of Leaf Water Content in Spring Wheat Based on Grey Relational Analysis |
| Umut Hasan1, 2, Mamat Sawut1, 2, 3*, Nijat Kasim1, 2, Nigela Taxipulati1, 3, WANG Jing-zhe1, 2, Irxat Ablat1, 2 |
1. College of Resources and Environmental Science, Xinjiang University, Urumqi 830046, China
2. Key Laboratory of Oasis Ecology of Ministry of Education, Urumqi 830046, China
3. Key Laboratory for Wisdom City and Environmental Modeling, Xinjiang University, Urumqi 830046, China |
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Abstract When vegetation index is used to retrieve water content, it is important to find the vegetation index which has the highest correlation between measured spectral data and vegetation water content. In this paper, Fukang science experimental base of Xinjiang University was selected as the study area. Based on spring wheat field spectral data and leaf water content data, this paper selected 5 typical water vegetation indices that have higher grey correlation degree with leaf water content through grey correlation analysis. And used 2 kinds of methods including the partial least squares regression (PLSR) and back propagation artificial neural network (BP ANN) to establish the quantitative inversion models of soil water content. The results showed that: (1) The first derivative of spectrum can effectively remove the noise influence and highlight the spectral characteristic information, especially in the range of 750～830，1 000～1 060，2 056～2 155 nm, which significantly improves the correlation with LWC. (2) The grey correlation method can better characterize the relationship between water vegetation indices and leaf water content, and the first 5 water vegetation indices based on original spectrum were two band ratio vegetation index, and the water vegetation indices based on the first derivative spectra were basically two band normalized difference vegetation index. (3) Among the two established models, R2 of PLSR and BP neural network model established by the first derivative reflectance were 0.80 and 0.81 respectively, which showed that the two models have good stability in inversion of leaf water content; the RMSE of the two models were 0.55 and RPD were 2.01 and 1.41 respectively, which indicated that the prediction accuracy of PLSR model was higher than that of BP neural network model. From the validation of the model, the PLSR model has some advantages in estimating leaf water content of spring wheat, which provides a reference for hyperspectral quantitative inversion of it.
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Received: 2017-11-26
Accepted: 2018-04-13
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Corresponding Authors:
Mamat Sawut
E-mail: korxat@xju.edu.cn
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