Conference Agenda

We analyzed a great deal of SAR images acquired over the German part of the Wadden Sea by the L-, C-, and X-band SARs aboard ALOS-2, Radarsat-2 and Sentinel-1, and TerraSAR-X, respectively. Using this wide range of multi-frequency / multi-polarization SAR data we investigated which combinations of radar band and polarization are best suited for a classification of different Wadden Sea surface types, including sandy and muddy sediments, sea grass meadows, and bivalve beds. New parameters, based on a decomposition of the complex SAR data, were used as input into a UNet-based semantic segmentation network with a texture-enhancement (TE) module to classify intertidal sediments and habitats. The experiment results verified the superiority of TE-UNet model compared with state-of-the-art semantic segmentation models.

Oceanic eddies are widely distributed in the global ocean, they play a crucial role in the global ocean energy cycle, the transport of heat and salt, and the distribution of nutrients. Synthetic Aperture Radar (SAR) is an ideal sensor for studying ocean eddies due to its high spatial resolution and independence of daytime and weather conditions. This paper proposes a method based on the deep learning networks of the YOLO family, named EOLO, to detect and extract geographic information of ocean eddies on C-band spaceborne SAR images. Several key enhancements were made to improve the performance of EOLO, including the introductions of spatial attention mechanism and new up-sampling operator, and improvements of the feature fusion method, loss function, and anchor box size, which contribute to an average precision of 91.5%. We also conducted experiments in the Baltic Sea, the Red Sea, and the Western Mediterranean Sea to verify the generalization of EOLO in different seas, reaching 95.7%, 96.8%, and 96.5% precision respectively. Furthermore, 8569 eddies were extracted by EOLO in the Western Mediterranean Sea in 2021, compared with the eddies from the altimeter data, the results show that the SAR eddies based on EOLO can detect at least 45% of the ocean eddies invisible to altimeters and are more realistic.

We introduce a neural network for the automatic detection of waterlines on Sentinel-1A/B SAR imagery of intertidal flats. The neural network is able to segregate water from intertidal flats for certain weather conditions in the German North Sea coast with high precision for calm weather conditions.
To simultaneously detect large structures and achieve a high resolution, the neural network consists of two stages of resolution. The neural network is structured as an image-to-image network and takes radar images and an ordinary land-water mask as input. The first stage produces a low resolution (640x640 m) allocation, whether a depicted area contains mostly land, mostly water or approximately equal parts. The allocation from the first stage is added to the input for the second stage, clarifying where islands and tideways are located. The second stage considers small parts of the radar image in a high resolution, accurately segregating water from intertidal flats at the resolution of the radar image (10x10m).
After re-merging the image, flood filling is performed to eliminate minor inaccuracies. However, relevant parts can be negated by this procedure. A more detailed land-water mask might mitigate that problem.

The tangential wind speed increases from the center to the eyewall of tropical cyclones (TC) along the radial direction and begins to decay when it extends outward. The tangential wind profile model is one of the most effective and widely used methods to reconstruct the TC radial wind speed. This paper proposes a parametric tangential wind profile (TWP) model based on high-spatial-resolution SAR imagery. The new model functions are piecewise with maximum tangential wind speed as a threshold, and all of them are designed as nonlinear. Notably, the derivative at the segmentation threshold is zero to ensure a smooth transition of the estimated wind speed profile. With the SAR-derived azimuth-averaged wind speed, we can determine the model parameters and get the tangential wind speed. The TWP model outperforms the commonly used SMRV model, as it better resolves the tangential wind profile shape as depicted by both SAR-derived winds and hurricane hunter Stepped-Frequency Microwave Radiometer (SFMR) derived winds. A comprehensive analysis of the TWP model parameters is carried out by fitting tangential winds for 620 hurricane hunter flights. Interestingly, the tangential wind profiles for major hurricanes show a similar shape. The proposed TWP model can be used for improved TC characterization and forecasting purposes.

Although The SAR is unanimously recognized as a key operational remote sensing instrument for oil spill surveillance and damage assessment owing to its all-day and almost all-weather observation ability together with its fine spatial resolution, the estimation of ancillary information related, e.g., to oil’s thickness and fraction water content is still a challenging task. Crude oil slicks and their emulsions can form thick layers ranging from micrometer to millimeter and can even reach centimeter thickness in the case of fresh oil under low sea state conditions [5]. The oil thickness distribution is as important as the spatial distribution of oil slicks, which is beneficial for proper choice of response methods and spatial allocation of response resources, as well as for legal purposes for prosecution.

In this study, the potential of the electromagnetic scattering model to retrieve quantitatively parameters of oil spills on the sea surface is investigated using the Artificial Neural Network (ANN) technique. In the SAR image plane, oil slicks appear as dark patches compared with ambient clean sea surface. This is due to both geometrical and electrical effects of the oil slick which, one on side, damps the short-gravity and capillary sea waves resulting in a lower backscattered signal and, on the other side, if the slick is thick enough or emulsified it can also change dielectric properties of the upper sea layer. To extract information about the oil slick from SAR imagery while limiting the effects of sea state conditions and incidence angle, the damping ratio (DR), i.e., the slick-free to slick-covered backscattering ratio, has been widely adopted. The backscattering DR of the oil slick is predicted using the AIEM augmented with MLB, effective dielectric constant model, and the composite medium model to include the effect of an oil slick. The simulated DR and their corresponding oil parameters, namely the oil thickness and seawater volume fraction, are utilized to train and test the NN with a relatively simple four-layer structure. The rationale consists of overcoming the lack of ground information using a forward scattering model. The adequately trained NN is then applied to the L-band UAVSAR image collected during the DWH oil spill accident to retrieve the slick thickness and volumetric fraction of seawater in the oil layer. The inversion results show that the thick emulsions are in the middle portion of the slick with 2 – 4 mm thickness surrounded by thin films with thickness less than 1mm. And the water content of the DWH oil slick is about 20% - 30%. Results are contrasted with optical data and previous studies of DWH oil spill and show qualitatively good agreement.

This study is supported by the ESA-NRSCC Dragon-5 cooperation project “Monitoring harsh coastal environments and ocean surveillance using radar remote sensing sensors” (ID 57979).

Ocean surface current (OSC) is one of the key marine dynamic elements which dominates the global circulation of carbon and heat. Measurements of the OSC are of particular significance for the studies and applications of marine environment monitoring, global climate change forecasting, marine search and emergency response, etc. [1-3]. By modulating ocean surface topography and roughness, the ocean currents could be characterized on synthetic aperture radar (SAR) images [4]. By far, two main-stream technical routines for OSC measurement based on SAR have been proposed, i.e., along-track interferometric SAR (along-track INSAR, ATI) [5, 6] and Doppler centroid anomaly (DCA) technique [7, 8]. Essentially, these techniques utilize the surface Doppler information to retrieve the corresponding velocity which are confronted with the challenge of separating the contributions from waves and currents. This requires a physical modeling of radar scattering from ocean current surface. Thus, in this study, we aim to numerically investigate the polarimetric SAR imaging responses to two-dimensional ocean surfaces with currents and waves. The well-developed radar imaging model (RIM) proposed by Kudryavtsev et al. [4] is employed to conduct the numerical simulations under various frequencies, incidence angles, wind speeds and full polarizations. The current surface with a typical internal wave phenomenon generated by the MITgcm numerical mode is used, which has resolution of about 1/200° in longitude direction and 1/60° in latitude direction. The current modulation of wave spectrum is considered in the KHCC03 spectrum. Experimental results indicate that current modulation on ocean scattering is more significant at lower wind speeds. It is also noted that the current modulation of ocean scattering performs most remarkable at cross-polarization, while has least effects at VV-polarization. The current modulation effect is nonlinear to current velocity. At large current velocity, the current modulation effect could be saturated, particularly for co-polarizations. More detailed numerical results will be given in the presentation.

Sea surface wind field is a parameter of key importance for several applications that span from weather forecast up to recreation activities. In addition, it also plays a role in the context of climate change as one of the effects of global warming is the increasing occurrence of strong winds that threaten coasts and critical infrastructures.

The wind can be estimated using either in situ or remotely sensed measurements. In the latter case, the main satellite remote sensor is the scatterometer, i.e., a meso-scale radar that is specifically designed to make normalized radar cross-section (NRCS) measurements that can be converted into the wind field using tailored geophysical model functions (GMFs).

There is an increasing interest in the exploitation of finer-spatial resolution NRCS measurements acquired by the Synthetic Aperture Radar (SAR), i.e.; an imaging radar that can achieve meter or sub-meter spatial resolutions even when operated by satellite. The main challenge relies on that fact that the SAR is not meant to be operationally used for wind field estimation. In fact, it can only provide one NRCS measure for each resolution cell and this makes the inversion of the sea surface wind field an under-determined problem.

To cope with this problem, two strategies are possible that rely on a wind direction information that is introduced into the inversion problem from either external source (e.g., scatterometer, ECMWF) or directly estimated from the SAR scene. This latter option is very promising since it allows forcing the wind field estimation process with a wind direction input on the same spatial grid of the wind to be estimated.

SAR-based wind direction has been addressed using different methods: a) the analysis of the orientation of the wind-induced streaks, using Fourier transforms [1],[2],[3], the discrete wavelet transform [4], the continuous wavelet transform [5] and gradient methods local [6]. In [7] a deep residual network is used to estimate the wind direction from the SAR images even in small turbulent areas, in the absence of streaks on the SAR images and in the presence of ships.

In this study, a sensitivity study is carried out to analyze the performance of neural network (NN) wind direction retrieval on Cosmo-SkyMed X - band SAR imagery. The main objective is to estimate the wind direction from the COSMO-SkyMed SAR imagery with artificial intelligence techniques. This is physically possible by exploiting wind-generated patterns in the SAR image, therefore by estimating the orientation of these patterns.

Experiments are carried out using 19 Cosmo-SkyMed SAR scenes that are augmented with ancillary spatially and time-collocated scatterometer information acquired between February and august 2022 in the North Sea.

References

[1] Wackerman, C., C. Rufenach, R. Schuchman, J. Johannessen, and K. Davidson, 1996: Wind vector retrieval using ERS-1 synthetic aperture radar imagery. IEEE Trans. Geosci. Rem. Sens., 34, 1343-1352.

[2] S. Lehner, J. Horstmann, W. Koch, and W. Rosenthal, “Mesoscale wind measurements using recalibrated ERS SAR images,” J. Geophys. Res., vol. 103, no. C4, pp. 7847–7856, Apr. 1998.

[3] P. W. Vachon and F. W. Dobson, “Validation of wind vector retrieval from ERS-1 SAR images over the ocean,” Global Atmos. Ocean Syst., vol. 5, pp. 177–187, 1996.

[4] Yong Du, Paris W Vachon & John Wolfe (2002) Wind direction estimation from SAR images of the ocean using wavelet analysis, Canadian Journal of Remote Sensing, 28:3, 498-509, DOI: 10.5589/m02-029

[5] Zecchetto, S., De Biasio, F., Della Valle, A., Quattrocchi, G., Cadau, E., Cucco, A., 2016a. Wind fields from C and X band SAR images at VV polarization in coastal area (Gulf of Oristano, Italy). IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9 (6).

[6] Koch, W., 2004. Directional analysis of SAR images aiming at wind direction. IEEE Trans. Geosci. Remote Sens. 42 (4), 702–710.

[7] A. Zanchetta, S. Zecchetto, “Wind direction retrieval from Sentinel-1 SAR images using ResNet,” Remote Sensing of Environment, Volume 253, 2021, 112178, 2021.

Coastal macroalgae blooms have a profound influence on marine ecosystem balance, tourism, and aquaculture. Since 2008, the western coasts of the Yellow Sea have been damaged every summer by a green tide caused by the overgrowth of ulva prolifera. Remote sensing has been the primary tool for monitoring this green tide. With the emergence of more free synthetic aperture radar (SAR) images with high resolution and the ability to image in cloudy conditions, SAR images play an increasingly important role in green tide monitoring. Deep learning is a powerful method in remote sensing images classification. However, current studies mainly focus on the image's backscattering coefficient, while ignoring the morphological characteristics. Moreover, the proportion of algae-pixels and seawater-pixels is significantly unbalanced, which will reduce the learning ability of the deep learning method. To address these issues, we propose a deep learning method to detect ulva prolifera. The proposed model is designed on four sides: 1) We add texture features to extract the morphological characteristics of green algae. 2) We design a new loss function to maintain the learning ability of the proposed deep learning method. 3）we build a texture-enhanced path called texture concatenation to help extract Ulva prolifera with fuzzy boundaries. 4）we embed the convolutional block attention module (CBAM) after each convolution layer. For texture features, we calculated four representative gray level co-occurrence matrix (GLCM) maps of the VV polarized images, i.e., ASM, entropy, correlation, mean. Thus, the input dataset includes two polarization channels and four texture channels. For the new loss function, we used the combination loss of binary crossentropy and focal loss via different weights. To construct the proposed model, we labeled 6317/2124 pairs of Sentinel-1 SAR image patches as the training/testing dataset. All of the used images were preprocessed through radiometric calibration, speckle filtering, terrain correction, and incident angle effect correction. Experiments show that when binary crossentropy is weighted for 0.70 and focal loss for 0.30, the model performs best, with a mean intersection over union (mIOU) of 86.31%, outperforming the well-known segmentation models using the identical dataset and hyperparameters. In addition, we also used the proposed model to analyze the interannual variation of green tide in 2019-2021, and found that 2019(2020) has the longest (shortest) bloom duration and biggest (smallest) coverage area; 2021(2020) has the biggest (smallest) nearshore damage to the southern coastlines of the Shandong Peninsula.

The detection and monitoring of ground surface changes using multi-temporal, remotely-sensed images represent the most important applications of remote sensing (RS) technologies. Various applications have extensively used optical RS sensors for change detection (CD). The CD process involves analyzing two or more images captured over the same geographical area at different times to identify significant land cover changes. While optical sensors have been widely used for CD, microwave RS images acquired by synthetic aperture radar (SAR) have been less exploited. However, SAR images in CD are attractive for operational purposes since SAR sensors are active instruments that can operate in any atmospheric and sunlight conditions. Remotely sensed data collected by several constellations of SAR sensors, such as the twin Sentinel-1A/B sensors of the European (EU) Copernicus, enable fast mapping of changes of Earth's surface and allow the time monitoring of areas prone to geohydrological disasters.

Coastal flood risk is a global challenge, as about 40 million people living in coastal port cities will likely be subject to one significant coastal flood event per century. SAR remote sensing is a valuable tool for detecting and monitoring flood phenomena and can differentiate between inundated and non-inundated areas. Flood risk increases due to urban growth, ground subsidence, and climate change. Identifying areas more prone to extreme floods helps optimize urban planners' civil protection actions and evaluate the damage. Recent advances in RS technology have allowed the generation of rapid damage prediction maps and associated models that are helpful in the occurrence of a flood event.

This work focuses on the impacts of floods and extreme weather events on coastal areas' cultural heritage preservation, particularly the case of the monumental city of Venice and the whole Venice Lagoon area. The Venice Lagoon represents Italy's most extensive lagoonal system, one of the largest in the Mediterranean Sea, and one of Italy's most important industrial areas. The lagoonal system includes Venice, an extraordinary archaeological, urban, architectural, artistic, and cultural heritage masterpiece. The Venice Lagoon ecosystem is subject to various drivers of change, such as land-based feeding activities, heavy metal extraction, ground-water extraction, etc., causing multiple environmental impacts on the Lagoon. The subsidence phenomenon of the terrain is one of the most critical drivers of change. A CD study was conducted to analyze the ground deformation (subsidence) that occurred in the Venice Lagoon in recent years using the multi-temporal interferometric Small Baseline Subset (SBAS) technique. The study examined the interlinked effects between the underlying subsidence in the area and the recent extreme flood events that occurred in November 2019. The time series of backscattered S-1 signals were analyzed to identify the extent of the flooded regions and the impact of the floods on the city. The study also leveraged the potential of a newly developed AI method based on Random Forest.

This methodology uses coherent/incoherent SAR change detection indices (CDIs) and their mutual interaction in a single corpus to rapidly map surface changes. The method has shown great success in quickly mapping land surface changes of areas affected by enormous wildfires in Sardinia and Sicily in the summer of 2021 and flooded areas in Houston and Galveston Bay due to Hurricane Harvey in 2017.

We conducted a detailed analysis using 180 Sentinel-1 images acquired from January 2017 to December 2021 to investigate ground deformation in the Venice Lagoon. We generated a stack of 1736 short baseline (SB) interferograms and computed the lagoon's time series of deformation and mean deformation velocity map. We also used a series of pre- and post-flood event acquisitions on 12 November 2019 to perform a change detection analysis of Venice using temporal multi-looked sigma nought maps and Coherence Changes Indexes (CCI) from an AI-based methodology.

For the change detection analysis, we selected a time series of acquisitions with a temporal baseline of ±6, ±12, ±18 days before (11 November, 5 November and 30 October), during (17 November), and after (23 and 29 November, and 5 December) the flood event. Each SAR image of the time series underwent post-processing using a de-speckling noise filtering algorithm and co-registration using Enhanced Spectral Diversity (ESD) to the 17 November acquisition. We computed sigma nought differences (), and we used triplet of SAR images with temporal baselines of ±6, ±12, ±18, ±30, ±36, and ±42 days with respect to the 12 November pre- and co-disaster InSAR () CCI to determine the coherence changes indexes.

The results of the interlinked analysis showed that only a tiny region of the emerged lands in the north and south of the Venice Lagoon is affected by subsidence. Flood events represent a severe threat to the integrity of these areas. The deformation analysis of the city of Venice showed no significant subsidence phenomena. Still, some spot regions over the low-lying lagoon terrains were affected by remarkable signals associated with sea level rise (SLR), which can seriously impact the hydrogeology of the area.

Coastal regions are with dense population, buildings and infrastructures, and vulnerable to natural disasters. Frequent natural disasters will cause huge economic losses and human casualties. Shanghai is a costal mega city and located in low-elevation coastal zones of the Yangtze River Delta. The city is frequently affected by typhoon and storm surge. Besides, The geological foundation of the city consists of soft alluvial deposits, including clay, silt, and sand. Due to its geological conditions, Shanghai is vulnerable to ground subsidence, flooding, and other geohazards.

In order to prevent, resist and reduce the impact of disasters, this study assesses the regional disaster reduction risk (DRR) capacity of a district of Shanghai with Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and established a machine learning aided evaluation models. We also retrieved long-term and recent ground deformation of the coastal areas of Shanghai with Small Baseline Subset (SBAS) technology and multi-sensor Synthetic Aperture Radar image time-series. We also simulate the possible flood inundation extent under different scenarios based on LISFLOOD-FP simulation model in coastal regions. For towns with weak DRR capacity, we analyze sensitivity of evaluation indicators to explore key indicators which affect improvement of DRR capacity. Finally, we proposed optimal strategies which could improve DRR capacity based on the assessment results of DRR capacity and regional disaster characteristics.

Wetland is known as the "kidney of the earth", and its ecological function, biodiversity, and various values ​​are irreplaceable. However, due to the rapid urbanization process, wetland resources are decreasing day by day, so the investigation of wetland resources is very important. The Yangtze estuarine wetland provides various ecosystem services. However, affected by human activities (upstream sediment reduction) and natural background (sea-level rise, SLR), the saltmarsh of the estuarine wetland is undergoing dramatic changes in recent years.

As one of the important ways of wetland monitoring, remote sensing technology is widely used in wetland monitoring. Traditional wetland monitoring remote sensing technology only uses optical remote sensing data for information extraction, but optical remote sensing data has high requirements for weather and specific characteristics of land cover. For wetland areas with more shallow water and higher rainfall than other areas, it is difficult to effectively obtain information on land cover for long time series using optical images. Comparing with optical remote sensing systems, Synthetic Aperture Radar (SAR) has all-weather, synoptic views of large areas, and day-night imaging capability. Microwave electromagnetic energy can penetrate shallow water and vegetation. In recent years, scholars have conducted extensive research on the information extraction of wetland characteristics based on SAR images.

In this paper, Chongming Dongtan Wetland is taken as the study area, and the multi-temporal Rardarsat-2 full-polarization SAR data and Sentinel-2A medium-resolution optical data are used as the data source. According to the particularity of the estuary wetland in the study area, different characteristic parameters are calculated, including vegetation index, water body index, spectral feature, radar feature, texture feature, and time feature. Six multi-dimensional feature data sets containing different feature parameters have been formulated. We perform object-oriented multi-scale inheritance segmentation on six feature data sets and use segmentation parameter optimization tools to select the optimal segmentation parameters. Combined with field investigation and visual interpretation of high-resolution remote sensing images, we build a classification system of Chongming Dongtan Wetland Saltmarsh Vegetation, which mainly includes three types of wetland saltmarsh vegetation: Phragmites australis, Spartina alterniflora, and Scirpus mariqueter. Different vegetation training samples and verification samples are selected on the segmented images, and the information on saltmarsh vegetation is extracted based on the random forest machine learning algorithm. To further study the interannual variation characteristics and phenological characteristics of wetland saltmarsh vegetation, this research also uses Sentinel-2, Sentinel-1, and Landsat-8 fusion images based on Google Earth Engine (GEE) to construct a medium-resolution long-term median image dataset, to obtain the spatiotemporal distribution results of saltmarsh vegetation in Chongming Dongtan Wetland from 2017 to 2022 and the quantitative migration of saltmarsh vegetation.

According to the results of this research, wetland saltmarsh vegetation protection and tidal flat utilization of Chongming Dongtan Wetland can be scientifically supported for Outline Development Plan for the Chongming world-class ecological island.

Change detection (CD) is to quantitatively analyze and determine the characteristics and process of earth surface change based on remote sensing data in different periods. It is widely used in disaster dynamic detection, urban planning, and other fields. Compared with optical remote sensing images, Synthetic Aperture Radar (SAR) images have the unique advantage for CD. As active remote sensing systems, SAR has all-weather, day-night imaging capability, and permits synoptic views of large areas. The backscatter intensity information of SAR images in urban areas is easily affected by comprehensive factors such as building layout, orientation, and surface materials, resulting in overall low radar echo intensity in some building areas. The introduction of coherence maps into change detection can improve the recognition of changed areas because urban buildings have high phase stability and coherence maps provide reliable information for building change detection (BCD). In this study, we mainly focus on two problems in practical application. First, speckle noise as an inherent characteristic of SAR images greatly influences the performance of BCD. Second, how to fuse intensity information and coherence information effectively is of significance for extracting the spatial features of changed areas. Thus, this study proposes a deeply supervised pseudo-siamese attention-guided network (DSPANet) for BCD, in which convolutional blocks have a strong ability in noise reduction owing to the large receptive fields, and the adopted pseudo-siamese structure does well in extracting intensity information and coherence information with the same network branch but different weights.

This study uses high-resolution TerraSAR-X (TSX) images covering Shanghai from a descending pass track. A set of four TSX images is acquired between 16 June 2019 and 10 September 2021. Before training the network, we pre-process TSX images to get intensity information and coherence information. Intensity information is obtained by pre-processing single-look complex (SLC) images, including radiometric correction, multi-looking processing, and geocoding. Coherence information is obtained by performing interferometry and computing coherence values of interferograms. DSPANet model is utilized to distinguish between changed and unchanged areas. It mainly consists of an encoder and a decoder. In the encoder, the convolutional block attention module (CBAM) is included to assign accurate labels to each pixel through the attention mechanism, thus enhance the feature learning of the network. In the decoder, the multi-level feature fusion (MFF) blocks with residual structure are used to avoid the gradient disappearance and gradient explosion caused by the deepening of the network, and the output of the encoder and decoder is concatenated with the skip connection for retaining shallow features. Besides, considering unstable weight updates and unsatisfactory performance while increasing the depth of the network, the deep supervision idea is introduced in DSPANet. It adds some auxiliary classifiers to realize gradient back-propagation by assisting middle layers in extracting features.

We evaluate the accuracy of the DSPANet model using five different metrics. The results show that the method is reliable in BCD and performs better than other advanced change detection methods (such as U-Net, FC-EF, and FC-Siam-Diff). The Precision, Recall, F1, Kappa, and Accuracy reach 88.02%, 84.13%, 86.03%, 85.09%, and 98.24% respectively.

Keywords: building change detection; Synthetic Aperture Radar (SAR); deep learning; coherence information

Shanghai is located in the coastal area of the Yangtze River Delta in eastern China, facing threats from various disasters such as typhoons, storm surges, flooding and ground subsidence. As a mega-city with an area of 6,340 km² and a population of over 24.75 million, Shanghai is densely populated with disaster-bearing bodies such as buildings, roads, and metro, which are easily affected by natural disasters. Frequent natural disasters will cause significant population and economic losses. Therefore, the objective of this study is to build a disaster-bearing body Comprehensive risk assessment model based on natural disaster scenario data and disaster-bearing body attribute data. Through analyzing the distribution of the disaster-bearing bodies’ comprehensive risk level, we can detect hazards in advance and reduce the potential impact of natural disasters.

Firstly, we constructed a comprehensive risk assessment indicator system according to three dimensions of hazard, vulnerability and exposure, based on the location and attribute information of buildings, and extracted the calculation indexes. We also simulated hazard Scenarios related to buildings and roads, including ground subsidence, typhoons, floods, and storm surges. Ground subsidence information is retrieved with 2017-2021 Sentinel-1A data and Small Baseline Subset (SBAS) technology. The comprehensive risk assessment model of disaster bearing bodies was constructed by weighted calculation input indexes, and realized automatic comprehensive risk assessment of the disaster-bearing bodies in large-scale. The weights of input indexes are determined by summarizing historical disasters data and scoring by experts. We use this model to evaluate the comprehensive risk level of the disaster-bearing bodies under multiple disaster scenarios, and analyze the distribution of risk levels. Finally, the regional disaster reduction risk (DRR) capacity of Shanghai and the comprehensive risk level of the disaster-bearing bodies were combined by disaster matrix to determine which high-risk disaster-bearing bodies are located in areas with low DRR capacity.

Synthetic aperture radar (SAR) Doppler shift measurements consist geophysical and non-geophysical contributions. The former is composed of the sea state (wind waves and swell) and underlying surface current. The latter contains geometry and scalloping errors, antenna electronic miss-pointing and unknown biases. In this letter, for the first time, we attempt to retrieve ocean surface radial current velocities from Sentinel-1A SAR Doppler shift observations acquired over Hurricane Maria. Doppler shifts caused by scalloping error are first estimated using linear fitting method. Doppler shifts arising from electronic miss-pointing and unknown biases are then calculated from mean value of Dopper observations over the land. Finally, we compute sea-state-induced Doppler shifts based on our recent ocean surface Doppler velocity model (so-called DPDop). The retrieved ocean surface radial current velocities are compared with the collocated high-frequency radar measurements, showing a bias of 0.02 m/s and a root-mean-square error (RMSE) of 0.19 m/s. These results suggest that the Doppler velocity model has potential to correct wave bias and can be used to derive reasonable radial current velocities under high wind conditions.