标题:Dynamic monitoring of soil salinization in Yellow River Delta utilizing MSAVI–SI feature space models with Landsat images
作者:Guo, Bing ;Yang, Fei ;Fan, Yewen ;Han, Baomin ;Chen, Shuting ;Yang, Wenna
作者机构:[Guo, Bing ;Han, Baomin ;Chen, Shuting ;Yang, Wenna ] School of Civil Architectural Engineering, Shandong University of Technology, Zibo; Shandong; 25 更多
通讯作者:Yang, Fei
来源:Environmental Earth Sciences
出版年:2019
卷:78
期:10
DOI:10.1007/s12665-019-8319-8
摘要:Considering the surface eco-environmental landscape of the Yellow River Delta, two different models based on Modified Soil-Adjusted Vegetation Index–Salinized Index (MSAVI–SI) feature spaces have been proposed, and then, comparisons and analyses among the above two models have been conducted to find and recommend the optimal monitoring model of soil salinization for the Yellow River Delta. Results showed that: (1) the MSAVI–SI feature space model considering the soil line had greater efficiency and applicability for monitoring salinized soil in the Yellow River Delta with R2 = 0.8975 and an overall precision of 86.7% validation of salinization classification; (2) the soil salinization was widely and discontinuously distributed over the whole region. During 1987–2016, soil salinization had improved with an increased area of slight salinization and a decreased area of severe and moderate salinization; (3) the relationship between salinization detection indices (SDI2) and organic content differed with increasing organic content. There was a positive relationship between SDI2 and organic content with the organic content (OC) < 0.8%, while relationship was negative with the OC > 0.8%. These results can be helpful for the dynamic and periodical monitoring of soil salinity, and provide a scientific basis for properly managing soil and water resource in the Yellow River Delta. In addition, the optimal MSAVI–SI feature space model (SDI2) can also be utilized to monitor the soil salinization of zones with similar environmental conditions to Yellow River Delta with monsoon climate and wetland ecosystem.
© 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
收录类别:EI
资源类型:期刊论文
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