In recent years, the Yellow River Delta has been affected by invasive species Spartina alterniflora (S. alterniflora), resulting in a fragile ecological environment. It is of great significance to monitor the ground object types in the Yellow River Delta wetlands. The classification accuracy based on Synthetic Aperture Radar (SAR) backscattering coefficient is limited by the small difference between some ground objects. To solve this problem, a decision tree classification method for extracting the ground object types in wetland combined time series SAR backscattering and coherence characteristics was proposed. The Yellow River Delta was taken as the study area and the 112 Sentinel-1A GRD data with VV/VH dual-polarization and 64 Sentinel-1A SLC data with VH polarization were used. The decision tree method was established, based on the annual mean VH and VV backscattering characteristics, the new constructed radar backscattering indices, and the annual mean VH coherence characteristics were suitable for extracting the wetlands in the Yellow River Delta. Then the classification results in the Yellow River Delta wetlands from 2018 to 2021 were obtained using the new method proposed in this paper. The results show that the overall accuracy and Kappa coefficient of the proposed method w5ere 89.504% and 0.860, which were 9.992% and 0.127 higher than multi-temporal classification by Support Vector Machine classifier. Compared with the decision tree without coherence, the overall accuracy and Kappa coefficient were improved by 8.854% and 0.108. The spatial distributions of wetland types in the Yellow River Delta from 2018 to 2021 were obtained using the constructed decision tree. The spatio-temporal evolution analysis was conducted. The results showed that the area of S. alterniflora decreased significantly in 2020 but it increased to the area of 2018 in 2021. In addition, S. alterniflora seriously affected the living space of Phragmites australis (P. australis) and in 4 years, 10.485 km² living space of P. australis was occupied by S. alterniflora. The proposed method can provide a theoretical basis for higher accuracy SAR wetland classification and the monitoring results can provide an effective reference for local wetland protection.

近年来，黄河三角洲受到入侵物种互花米草（Spartina interniflora）的影响。互花米草)，导致生态环境脆弱。黄河三角洲湿地地物类型监测具有重要意义。基于合成孔径雷达（SAR）后向散射系数的分类精度受限于一些地物之间的微小差异。针对这一问题，提出了一种结合时间序列SAR后向散射和相干特征提取湿地地物类型的决策树分类方法。以黄河三角洲为研究区域，使用了112个VV/VH双极化Sentinel-1A GRD数据和64个VH极化Sentinel-1A SLC数据。建立决策树方法，基于年均VH和VV后向散射特征，新构建的雷达后向散射指标，年平均VH相干特征适合黄河三角洲湿地的提取。然后利用本文提出的新方法得到黄河三角洲湿地2018-2021年的分类结果。结果表明，该方法的整体准确率和Kappa系数分别为89.504%和0.860，分别比支持向量机分类器的多时态分类分别高出9.992%和0.127。与没有相干性的决策树相比，整体准确率和 Kappa 系数分别提高了 8.854% 和 0.108。使用构建的决策树获得2018-2021年黄河三角洲湿地类型的空间分布。进行了时空演化分析。结果表明，面积 然后利用本文提出的新方法得到黄河三角洲湿地2018-2021年的分类结果。结果表明，该方法的整体准确率和Kappa系数分别为89.504%和0.860，分别比支持向量机分类器的多时态分类分别高出9.992%和0.127。与没有相干性的决策树相比，整体准确率和 Kappa 系数分别提高了 8.854% 和 0.108。使用构建的决策树获得2018-2021年黄河三角洲湿地类型的空间分布。进行了时空演化分析。结果表明，面积 然后利用本文提出的新方法得到黄河三角洲湿地2018-2021年的分类结果。结果表明，该方法的整体准确率和Kappa系数分别为89.504%和0.860，分别比支持向量机分类器的多时态分类分别高出9.992%和0.127。与没有相干性的决策树相比，整体准确率和 Kappa 系数分别提高了 8.854% 和 0.108。使用构建的决策树获得2018-2021年黄河三角洲湿地类型的空间分布。进行了时空演化分析。结果表明，面积 结果表明，该方法的整体准确率和Kappa系数分别为89.504%和0.860，分别比支持向量机分类器的多时态分类分别高出9.992%和0.127。与没有相干性的决策树相比，整体准确率和 Kappa 系数分别提高了 8.854% 和 0.108。使用构建的决策树获得2018-2021年黄河三角洲湿地类型的空间分布。进行了时空演化分析。结果表明，面积 结果表明，该方法的整体准确率和Kappa系数分别为89.504%和0.860，分别比支持向量机分类器的多时态分类分别高出9.992%和0.127。与没有相干性的决策树相比，整体准确率和 Kappa 系数分别提高了 8.854% 和 0.108。使用构建的决策树获得2018-2021年黄河三角洲湿地类型的空间分布。进行了时空演化分析。结果表明，面积 整体准确率和 Kappa 系数分别提高了 8.854% 和 0.108。使用构建的决策树获得2018-2021年黄河三角洲湿地类型的空间分布。进行了时空演化分析。结果表明，面积 整体准确率和 Kappa 系数分别提高了 8.854% 和 0.108。使用构建的决策树获得2018-2021年黄河三角洲湿地类型的空间分布。进行了时空演化分析。结果表明，面积互花米草2020 年显着下降，但 2021 年增加到 2018 年的面积。此外，互花米草严重影响了芦苇的生存空间（P. australis) 和 4 年内，10.485 km ²的生活空间P. australis被占用互花米草. 该方法可为更高精度的SAR湿地分类提供理论依据，监测结果可为当地湿地保护提供有效参考。