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首页> 外文期刊>Geomatics,Natural Hazards & Risk >Salinization information extraction model based on VI–SI feature space combinations in the Yellow River Delta based on Landsat 8 OLI image
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Salinization information extraction model based on VI–SI feature space combinations in the Yellow River Delta based on Landsat 8 OLI image

机译：基于VI-SI的盐渍化信息提取模型在Landsat 8 Oli图像中黄河三角洲的vi-si特征空间组合

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摘要

The interference of soil salt content, vegetation, and other factors greatly constrain soil salinization monitoring via remote sensing techniques. However, traditional monitoring methods often ignore the vegetation information. In this study, the vegetation indices–salinity indices (VI–SI) feature space was utilized to improve the inversion accuracy of soil salinity, while considering the bare soil and vegetation information. By fully considering the surface vegetation landscape in the Yellow River Delta, twelve VI–SI feature spaces were constructed, and three categories of soil salinization monitoring index were established; then, the inversion accuracies among all the indices were compared. The experiment results showed that remote sensing monitoring index based on MSAVI–SI 1 with SDI 2 had the highest inversion accuracy ( R 2 = 0.876), while that based on the ENDVI–SI 4 feature space with SDI 1 had the lowest ( R 2 = 0.719). The reason lied in the fact that MSAVI fully considers the bare soil line and thus effectively eliminates the background influence of soil and vegetation canopy. Therefore, the remote sensing monitoring index derived from MSAVI–SI 1 can greatly improve the dynamic and periodical monitoring of soil salinity in the Yellow River Delta.
机译：土壤盐含量，植被等因素的干扰极大地限制了通过遥感技术的土壤盐渍化监测。然而，传统的监测方法通常忽略植被信息。在这项研究中，利用植被指数 - 盐度指数（VI-Si）特征空间来提高土壤盐度的反转精度，同时考虑裸露的土壤和植被信息。通过完全考虑黄河三角洲的表面植被景观，建造了十二个VI-Si特征空间，建立了三类土壤盐渍化监测指数;然后，比较所有索引中的反转精度。实验结果表明，基于MSAVI-SI 1的遥感监测索引具有最高的反转精度（R 2 = 0.876），而基于具有SDI 1的ENDVI-SI 4特征空间具有最低（R 2 = 0.719）。 MSAVI充分考虑了裸土线，从而有效地消除了土壤和植被冠层的背景影响的原因。因此，来自MSAVI-SI 1的遥感监测指数可以大大提高黄河三角洲土壤盐度的动态和周期性监测。

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