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Dynamic monitoring of soil salinization in Yellow River Delta utilizing MSAVI–SI feature space models with Landsat images

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Abstract

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.

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Acknowledgements

This work was supported by The National Key R&D Program of China (Grant no. 2017YFA0604804); Natural Science Foundation of Shandong Province (Grant nos. ZR2018BD001); Project of Shandong Province Higher Educational Science and Technology Program (Grant no. J18KA181); Open Fund of Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University (Grant no. KLGIS2017A02); Open Fund of State Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University (Grant no. 17I04); Open Fund of Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation (Grant no. 2016NGCM02); Project of Hubei Key Laboratory of Regional Development and Environmental Response (Hubei University) (no. 2017 (B) 003).

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Authors and Affiliations

  1. School of Civil Architectural Engineering, Shandong University of Technology, Zibo, 255000, Shandong, China

    Bing Guo, Baomin Han, Shuting Chen & Wenna Yang

  2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research of Chinese Academy of Sciences, Datun Road 20, Chaoyang District, Beijing, 100101, China

    Fei Yang

  3. Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China

    Bing Guo

  4. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China

    Bing Guo & Yewen Fan

  5. Hubei Key Laboratory of Regional Development and Environmental Response (Hubei University), Wuhan, 430062, China

    Bing Guo

  6. Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation, Wuhan, 433079, China

    Bing Guo

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  1. Bing Guo
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  2. Fei Yang
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  4. Baomin Han
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Correspondence to Fei Yang.

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Guo, B., Yang, F., Fan, Y. et al. Dynamic monitoring of soil salinization in Yellow River Delta utilizing MSAVI–SI feature space models with Landsat images. Environ Earth Sci 78, 308 (2019). https://doi.org/10.1007/s12665-019-8319-8

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