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Estimating forest soil organic carbon content using vis-NIR spectroscopy: Implications for large-scale soil carbon spectroscopic assessment

文献类型：期刊论文

作者Liu, Shangshi2; Shen, Haihua2; Chen, Songchao3,7; Zhao, Xia; Biswas, Asim6; Jia, Xiaolin5; Shi, Zhou5; Fang, Jingyun2,4
刊名GEODERMA
出版日期2019
卷号348页码:37-44
关键词China's forest Cubist model Soil organic carbon Visible-near-infrared spectroscopy
ISSN号0016-7061
DOI10.1016/j.geoderma.2019.04.003
文献子类Article
英文摘要Large-scale soil organic carbon (SOC) stock assessment is expensive as a large number of samples must be collected and then their time-consuming measurements must be made in the laboratory. Previous studies have shown that visible-near-infrared reflectance (vis-NIR) spectroscopy can quickly predict SOC content at a low cost. However, the application of this method at the large scale remains challenging due to the high spatial heterogeneity of SOC and the spatially dependent relationships of soil spectra and SOC content. Here, we conducted large-scale soil sampling across China's forests and established the Chinese forest soil spectral library (CFSSL) by measuring SOC content and scanning the vis-NIR reflectance of 11, 213 soil samples. Compared with the traditional global partial least squares regression (PLSR) modeling method (R-2 = 0.75, RPIQ = 1.95), the clustering by fast research and find of density peak in combination with the Cubist model significantly improved the prediction ability of SOC content (R-2 = 0.96, RPIQ = 5.83). This study provided a cost-efficient spectroscopic methodology, including measurement and prediction modeling, for large-scale SOC estimation.
学科主题Soil Science
出版地AMSTERDAM
电子版国际标准刊号1872-6259
WOS关键词LEAST-SQUARE REGRESSION ; LOCAL SCALE ; PREDICTION ; MATTER ; CHEMISTRY ; DYNAMICS
WOS研究方向Agriculture
语种英语
WOS记录号WOS:000470042500004
出版者ELSEVIER
资助机构Key Research Program of Frontier Sciences, CAS [QYZDY-SSW-SMC011] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [31330012] ; National Key Research and Development Program [2017YFC0503901]
源URL[http://ir.ibcas.ac.cn/handle/2S10CLM1/19554]  
专题植被与环境变化国家重点实验室
作者单位1.Peking Univ, Minist Educ, Inst Ecol, Key Lab Earth Surface Proc, Beijing 100871, Peoples R China
2.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Zhejiang Univ, Coll Environm & Resource Sci, Inst Appl Remote Sensing & Informat Technol, Hangzhou 310058, Zhejiang, Peoples R China
5.Univ Guelph, Sch Environm Sci, 50 Stone Rd East, Guelph, ON N1G 2W1, Canada
6.Agrocampus Ouest, INRA, SAS, F-35042 Rennes, France
7.INRA, Unite InfoSol, F-45075 Orleans, France
推荐引用方式
GB/T 7714		 Liu, Shangshi,Shen, Haihua,Chen, Songchao,et al. Estimating forest soil organic carbon content using vis-NIR spectroscopy: Implications for large-scale soil carbon spectroscopic assessment[J]. GEODERMA,2019,348:37-44.
APA Liu, Shangshi.,Shen, Haihua.,Chen, Songchao.,Zhao, Xia.,Biswas, Asim.,...&Fang, Jingyun.(2019).Estimating forest soil organic carbon content using vis-NIR spectroscopy: Implications for large-scale soil carbon spectroscopic assessment.GEODERMA,348,37-44.
MLA Liu, Shangshi,et al."Estimating forest soil organic carbon content using vis-NIR spectroscopy: Implications for large-scale soil carbon spectroscopic assessment".GEODERMA 348(2019):37-44.

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来源：植物研究所

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