中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 地理科学与资源研究所
  3. 中国科学院地理科学与资源研究所
  4. 资源与环境信息系统国家重点实验室
  5. 外文论文
Soil organic matter content prediction using Vis-NIRS based on different wavelength optimization algorithms and inversion models

文献类型：期刊论文

作者Zhou, Wei2; Xiao, Jieyun; Li, Haoran; Chen, Qi3; Wang, Ting; Wang, Qian1; Yue, Tianxiang2
刊名JOURNAL OF SOILS AND SEDIMENTS
出版日期2023-04-01
卷号N/A
关键词Soil organic matter Visible near-infrared spectroscopy Competitive adaptive reweighted sampling Random forest Three-rivers source region
ISSN号1439-0108
DOI10.1007/s11368-023-03480-4
文献子类Article; Early Access
英文摘要Purpose Visible and near-infrared reflectance spectroscopy has been proven to be an efficient method for predicting soil properties, and the wavelength optimization can improve the simulation accuracy of SOM (soil organic matter), but the best combination of wavelength optimization algorithms and inversion model is unknown for alpine ecosystem soil. Methods In this study, 269 topsoil samples were collected in the Three-Rivers Source Region of China and were used to build the inversion model of SOM content. Four kinds of wavelength optimization algorithms were conducted, i.e., correlation analysis, uninformative variable elimination (UVE), successive projection algorithm, and competitive adaptive reweighted sampling (CARS) after spectral pre-treatments. Then, partial least squares regression, support vector machine, and random forest (RF) were used to develop the inversion model of SOM content. Various combinations of wavelength optimization algorithms and inversion models were constructed, and the accuracies were compared. Results The combination of the UVE-CARS-RF achieved the highest simulation accuracy (R-2 = 0.902, RPD = 3.218). For the single band selection method, the CARS algorithm has the highest simulation accuracy, especially the combination of CARS-RF (R-2 = 0.899, RPD = 3.133). Conclusion Appropriate combination of the wavelength optimization algorithm and inversion model not only can significantly reduce computational load but improve the prediction precision. In total, RF obtained the best predication effect.
WOS关键词INFRARED REFLECTANCE SPECTROSCOPY ; VARIABLES SELECTION METHODS ; CARBON ; NITROGEN ; REGRESSION ; MOUNTAIN ; TEXTURE ; FORESTS ; STOCKS
WOS研究方向Environmental Sciences & Ecology ; Agriculture
WOS记录号WOS:000950086100002
出版者SPRINGER HEIDELBERG
源URL[http://ir.igsnrr.ac.cn/handle/311030/190486]  
专题资源与环境信息系统国家重点实验室_外文论文
作者单位1.Shandong Agr Univ, Coll Resources & Environm, Tai An 271018, Peoples R China
2.Southwest Univ, Sch Geog Sci, Chongqing Jinfo Mt Karst Ecosyst Natl Observat & R, Chongqing 400715, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
4.Liaocheng Univ, Sch Geog & Environm, Liaocheng 252059, Shandong, Peoples R China
推荐引用方式
GB/T 7714		 Zhou, Wei,Xiao, Jieyun,Li, Haoran,et al. Soil organic matter content prediction using Vis-NIRS based on different wavelength optimization algorithms and inversion models[J]. JOURNAL OF SOILS AND SEDIMENTS,2023,N/A.
APA Zhou, Wei.,Xiao, Jieyun.,Li, Haoran.,Chen, Qi.,Wang, Ting.,...&Yue, Tianxiang.(2023).Soil organic matter content prediction using Vis-NIRS based on different wavelength optimization algorithms and inversion models.JOURNAL OF SOILS AND SEDIMENTS,N/A.
MLA Zhou, Wei,et al."Soil organic matter content prediction using Vis-NIRS based on different wavelength optimization algorithms and inversion models".JOURNAL OF SOILS AND SEDIMENTS N/A(2023).

入库方式： OAI收割

来源：地理科学与资源研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。