文献类型： 外文期刊

作者： Guo, Peng-Tao ¹ ; Shi, Zhou ⁵ ; Li, Mao-Fen ⁶ ; Luo, Wei ¹ ; Cha, Zheng-Zao 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.Zhejiang Univ, Coll Environm & Resource Sci, Hangzhou 310058, Peoples R China

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

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

关键词： Global model; Parent material; Artificial neural networks; Partial least squares regression

期刊名称：INFRARED PHYSICS & TECHNOLOGY （ 影响因子：3.3； 五年影响因子：3.2 ）

ISSN： 1350-4495

年卷期： 2023 年 131 卷

页码：

收录情况： SCI

摘要： The globally modelling method was commonly used to build spectral models for estimating leaf nutrients of plants. However, predictive ability of such models was rather limited at regional scale because of the unrea-sonable assumption inherent in the globally modelling method which assumed that relationships between leaf nutrients and leaf spectra were stable over space. In fact, the leaf nutrients-spectra relationships generally varied across extensive area due to influences of various environmental factors. In order to accommodate the varied leaf nutrients-spectra relationships at regional scale, the locally modelling approach was proposed to develop spectral models for predicting leaf nutrients. The locally modelling approach assumed that the leaf nutrients-spectra relationships were stable at local spaces within which environmental conditions were relatively uniform. Ac-cording to this basic idea, the proposed approach used environmental factor as grouping variable to classify leaf samples into several homogenous sub-datasets. Within these sub-datasets, the leaf nutrients-spectra relationships would be more stable. Then, linear or non-linear method were employed to fit model for each sub-dataset. In order to verify the effectiveness of this method, the locally modelling approach was applied to a case study to estimate leaf phosphorus concentration (LPC) of rubber trees (Hevea brasiliensis) in Hainan Island, China. In this case study, three scenarios were set up. In the first scenario, the local model was built using the partial least squares regression (PLSR) with full wavelengths as input variables. In the second scenario, the local model was also developed using the PLSR but with manually selected wavelengths as input variables. In the third scenario, the local model was constructed using the artificial neural networks with feature bands as input variables. Performances of these local models were compared to that of the corresponding global models. Results showed that for the three scenarios local models always yielded higher prediction accuracies than that of the global models in the test dataset. For the first scenario, values of coefficient of determination (r2) and root mean square error (RMSE) ranged from 0.652 to 0.735 and from 0.030% to 0.034%, respectively, for the local model, whereas those varied from 0.458 to 0.637 and from 0.037% to 0.041%, respectively, for the global model. For the second scenario, values of r2 and RMSE ranging from 0.565 to 0.734 and from 0.031% to 0.046%, respectively, for the local model, while those varied from 0.275 to 0.559 and from 0.038% to 0.053%, respectively, for the global model. For the third scenario, values of r2 and RMSE ranging from 0.678 to 0.863 and from 0.021% to 0.033%, respectively, for the local model, while those varied from 0.441 to 0.726 and from 0.030% to 0.043%, respec-tively, for the global model. This result demonstrates that the locally modelling approach was more effective in estimating LPC of rubber trees than the globally modelling method at the regional scale. In the future, more environmental factors should be considered and their combined effects on leaf sample classification need to be investigated.