This paper addresses the challenges of an early flood warning caused by complex convective systems (CSs), by using Low-Earth Orbit and Geostationary satellite data. We focus on a sequence of extreme events that took place in central Vietnam during October 2020, with a specific emphasis on the events leading up to the floods. In this critical phase, several hydrometeorological indicators could be identified thanks to Earth Observation satellites, which enable the characterization and monitoring of a CS, in terms of low-temperature clouds and heavy rainfall. Himawari-8 (H8) images, both individually and in time-series, allow identifying and tracking convective clouds. This is complemented by the observation of heavy/violent rainfall through GPM IMERG data, and the detection of strong winds using radiometers/scatterometers. Collectively, these datasets, along with the estimated intensity and duration of the event from each source, form a comprehensive dataset detailing the intricate behaviors of CSs. All of these factors are significant contributors to the magnitude of flooding and the short-term dynamics anticipated in the studied region.

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.

We propose a novel deep learning-based method that adapts the domains of images acquired by different remote sensing sensors. It adapts a lower resolution image to the domain of an an higher resolution targeted sensor. This is effective in the case of change detection, where differences between sensors, such as spatial resolution and radiometry, can hinder the detection performance and where model hallucination artifacts are unwanted. The proposed technique divides the input image into patches and uses a diffusion-based model to generate translated patches in the style of the target sensor. The translated patches are stitched together to form the output image, which provides global generative consistency. Our approach can handle images with different resolutions and tonalities. We show its effectiveness on a Sentinel-II + Planet Dove data set and demonstrate its high generation quality and contribution to enhance change detection performance.

Climate change is increasing the frequency and severity of hydrological extremes in many parts of the world. In Europe as well as in Luxembourg, droughts have worsened in intensity and duration in recent years, causing significant loss to several sectors, such as agriculture and forestry. There is a pressing need for developing and setting up advanced drought monitoring and prediction systems. In this context, this research work aims to improve drought prediction accuracy by jointly assimilating, into a hydrological model, various EO-based datasets, namely soil moisture (SM) and total water storage (TWS) derived from S-1 and GRACE & GRACE-FO satellite missions respectively. The assimilation of satellite-observed water content enables an integrated assessment and modeling of water resources through the monitoring and modeling of SM and groundwater availability in Luxembourg and the Greater Region, between 2012 and 2022.

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.

The accuracy of soil moisture estimated from Synthetic Aperture Radar backscatter data at high resolution is limited by speckle. Common practice to mitigate speckle is to multilook the data prior to retrieving soil moisture. While multilooking indeed reduces speckle, it also decreases the spatial resolution and removes possibly useful high resolution information from the data. We therefore hypothesised that using higher resolution backscatter data for soil moisture retrieval would lead to higher retrieval accuracies. A high-resolution field study combined with a synthetic experiment showed that calculating soil moisture prior to multilooking to the final target resolution (calculate-then-average, CtA) has substantial advantages over the average-then-calculate (AtC) approach. Currently, the AtC strategy is most often applied in soil moisture studies, mainly due to its computational advantage compared to the CtA approach. We show that by making use of a higher source resolution backscatter data than the target resolution, we could improve the soil moisture retrieval over an agricultural field.

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.

The accuracy of high resolution soil moisture estimated from SAR backscatter data is limited due to speckle in the native resolution backscatter data. However, reducing this speckly by means of spatial aggregation also removes useful information from the data. We therefore hypothesised that using unfiltered backscatter data in a soil moisture inversion model can be valuable in high resolution soil moisture applications. A field study combined with a synthetic experiment showed that calculating soil moisture prior to spatial averaging to the final target resolution (CtA) has substantial advantages over the average-then-calculate (AtC) approach. Currently, the AtC strategy is most often applied in soil moisture studies, mainly due to its computational advantage compared to the CtA approach. However, especially at high resolutions, using a slightly higher source resolution backscatter data than the target soil moisture resolution, can already improve accuracy of the soil moisture estimates.

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.

Space-borne SAR instruments can provide backscatter on a high spatial resolution, and with the introduction of the Sentinel-1 satellites, these can co-exist with relatively high temporal resolutions. Here, we use a combination of active microwave Sentinel-1 and optical Sentinel-2 data in the MULESME algorithm to estimate soil moisture on a field in Southeastern Luxembourg. Satellite data were compared to data gathered in the field and semi-continuous measurements from a nearby permanent station. Our results indicate that the accuracy of MULESME soil moisture estimates increases with a decrease in spatial resolution, but that this increase stagnates rather soon after the first few spatial aggregations, thus confirming the value of high resolution data. Future endeavours will focus on the analysis of soil moisture variation in time, compared to soil moisture measurements from a nearby permanent station.

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.

The new, systematic Global Flood Monitoring (GFM) product of the Copernicus Emergency Management Service will provide a continuous monitoring of floods worldwide by immediately processing and analysing all incoming S-1 Interferometric Wide Swath data and making use of the data cube approach enabling a high product timeliness and the implementation of flood mapping algorithms that require data-driven model training. It integrates three independently developed flood mapping algorithms to improve the robustness and accuracy of the flood and water extent maps and to build a high degree of redundancy into the service.

With the ongoing proliferation of satellite data, in particular open-access satellite imagery, from both optical and synthetic aperture radar (SAR) sensors, the number of downstream applications is rapidly growing. Developers of Earth Observation (EO)-based products and services, as well as expert and non-expert users of such tools, thus need access to a cloud computing infrastructure offering interoperable analysis functionality. Here, we present the versatility of such a cloud-based infrastructure called WASDI. WASDI, a web-advanced space development interface, is an online EO analytics platform where EO experts can develop and deploy applications (apps) and users can use them to processes satellite images on demand to generate value-added content.

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.

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.

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.

Monitoring of wetland inundation dynamics is important for flood management and the characterisation of hydrological connectivity. SAR-based inundation extent monitoring in wetlands is often challenging due to different factors, such as waves, vegetation cover and wet snow. The presented study targets the mapping of inundation dynamics in the Prairie Pothole Region (PPR) of North Dakota, USA. A 3-year water extent time series was derived from Sentinel-1 SAR data by first delineating permanent water bodies using a clustering approach. In a second step, water body dynamics were mapped using region growing and automatic thresholding. Results suggest that there is considerable potential for mapping surface water dynamics in late spring, summer and autumn, whereas confusion with wet snow may take place in early spring.

As part of the Copernicus Programme, Sentinel 1 (S1) synthetic aperture radar (SAR) mission represents a unique monitoring tool whose potentialities for hydrological risk mitigation need to be evaluated. To this aim, S1-A derived soil moisture maps with high spatial resolution (100 m) and moderate temporal resolution (12 days) were assimilated within a time-continuous, spatially-distributed, physically-based hydrological model (Continuum) with the specific objective to evaluate the impact on discharge predictions and (flash) flood modelling. A Nudging assimilation scheme was chosen for the DA experiment due to its computational efficiency, particularly useful for operational applications. Results were evaluated in the Orba River catchment (Italy) in the time period October 2014 — November 2016, corresponding to the first two years of activity of the S1-A mission.

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.

Parametric thresholding algorithms applied to synthetic aperture radar (SAR) imagery typically require the estimation of two distribution functions, i.e., one representing the target class and one its background. They are eventually used for selecting the threshold that allows binarizing the image in an optimal way. In this context, one of the main difficulties in parameterizing these functions originates from the fact that the target class often represents only a small fraction of the image. Under such circumstances, the histogram of the image values is often not obviously bimodal and it becomes difficult, if not impossible, to accurately parameterize distribution functions. Here we introduce a hierarchical split-based approach that searches for tiles of variable size allowing the parameterization of the distributions of two classes. The method is integrated into a flood-mapping algorithm in order to evaluate its capacity for parameterizing distribution functions attributed to floodwater and changes caused by floods. We analyzed a data set acquired during a flood event along the Severn River (U.K.) in 2007. It is composed of moderate (ENVISAT-WS) and high (TerraSAR-X)-resolution SAR images. The obtained classification accuracies as well as the similarity of performance levels to a benchmark obtained with an established method based on the manual selection of tiles indicate the validity of the new method.

First results of the assimilation of high-resolution Sentinel-1A based soil moisture products in a distributed, physically based, hydrological model are presented. A comprehensive evaluation of the assimilation's impact on discharge predictions is provided. Results are further compared to those obtained when assimilating the lower-resolution ASCAT-based soil moisture product. The exercise was carried out within the MIDA project framework (funded by the Italian Space Agency) aiming at producing root zone soil moisture maps useful for flood risk management applications. The experimental site is the Orba River Catchment (Italy). The period of investigation is October 2014-February 2015. Using a relatively simple data assimilation technique (Nudging) the results of our case study show that overall the assimilation of currently available Sentinel-1 data only marginally improves discharge simulations. However, the impact becomes more significant when specifically considering predictions of high flow. Further improvements are expected when both Sentinel-1A and B data will be available.

Classification algorithms for Synthetic Aperture Radar (SAR) imagery that are based on statistical modelling typically require the parameterization of distribution functions of backscatter values of different land cover classes to classify a scene. To parameterize accurately the distribution functions of the individual classes a sufficient number of pixels is needed and this criterion is not always satisfied, especially for classes occupying only a small fraction of the scene Here we propose an automatic algorithm that aims to map buildings based on the SAR intensity backscattering feature. It makes use of a hierarchical split-based approach that does not fix the size of the tiles a priori but, rather, searches for tiles of variable size where the distribution functions attributed to classes of interest can be parameterized in a robust way. The algorithm has been developed in the framework of the Urban Round-Robin exercise, supported by the European Space Agency (ESA) through the ESA Land Cover Climate Change Initiative (CCI), and tested on Sentinel-1 data from five different test sites located in semiarid and arid regions in the Mediterranean region and Northern Africa.

By providing high quality flood maps a spaceborne SAR can be an effective source of information. These maps support civil protection authorities for disaster risk reduction. Here we propose a methodology for identifying floods that occur on different types of land cover, such as urban areas, bare and poorly vegetated soil and vegetated areas. Our approach takes advantage of polarimetric SAR data and InSAR coherence to better characterize the landscape. Indeed, the Sentinel-1 repeat cycle of six days, and its systematic acquisition of dual-pol SAR data, provides an unprecedented chance to develop automatic, high frequency flood mapping algorithms for complex environments. The algorithm has been tested on two different Sentinel-1 datasets, acquired, respectively over Greece and Italy, showing promising results.

Observations of the temporal and spatial variations of water depth in rivers and floodplains are very important in operational hydrology. However, our capacity to monitor water depth at large scale is still very limited. As a result, the need to measure water storage changes in all wetlands, lakes, and reservoirs has motivated the radar interferometry-based Surface Water Ocean Topography Mission (SWOT) scheduled for launch in 2020.

Probabilistic flood mapping offers flood managers, decision makers, insurance agencies, and humanitarian relief organizations a useful characterization of uncertainty in flood mapping delineation. Probabilistic flood maps are also of high interest for data assimilation into numerical models. The direct assimilation of probabilistic flood maps into hydrodynamic models would be beneficial because it would eliminate the intermediate step of having to extract water levels first. This paper introduces a probabilistic flood mapping procedure based on synthetic aperture radar (SAR) data. Given a SAR image of backscatter values, we construct a total histogram of backscatter values and decompose this histogram into probability distribution functions of backscatter values associated with flooded (open water) and non-flooded pixels, respectively. These distributions are then used to estimate, for each pixel, its probability of being flooded. The new approach improves on binary SAR-based flood mapping procedures, which do not inform on the uncertainty in the pixel state. The proposed approach is tested using four SAR images from two floodplains, i.e., the Severn River (U.K.) and the Red River (U.S.). In all four test cases, reliability diagrams, with error values ranging from 0.04 to 0.23, indicate a good agreement between the SAR-derived probabilistic flood map and an independently available validation map, which is obtained from aerial photography.

A new method for flood hazard mapping that integrates global flood inundation modeling and microwave remote sensing is presented. It combines the time and space continuity of a global inundation model with the limited revisit time but high spatial resolution of satellite observations. The availability of model simulations over a long time period allows a robust estimate of non-exceedance probabilities that can be attributed to the corresponding satellite observations. The resulting flood hazard map will have a spatial resolution equal to that of the used satellite images, generally higher than that of the global inundation model. This can theoretically be done for any point in the world, allowing the estimation of flood hazard at a global scale, provided that a sufficient number of remote sensing images are available. The method is tested on the Severn River (UK), with a high number of flood events observed by ENVISAT ASAR. The global ECMWF flood inundation model is considered for this study.

Predictions of hydrological models are highly uncertain due to both the nature of the modelled system and the meteorological forcings. Soil moisture information derived from satellite data can help to reduce this uncertainty. Indeed, data assimilation techniques offer the possibility to dynamically correct the model evolution in order to improve the model output. However, several questions concerning the use of these techniques are still without answer. The aim of this work is trying to better understand what conditions allow a successful assimilation of satellite soil moisture products. To this end, three different products, along with several options in terms of filter design, were tested and their impact on data assimilation performances was evaluated.

Very high resolution synthetic aperture radar (SAR) sensors represent an alternative to aerial photography for delineating floods in built-up environments where flood risk is highest. However, even with currently available SAR image resolutions of 3 m and higher, signal returns from man-made structures hamper the accurate mapping of flooded areas. Enhanced image processing algorithms and a better exploitation of image archives are required to facilitate the use of microwave remote-sensing data for monitoring flood dynamics in urban areas. In this paper, a hybrid methodology combining backscatter thresholding, region growing, and change detection (CD) is introduced as an approach enabling the automated, objective, and reliable flood extent extraction from very high resolution urban SAR images. The method is based on the calibration of a statistical distribution of “open water” backscatter values from images of floods. Images acquired during dry conditions enable the identification of areas that are not “visible” to the sensor (i.e., regions affected by “shadow”) and that systematically behave as specular reflectors (e.g., smooth tarmac, permanent water bodies). CD with respect to a reference image thereby reduces overdetection of inundated areas. A case study of the July 2007 Severn River flood (UK) observed by airborne photography and the very high resolution SAR sensor on board TerraSAR-X highlights advantages and limitations of the method. Even though the proposed fully automated SAR-based flood-mapping technique overcomes some limitations of previous methods, further technological and methodological improvements are necessary for SAR-based flood detection in urban areas to match the mapping capability of high-quality aerial photography.

Soil Moisture (SM), being one of the main variables within the system that controls the hydrological interactions among soil, vegetation and atmosphere, plays a key role in the water cycle. Satellite systems, both active and passive, have already demonstrated their capability to provide reliable SM measurements. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, launched in November 2009, was the first specific SM satellite mission. In this work we assessed the capability of SMOS data to accurately capture SM dynamics over a long time period by comparing them with in situ observations. To better assess the performance of such results, they were also compared with those obtained with alternative satellite-based SM products, considering in particular those generated by Advanced Microwave Sounding Radiometer (AMSR-E) and Advanced SCATterometer (ASCAT) data.

The European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission was launched on November 2, 2009. Providing accurate soil moisture (SM) estimation is one of its main scientific objectives. Since the end of the commissioning phase, preliminary global SMOS SM data [Level 2 (L2) product] are distributed to users. In this paper, we carried out a first assessment of the reliability of this product through a comparison with in situ observed and modeled SM over three different sites: One is located in Luxemburg, and two are located in Italy. The period from August 1, 2010, to July 1, 2011, has been analyzed, giving us the opportunity to evaluate the satellite response to different SM states. The selected period is important for hydrological predictions as it is typically characterized by a sequence of transitions from dry to wet and from wet to dry conditions. In order to compare SMOS and ground SM measurements, a two-step approach has been applied. First, an exponential filter has been applied to approximate root-zone SM, and second, a cumulative distribution function matching has been employed to remove systematic differences between satellite and in situ observations and model simulations of SM. Our results indicate rather good reliability of the filtered and bias-corrected SM estimates derived from the first SMOS L2 products. Bearing in mind that an updated/advanced version of the SMOS SM product has been recently produced, our preliminary results already seem to confirm the potential of SMOS for monitoring of water in soils.

Exploitation of river inundation satellite images, particularly for operational applications, is mostly restricted to flood extent mapping. However, there lies significant potential for improvement in a 3-D characterization of floods (i.e., flood depth maps) and an integration of the remote-sensing-derived (RSD) characteristics in hydraulic models. This paper aims at developing synthetic aperture radar (SAR) image analysis methods that go beyond flood extent mapping to assess the potential of these images in the spatiotemporal characterization of flood events. To meet this aim, two research issues were addressed. The first issue relates to water level estimation. The proposed method, which is an adaptation to SAR images of the method developed for water level estimation using flood aerial photographs, is composed of three steps: (1) extraction of flood extent limits that are relevant for water level estimation; (2) water level estimation by merging relevant limits with a Digital Elevation Model; and (3) constraining of the water level estimates using hydraulic coherence concepts. Applied to an ENVISAT image of an Alzette River flood (2003, Grand Duchy of Luxembourg), this provides plusmn54-cm average vertical uncertainty water levels that were validated using a sample of ground surveyed high water marks. The second issue aims at better constraining hydraulic models using these RSD water levels. To meet this aim, a "traditional" calibration using recorded hydrographs is completed via comparison between simulated and RSD water levels. This integration of the RSD characteristics proves to better constrain the model (i.e., the number of parameter sets providing acceptable results with respect to observations has been reduced). Furthermore, simulations of a flood event of a different return period (2007) using the model calibrated for the 2003 flood event shows the reliability of the latter for flood forecasting.

The present case study focuses on monitoring the wetness state of the experimental Bibeschbach catchment (10.8 km2), located within the Alzette river basin in the Grand-Duchy of Luxemburg over the last three winters (2005-2008). The objectives of this study are (1) to retrieve soil moisture from spaceborne active and passive microwave sensors, namely AMSR-E and ERS-2 SAR, (2) to compare the remote sensing-derived estimates of basin-averaged soil moisture with ground measurements that are performed throughout the catchment.

Observed spatially distributed water stages with uncertainty are of considerable importance for flood modeling and management purposes but are difficult to collect in the field during a flood event. Synthetic aperture radar (SAR) remote sensing offers an inviting alternative to provide this kind of data. A straightforward technique to derive water stages from a single SAR flood image is to extract heights from a digital elevation model at the flood boundaries. Schumann et al. have presented a regression modeling approach as an improvement to this simple technique. However, regression modeling associated with their model may restrict output to mapping purposes rather than extend it to integration with other data or models. This letter introduces an inviting alternative that conducts statistical analysis on river cross-sectional data points, thereby allowing uncertainty assessment of remote-sensing-derived water stages without any regression modeling constraint. This renders remote-sensing data fit for, e.g., flood inundation model evaluation with uncertainty in observations and data assimilation studies, where (linear) ldquotransformation,rdquo i.e., modeling, to observed data should be minimal.

This paper presents a remote-sensing-based steady-state flood inundation model to improve preventive flood-management strategies and flood disaster management. The Regression and Elevation-based Flood Information eXtraction (REFIX) model is based on regression analysis and uses a remotely sensed flood extent and a high-resolution floodplain digital elevation model to compute flood depths for a given flood event. The root mean squared error of the REFIX, compared to ground-surveyed high water marks, is 18 cm for the January 2003 flood event on the River Alzette floodplain (G.D. of Luxembourg), on which the model is developed. Applying the same methodology on a reach of the River Mosel, France, shows that for some more complex river configurations (in this case, a meandering river reach that contains a number of hydraulic structures), piecewise regression is required to yield more accurate flood water-line estimations. A comparison with a simulation from the Hydrologic Engineering Centers River Analysis System hydraulic flood model, calibrated on the same events, shows that, for both events, the REFIX model approximates the water line reliably