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Remote Sensing Technology and Application  2020, Vol. 35 Issue (1): 211-218    DOI: 10.11873/j.issn.1004-0323.2020.1.0211
    
Remote Sensing Extraction of Soil Salinity in Yellow River Delta Kenli County based on Feature Space
Lingling Bian1,2(),Juanle Wang2,4(),Bing Guo1,Kai Cheng2,3,Haishuo Wei1,2
1. School of Architecture Engineering, Shandong University of Technology, Zibo 255000, China
2. State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3. University of Chinese Academy of Sciences, Beijing 100049
4. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
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Abstract  

Soil salinization is an important challenge to achieve sustainable use of land resources. The appropriate method for remote sensing quantitative inversion in the coastal Yellow River Delta region of China can provide technical reference for regional salinization monitoring and prevention. Utilizing Landsat 8 OLI image and field measured data, we extracted key surface characteristic parameters, quantitatively discussed the law and relationship between soil salinity and surface biophysical parameters and established a soil salinity inversion model. The results show that the inversion precisions of Albedo-MSAVI, SI-Albedo and SI-NDVI feature space are 83.4%, 88.8% and 80.6% respectively. The analysis shows the SI-Albedo model is suitable for the inversion of salinization level in Binhai areas. For Albedo-MSAVI and SI-NDVI models, they have certain reference significance for salinization information extraction in inland arid and semi-arid areas. Based on the inversion of the SI-Albedo feature space with the highest accuracy, the level of salinization in Kenli County is generally high-low-high trends from the east to the west, which is consistent with the formation mechanism of salt accumulation in this area.

Key words:  Salinization      Feature space      Remote sensing inversion      Yellow River Delta      Kenli County     
Received:  04 December 2018      Published:  01 April 2020
ZTFLH:  TP753  
Corresponding Authors:  Juanle Wang     E-mail:  bianll@lreis.ac.cn;wangjl@igsnrr.ac.cn
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Lingling Bian
Juanle Wang
Bing Guo
Kai Cheng
Haishuo Wei

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Lingling Bian,Juanle Wang,Bing Guo,Kai Cheng,Haishuo Wei. Remote Sensing Extraction of Soil Salinity in Yellow River Delta Kenli County based on Feature Space. Remote Sensing Technology and Application, 2020, 35(1): 211-218.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2020.1.0211     OR     http://www.rsta.ac.cn/EN/Y2020/V35/I1/211

Fig.1  Geographic location and sampling point distribution
Fig.2  Principle of feature space
Fig.3  Albedo-MSAVI scatter plot
Fig.4  SI-Albedo scatter plot
Fig.5  SI-NDVI scatter plot
盐渍化程度 非盐渍化 轻度盐渍化 中度盐渍化 重度盐渍化 盐土
SDI ≤0.68 >0.68, ≤0.84 >0.84, ≤0.89 >0.89, ≤0.98 >0.98, ≤1.21
ASI ≤0.58 >0.58, ≤0.66 >0.66, ≤0.72 >0.72, ≤0.80 >0.80, ≤1.40
SDI ≤0.007 >0.007, ≤0.08 >0.08, ≤0.15 >0.15, ≤0.23 >0.23, ≤0.57
Table 1  Monitoring indicators of salinization in Kenli County
Fig.6  Distribution chart of salinization distribution in Kenli County
模型 分类正确 分类错误 总体精度
Albedo-MSAVI 30 6 83.4%
SI-Albedo 32 4 88.8%
SI-NDVI 29 7 80.6%
Table 2  Accuracy verification of the models
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