干旱区生态环境知识资源中心(Arid): Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images

Arid

DOI	10.3390/rs11070736
	Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images
	Hu, Jie 1; Peng, Jie 1,2; Zhou, Yin 1,3; Xu, Dongyun 1; Zhao, Ruiying 1; Jiang, Qingsong 1,4; Fu, Tingting 1; Wang, Fei 5; Shi, Zhou 1
通讯作者	Shi, Zhou
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2019
卷号	11 期号:7
英文摘要	Soil salinization is a global issue resulting in soil degradation, arable land loss and ecological environmental deterioration. Over the decades, multispectral and hyperspectral remote sensing have enabled efficient and cost-effective monitoring of salt-affected soils. However, the potential of hyperspectral sensors installed on an unmanned aerial vehicle (UAV) to estimate and map soil salinity has not been thoroughly explored. This study quantitatively characterized and estimated field-scale soil salinity using an electromagnetic induction (EMI) equipment and a hyperspectral camera installed on a UAV platform. In addition, 30 soil samples (0 similar to 20 cm) were collected in each field for the lab measurements of electrical conductivity. First, the apparent electrical conductivity (ECa) values measured by EMI were calibrated using the lab measured electrical conductivity derived from soil samples based on empirical line method. Second, the soil salinity was quantitatively estimated using the random forest (RF) regression method based on the reflectance factors of UAV hyperspectral images and satellite multispectral data. The performance of models was assessed by Lin's concordance coefficient (CC), ratio of performance to deviation (RPD), and root mean square error (RMSE). Finally, the soil salinity of three study fields with different land cover were mapped. The results showed that bare land (field A) exhibited the most severe salinity, followed by dense vegetation area (field C) and sparse vegetation area (field B). The predictive models using UAV data outperformed those derived from GF-2 data with lower RMSE, higher CC and RPD values, and the most accurate UAV-derived model was developed using 62 hyperspectral bands of the image of the field A with the RMSE, CC, and RPD values of 1.40 dS m(-1), 0.94, and 2.98, respectively. Our results indicated that UAV-borne hyperspectral imager is a useful tool for field-scale soil salinity monitoring and mapping. With the help of the EMI technique, quantitative estimation of surface soil salinity is critical to decision-making in arid land management and saline soil reclamation.
英文关键词	soil salinity unmanned aerial vehicle hyperspectral imager random forest regression electromagnetic induction
类型	Article
语种	英语
国家	Peoples R China
开放获取类型	gold, Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000465549300001
WOS关键词	SALT-AFFECTED SOILS ; SPECTRAL CHARACTERISTICS ; ELECTRICAL-CONDUCTIVITY ; CROSS-VALIDATION ; ORGANIC-CARBON ; RANDOM FORESTS ; VEGETATION ; REGRESSION ; IRRIGATION ; SELECTION
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
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/2XILL650/218355
作者单位	1.Zhejiang Univ, Inst Agr Remote Sensing & Informat Technol Applic, Coll Environm & Resource Sci, Hangzhou 310029, Zhejiang, Peoples R China; 2.Tarim Univ, Coll Plant Sci, Alar 843300, Peoples R China; 3.Zhejiang Univ, Inst Land Sci & Property, Sch Publ Affairs, Hangzhou 310029, Zhejiang, Peoples R China; 4.Tarim Univ, Coll Informat Engn, Alar 843300, Peoples R China; 5.Xinjiang Univ, Xinjiang Common Univ Key Lab Smart City & Environ, Coll Resource & Environm Sci, Urumqi 830046, Peoples R China
推荐引用方式 GB/T 7714	Hu, Jie,Peng, Jie,Zhou, Yin,et al. Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images[J]. 新疆大学,2019,11(7).
APA	Hu, Jie.,Peng, Jie.,Zhou, Yin.,Xu, Dongyun.,Zhao, Ruiying.,...&Shi, Zhou.(2019).Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images.REMOTE SENSING,11(7).
MLA	Hu, Jie,et al."Quantitative Estimation of Soil Salinity Using UAV-Borne Hyperspectral and Satellite Multispectral Images".REMOTE SENSING 11.7(2019).