Rain regime segmentation of Sentinel-1 observation learning from NEXRAD collocations with Convolution Neural Networks
This work addresses the need for improved rainfall estimation over areas not covered by ground-based radars, such as oceans, offering a tool for enhancing SAR-derived wind fields and studying rain cells, though it is incremental as it builds on existing methods with a learning-based approach.
The paper tackles the problem of remote sensing rainfall events by proposing a deep learning approach for three-class segmentation of SAR observations, demonstrating that a convolutional neural network trained on collocated Sentinel-1/NEXRAD data outperforms state-of-the-art filtering schemes like Koch's filters with high performance delineated by thresholds at 24.7, 31.5, and 38.8 dBZ.
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.