Soil Salinity Inversion Model of Oasis in Arid Area Based on UAV Multispectral Remote Sensing

doi:10.3390/rs14081804

	Soil Salinity Inversion Model of Oasis in Arid Area Based on UAV Multispectral Remote Sensing
	Zhao, Wenju 1; Zhou, Chun 1; Zhou, Changquan 1,2; Ma, Hong 1; Wang, Zhijun 1,3
刊名	Remote Sensing
	2022-04-02
卷号	14 期号:8
关键词	Agriculture Antennas Arid regions Decision trees Food supply Mean square error Neural networks Soil surveys Soils Statistical tests Support vector machines Unmanned aerial vehicles (UAV) Vegetation Arid area Inversion models Multi-spectral image data Multispectral remote sensing Remote sensing inversion model Remote-sensing Soil salinity Soil salinization Soil salt content Soil salts
DOI	10.3390/rs14081804
英文摘要	Soil salinization severely restricts the development of global industry and agriculture and affects human beings. In the arid area of Northwest China, oasis saline-alkali land threatens the development of agriculture and food security. This paper develops and optimizes an inversion monitoring model for monitoring the soil salt content using unmanned aerial vehicle (UAV) multispectral remote sensing data. Using the multispectral remote sensing data in three research areas, the soil salt inversion models based on the support vector machine regression (SVR), random forest (RF), backpropagation neural network (BPNN), and extreme learning machine (ELM) were constructed. The results show that the four constructed models based on the spectral index can achieve good inversion accuracy, and the red edge band can effectively improve the soil salt inversion accuracy in saline-alkali land with vegetation cover. Based on the obtained results, for bare land, the best model for soil salt inversion is the ELM model, which reaches the determination coefficient (Rv2) of 0.707, the root mean square error RMSEv of 0.290, and the performance deviation ratio (RPD) of 1.852 on the test dataset. However, for agricultural land with vegetation cover, the best model for soil salinity inversion using the vegetation index is the BPNN model, which achieves Rv2 of 0.836, RMSEv of 0.027, and RPD of 2.100 on the test dataset. This study provides technical support for rapid monitoring and inversion of soil salinization and salinization control in irrigation areas. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
语种	英语
出版者	MDPI
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/158411]
专题	能源与动力工程学院
作者单位	1.College of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou; 730050, China; 2.School of Civil Engineering, Lanzhou College of Information Science and Technology, Lanzhou; 730300, China; 3.Baiyin New Material Research Institute of Lanzhou University of Technology, Baiyin; 730900, China
推荐引用方式 GB/T 7714	Zhao, Wenju,Zhou, Chun,Zhou, Changquan,et al. Soil Salinity Inversion Model of Oasis in Arid Area Based on UAV Multispectral Remote Sensing[J]. Remote Sensing,2022,14(8).
APA	Zhao, Wenju,Zhou, Chun,Zhou, Changquan,Ma, Hong,&Wang, Zhijun.(2022).Soil Salinity Inversion Model of Oasis in Arid Area Based on UAV Multispectral Remote Sensing.Remote Sensing,14(8).
MLA	Zhao, Wenju,et al."Soil Salinity Inversion Model of Oasis in Arid Area Based on UAV Multispectral Remote Sensing".Remote Sensing 14.8(2022).