Flood mapping and water depth estimation from Synthetic Aperture Radar (SAR) imagery are crucial for calibrating and validating hydraulic models. This study uses SAR imagery to evaluate various preprocessing (especially speckle noise reduction), flood mapping, and water depth estimation methods. The impact of the choice of method at different steps and its hyperparameters is studied by considering an ensemble of preprocessed images, flood maps, and water depth fields. The evaluation is conducted for two flood events on the Garonne River (France) in 2019 and 2021, using hydrodynamic simulations and in-situ observations as reference data. Results show that the choice of speckle filter alters flood extent estimations with variations of several square kilometers. Furthermore, the selection and tuning of flood mapping methods also affect performance. While supervised methods outperformed unsupervised ones, tuned unsupervised approaches (such as local thresholding or change detection) can achieve comparable results. The compounded uncertainty from preprocessing and flood mapping steps also introduces high variability in the water depth field estimates. This study highlights the importance of considering the entire processing pipeline, encompassing preprocessing, flood mapping, and water depth estimation methods and their associated hyperparameters. Rather than relying on a single configuration, adopting an ensemble approach and accounting for methodological uncertainty should be privileged. For flood mapping, the method choice has the most influence. For water depth estimation, the most influential processing step was the flood map input resulting from the flood mapping step and the hyperparameters of the methods.

翻译：基于合成孔径雷达（SAR）影像的洪水制图与水深估算对于率定和验证水动力模型至关重要。本研究利用SAR影像评估了多种预处理（特别是斑点噪声抑制）、洪水制图及水深估算方法。通过构建预处理影像、洪水淹没图与水深场的集成结果，分析了不同处理阶段的方法选择及其超参数的影响。研究以2019年和2021年法国加龙河两次洪水事件为案例，采用水动力模拟和现场观测数据作为参考基准进行评估。结果表明，斑点滤波器的选择会导致数平方公里的洪水范围估算差异；洪水制图方法的选择与调优同样显著影响结果性能。尽管监督学习方法整体优于无监督方法，但经过调优的无监督方法（如局部阈值分割或变化检测）可获得与之相当的结果。预处理与洪水制图环节累积的不确定性还会导致水深场估算产生高度变异性。本研究强调了需统筹考虑涵盖预处理、洪水制图与水深估算方法及其超参数的完整处理流程的重要性。相较于依赖单一配置，更应优先采用集成化处理策略并充分考虑方法学不确定性。对于洪水制图，方法选择的影响最为显著；而对于水深估算，最具影响力的处理环节是洪水制图阶段生成的淹没图输入以及各方法的超参数设置。