干旱区生态环境知识资源中心(Arid): Inversion of Soil Salinity Using Multisource Remote Sensing Data and Particle Swarm Machine Learning Models in Keriya Oasis, Northwestern China

Arid

DOI	10.3390/rs14030512
	Inversion of Soil Salinity Using Multisource Remote Sensing Data and Particle Swarm Machine Learning Models in Keriya Oasis, Northwestern China
	Wei, Qinyu; Nurmemet, Ilyas; Gao, Minhua; Xie, Boqiang
通讯作者	Gao, MH
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:3
英文摘要	Soil salinization is a global problem that damages soil ecology and affects agricultural development. Timely management and monitoring of soil salinity are essential to achieve the most sustainable development goals in arid and semi-arid regions. It has been demonstrated that Polarimetric Synthetic Aperture Radar (PolSAR) data have a high sensitivity to the soil dielectric constant and soil surface roughness, thus having great potential for the detection of soil salinity. However, studies combining PALSAR-2 data and Landsat 8 data to invert soil salinity information are less common. The particle swarm optimization (PSO) algorithm is characterized by simple operation, fast computation, and good adaptability, but there are relatively few studies applying it to soil salinity as well. This paper takes the Keriya Oasis as an example, proposing the PSO-SVR and PSO-BPNN models by combining PSO with support vector machine regression (SVR) and back-propagation neural network (BPNN) models. Then, PALSAR-2 data, Landsat 8 data, evapotranspiration data, groundwater burial depth data, and DEM data were combined to conduct the inversion study of soil salinity in the study area. The results showed that the introduction of PSO generated a satisfactory estimating performance. The SVR model accuracy (R-2) improved by 0.07 (PALSAR-2 data), 0.20 (Landsat 8 data), and 0.19 (PALSAR + Landsat data); the BP model accuracy (R-2) improved by 0.03 (PALSAR-2 data), 0.24 (Landsat 8 data), and 0.12 (PALSAR + Landsat data), and then combined with the model inversion plots, we found that PALSAR + Landsat data combined with the PSO-SVR model could achieve better inversion results. The fine texture information of PALSAR-2 data can be used to better invert the soil salinity in the study area by combining it with the rich spectral information of Landsat 8 data. This study complements the research ideas and methods for soil salinization using multi-source remote sensing data to provide scientific support for salinity monitoring in the study area.
英文关键词	soil salinization particle swarm optimization support vector regression back-propagation neural network PALSAR
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000756081800001
WOS关键词	SALINIZATION ; REGRESSION ; PROVINCE ; SCALE ; OPTIMIZATION ; ALGORITHMS ; IRRIGATION ; PREDICTION ; MOISTURE ; OASES
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
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376429
推荐引用方式 GB/T 7714	Wei, Qinyu,Nurmemet, Ilyas,Gao, Minhua,et al. Inversion of Soil Salinity Using Multisource Remote Sensing Data and Particle Swarm Machine Learning Models in Keriya Oasis, Northwestern China[J],2022,14(3).
APA	Wei, Qinyu,Nurmemet, Ilyas,Gao, Minhua,&Xie, Boqiang.(2022).Inversion of Soil Salinity Using Multisource Remote Sensing Data and Particle Swarm Machine Learning Models in Keriya Oasis, Northwestern China.REMOTE SENSING,14(3).
MLA	Wei, Qinyu,et al."Inversion of Soil Salinity Using Multisource Remote Sensing Data and Particle Swarm Machine Learning Models in Keriya Oasis, Northwestern China".REMOTE SENSING 14.3(2022).