Remote sensing of rainfall events is critical for both operational and scientific needs, including for example weather forecasting, extreme flood mitigation, water cycle monitoring, etc. Ground-based weather radars, such as NOAA's Next-Generation Radar (NEXRAD), provide reflectivity and precipitation estimates of rainfall events. However, their observation range is limited to a few hundred kilometers, prompting the exploration of other remote sensing methods, particularly over the open ocean, that represents large areas not covered by land-based radars. Here we propose a deep learning approach to deliver a three-class segmentation of SAR observations in terms of rainfall regimes. SAR satellites deliver very high resolution observations with a global coverage. This seems particularly appealing to inform fine-scale rain-related patterns, such as those associated with convective cells with characteristic scales of a few kilometers. We demonstrate that a convolutional neural network trained on a collocated Sentinel-1/NEXRAD dataset clearly outperforms state-of-the-art filtering schemes such as the Koch's filters. Our results indicate high performance in segmenting precipitation regimes, delineated by thresholds at 24.7, 31.5, and 38.8 dBZ. Compared to current methods that rely on Koch's filters to draw binary rainfall maps, these multi-threshold learning-based models can provide rainfall estimation. They may be of interest in improving high-resolution SAR-derived wind fields, which are degraded by rainfall, and provide an additional tool for the study of rain cells.

翻译：降雨事件的遥感观测对业务和科研需求至关重要，例如天气预报、极端洪水减灾、水循环监测等。地基天气雷达（如美国国家海洋和大气管理局的下一代雷达NEXRAD）可提供降雨事件的反射率与降水估计，但其观测范围仅限数百公里，这促使人们探索其他遥感手段——尤其是在陆地雷达无法覆盖的广阔开阔海域。本文提出一种深度学习方法，实现合成孔径雷达（SAR）观测的三类降雨区域分割。SAR卫星具有全球覆盖能力且分辨率极高，这对解析对流单体等特征尺度仅为数公里的降雨相关精细结构尤为有利。研究表明，基于共位Sentinel-1/NEXRAD数据集训练的卷积神经网络，显著优于科赫滤波器等现有最优滤波方案。该模型能以24.7、31.5和38.8 dBZ为阈值对降水区域进行高精度分割。与当前依赖科赫滤波器生成二元降雨分布图的方法不同，这种多阈值学习模型可提供降雨强度估计，有望改善受降雨退化的高分辨率SAR风场反演结果，并为降雨细胞研究提供新的分析工具。