干旱区生态环境知识资源中心(Arid): Integrating Active and Passive Remote Sensing Data for Mapping Soil Salinity Using Machine Learning and Feature Selection Approaches in Arid Regions

Arid

DOI	10.3390/rs15071751
	Integrating Active and Passive Remote Sensing Data for Mapping Soil Salinity Using Machine Learning and Feature Selection Approaches in Arid Regions
	Mohamed, Sayed A.; Metwaly, Mohamed M.; Metwalli, Mohamed R.; AbdelRahman, Mohamed A. E.; Badreldin, Nasem
通讯作者	Badreldin, N
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2023
卷号	15 期号:7
英文摘要	The prevention of soil salinization and managing agricultural irrigation depend greatly on accurately estimating soil salinity. Although the long-standing laboratory method of measuring salinity composition is accurate for determining soil salinity parameters, its use is frequently constrained by the high expense and difficulty of long-term in situ measurement. Soil salinity in the northern Nile Delta of Egypt severely affects agriculture sustainability and food security in Egypt. Understanding the spatial distribution of soil salinity is a critical factor for agricultural development and management in drylands. This research aims to improve soil salinity prediction by using a combined data collection method consisting of Sentinel-1 C radar data and Sentinel-2 optical data acquired simultaneously via integrated radar and optical sensor variables. The modelling approach focuses on feature selection strategies and regression learning. Feature selection approaches that include the filter, wrapper, and embedded methods were used with 47 selected variables depending on a genetic algorithm to scrutinize whether regions of the spectrum from optical indices and SAR texture choose the optimum combinations of selected variables. The sub-setting variables resulting from each feature selection method were used to train the regression learners' random forest (RF), linear regression (LR), backpropagation neural network (BPNN), and support vector regression (SVR). Combining the BPNN feature selection method with the RF regression learner better predicted soil salinity (RME 0.000246; sub-setting variables = 18). Integrating different remote sensing data and machine learning provides an opportunity to develop a robust prediction approach to predict soil salinity in drylands. This research evaluated the performances of various machine learning models, overcame the limitations of conventional techniques, and optimized the variable input combinations. This research can assist farmers in soil-salinization-affected areas in better managing planting procedures and enhancing the sustainability of their lands.
英文关键词	machine learning feature selection soil salinity arid regions
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:000970258800001
WOS关键词	ORGANIC-CARBON ; SENTINEL-2 MSI ; ALGORITHMS ; REGRESSION ; MOISTURE ; LANDS ; MODEL ; AREA
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/2XILL650/398252
推荐引用方式 GB/T 7714	Mohamed, Sayed A.,Metwaly, Mohamed M.,Metwalli, Mohamed R.,et al. Integrating Active and Passive Remote Sensing Data for Mapping Soil Salinity Using Machine Learning and Feature Selection Approaches in Arid Regions[J],2023,15(7).
APA	Mohamed, Sayed A.,Metwaly, Mohamed M.,Metwalli, Mohamed R.,AbdelRahman, Mohamed A. E.,&Badreldin, Nasem.(2023).Integrating Active and Passive Remote Sensing Data for Mapping Soil Salinity Using Machine Learning and Feature Selection Approaches in Arid Regions.REMOTE SENSING,15(7).
MLA	Mohamed, Sayed A.,et al."Integrating Active and Passive Remote Sensing Data for Mapping Soil Salinity Using Machine Learning and Feature Selection Approaches in Arid Regions".REMOTE SENSING 15.7(2023).