干旱区生态环境知识资源中心(Arid): Exploring the potential of UAV hyperspectral image for estimating soil salinity: Effects of optimal band combination algorithm and random forest

Arid

DOI	10.1016/j.saa.2022.121416
	Exploring the potential of UAV hyperspectral image for estimating soil salinity: Effects of optimal band combination algorithm and random forest
	Zhu, Chuanmei; Ding, Jianli; Zhang, Zipeng; Wang, Zheng
通讯作者	Ding, JL
来源期刊	SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
ISSN	1386-1425
EISSN	1873-3557
出版年	2022
卷号	279
英文摘要	Hyperspectral remote sensing by unmanned aerial vehicle (UAV) is an important technical tool for rapid, ac-curate, and real-time monitoring of soil salinity in arid zone agroecosystems. However, the key to effective soil salinity (electrical conductivity, EC) prediction by UAV visible and near-infrared (Vis-NIR) spectroscopy depends on the selection of effective features selection techniques and robust prediction characteristics algorithms. Therefore, in this study, two advanced feature selection methods and two commonly used modeling methods were applied to predict and characterize the spatial patterns of soil salinity (EC). The aim of this study was to explore the predictive performance of different feature band selection methods and to identify a robust soil salinity mapping strategy. The results demonstrated that standard normal variate (SNV) pre-processing broad-ened the absorption characteristics of the spectrum. Compared with competitive adaptive reweighted sampling (CARS), the optimal band combination algorithm (OBCA) strengthened the correlation with soil salinity and had a higher variable importance in the modeling. Random forest (RF) was more stable in mapping the spatial pattern of surface soil salinity compared to the partial least squares regression model (PLSR). Our results confirm the effectiveness of OBCA and RF in the developing UAV remote sensing models for surface soil salinity estimation and mapping.
英文关键词	Visibleandnear-infraredspectroscopy Featureselection CompetitiveadaptiveReweightedsampling Digitalsoilmapping Soilsalinity
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000822673400003
WOS关键词	REFLECTANCE ; SPECTROSCOPY ; CAPABILITY ; PREDICTION
WOS类目	Spectroscopy
WOS研究方向	Spectroscopy
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394527
推荐引用方式 GB/T 7714	Zhu, Chuanmei,Ding, Jianli,Zhang, Zipeng,et al. Exploring the potential of UAV hyperspectral image for estimating soil salinity: Effects of optimal band combination algorithm and random forest[J],2022,279.
APA	Zhu, Chuanmei,Ding, Jianli,Zhang, Zipeng,&Wang, Zheng.(2022).Exploring the potential of UAV hyperspectral image for estimating soil salinity: Effects of optimal band combination algorithm and random forest.SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY,279.
MLA	Zhu, Chuanmei,et al."Exploring the potential of UAV hyperspectral image for estimating soil salinity: Effects of optimal band combination algorithm and random forest".SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 279(2022).