干旱区生态环境知识资源中心(Arid): Reducing moisture effects on soil organic carbon content prediction in visible and near-infrared spectra with an external parameter othogonalization algorithm

Arid

DOI	10.1139/cjss-2020-0009
	Reducing moisture effects on soil organic carbon content prediction in visible and near-infrared spectra with an external parameter othogonalization algorithm
	Cao, Yue; Bao, Nisha; Liu, Shanjun; Zhao, Wei; Li, Shimeng
通讯作者	Bao, NS
来源期刊	CANADIAN JOURNAL OF SOIL SCIENCE
ISSN	0008-4271
EISSN	1918-1841
出版年	2020
卷号	100 期号:3 页码:253-262
英文摘要	Field spectroscopy and other efficient hyperspectral techniques have been widely used to measure soil properties, including soil organic carbon (SOC) content. However, reflectance measurements based on field spectroscopy are quite sensitive to uncontrolled variations in surface soil conditions, such as moisture content; hence, such variations lead to drastically reduced prediction accuracy. The goals of this work are to (i) explore the moisture effect on soil spectra with different SOC levels, (ii) evaluate the selection of optimal parameter for external parameter othogonalization (EPO) in reducing moisture effect, and (iii) improve SOC prediction accuracy for semi-arid soils with various moisture levels by combing the EPO with machine learning method. Soil samples were collected from grassland regions of Inner Mongolia in North China. Rewetting laboratory experiments were conducted to make samples moisturized at five levels. Visible and near-infrared spectra (350-2500 nm) of soil samples rewetted were observed using a hand-held SVC HR-1024 spectroradiometer. Our results show that moisture influences the correlation between SOC content and soil reflectance spectra and that moisture has a greater impact on the spectra of samples with low SOC. An EPO algorithm can quantitatively extract information of the affected spectra from the spectra of moist soil samples by an optimal singular value. A SOC model that effectively couples EPO with random forest (RF) outperforms partial least-square regression (PLSR)-based models. The EPO-RF model generates better results with R-2 of 0.86 and root-mean squared error (RMSE) of 3.82 g kg(-1), whereas a PLSR model gives R-2 of 0.79 and RMSE of 4.68 g kg(-1).
英文关键词	moisture effect external parameter orthogonalization soil organic carbon random forest model VIS-NIR spectroscopy
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:000566331500008
WOS关键词	DIFFUSE-REFLECTANCE SPECTROSCOPY ; WATER ; REGRESSION ; MATTER ; VNIR
WOS类目	Soil Science
WOS研究方向	Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/326052
作者单位	[Cao, Yue; Bao, Nisha; Liu, Shanjun; Zhao, Wei; Li, Shimeng] Northeastern Univ, Sch Resources & Civil Engn, Shenyang 110819, Peoples R China
推荐引用方式 GB/T 7714	Cao, Yue,Bao, Nisha,Liu, Shanjun,et al. Reducing moisture effects on soil organic carbon content prediction in visible and near-infrared spectra with an external parameter othogonalization algorithm[J],2020,100(3):253-262.
APA	Cao, Yue,Bao, Nisha,Liu, Shanjun,Zhao, Wei,&Li, Shimeng.(2020).Reducing moisture effects on soil organic carbon content prediction in visible and near-infrared spectra with an external parameter othogonalization algorithm.CANADIAN JOURNAL OF SOIL SCIENCE,100(3),253-262.
MLA	Cao, Yue,et al."Reducing moisture effects on soil organic carbon content prediction in visible and near-infrared spectra with an external parameter othogonalization algorithm".CANADIAN JOURNAL OF SOIL SCIENCE 100.3(2020):253-262.