Exploring the potential of time series InSAR for coastal wetland fine mapping using auto-weighted ensemble machine learning - Zhenjin Li; Zhiyong Wang; Jingrui Pan; Xiaomei Jiao; Yuchu Fan; Zhenhai Gao; Yulei Si; Silong Cheng; Haoran Song (International Journal of Digital Earth)
Wetland monitoring is essential for understanding dynamics, protecting ecological environments, and supporting sustainable development. The potential of multiple spatial–temporal SAR features derived from time series InSAR for fine-scale wetland mapping is not yet fully understood. In this study, we utilized time series Sentinel-1 VH/VV images to derive multi-modal features (Amplitude, coherence, and phase) and explored the accuracy of these features in fine-scale coastal wetland classification. Furthermore, we proposed an auto-weighted ensemble machine learning (AWEML) classifier to optimize wetland monitoring accuracy. Finally, we employed SHAP to assess the influence of features with different temporal baselines on various ground objects. The results of the Yellow River Delta wetland indicate that: (1) Integrating multi-modal features improved classification accuracy under XGBoost by 13.1%, 6.55%, and 27.21% compared to single input amplitude, coherence, and phase features, respectively. (2) AWEML demonstrated superior performance, achieving OA of 94.36%, which is 2.42% and 2.81% higher than ensemble machine learning with soft-voting and XGBoost. (3) VH short temporal baseline coherence features significantly improved the extraction accuracy of salt marsh vegetation, while VV long temporal baseline coherence features performed well for identifying stable ground objects. Our method provides a new reference for coastal wetland fine mapping using SAR data.Abbreviations: SA: Spartina alterniflora; PA: Phragmites australis; TC: Tamarix chinensis; WT: water; FL: farmland; TF: tidal flat; BB: building and bare land; SS: Sueada salsa; IC: Imperata cylindrica.