Deep neural networks (DNNs) have demonstrated remarkable success across various domains, including Earth Observation applications. Despite their achievements, DNNs do not quantify the uncertainty of their predictions, which is particularly crucial for high-stakes applications such as flood mapping. We applied density-aware deep neural networks for uncertainty quantification in SAR-based flood mapping through a single forward pass. The aleatoric uncertainty is captured through softmax entropy, while epistemic uncertainty is quantified using density in the latent feature space. Our image segmentation results illustrate that the employed density-aware deep neural networks exhibit good performance in uncertainty quantification, surpassing Deep Ensembles for out-of-distribution (OOD) data detection.

This paper proposes the observations of oil drift and the changes in oil shape and size based on the collocation of Sentinel-1 descending and ascending images with a time lag of 12 hours offshore Nigeria. The oil slicks are first detected from the descending and ascending images using the hierarchical split-based approach to identify oil objects and non-linear filters (mean and standard deviation) to determine oil contours. Then, the detected oil is collocated to estimate the distance and direction of its movements. Finally, surface wind and current data are used for analyzing the relationship between met-ocean conditions and the evolution of oil slicks.

Previous references indicated that surface wind gusts associated with deep convection can be observed and estimated from Sentinel-1 images. They also presented the relationship between surface wind patterns and deep convective clouds observed on Meteosat geostationary (GEO) images. To strengthen this relationship, this paper presents the comparison between surface wind speed retrieved from Sentinel-1 data, wind magnitude measured by the weather stations, and deep convective clouds observed on GOES-16 GEO images over the Gulf of Mexico. The results show that a mesoscale surface wind pattern (a squall line) observed on Sentinel-1 images corresponds to deep convective cloud locations. In particular, the peaks of wind intensity measured by the weather stations match the Sentinel-1 wind gusts and the deep convective clouds.

Due to the complexity of urban environments, the synthetic aperture radar (SAR) based mapping of floodwater is impacted by different factors such as water depth, building orientation and the density of built-up areas. Several studies have proven that both SAR multitemporal intensity and interferometric SAR (InSAR) coherence data acquired in VV and VH polarizations support the urban flood mapping. We propose a deep learning (DL) based method using dual-polarization Sentinel-1 multitemporal intensity and coherence data combined with prior information to map floodwater in urbanized areas. The proposed method aims at mapping flooded areas in urbanized regions and bare soils/sparsely vegetated areas within the entire frame of a Sentinel-1 image. In this paper, our method is evaluated for the Houston (US) urban flood event in 2017 via a qualitative and quantitative comparison with two established DL models. The proposed method has the lowest number of false alarms in flooded urban areas, indicating that the prior information from the probabilistic urban mask is valuable.

Due to the complexity of backscattering mechanisms in built-up areas, the synthetic aperture radar (SAR)-based mapping of floodwater in urban areas remains challenging. Open areas affected by flooding have low backscatter due to the specular reflection of calm water surfaces. Floodwater within built-up areas leads to double-bounce effects, the complexity of which depends on the configuration of floodwater concerning the facades of the surrounding buildings. Hence, it has been shown that the analysis of interferometric SAR coherence reduces the underdetection of floods in urbanized areas. Moreover, the high potential of deep convolutional neural networks for advancing SAR-based flood mapping is widely acknowledged. Therefore, we introduce an urban-aware U-Net model using dual-polarization Sentinel-1 multitemporal intensity and coherence data to map the extent of flooding in urban environments. It usesa prioriinformation (i.e., an SAR-derived probabilistic urban mask) in the proposed urban-aware module, consisting of channel-wise attention and urban-aware normalization submodules to calibrate features and improve the final predictions. In this study, Sentinel-1 single-look complex data acquired over four study sites from three continents have been considered. The qualitative evaluation and quantitative analysis have been carried out using six urban flood cases. A comparison with previous methods reveals a significant enhancement in the accuracy of urban flood mapping: the F1 score of flooded urban increased from 0.3 to 0.6 with few false alarms in urban area using our method. Experimental results indicate that the proposed model trained with limited datasets has strong potential for near-real-time urban flood mapping.

Operational services in the fields of flood monitoring and prevention are benefitting from the large scale and systematic availability of synthetic aperture radar (SAR) data. The main advantages of SAR data are that they provide synoptic views over wide areas, day and night and all-weather condition acquisitions and a reliable data acquisition schedule. Satellite SAR data availability has increased over the past few years due to renewed efforts of several space agencies to put in place new satellite constellations. The latter enable the reduction of the satellite time access to areas of interest and provide enriched information with increased spatial resolution as well as variable polarizations and frequencies. The current situation tells us that there are regions in the world and land cover classes where SAR-derived flood maps are very reliable and accurate, but others where uncertainty is still very high, or where SAR is even unable to provide flood extent information. Therefore, the aim of this paper is to provide an overall picture of SAR-based floodwater mapping algorithms and their suitability for operational applications.