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.

Previous studies have shown that the decrease of temporal interferometric synthetic aperture radar (InSAR) coherence could be exploited to detect the appearance of floodwater in urban areas. However, as of today, approaches based on this principle only make use of single co-polarization images for identifying the presence of floodwater in the double-bounce feature. In this study, we take advantage of both co- and cross-polarization images to detect significant decreases of the multitemporal InSAR coherence in order to enhance the mapping of floodwater in urban areas. We consider that not only double-bounce scattering, but also multiple-bounce may occur in urban areas depending on how the building facades are oriented with respect to the synthetic aperture radar (SAR) sensor’s line of sight. The Sentinel-1 (S-1) mission is particularly well suited for applying and testing this kind of approach due to the systematic availability of dual-polarization data. Using as a test case, the widespread flooding in the city of Houston, USA, caused by Hurricane Harvey in 2017, we demonstrate that the proposed methodology leads to an increase of the accuracy of the urban flood maps from 75.2% when only using the VV polarization, to 82.9% when using the dual polarization information.

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.

This study addresses the effects of SAR signatures of moving vessels extracted from Sentinel-1 imagery, that suffer from a loss of focus due to the azimuthal velocity, e.g. target defocusing. The effects generated by SAR moving targets in the azimuthal direction result in residual Doppler chirps that can be estimated and characterized by processing Sentinel-1 Single Look Complex (SLC) images. We propose to employ the fractional Fourier transform (FrFT) in order to compensate the moving target defocusing effects in the SLC domain. In addition, different Sentinel-1 polarimetric representations of a target within the FrFT domain are also addressed and fused. The experimental results are based on Sentinel-1 Stripmap images and are cross compared with AIS data.

Due to the similarity of the radar backscatter in flooded and unflooded conditions over particular areas, it is not possible to carry out a comprehensive SAR-based flood mapping at large scale. In this paper, an additional information layer derived from Sentinel-l time series data, called Exclusion map (EX-map), is introduced. Its aim is to enhance and complement the results of automatic change detection-based flood mapping methods. The EX-map aims at delineating areas where observed variations of SAR backscatter do not allow detecting the appearance of floodwater. The EX-map is mainly composed of the following land cover classes: topographic shadow/layover, double bounce and smooth tarmac in urban areas, arid areas, dense vegetation and permanent water bodies. The method is evaluated over six study sites across the globe and tested for different flood events. The EX-map not only increases the classification accuracy of change detection-based flood maps derived from Sentinel-l data from 95.92% to 97.02%, but also enables a better interpretation of any SAR-based floodwater map.

This letter addresses the use of the Sentinel-1 time series with the aim of proposing an automatic and unsupervised coastline detection method that averages the dynamical variations of coastal areas over a limited period of time, e.g., one year. First, we propose applying a temporal averaging filter that allows the temporal variations in coastal areas, e.g., due to tides or vegetation, to be encapsulated, and, at the same time, the speckle to be reduced, without decreasing the spatial resolution of the synthetic aperture radar (SAR) time series. Then, based on the distinctive backscattering values of the sea and land pixels, we will employ an iterative hierarchical tiling method in order to accurately characterize the two classes using bimodal distribution. The distribution is then segmented by a thresholding and region-growing procedure to separate the sea and land classes. A large-scale quantitative comparison between the SAR-derived and open street map (OSM) coastlines allows for a numerical evaluation of the results, i.e., an overall agreement ranging from 80% to 90%. In addition, Sentinel-2 images are used to evaluate the estimated SAR coastline qualitatively. Furthermore, the benefits of having an accurate SAR coastline are shown in the case of two well-known Earth observation-monitoring applications, ship detection, and floodwater mapping.

We introduce a new SAR-based flood extent mapping algorithm enabling systematic and automatic monitoring of water bodies at large scale in near real time. The algorithm is both efficient and robust, especially in areas where flood events are not sporadic but long lasting (e.g. monsoon-related floods). It is based on the regular processing of subsequently acquired pairs of Sentinel-1 images. The algorithm has been developed in the framework of the ESA-funded e-DRIFT project, with the aim to respond to the needs of the disaster risk financing sector in Southeast Asia. The approach has been validated after intensive testing over different areas of interest in South East Asia i.e. Myanmar and Laos, where risks associated with flooding are currently not well-known. Moreover, the algorithm is implemented on a virtual platform that efficiently handles large collections of Sentinel-1 data from all the orbits and dates available over areas of interest affected by floods. The output of this near real-time system are reliable and useful input data for the parametric modelling carried out by the (re-)insurance companies, allowing them to better anticipate risk of natural disasters.

In this paper, we present a fully automatic algorithm capable of mapping floodwater in urban areas using 20 m Sentinel-1 SAR data. It is composed of a two-steps approach that first uses the SAR data to identify buildings and then takes advantage of the Interferometric SAR coherence feature from both co- and cross-polarizations to detect the presence of floodwater in urbanized areas. The preliminary detection of buildings is a pre-requisite for classifying them as flooded based on the InSAR coherence temporal decrease when water is present in urban areas, given that in general buildings show a strong temporal coherence. In addition, the short temporal and perpendicular baselines of the intereferomeric Sentinel-1 image acquisitions is an advantage for this kind of approach. The algorithm is applied to Sentinel-1 images acquired during the major flood event that hit Jakarta (Indonesia) in January 2020.

This research addresses the use of Sentinel-1 time series with the aim of detecting spatio-temporal changes in the coastal environment. To this end an automatic and unsupervised coastline detection method is proposed. First, we apply a temporal averaging filter that allows encapsulating the temporal variations in coastal areas, e.g. due to tides or vegetation, and at the same time it allows reducing the speckle, without decreasing the spatial resolution of the Synthetic Aperture Radar (SAR) images. Then, based on the distinctive backscattering values of the sea and land classes we employ an iterative hierarchical tiling method in order to accurately characterize the two classes by a bimodal distribution. The latter is then segmented by a thresholding and region-growing procedure to separate the sea and land classes. The proposed method is applied to two different SAR time-series, each one acquired throughout one year. The extracted yearly coastlines are then analyzed in order to identify spatio-temporal changes. Experimental results showcase coastal area changes between occuring 2018 and 2019 and that were caused by the hurricane Michael hitting Northwest Florida in October 2018.

This research addresses the use of machine learning for detecting building footprints from dual-polarization multitemporal SAR data. We assume that in SAR images, built-up areas exhibit very high backscattering values, making them brighter than other land cover classes in both the co- and cross-polarization channels. In addition the building class is considered to be stable in time. In this context, we propose to employ a convolutional neural network that integrates a set of SAR images of different dates and polarizations for delineating the building footprint. The algorithm was tested on Sentinel-1 datasets from eight different test sites located in Iraq. Four of the test sites along with their corresponding annotations (i.e. ground truth) constitute the training dataset, while the other four test sites are used for evaluation. All the datasets were provided in the framework of a humaritan AI4EO UNOSAT challenge launched in 2019 by Phi-Unet. The results evaluated by the F1 score with values of about 0.75 indicate that the proposed method is able to accurately detect the building footprint.

We propose a novel approach for rapid segmentation of flooded buildings by fusing multiresolution, multisensor, and multitemporal satellite imagery in a convolutional neural network. Our model significantly expedites the generation of satellite imagery-based flood maps, crucial for first responders and local authorities in the early stages of flood events. By incorporating multitemporal satellite imagery, our model allows for rapid and accurate post-disaster damage assessment and can be used by governments to better coordinate medium- and long-term financial assistance programs for affected areas. The network consists of multiple streams of encoder-decoder architectures that extract spatiotemporal information from medium-resolution images and spatial information from high-resolution images before fusing the resulting representations into a single medium-resolution segmentation map of flooded buildings. We compare our model to state-of-the-art methods for building footprint segmentation as well as to alternative fusion approaches for the segmentation of flooded buildings and find that our model performs best on both tasks. We also demonstrate that our model produces highly accurate segmentation maps of flooded buildings using only publicly available medium-resolution data instead of significantly more detailed but sparsely available very high-resolution data. We release the first open-source dataset of fully preprocessed and labeled multiresolution, multispectral, and multitemporal satellite images of disaster sites along with our source code.

In this work we present an automatic algorithm for providing probabilistic flood maps, not only on bare soils, but also within urban areas. The probabilistic flood mapping procedure is based on synthetic aperture radar (SAR) data and the Bayesian inference. Both intensity data and Interferometric SAR (InSAR) coherence feature are used. The approach improves the information content of a binary SAR-based floodwater map, which does not give any indication on the uncertainty in the pixel state.The proposed methodology is tested for the flood event that heavily affected the city of Houston (Texas) during the 2017 hurricane season. Data provided by the Sentinel-1 mission are used, with a geometric resolution of 20m. The algorithm takes fully advantage of the Sentinel-1 mission's repeat cycle of six days and narrow orbital tube to fully exploit the potentialities of InSAR coherence feature to detect floodwater in complex environments. The application of the proposed method to the Houston case study showed promising results.

This study addresses the use of Sentinel-1 data for innovative improvements of automatic classic ship detection detection chains. Firstly, we propose to extract the complex coherence from the two polarization channels and to perform the vessel detection the vessels in this domain. A comparative assessment between the use of the complex coherence and the intensity images together with AIS validation demonstrates that the fusion of the different results allows to reduce the number of false alarms while maintaining an optimal detection rate. Secondly, we propose to make use of Sentinel-1 time series in order to delineate the coastline which is an essential parameter for ship detection chains. Experimental results are conducted on Sentinel-1 images acquired in different areas of interest for maritime surveillance such as the Gulf of Califoria (Mexico) or the English Channel.

The main objective of this study is to introduce and evaluate a SAR-based flood mapping algorithm enabling the automatic generation of a large-scale flood record from the ENVISAT ASAR data archive. The flood mapping algorithm is based on a change detection approach and requires an automatic selection of optimal reference images. The flood mapping algorithm is applied to selected pairs of images to sequentially generate a record of flood extent maps. False alarms caused by water-like areas are reduced using auxiliary data sources such as the Height Above Nearest Drainage (HAND) index derived from topography data. The proposed method is applied to several ENVISAT WS ASAR datasets acquired over the UK and results are validated with a flood extent map derived from aerial photography. Results presented in this paper demonstrate the effectiveness of the methodology.

This study aims to map built-up areas using SAR data provided by the Sentinel-1 mission. The proposed algorithm exploits several features offered by the satellite mission such as: high revisit time, dual-polarization data and Interferometric SAR coherence. The algorithm is based on an adaptive parametric thresholding methodology that identifies pixels with high backscattering values in both VV and VH channels corresponding to built-up areas. The Interferometric SAR coherence allows discriminating false alarms caused by other land cover classes characterized by high backscattering values which are not coherent in time (e.g. certain types of vegetated areas). Both the intensity in VV and VH, as well as coherence features are obtained by averaging multi-temporal SAR series. This allows reducing the speckle without any loss in spatial resolution. The algorithm has been tested on Sentinel-1 Interferometric Wide Swath data from five different test sites located in semiarid and arid regions in the Mediterranean region and Northern Africa.

As of today, SAR imagery represents the most commonly used data source for remote sensing-based flood mapping. The data are characterized by a good sensitivity to water and are available day and night, regardless of cloud cover. Many studies have demonstrated that SAR systems are suitable tools for flood mapping on bare soils and scarcely vegetated areas. In spite of the progress in the development of Near Real Time SAR based flood mapping algorithms, the detection of inundation in urban areas still represents a critical issue. Here we propose a methodology for identifying floods that heavily affected the city of Houston (Texas) during the 2017 hurricane season. Our approach takes advantage of the Interferometric SAR coherence feature to detect the presence of floodwater in urbanized areas. In particular, data provided by the Sentinel-1 mission in both, Strip Map and Interferometric Wide Swath modes, have been used, with a geometric resolution of 5m and 20m, respectively. The algorithm takes fully advantage of the Sentinel-1 mission's repeat cycle of six days, thereby providing an unprecedented possibility to develop an automatic, high frequency flood mapping application that is suitable for complex environments. The test of the algorithm for the Houston case study showed promising results for mapping flood in urban areas.

For high accuracy flood mapping, an algorithm that integrates multiple satellite data sources is essential to maximize the sensor ability and compensate the limitations of optical and SAR data. The main objective of this study is to propose an algorithm of dynamic flood detection using optical and Synthetic Aperture Radar (SAR) images that compares and combines two different statistical thresholding approaches. To improve the flood detection accuracy, image fusion technique was investigated to maximize the utilization of calibrated and optimized flood maps as the integrated flood detection approach. To showcase the advantages of the proposed methodology, we employ MODIS, Landsat-8 and Sentinel-IA images acquired over a challenging area along the Brahmaputra River where flood events often occur.

This study addresses the use of dual-polarimetric descriptors for ship detection and characterization from Synthetic Aperture Radar (SAR) data. Ship detection is usually performed independently on each polarization channel and the results are merged subsequently. We propose to extract polarimetric descriptors from the two polarization channels and to perform the vessel detection the vessels in this domain. Several polarimetric descriptors, such as those derived from the the Eigenvector-Eigenvalue decomposition, are employed for this purpose. A comparative assessment between the use of intensity images and polarimetric descriptors for the detection and identification of ships is then realized. The proposed methodology is tested on Sentinel-1 data acquired over the English channel. Automatic Identification System (AIS) data flows are considered as ground truth.

The main objective of this study is to generate inundation maps of past flood events based on an archive of Synthetic Aperture Radar (SAR) data. Within a hierarchical image splitting framework, the flood mapping algorithm uses a histogram thresholding operation and a region growing process to delineate the flood extent. This algorithm is applied to an archive of SAR images in order to generate a flood frequency map. We define the flood frequency of a specific area as the ratio between the number of images where the area was detected as flooded and the total number of images within the employed data collection. SAR water-like ambiguities (e.g. urban areas, crops or shadow regions) are filtered out using auxiliary data sources such as the Height Above Nearest Drainage (HAND) index or land cover maps. The proposed methodology is applied to an ENVISAT ASAR image archive over the UK area. Results presented in this article demonstrate the effectiveness of this methodology.

This paper studies the effects of stationary-based processing of moving ship signatures in synthetic aperture radar (SAR) imagery and introduces a methodology to estimate and compensate for them. SAR imaging of moving targets usually results in residual chirps in the azimuthal SLC processed signal. The fractional Fourier transform (FrFT) makes it possible to represent the SAR signal in a rotated joint time-frequency plane and performs optimal processing and analysis of these residual chirp signals. The along-track defocus can thus be compensated for and the target's azimuthal speed estimated. The impact of higher order motion terms (e.g., acceleration) has been also considered. Experiments were conducted on a large number of ship signatures extracted from Radarsat-2 Multi Look Fine and Ultra Fine SAR images. An intercomparison with a standard Doppler Sublook Decomposition Method (SDM) is carried out, as well as a complete performance analysis with AIS data as ground truth.

This study addresses the performances of ship detection with data acquired by the newly launched Sentinel-1 SAR sensor. An automatic validation approach based on coastal AIS data is employed for measuring the detection efficiency. Results are compared with ship detection capabilities conducted on Radarsat-2 and CosmoSkymed datasets. The influence of different key parameters, such as SAR imaging characteristics (polarization, incidence angle) or meteorological conditions, is addressed. Such an analysis is useful for operational services to determine data specifications that assure optimum vessel detection for maritime surveillance applications.

This paper considers the impact of dynamical targets on SAR imagery, when processed with stationary based techniques. We propose to employ the Fractional Fourier Transform as a tool for estimating the residual Doppler rate corresponding to moving vessels. Hence, the defocusing effect can be corrected and the associated azimuthal velocity can be estimated. The capabilities of the proposed methodology are illustrated with moving vessels extracted from Radarsat-2 Multilook Fine images and AIS data flows as ground truth.

In this paper paper we address performances of ship detection algorithms in medium resolution SAR imagery. An automatic validation approach based on coastal AIS data allows to evaluate detectors efficiency. Detection capabilities remain sensitive to dataset features such as SAR imaging characteristics, meteorological conditions or vessel size. The influence of this different key parameters is fully assessed in this study. This analysis is valuable for operational services, allowing to select the most appropriate type of data for different applications in maritime surveillance.