The UC Berkeley Random Walk Algorithm WaterMask from CYGNSS (Berkeley-RWAWC) is a new data product designed to address the challenges of monitoring inundation in regions hindered by dense vegetation and cloud cover as is the case in most of the Tropics. The Cyclone Global Navigation Satellite System (CYGNSS) constellation provides data with a higher temporal repeat frequency compared to single-satellite systems, offering the potential for generating moderate spatial resolution inundation maps with improved temporal resolution while having the capability to penetrate clouds and vegetation. This paper details the development of a computer vision algorithm for inundation mapping over the entire CYGNSS domain (37.4°N–37.4°S). The sole reliance on CYGNSS data sets our method apart in the field, highlighting CYGNSS's indication of water existence. Berkeley-RWAWC provides monthly, low-latency inundation maps starting in August 2018 and across the CYGNSS latitude range, with a spatial resolution of 0.01° × 0.01°. Here we present our workflow and parameterization strategy, alongside a comparative analysis with established surface water data sets (SWAMPS, WAD2M) in four regions: the Amazon Basin, the Pantanal, the Sudd, and the Indo-Gangetic Plain. The comparisons reveal Berkeley-RWAWC's enhanced capability to detect seasonal variations, demonstrating its usefulness in studying tropical wetland hydrology. We also discuss potential sources of uncertainty and reasons for variations in inundation retrievals. Berkeley-RWAWC represents a valuable addition to environmental science, offering new insights into tropical wetland dynamics.

中文翻译：

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Berkeley-RWAWC：基于 CYGNSS 的新型水罩揭示了热带季节动态的独特观测


CYGNSS (Berkeley-RWAWC) 的加州大学伯克利分校随机游走算法 WaterMask 是一种新的数据产品，旨在解决在大多数热带地区受茂密植被和云层覆盖阻碍的地区监测洪水的挑战。与单卫星系统相比，旋风全球导航卫星系统（CYGNSS）星座提供的数据具有更高的时间重复频率，有可能生成具有改进时间分辨率的中等空间分辨率淹没图，同时具有穿透云层和植被的能力。本文详细介绍了用于整个 CYGNSS 域（37.4°N–37.4°S）淹没测绘的计算机视觉算法的开发。完全依赖 CYGNSS 数据使我们的方法在该领域脱颖而出，突出了 CYGNSS 对水存在的指示。 Berkeley-RWAWC 从 2018 年 8 月开始提供月度低延迟淹没地图，覆盖 CYGNSS 纬度范围，空间分辨率为 0.01° × 0.01°。在这里，我们介绍了我们的工作流程和参数化策略，以及与四个地区已建立的地表水数据集（SWAMPS、WAD2M）的比较分析：亚马逊盆地、潘塔纳尔湿地、苏德河和印度恒河平原。这些比较揭示了 Berkeley-RWAWC 检测季节变化的能力增强，证明了其在研究热带湿地水文方面的有用性。我们还讨论了不确定性的潜在来源以及洪水反演变化的原因。 Berkeley-RWAWC 代表了环境科学的宝贵补充，为热带湿地动态提供了新的见解。