{"title":"Zoning the soil salinization levels in the northern China’s coastal areas based on high-resolution soil mapping","authors":"Yuan Chi , Minglei Fan , Zhiwei Zhang , Yubing Qu","doi":"10.1016/j.ecolind.2025.113303","DOIUrl":null,"url":null,"abstract":"<div><div>Zoning the soil salinization levels in large-scale coastal areas is vital for understanding the spatiotemporal mechanism of soil salinization and guiding the coastal integrated management. However, it is difficult due to the complicated influencing factors and the high demands for a fine mapping resolution and precise simulation results. In the present study, the northern China’s coastal areas (> 6 4000 km<sup>2</sup>) were selected as the study area, and extensive field investigation, multispectral remote sensing images, and open-source land cover data served as the data source. A simulation unit of 100 m × 100 m was employed to precisely map the soil salinity (SS) based on a predictor system that covered different aspects of influencing factors, and high-resolution soil salinization maps were generated for the entire study area and different cities. Results indicated that the mapping obtained a relative-root mean squared error of 0.31, which was in a low level and denoted a high accuracy compared with previous studies. The soil salinization levels presented the following spatial heterogeneities: (1) The levels showed distinct polarization, that is, extremely low and high levels covered the most of the study area. (2) The SS exhibited a distinct decrease from the coastline to the inner land, and wetlands and water areas exhibited much higher SS than the remaining land cover types. (3) Muddy coasts suffered more severe soil salinization than rocky and sandy coasts in the alongshore areas. (4) The inner land was generally free from the salinization but some small patches of bare lands and water areas were still at risk. (5) Dongying, Binzhou, Weifang, and Yancheng Cities suffered much more distinct soil salinization than the remaining cities. The coastline and ecological quality were the major factors that determine the spatial pattern of soil salinization in the alongshore and inland areas, respectively.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"172 ","pages":"Article 113303"},"PeriodicalIF":7.0000,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Indicators","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1470160X25002341","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Zoning the soil salinization levels in large-scale coastal areas is vital for understanding the spatiotemporal mechanism of soil salinization and guiding the coastal integrated management. However, it is difficult due to the complicated influencing factors and the high demands for a fine mapping resolution and precise simulation results. In the present study, the northern China’s coastal areas (> 6 4000 km2) were selected as the study area, and extensive field investigation, multispectral remote sensing images, and open-source land cover data served as the data source. A simulation unit of 100 m × 100 m was employed to precisely map the soil salinity (SS) based on a predictor system that covered different aspects of influencing factors, and high-resolution soil salinization maps were generated for the entire study area and different cities. Results indicated that the mapping obtained a relative-root mean squared error of 0.31, which was in a low level and denoted a high accuracy compared with previous studies. The soil salinization levels presented the following spatial heterogeneities: (1) The levels showed distinct polarization, that is, extremely low and high levels covered the most of the study area. (2) The SS exhibited a distinct decrease from the coastline to the inner land, and wetlands and water areas exhibited much higher SS than the remaining land cover types. (3) Muddy coasts suffered more severe soil salinization than rocky and sandy coasts in the alongshore areas. (4) The inner land was generally free from the salinization but some small patches of bare lands and water areas were still at risk. (5) Dongying, Binzhou, Weifang, and Yancheng Cities suffered much more distinct soil salinization than the remaining cities. The coastline and ecological quality were the major factors that determine the spatial pattern of soil salinization in the alongshore and inland areas, respectively.
期刊介绍:
The ultimate aim of Ecological Indicators is to integrate the monitoring and assessment of ecological and environmental indicators with management practices. The journal provides a forum for the discussion of the applied scientific development and review of traditional indicator approaches as well as for theoretical, modelling and quantitative applications such as index development. Research into the following areas will be published.
• All aspects of ecological and environmental indicators and indices.
• New indicators, and new approaches and methods for indicator development, testing and use.
• Development and modelling of indices, e.g. application of indicator suites across multiple scales and resources.
• Analysis and research of resource, system- and scale-specific indicators.
• Methods for integration of social and other valuation metrics for the production of scientifically rigorous and politically-relevant assessments using indicator-based monitoring and assessment programs.
• How research indicators can be transformed into direct application for management purposes.
• Broader assessment objectives and methods, e.g. biodiversity, biological integrity, and sustainability, through the use of indicators.
• Resource-specific indicators such as landscape, agroecosystems, forests, wetlands, etc.