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.

Since 2018, for the first time, space measurements of colocated wind vectors and wave spectral characteristics are available thanks to the French/Chinese CFOSAT mission, which carries a wind scatterometer (SCAT) and a wave scatterometer (SWIM). Four years after its launch, CFOSAT data processing has been improved to reach a high level quality, leading to a reprocessing of the whole mission dataset. This paper focuses on the CFOSAT SWIM data reprocessing, the product performance, now homogeneous over mission lifetime, the complementarity with SAR observations and some scientific contributions to oceanography.

Precipitation measurement is an important prior for several operational and scientific applications, including weather forecasting, hazard prevention, agriculture, etc. Weather radars, such as NEXRAD, observe the air volume reflectivity and infer precipitation intensity at high resolution. However, their capabilities are limited over the ocean. C-band SAR imagery, which is sensitive to ocean surface roughness, is known to be sensitive to the effect of rain. In this study, we improve existing NEXRAD/Sentinel-1 collocations and train a U-Net deep learning model to estimate NEXRAD radar reflectivity from Sentinel-1 observations. Precipitation fore-casts are returned as segmentations with thresholds at 1, 3 and 10 mm/hr. The results indicate high performance over a wide range of wind speeds and thus can provide an accurate estimate of precipitation in the absence of weather radar.

Segmentations of ocean SAR images (Sentinel-1 A and B) into 10 classes of metoceanic phenomena are for the first time presented, with a 400 m resolution. Ocean SAR images segmentation differs from classic deep learning problems with a high variety of shapes and a particular importance of high-frequency patterns. To this end, an assessment of deep learning frameworks is performed, with a focus on the comparison between weakly supervised and supervised methods. Metrics based on the Wassertein distance indicate best performances by the supervised segmentation (U-Net) given operational constraints, thus highlighting the significance of properly annotated data sets. While available training data sets are made of small$20 \times 20 \text{km}$imagettes, the extension of the inference from imagettes to wide swath images, with a wider variety of incidence angles, presents promising results and opens the way to more extensive oceanographic applications in SAR imagery.

The CFOSAT (China France Oceanography Satellite) mission launched in 2018 now routinely provides at the global scale, directional spectra of ocean waves. The principle is based on the analysis of the normalized radar cross-section measured by the instrument SWIM (Surface Waves Investigation and Monitoring), a near-nadir pointing Ku-Band real-aperture scanning radar. From the ocean wave spectra derived from SWIM, the principal parameters of ocean wave spectra as significant wave height, peak wavelength, and peak direction are now available to better characterize the sea-state. However, it is known that these principal parameters are not sufficient not fully characterize the distribution of wave energy and understand or validate the physical processes impacting its evolution during growth order decay. Here we show that the parameters characterizing the shape of the wave spectra (e.g directional and frequency spread) can be estimated at the global scale from the SWIM measurements. We also show that they can provide consistent values of the Benjamin-Feir index, an index proposed to estimate the probability of extreme waves. Similarities of differences with the shape parameters of the MFWAM numerical wave model are also discussed.