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热带海洋学报

• • 上一篇    

基于扩展指数的黄河三角洲耕地盐分遥感反演研究

栾心怡1，2，宁吉才1，崔欣1
  

  1. 1. 中国科学院烟台海岸带研究所，山东 烟台 264000；

    2. 中国科学院大学，北京 100049



  • 收稿日期:2025-09-02 修回日期:2025-10-19 接受日期:2025-11-26
  • 通讯作者: 宁吉才
  • 基金资助:
    国家盐碱地综合利用技术创新中心“揭榜挂帅”项目（GYJ2023001）;中国科学院烟台海岸带研究所种子项目（YIC E351030601）

Remote sensing inversion of soil salinity in the Yellow River Delta based on extended indices

LUAN Xinyi1，2,NING Jicai1,CUI Xin1   

  1. 1. Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264000, China；

    2. University of Chinese Academy of Sciences，Beijing 100049，China

  • Received:2025-09-02 Revised:2025-10-19 Accepted:2025-11-26
  • Supported by:

    “Unveiling and Commanding” Project (GYJ2023001) from National Center of Technology Innovation for Comprehensive Utilization of Salt-alkali Land, China；The seed project of Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences(YIC E351030601)

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摘要： 高效获取土壤盐分含量数据对于滨海盐渍土的治理和改善具有重要意义，其关键在于提高结果数据的准确度。本文以黄河三角洲地区耕地土壤为研究对象，基于Landsat系列遥感影像数据，引入短波红外波段对传统波段指数进行扩展，在传统波段指数和改进后的扩展波段指数的基础上分别采用MLR（multiple linear regression）、RF(random forest)和SVM(support vector machine)构建土壤盐分反演模型，对比各个模型的精度，筛选得到最优模型，使用最优模型对研究区2014-2024年的盐分含量进行反演和时空变化分析。结果表明：相比于传统波段指数，使用扩展波段指数能够提高反演精度，获得更加准确的反演结果，使用上述机器学习方法进行建模，改进后的模型精度有所提高，R2（coefficient of determination）提高0.13～0.14，RMSE（root mean square error）降低0.12～0.28，MAE（mean absolute error）降低0.08～0.13；对比三种建模方法，SVM反演精度最高，其次是RF，MLR的效果相对较差，本次研究最优模型为基于扩展波段指数的支持向量机模型，其R2、RMSE和MAE为0.74、1.1和0.7，模型较为可靠；研究区近十年土壤盐渍化状况呈现较为明显的波动特征，呈现减缓-加重不断交替的规律和趋势，2018年和2021年盐渍化状况较为严重，区域盐渍化治理仍需高度重视和持续关注。

关键词: 土壤盐分反演, 波段指数, 机器学习, 黄河三角洲, 时空分析

Abstract: Efficiently obtaining soil salinity data is of great significance for the management and improvement of coastal saline-alkali soils, with the key lying in enhancing the accuracy of the results. This study takes the cultivated soil in the Yellow River Delta region as its research object, utilizing Landsat series remote sensing imagery data. It introduces the shortwave infrared band to expand the traditional band indices, and based on both the traditional band indices and the improved expanded band indices, employs MLR (multiple linear regression), RF (random forest), and SVM (support vector machine) to construct soil salinity inversion models. The accuracy of each model is compared, and the optimal model is selected. The optimal model is then used to inversely estimate and analyze the spatial-temporal changes in salinity content in the study area from 2014 to 2024.The results indicate that compared to traditional band indices, the use of extended band indices can improve inversion accuracy and yield more precise inversion results. When modeling using the aforementioned machine learning methods, the improved model accuracy shows an increase in R² (coefficient of determination) by 0.13～0.14, a decrease in RMSE (root mean square error) by 0.12～0.28, and a decrease in MAE (mean absolute error) by 0.08～0.13; When comparing the three modeling methods, SVM achieved the highest inversion accuracy, followed by RF, while MLR performed relatively poorly. The optimal model in this study was the support vector machine model based on the extended band index, with R², RMSE, and MAE values of 0.74, 1.1, and 0.7, respectively, indicating a reliable model; The soil salinization status in the study area over the past decade has shown significant fluctuations, alternating between mitigation and exacerbation. The salinization status was particularly severe in 2018 and 2021. Therefore, soil salinization management in the region still requires high priority and continuous attention

Key words: soil salinity inversion, band index, machine learning, Yellow River Delta, spatio-temporal analysis