文献类型： 外文期刊

作者： Guo, Peng-Tao ¹ ; Zhu, A-Xing ⁵ ; Cha, Zheng-Zao ¹ ; Li, Mao-Fen ⁹ ; Luo, Wei ¹ ;

作者机构： 1.Chinese Acad Trop Agr Sci, Rubber Res Inst, Haikou 571101, Hainan, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Biol & Genet Resources Rubber Tree, Haikou 571101, Hainan, Peoples R China

3.State Key Lab Incubat Base Cultivat & Physiol Trop, Haikou 571101, Hainan, Peoples R China

4.Chinese Acad Trop Agr Sci, Soil & Fertilizer Res Ctr, Haikou 571101, Hainan, Peoples R China

5.Nanjing Normal Univ, Sch Geog Sci, 1 Wenyuan Rd, Nanjing 210023, Peoples R China

6.Univ Wisconsin Madison, Dept Geog, 550 North Pk St, Madison, WI 53706 USA

7.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, 11A Datun Rd, Beijing 100101, Peoples R China

8.Southern Univ Sci & Technol, Ctr Social Sci, Shenzhen, Peoples R China

9.Chinese Acad Trop Agr Sci, Inst Sci & Tech Informat, Haikou 571101, Hainan, Peoples R China

10.Hainan Prov Key Lab Pract Res Trop Crops Informat, Haikou, Peoples R China

关键词： K -means clustering; Partial least squares regression; Hyperspectral reflectance; Regional scale; Environmental factors

期刊名称：HELIYON （ 影响因子：3.776； 五年影响因子：3.752 ）

ISSN：

年卷期： 2022 年 8 卷 6 期

页码：

收录情况： SCI

摘要： Existing local models based on multiple environmental variables clustering (LM-MEVC) treat the influences of environmental factors on leaf phosphorus concentration (LPC) of rubber trees (Hevea brasiliensis) equally when grouping samples. In fact, the effects that environmental factors assert on LPC are different. So, environmental factors need to be treated differently so that the different effects can be taken into consideration when dividing samples into clusters or groups. According to this basic idea, a local model based on weighted environmental variables clustering (LM-WEVC) was developed. This approach consists of four steps. Firstly, the most important environmental variables that influence LPC were selected. Then, the weights of the selected environmental variables were determined. In the following, the selected environmental variables were weighted and used as clustering variables to group samples. Finally, within each cluster or group of samples, an estimation model was established. In order to verify its effectiveness in predicting LPC of rubber trees, the proposed method was applied to a case study in Hainan Island, China. Rubber tree (cultivar CATAS-7-33-97) leaf samples were collected from three different sampling periods. Spectral reflectance of the collected leaf samples was measured using an ASD spectroradiometer, FieldSpec 3. Leaf samples collected from the three different sampling periods were used separately to test LM-WEVC. Coefficient of determination (R-2), root mean squared error (RMSE), and ratio of prediction deviation (RPD) were employed as evaluation criterion. Performance of LM-WEVC was compared with that of the existing LM-MEVC. Results indicated that for the three sampling periods, the prediction accuracies of LM-WEVC were always higher than those of LM-MEVC. The values of R-2 and RPD for LM-WEVC were increased by 8.15%-36.68%, and by 11.33%-59.40% respectively, while values of RMSE were reduced by 9.09%-37.5%, compared with those for LM-MEVC. These results demonstrate that LM-WEVC was effective in estimating LPC of rubber trees, and also confirmed our hypothesis that environmental factors unequally influenced LPC of rubber trees.