1. 新疆大学资源与环境科学学院，新疆 乌鲁木齐 830046
2. 新疆大学数学与系统科学学院，新疆 乌鲁木齐 830046
3. 新疆林业科学院现代林业研究所，新疆 乌鲁木齐 830063
4. Department of Earth and Environmental Studies, Montclair State University, New Jersey 07043，USA
Chlorophyll Content Estimation of Jujube Leaves Based on GWLS-SVR Model
1. College of Resources and Environmental Science, Xinjiang University, Urumqi 830046, China
2. College of Mathematics and System Science, Xinjiang University，Urumqi 830046, China
3. Institute of Modern Forestry, Xinjiang Academy of Forestry Sciences, Urumqi 830063, China
4. Department of Earth and Environmental Studies, Montclair State University, New Jersey 07043，USA
Abstract：Chlorophyll Contentis an indicator of the photosynthetic capacity, growth and nutritional status of jujube trees. The distribution of chlorophyll content is different in jujube trees planted in different geographical locations under the influence of natural and human-made factors. The Hyperspectral reflectance of jujube leaves and the SPAD value of jujube leaves that representing chlorophyll content in Ruoqiang county were measured on the spots. To estimate the SPAD value of jujube leaves efficiently and losslessly, the global Moran’s I of jujube SPAD value was calculated, The statistics was calculated based on the correlation between SPAD value and Hyperspectral bands to choose the most important characteristic bands. The GWLS-SVR（Geographically Weighted Least Squares-Support Vector Regression）model was used to predict the SPAD value and compared with multiple linear regression (MLR) and support vector regression (SVR) models, and explored the ability of the model to estimate the SPAD value of the jujube leaves. The results show that: (1) the First derivative of the spectrum can effectively remove the noise and highlight the spectral information, especially in the range of 492～510, 542～543, 642～652, 657～670 and 682～692 nm, and significantly improve the correlation of the spectrum with SPAD value. (2) statistics method can effectively select the feature bands of the sensitive range, thus improves the model estimation accuracy. The two variables with the highest importance of the original spectrum were 595 and 696 nm, and the feature band of the first derivative of the spectrum was 688 nm. Among them, the statistics of a single band were always lower than those of multiple band combinations of the same sensitive band interval, which may be caused by the strong collinearity between the adjacent bands. (3) There was significant spatial aggregation on the SPAD value of jujube leaves in Ruoqiang county, the global Moran’s I was 0.125 8 (p<0.1), which is suitable for the establishment of GWLS-SVR model that considers the spatial location. (4) By combining Bootstrap resampling and t-test, the GWLS-SVR model that combined with geographic location information was generally better than the support vector regression and multiple linear regression model, and the results were highly significant (p<0.001). Among the models, the GWLS-SVR model based on the First derivative of the spectrum was the optimal estimation of SPAD value for jujube leaves (R2=0.975, MSE=1.082), which can provide a certain reference for the Hyperspectral quantitative inversion of the SPAD value of jujube and the rapid and non-destructive monitoring of jujube growth.
Key words：Chlorophyll content; GWLS-SVR model; Hyperspectral; Red jujube tree; Bootstrap resampling; t test; SPAD value
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