干旱区生态环境知识资源中心(Arid): Combination of Fractional Order Differential and Machine Learning Algorithm for Spectral Estimation of Soil Organic Carbon Content

Arid

DOI	10.3788/LOP57.153001
	Combination of Fractional Order Differential and Machine Learning Algorithm for Spectral Estimation of Soil Organic Carbon Content
	Zhao Qidong; Ge Xiangyu; Ding Jianli; Wang Jingzhe; Zhang Zhenhua; Tian Meiling
通讯作者	Ding, JL
来源期刊	LASER & OPTOELECTRONICS PROGRESS
ISSN	1006-4125
出版年	2020
卷号	57 期号:15
英文摘要	In this study, 96 surface soil samples arc obtained from the typical oasis of the Ugan-Kuqa River in the Xinjiang Uyghur Autonomous Region and their spectral reflectance and soil organic carbon (SOC) content arc evaluated. Using fractional order differential technique (with an order value range of 0-2 and a step size of 0. 2) is combined with five machine learning algorithms, including the extreme learning machine, random forest, multiple adaptive regression spline function, clastic network regression, and gradient lifting regression tree (GBRT) algorithms, and high-precision estimation of SOC content. The experimental results show that the pretreatment effect obtained using a fractional order differential is better than that obtained using an integer order differential. The correlation at a specific band is significantly improved, and the maximum correlation is enhanced by approximately 0.220. In case of the GBRT, the verification concentration determination coefficient is 0.878 and the relative analysis error is 3.142, indicating that this type of integrated learning is superior to other models of different orders. GBRT based on a 1.6-ordcr spectral reflectance should be used to estimate the SOC content of the oasis in arid areas. Thus, a new scheme based on the combination of visible light-near infrared (VIS-NIR) with the fractional order differential technology and machine learning algorithms is proposed in this study to improve the accuracy of the model used for estimating the SOC content of the oasis in arid areas.
英文关键词	spectroscopy soil organic carbon visible-near infrared spectroscopy machine learning fractional order differential
类型	Article
语种	中文
收录类别	ESCI
WOS记录号	WOS:000557863300041
WOS关键词	PREDICTION ; MATTER ; REGRESSION ; IMPROVE ; MODEL
WOS类目	Engineering, Electrical & Electronic ; Optics
WOS研究方向	Engineering ; Optics
Scopus学科分类	Xinjiang Univ, Coll Resource & Environm Sci, Key Lab Smart City & Environm Modelling, Higher Educ Inst, Urumqi 83004, Xinjiang, Peoples R China.
来源机构	新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/334530
作者单位	[Zhao Qidong; Ge Xiangyu; Ding Jianli; Wang Jingzhe; Zhang Zhenhua; Tian Meiling] Xinjiang Univ, Minist Educ, Key Lab Oasis Ecol, Urumqi 830046, Xinjiang, Peoples R China; [Zhao Qidong; Ge Xiangyu; Ding Jianli; Wang Jingzhe; Zhang Zhenhua; Tian Meiling] Xinjiang Univ, Coll Resource & Environm Sci, Key Lab Smart City & Environm Modelling, Higher Educ Inst, Urumqi 83004, Xinjiang, Peoples R China; [Wang Jingzhe] Guangdong Inst Ecoenvironm Sci & Technol, Guangzhou 510650, Guangdong, Peoples R China
推荐引用方式 GB/T 7714	Zhao Qidong,Ge Xiangyu,Ding Jianli,et al. Combination of Fractional Order Differential and Machine Learning Algorithm for Spectral Estimation of Soil Organic Carbon Content[J]. 新疆大学,2020,57(15).
APA	Zhao Qidong,Ge Xiangyu,Ding Jianli,Wang Jingzhe,Zhang Zhenhua,&Tian Meiling.(2020).Combination of Fractional Order Differential and Machine Learning Algorithm for Spectral Estimation of Soil Organic Carbon Content.LASER & OPTOELECTRONICS PROGRESS,57(15).
MLA	Zhao Qidong,et al."Combination of Fractional Order Differential and Machine Learning Algorithm for Spectral Estimation of Soil Organic Carbon Content".LASER & OPTOELECTRONICS PROGRESS 57.15(2020).