• 方法与技术 •

### 基于优化SVR高光谱指数的独尾草叶绿素含量估算

1. (1新疆大学资源与环境科学学院, 乌鲁木齐 830046; 2绿洲生态重点实验室, 乌鲁木齐 830046; 3智慧城市与环境建模自治区高校重点实验室， 乌鲁木齐 830046)
• 出版日期:2017-02-10 发布日期:2017-02-10

### Estimation of chlorophyll content of Eremurus chinensis based on optimization support vector regression machine.

TAN lin1,2,3, HE Bing-yu1,2,3, LIU Wei-guo1,2,3*, PANG dong1,2,3#br#

1. (1College of Resources and Environment Science, Xinjiang University, Urumqi 830046, China; 2Key Laboratory of Oasis Ecology, Urumqi 830046, China; 3Key Laboratory of Intelligent City and Environmental Modeling of Autonomous Region, Urumqi 830046, China).

• Online:2017-02-10 Published:2017-02-10

Abstract: Spectral reflectance and chlorophyll content of the leaves were determined in the seedling stage and flowering stage of Eremurus chinensis in Gurbantunggut desert located in the center of the Junggar basin. Relationship was developed between 24 kinds of spectral index and chlorophyll, and estimation model was established by support vector regression (SVR) using the optimal spectral index which had high interrelationship. The results showed that (1) the chlorophyll content in flowering stage was higher than that in seedling stage, which was mainly related to the photosynthesis of vegetation, and the spectral reflectance of the flowering stage was lower than that of seedling stage. The spectral reflectance of two phases was consistent with the spectral reflectance of common plants. (2) The GNDVI and chlorophyll content showed the highest correlation with coefficient of determination (R2) values of 0.664, among all the spectral indices. In the flowering stage, the correlation coefficient between GM-2 and chlorophyll content showed the highest correlation with R2 values of 0.711. According to the rankings of correlation coefficients (R2), the first 7 of the spectral index were the same in the two periods. (3) Seven sensitive spectral indices were used as input factors, and the optimal SVR estimation model was established by selecting the optimal parameters (c,g) from 3 kinds of optimization algorithms. The model accuracy was high during both the seedling stage and flowering stage. The model accuracy was ranked as, PSO-SVR>GA-SVR>GS-SVR, with the PSO-SVR coefficient being the highest and the root mean square error (RMSE) the least. In the seedling stage, the PSOSVR and coefficient was 0.812 with the RMSE being 0.728. In the flowering stage, the PSOSVR coefficient was 0.841 with the RMSE being 0.247. The results showed that the SVR model based on PSO-SVR algorithm had high precision with low error, which can estimate the chlorophyll content accurately. Moreover, the estimation of chlorophyll content in the flowering period was better than that in the seedling stage. The result would provide scientific basis and technical support for monitoring and estimating ecological characteristics of desert vegetation, spatial and temporal distribution of desert vegetation, and biochemical parameter inversion.