干旱区生态环境知识资源中心(Arid): Application of Machine Learning Algorithms for Digital Mapping of Soil Salinity Levels and Assessing Their Spatial Transferability in Arid Regions

Arid

DOI	10.3390/land12091680
	Application of Machine Learning Algorithms for Digital Mapping of Soil Salinity Levels and Assessing Their Spatial Transferability in Arid Regions
	Sulieman, Magboul M.; Kaya, Fuat; Elsheikh, Mohammed A.; Basayigit, Levent; Francaviglia, Rosa
通讯作者	Francaviglia, R
来源期刊	LAND
EISSN	2073-445X
出版年	2023
卷号	12 期号:9
英文摘要	A comprehensive understanding of soil salinity distribution in arid regions is essential for making informed decisions regarding agricultural suitability, water resource management, and land use planning. A methodology was developed to identify soil salinity in Sudan by utilizing optical and radar-based satellite data as well as variables obtained from digital elevation models that are known to indicate variations in soil salinity. The methodology includes the transfer of models to areas where similar conditions prevail. A geographically coordinated database was established, incorporating a variety of environmental variables based on Google Earth Engine (GEE) and Electrical Conductivity (EC) measurements from the saturation extract of soil samples collected at three different depths (0-30, 30-60, and 60-90 cm). Thereafter, Multinomial Logistic Regression (MNLR) and Gradient Boosting Algorithm (GBM), were utilized to spatially classify the salinity levels in the region. To determine the applicability of the model trained at the reference site to the target area, a Multivariate Environmental Similarity Surface (MESS) analysis was conducted. The producer's accuracy, user's accuracy, and Tau index parameters were used to evaluate the model's accuracy, and spatial confusion indices were computed to assess uncertainty. At different soil depths, Tau index values for the reference area ranged from 0.38 to 0.77, whereas values for target area samples ranged from 0.66 to 0.88, decreasing as the depth increased. Clay normalized ratio (CLNR), Salinity Index 1, and SAR data were important variables in the modeling. It was found that the subsoils in the middle and northwest regions of both the reference and target areas had a higher salinity level compared to the topsoil. This study highlighted the effectiveness of model transfer as a means of identifying and evaluating the management of regions facing significant salinity-related challenges. This approach can be instrumental in identifying alternative areas suitable for agricultural activities at a regional level.
英文关键词	dryland digital soil mapping environmental similarity Google Earth Engine remote sensing SAR Sentinel 2 MSI salinization transfer learning
类型	Article
语种	英语
开放获取类型	gold
收录类别	SSCI
WOS记录号	WOS:001079160800001
WOS关键词	LOGISTIC-REGRESSION ; CLASSIFICATION ; EXTRAPOLATION ; MODELS ; SCALE
WOS类目	Environmental Studies
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/397689
推荐引用方式 GB/T 7714	Sulieman, Magboul M.,Kaya, Fuat,Elsheikh, Mohammed A.,et al. Application of Machine Learning Algorithms for Digital Mapping of Soil Salinity Levels and Assessing Their Spatial Transferability in Arid Regions[J],2023,12(9).
APA	Sulieman, Magboul M.,Kaya, Fuat,Elsheikh, Mohammed A.,Basayigit, Levent,&Francaviglia, Rosa.(2023).Application of Machine Learning Algorithms for Digital Mapping of Soil Salinity Levels and Assessing Their Spatial Transferability in Arid Regions.LAND,12(9).
MLA	Sulieman, Magboul M.,et al."Application of Machine Learning Algorithms for Digital Mapping of Soil Salinity Levels and Assessing Their Spatial Transferability in Arid Regions".LAND 12.9(2023).