干旱区生态环境知识资源中心(Arid): Ensemble machine-learning-based framework for estimating total nitrogen concentration in water using drone-borne hyperspectral imagery of emergent plants: A case study in an arid oasis, NW China

Arid

DOI	10.1016/j.envpol.2020.115412
	Ensemble machine-learning-based framework for estimating total nitrogen concentration in water using drone-borne hyperspectral imagery of emergent plants: A case study in an arid oasis, NW China
	Wang, Jingzhe; Shi, Tiezhu; Yu, Danlin; Teng, Dexiong; Ge, Xiangyu; Zhang, Zipeng; Yang, Xiaodong; Wang, Hanxi; Wu, Guofeng
通讯作者	Shi, TZ
来源期刊	ENVIRONMENTAL POLLUTION
ISSN	0269-7491
EISSN	1873-6424
出版年	2020
卷号	266
英文摘要	In arid and semi-arid regions, water-quality problems are crucial to local social demand and human well-being. However, the conventional remote sensing-based direct detection of water quality parameters, especially using spectral reflectance of water, must satisfy certain preconditions (e.g., flat water surface and ideal radiation geometry). In this study, we hypothesized that drone-borne hyperspectral imagery of emergent plants could be better applied to retrieval total nitrogen (TN) concentration in water regardless of preconditions possibly due to the spectral responses of emergent plants on nitrogen removal and water purification. To test this hypothesis, a total of 200 groups of bootstrap samples were used to examine the relationship between the extracted TN concentrations from the drone-borne hyperspectral imagery of emergent plants and the experimentally measured TN concentrations in Ebinur Lake Oasis using four machine learning (ML) models (Partial Least Squares (PLS), Random Forest (RF), Extreme Learning Machine (ELM), and Gaussian Process (GP)). Through the introduction of the fractional order derivative (FOD), we build a decision-level fusion (DLF) model to minimize the regression results' biases of individual ML models. For individual ML model, GP performed the best. Still, the amount of uncertainty in individual ML models renders their performance to be subpar. The introduction of the DLF model greatly minimizes the regression results' biases. The DLF model allows to reduce potential uncertainties without sacrificing accuracy. In conclusion, the spectral response caused by nitrogen removal and water purification on emergent plants could be used to retrieve TN concentration in water with a DLF model framework. Our study offers a new perspective and a basic scientific support for water quality monitoring in arid regions. (C) 2020 Elsevier Ltd. All rights reserved.
英文关键词	Water resources Remote sensing Total nitrogen Hyperspectral imagery Machine learning Bootstrap
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000571408700007
WOS关键词	SOIL ORGANIC-MATTER ; NATURE-RESERVE ELWNNR ; REFLECTANCE ; REGRESSION ; QUALITY ; REMOVAL ; CLASSIFICATION ; MULTISOURCE ; SENTINEL-2 ; VEGETATION
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
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/2XILL650/326403
作者单位	[Wang, Jingzhe; Shi, Tiezhu; Wu, Guofeng] Shenzhen Univ, MNR Key Lab Geoenvironm Monitoring Great Bay Area, Shenzhen 518060, Peoples R China; [Wang, Jingzhe; Shi, Tiezhu; Wu, Guofeng] Shenzhen Univ, Guangdong Key Lab Urban Informat, Shenzhen 518060, Peoples R China; [Wang, Jingzhe; Shi, Tiezhu; Wu, Guofeng] Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China; [Shi, Tiezhu; Wu, Guofeng] Shenzhen Univ, Sch Architecture & Urban Planning, Shenzhen 518060, Peoples R China; [Wang, Jingzhe] Shenzhen Univ, Coll Life Sci & Oceanog, Shenzhen 518060, Peoples R China; [Yu, Danlin] Renmin Univ China, Sch Sociol & Populat Studies, Beijing 100872, Peoples R China; [Yu, Danlin] Montclair State Univ, Dept Earth & Environm Studies, Montclair, NJ 07043 USA; [Teng, Dexiong; Ge, Xiangyu; Zhang, Zipeng] Xinjiang Univ, Coll Resources & Environm Sci, Urumqi 800046, Peoples R China; [Yang, Xiaodong] Ningbo Univ, Dept Geog & Spatial Informat Technol, Ningbo 315211, Peoples R China; [Wang,...
推荐引用方式 GB/T 7714	Wang, Jingzhe,Shi, Tiezhu,Yu, Danlin,et al. Ensemble machine-learning-based framework for estimating total nitrogen concentration in water using drone-borne hyperspectral imagery of emergent plants: A case study in an arid oasis, NW China[J]. 新疆大学,2020,266.
APA	Wang, Jingzhe.,Shi, Tiezhu.,Yu, Danlin.,Teng, Dexiong.,Ge, Xiangyu.,...&Wu, Guofeng.(2020).Ensemble machine-learning-based framework for estimating total nitrogen concentration in water using drone-borne hyperspectral imagery of emergent plants: A case study in an arid oasis, NW China.ENVIRONMENTAL POLLUTION,266.
MLA	Wang, Jingzhe,et al."Ensemble machine-learning-based framework for estimating total nitrogen concentration in water using drone-borne hyperspectral imagery of emergent plants: A case study in an arid oasis, NW China".ENVIRONMENTAL POLLUTION 266(2020).