Although wetlands are widely recognized for thier important role in providing ecosystem services, their abundance, spatial extent, and condition remain poorly constrained and at-risk of decline. Accurate mapping and monitoring are therefore essential for their protection. However, distinguishing swamps from upland forests and shrublands is especially challenging because optical sensors cannot detect water and/or saturated soil under dense canopies. Synthetic Aperture Radar (SAR) offers distinct advantages in this regard: (1) under certain conditions, microwaves can penetrate vegetation and provide a strong backscattered signal from double bounce when surface water or very wet soil are present, and (2) microwaves can penetrate clouds, providing an opportunity to monitor changes in moisture or the extent of flooding through time. In spite of these advantages, users may still find it difficult to know which wavelengths, incidence angles, polarization states, and times of year can be used to detect swamps because of the complexity of choices, and some confusing and conflicting results presented in the literature. The goal of this research was therefore to better elucidate the impacts of sensor and environmental characteristics on the seasonal backscattering behaviour observed in and separability between swamps and dry, upland forests and shrublands, as well as determine the need for additional ancillary data like digital elevation models and derivatives to improve mapping accuracy. Using SAR data from three sensors with two different wavelengths, various polarization states, and a range of incidence angles we: (1) investigate the drivers of variations in seasonal trends and the frequency and timing of changes among different SAR time series, and assess their impact on separability, (2) quantify the importance of acquisition timing, type, number of derivatives on the accuracy of Random Forest models. Our results show that a common pre-conception that longer wavelengths are preferred for distinguishing flooded versus upland forests and shrublands has proven overly general, that data acquired before leaf flush in the spring provides superior results, and that DEM data only provides an advantage when using sub-optimal SAR data.

中文翻译：

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利用 SAR 区分温带落叶沼泽与高地森林和灌木丛的新见解


尽管湿地在提供生态系统服务方面发挥着重要作用，但其丰度、空间范围和状况仍然缺乏约束，并面临衰退的风险。因此，准确的测绘和监测对于保护它们至关重要。然而，区分沼泽与高地森林和灌木丛尤其具有挑战性，因为光学传感器无法检测浓密树冠下的水和/或饱和土壤。合成孔径雷达 (SAR) 在这方面具有明显的优势：(1) 在某些条件下，微波可以穿透植被，并在存在地表水或非常潮湿的土壤时通过双反射提供强烈的反向散射信号，(2) 微波可以穿透云，提供了监测水分变化或洪水程度随时间变化的机会。尽管有这些优点，用户可能仍然发现很难知道哪些波长、入射角、偏振态和一年中的时间可以用来检测沼泽，因为选择的复杂性，以及文献中提出的一些令人困惑和相互矛盾的结果。因此，这项研究的目标是更好地阐明传感器和环境特征对沼泽和干燥、高地森林和灌木丛中观察到的季节性反向散射行为以及它们之间的可分离性的影响，并确定是否需要额外的辅助数据，例如数字高程模型和导数以提高绘图精度。 使用来自具有两种不同波长、不同偏振状态和一系列入射角的三个传感器的 SAR 数据，我们：(1) 研究季节趋势变化的驱动因素以及不同 SAR 时间序列之间变化的频率和时间，并评估它们的变化对可分离性的影响，（2）量化采集时机、类型、导数数量对随机森林模型准确性的重要性。我们的结果表明，一种普遍的先入为主的观念，即较长的波长更适合区分被淹没的森林和高地森林和灌木丛，已被证明过于笼统，在春季叶子冲刷之前获取的数据提供了更好的结果，并且 DEM 数据仅在使用时才提供优势次优 SAR 数据。