从Sentinel-1 InSAR数据学习区域雪水当量和雪高变化
Learning Regional Snow Water Equivalent and Snow Height Variations from Sentinel-1 InSAR Acquisitions
- Politecnico di Torino（都灵理工大学）
- Fondazione LINKS（LINKS基金会）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究评估三种机器学习模型（XGBoost、U-Net、SegFormer）利用Sentinel-1 InSAR数据估计意大利阿尔卑斯山区雪水当量和雪高变化，SegFormer表现最佳，并揭示误差主要源于系统性偏移。
AI中文摘要：
山区水资源管理在很大程度上依赖于可靠的雪水当量（SWE）和雪高（HS）数据，然而这些变量在大尺度上仍然难以追踪。本研究评估了三种机器学习架构（XGBoost、U-Net和SegFormer），用于从意大利阿尔卑斯山区的Sentinel-1 InSAR数据联合估计SWE和HS变化，并以IT-SNOW再分析数据作为参考。SegFormer在两个目标上均取得了最佳结果，HS的MAE为10.391厘米，SWE的MAE为27.113毫米水当量，且在不同初始化下变异性最低。特征敏感性分析表明，包含所有可用特征并不能保证最低误差，存在模型和任务特定的敏感性。空间指标（R²、Pearson相关系数）比平均误差（MAE、RMSE）更能清晰地区分三种架构，而按窗口分解误差显示，大部分误差归因于估计平均变化中的系统性偏移，而非空间模式。
英文摘要：
Managing water resources in mountainous regions depends heavily on reliable Snow Water Equivalent (SWE) and Snow Height (HS) data, yet these variables remain difficult to track at scale. This study evaluates three machine learning architectures (XGBoost, U-Net and SegFormer) for the joint estimation of SWE and HS variations from Sentinel-1 InSAR data over the Italian Alps, using the IT-SNOW reanalysis as reference. SegFormer achieves the best results on both targets, with an MAE of 10.391 cm for HS and 27.113 mm w.e. for SWE and the lowest variability across initializations. A feature sensitivity analysis shows that including all available features does not guarantee the lowest error, with model- and task-specific sensitivities. Spatial metrics (R2, Pearson's r) separate the three architectures far more clearly than mean error (MAE, RMSE) does, and decomposing the error per window attributes most of it to a systematic offset in the estimated mean variation rather than to the spatial pattern.
