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遥感技术与应用  2020, Vol. 35 Issue (1): 211-218    DOI: 10.11873/j.issn.1004-0323.2020.1.0211
遥感应用     
基于特征空间的黄河三角洲垦利县土壤盐分遥感提取
边玲玲1,2(),王卷乐2,4(),郭兵1,程凯2,3,魏海硕1,2
1. 山东理工大学建筑工程学院,山东 淄博 255049
2. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101
3. 中国科学院大学,北京 100049
4. 江苏省地理信息资源开发与利用协同创新中心,江苏 南京 210023
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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摘要:

土壤盐渍化是实现土地资源可持续利用所面临的重要挑战,在我国滨海的黄河三角洲区域遥感定量反演适宜方法可为区域盐渍化监测与防治提供技术方法参考。研究以Landsat 8 OLI数据和野外实测数据为基础,提取关键地表特征参量,定量化探讨土壤盐分与地表生物物理参数之间的规律及关系,建立黄河三角洲土壤盐分最优反演模型。结果表明:Albedo-MSAVI、SI-Albedo、SI-NDVI反演精度分别为83.4%、88.8%和80.6%。分析认为SI-Albedo模型最适用于滨海地区盐渍化程度反演,对滨海地区土壤盐分的预测能力较强;Albedo-MSAVI、SI-NDVI模型对内陆干旱、半干旱地区的盐渍化信息提取具有一定的参考意义。基于精度最高的SI-Albedo所反演的结果来看,垦利县盐渍化程度自东向西总体呈高低高走向,与该区域盐分积聚的成因机理相符。

关键词: 盐渍化特征空间遥感反演黄河三角洲垦利县    
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
收稿日期: 2018-12-04 出版日期: 2020-04-01
ZTFLH:  TP753  
基金资助: 中国科学院战略性先导科技专项(A类)资助(XDA19040501);防灾减灾知识服务系统(CKCEST?2018?2?8);中国科学院“十三五”信息化专项科学大数据工程项目(XXH13505?07)
通讯作者: 王卷乐     E-mail: bianll@lreis.ac.cn;wangjl@igsnrr.ac.cn
作者简介: 边玲玲(1994-),女,山东济南人,硕士研究生,主要从事土地资源与遥感应用研究。E?mail:bianll@lreis.ac.cn
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引用本文:

边玲玲,王卷乐,郭兵,程凯,魏海硕. 基于特征空间的黄河三角洲垦利县土壤盐分遥感提取[J]. 遥感技术与应用, 2020, 35(1): 211-218.

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.

链接本文:

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

图1  研究区位置与采样点分布图
图2  特征空间原理
图3  Albedo-MSAVI散点图
图4  SI-Albedo散点图
图5  SI-NDVI散点图
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
表1  垦利县盐渍化监测指标
图6  垦利县盐渍化分布等级图
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%
表2  模型精度验证
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