Satellite-based Synthetic Aperture Radar Images Research Articles

Finding Navigable Paths through Tidal Flats with Synthetic Aperture Radar

Tidal flats are some of the most dynamic coastal environments in the world, where traditional coastal mapping and monitoring provide insufficient temporal resolution to reliably map channels and sand flats. Satellite-based Synthetic Aperture Radar (SAR) enables regular cloud-penetrating detection of water flowing through channels within the tidal flats, referred to as tidal channels. This paper presents a method for detecting a path through tidal channels, using satellite imagery, that supports our understanding and safe exploitation of this valuable coastal environment. This approach is the first proposed to identify navigable paths in all conditions, with SAR images susceptible to variation due to weather and tidal conditions. Tidal channels are known to vary in SAR presentation, and we find that tidal flat presentation is also influenced by conditions. The most influential factor is the wind, with high winds causing an inversion in how both tidal flats and tidal channels present in SAR images. The presented method for the automatic detection of tidal channels accounts for this variability by using previous channel paths as a reference to reliably correct imagery and detect the latest path. The final algorithm produces paths with minor errors in 17.6% of images; the error rate increases to 71.7%, with an almost tenfold increase in errors, when the SAR image and paths are not adjusted to account for conditions. This capability has been used to support the Nith Inshore Rescue in attending call-outs from their base in Glencaple, UK, while the insights from monitoring tidal channels for a year demonstrate how periods of high river flow preceded major changes in the channel path.

RETROSPECTIVE STUDY OF VERTICAL GROUND DEFORMATION IN COMO, NORTHERN ITALY: INTEGRATION OF LEVELLING AND PSI MEASUREMENTS

Abstract. Subsidence-related vertical ground deformation due to natural and anthropogenic factors may lead to considerable damages to structures and infrastructures, and may increase the occurrence probability of consequential events, such as floods, especially on sea and lake shores. Como city, placed in the north of Italy, adjacent to Como Lake, is subjected to significant subsidence phenomenon, which has been monitored by geodetic levelling networks. In this work the historical geodetic levelling measurements and satellite-based Synthetic Aperture Radar (SAR) data archive are integrated to assess the accuracy of Atmospheric Delay and Deformation Rate estimations obtained through Persistent Scatter Interferometry (PSI) techniques. Tree levelling measurement datasets acquired in 1990, 1997 and 2004 are used in order to obtain the precise deformation rate at the benchmarks for two periods of 1990–1997 and 1997–2004. For multi-temporal InSAR analysis, 106 SAR images (1992–2004) and 41 SAR images (1992–2004) in Ascending Track orbit from ERS-1/2 missions are used in this study. The assessment is performed through a statistical comparison between two sets of vertical land deformation rates obtained by integrated methods. The results of the validation represented a good consistency between deformation rates derived by both techniques. Also, this study has revealed the potential of SAR images acquired in gyro-less mode by ERS-2 mission (2001–2004) in terms of the estimation of ground deformation.

Revealing the global longline fleet with satellite radar

Because many vessels use the Automatic Identification System (AIS) to broadcast GPS positions, recent advances in satellite technology have enabled us to map global fishing activity. Understanding of human activity at sea, however, is limited because an unknown number of vessels do not broadcast AIS. Those vessels can be detected by satellite-based Synthetic Aperture Radar (SAR) imagery, but this technology has not yet been deployed at scale to estimate the size of fleets in the open ocean. Here we combine SAR and AIS for large-scale open ocean monitoring, developing methods to match vessels with AIS to vessels detected with SAR and estimate the number of non-broadcasting vessels. We reveal that, between September 2019 and January 2020, non-broadcasting vessels accounted for about 35% of the longline activity north of Madagascar and 10% of activity near French Polynesia and Kiribati’s Line Islands. We further demonstrate that this method could monitor half of the global longline activity with about 70 SAR images per week, allowing us to track human activity across the oceans.

Phase calibration for ideal wideband chirp in satellite-based synthetic aperture radar

With the development of launch vehicles, the number of satellite-based synthetic aperture radar (SAR) has been increasing. Satellite-based SAR can acquire high-resolution images at day and night regardless of the weather. Since SAR resolution is determined by a chirp bandwidth, corresponding a generation technology of wideband chirp is essentially required for high-resolution SAR images under consideration of relatively small and light weight SAR system. Therefore, an ideal chirp waveform generation, which is challenging task in actual hardware, is proposed by modifying a chirp equation for phase calibration, and is verified based on Sentinel-1 satellite SAR parameters. The proposed method is applied to our SAR hardware and we confirm the calibrated waveform. Evaluation results using SAR observation simulation show that the impulse response with the proposed method has better performances.

Permanent Laser Scanner and Synthetic Aperture Radar Data: Correlation Characterisation at a Sandy Beach.

In recent years, our knowledge of coastal environments has been enriched by remotely sensed data. In this research, we co-analyse two sensor systems: Terrestrial Laser Scanning (TLS) and satellite-based Synthetic Aperture Radar (SAR). To successfully extract information from a combination of different sensors systems, it should be understood how these interact with the common environment. TLS provides high-spatiotemporal-resolution information, but it has high economic costs and limited field of view. SAR systems, despite their lower resolution, provide complete, repeated, and frequent coverage. Moreover, Sentinel-1 SAR images are freely available. In the present work, Permanent terrestrial Laser Scanning (PLS) data, collected in Noordwijk (The Netherlands), are compared with simultaneous Sentinel-1 SAR images to investigate their combined use on coastal environments: knowing the relationship between SAR and PLS data, the SAR dataset could be correlated to beach characteristics. Meteorological and surface roughness have also been taken into consideration in the evaluation of the correlation between PLS and SAR data. A generally positive linear correlation factor up to 0.5 exists between PLS and SAR data. This correlation occurs for low- or moderate-wind-speed conditions, whilst no particular correlation has been highlighted for high wind intensity. Furthermore, a dependence of the linear correlation on the wind direction has been detected.

Generating landslide density heatmaps for rapid detection using open-access satellite radar data in Google Earth Engine

Abstract. Rapid detection of landslides is critical for emergency response, disaster mitigation, and improving our understanding of landslide dynamics. Satellite-based synthetic aperture radar (SAR) can be used to detect landslides, often within days of a triggering event, because it penetrates clouds, operates day and night, and is regularly acquired worldwide. Here we present a SAR backscatter change approach in the cloud-based Google Earth Engine (GEE) that uses multi-temporal stacks of freely available data from the Copernicus Sentinel-1 satellites to generate landslide density heatmaps for rapid detection. We test our GEE-based approach on multiple recent rainfall- and earthquake-triggered landslide events. Our ability to detect surface change from landslides generally improves with the total number of SAR images acquired before and after a landslide event, by combining data from both ascending and descending satellite acquisition geometries and applying topographic masks to remove flat areas unlikely to experience landslides. Importantly, our GEE approach does not require downloading a large volume of data to a local system or specialized processing software, which allows the broader hazard and landslide community to utilize and advance these state-of-the-art remote sensing data for improved situational awareness of landslide hazards.

Environment Monitoring of Shanghai Nanhui Intertidal Zone With Dual-Polarimetric SAR Data Based on Deep Learning

Satellite-based synthetic aperture radar (SAR) can provide a low-cost, frequent environment monitoring for dynamic intertidal zones. The critical problem is to realize pixel-level classification of SAR images of the intertidal zones with excellent and robust performance. Recently, deep learning, in particular deep convolutional neural networks, has provided us with promising solutions to this problem. Based on a sophisticated deep learning-based pixel-level classification model U²-Net, we propose an MB-U²-ACNet model suitable for intertidal zone land cover classification using dual-polarimetric SAR data integrated with environmental information, such as wind speed and tide level information. The MB-U²-ACNet model has a multi-branch nested U-shaped encoding-decoding structure. We extract and fuse features from multiple data sources, including satellite remote sensing and environmental information, by establishing the multi-branch structure. Furthermore, we propose an asymmetric convolution residual U-block for each encoding-decoding stage to improve the model’s feature extraction ability. Moreover, the model with attention mechanisms better distinguishes the importance of features from the channel’s perspectives and spatial dimensions. We construct a dataset with 106 Sentinel-1 SAR images from 2016 to 2020 for environment monitoring in the intertidal zone of Shanghai Nanhui. On the dataset, the proposed model reaches the overall classification accuracy of 96.40% and the mean intersection over union score of 0.8307. The experiments show the advantages of the proposed model compared with the benchmarking models due to better feature extraction and multi-source information fusion. In addition, the contributions of every added sub-structure are analyzed systematically.

Flood Mapping in Valley Districts of Manipur Using Satellite-based Synthetic Aperture Radar (SAR) Images

Impacts from flood pose high risk to lives and properties of river plain inhabitants across the globe. Plains of Northeast India, including central valley in Manipur, often experience floods of variable intensities in different seasons of the year. Current flood mitigation measures of the region scarcely consider the dissimilarity in inundation patterns produced by floods of different seasons. Considering this, the current study examined the inundation pattern of pre-monsoon and monsoon floods in five valley districts of Manipur using Synthetic Aperture Radar (SAR) data of Sentinel-1 satellite. SAR images of April (premonsoon) and July (monsoon), and a pre-flood image (March) of the year 2017 were employed for the analysis. The results indicate that the extent of inundation during monsoon flood (20222 ha) was about four times larger than the pre-monsoon flood (4297 ha). Imphal West and Thoubal districts witnessed largest area under inundation during pre-monsoon and monsoon flood respectively. And moving from premonsoon to monsoon, Kaching, Thoubal Imphal East, and Bisnupur experienced the largest percentage climb in inundation area while it only changed slightly in Imphal West. This suggest that spatial pattern of flooding exhibited significant variations among the districts between the floods. Therefore, to be more effective in future, flood management strategies need inputs from flood mapping studies performed using space-based SAR data.

Dryland Crop Classification Combining Multitype Features and Multitemporal Quad-Polarimetric RADARSAT-2 Imagery in Hebei Plain, China.

The accuracy of dryland crop classification using satellite-based synthetic aperture radar (SAR) data is often unsatisfactory owing to the similar dielectric properties that exist between the crops and their surroundings. The main objective of this study was to improve the accuracy of dryland crop (maize and cotton) classification by combining multitype features and multitemporal polarimetric SAR (PolSAR) images in Hebei plain, China. Three quad-polarimetric RADARSAT-2 scenes were acquired between July and September 2018, from which 117 features were extracted using the Cloude–Pottier, Freeman–Durden, Yamaguchi, and multiple-component polarization decomposition methods, together with two polarization matrices (i.e., the coherency matrix and the covariance matrix). Random forest (RF) and support vector machine (SVM) algorithms were used for classification of dryland crops and other land-cover types in this study. The accuracy of dryland crop classification using various single features and their combinations was compared for different imagery acquisition dates, and the performance of the two algorithms was evaluated quantitatively. The importance of all investigated features was assessed using the RF algorithm to optimize the features used and the imagery acquisition date for dryland crop classification. Results showed that the accuracy of dryland crop classification increases with evolution of the phenological period. In comparison with SVM, the RF algorithm showed better performance for dryland crop classification when using full polarimetric RADARSAT-2 data. Dryland crop classification accuracy was not improved substantially when using only backscattering intensity features or polarization decomposition parameters extracted from a single-date image. Satisfactory classification accuracy was achieved using 11 optimized features (derived from the Cloude–Pottier decomposition and the coherency matrix) from 2 RADARSAT-2 images (acquisition dates corresponding to the middle and late stages of dryland crop growth). This study provides an important reference for timely and accurate classification of dryland crop in Hebei plain, China.

MYI Floes Identification Based on the Texture and Shape Feature from Dual-Polarized Sentinel-1 Imagery

The Northwest Passage (NWP) in the Arctic is usually covered with hazardous multi-year ice (MYI) and seasonal first-year ice (FYI) in winter, with possible thin ice and open-water areas during transition seasons. Ice classification is important for both marine navigation and climate change studies. Satellite-based Synthetic Aperture Radar (SAR) systems have shown advantages of retrieving this information. Operational ice mapping relies on visual analysis of SAR images along with ancillary data. However, these maps estimate ice types and concentrations within large-size polygons of a few tens or hundreds of kilometers, which are subjectively identified and selected by analysts. This study aims at developing an automated algorithm to identify individual MYI floes from SAR images then classify the rest of the image as FYI and other ice types. The algorithm identifies the MYI floes using extended-maximum operator, morphological image processing, and a few geometrical features. Classifying the rest of the image uses texture and neural network model. The input data is a set of Sentinel-1 A/B Extended Wide (EW) mode images, acquired between September and March 2016–2019. Although the overall accuracy (for all type classification) from the new method scored 93.26%, the accuracy from using the texture classifier only was 75.81%. The kappa coefficient from the former was higher than the latter by 0.25. Compared with the operational ice charts from the Canadian Ice Service, ice type maps from the new method show better distribution of MYI at the fine scale of individual floes. Comparison against MYI concentration from two automated algorithms that use a combination of coarse-resolution passive and active microwave data also confirms the advantage of resolving MYI floes from the fine-resolution SAR.

Near-Field Ground Motions and Shaking from the 2019 Mw 7.1 Ridgecrest, California, Mainshock: Insights from Instrumental, Macroseismic Intensity, and Remote-Sensing Data

ABSTRACTShaking from the 6 July 2019 Mw 7.1 Ridgecrest, California, mainshock was strongly felt through southern California, but generated relatively minimal structural damage in Ridgecrest. We consider the extent to which a damage proxy map (DPM) generated from satellite-based Synthetic Aperture Radar images can detect minor damage throughout the town of Ridgecrest. The DPM does not, as expected, detect all minor structural damage to individual structures, nor can it distinguish between structural damage and earthquake-related movement that is not consequential. However, the DPM does confirm many instances of minor structural damage to larger structures and groups of smaller structures and in some instances suggests minor structural damage that is not apparent upon visual inspection. Although ambiguous identification of minor damage may not be useful to guide earthquake response, the identification of minor, possibly hidden damage is potentially useful for other purposes. Overall, the DPM confirms that structural damage was commensurate with modified Mercalli intensity no higher than 7 throughout Ridgecrest. We consider both instrumental and intensity data to explore further the distribution of near-field ground motions over the frequency range of engineering concern. Peak ground accelerations and peak ground velocities estimated from “Did You Feel It?” intensity data using the Worden et al. (2012) ground-motion intensity conversion equation (GMICE) are consistent with recorded instrumental data. Both instrumental and estimated mainshock peak accelerations are further consistent with predictions from both the Boore et al. (2014) ground-motion prediction equation (GMPE), but lower than predicted by the Atkinson and Wald (2007) and Atkinson et al. (2014) intensity prediction equations (IPEs). A GMPE such as Boore et al. (2014), which is constrained by a large global dataset, together with a well-constrained GMICE, may thus characterize expected shaking intensities for large earthquakes better than an IPE based on more limited intensity data.

Advanced Remote Sensing of Internal Waves by Spaceborne Along-Track InSAR—A Demonstration With TerraSAR-X

Since the SEASAT mission in 1978, satellite-based synthetic aperture radar (SAR) images have been used to study oceanic internal waves. Internal waves become visible in SAR images because their orbital currents modulate the surface roughness. While this leads to an accurate spatial representation of internal wave patterns, the complexity of the imaging mechanism makes it difficult to derive actual currents and internal wave amplitudes from SAR signatures. We demonstrate in this paper how a more robust parameter retrieval is possible with along-track interferometric SAR (InSAR) data that resolve amplitudes and temporal phase changes of the backscattered signal together, the latter of which are directly related to the scatterers' line-of-sight velocities. Our example data set, which was acquired by TerraSAR-X in Dual Receive Antenna mode at Dongsha (South China Sea), exhibits strong signatures of internal waves in the interferogram amplitude and phase. We use a simple internal soliton parameterization and a numerical radar imaging model to find a plausible combination of internal wave parameters, which leads to good agreement between simulated and observed signatures. Testing the sensitivity of radar amplitude and phase signatures to various parameters, we show that along-track InSAR data should generally permit more accurate and less ambiguous internal wave parameter retrievals than conventional SAR images.

ANALYSIS OF X-BAND VERY HIGH RESOLUTION PERSISTENT SCATTERER INTERFEROMETRY DATA OVER URBAN AREAS

Abstract. Persistent Scatterer Interferometry (PSI) is a satellite-based Synthetic Aperture Radar (SAR) remote sensing technique used to measure and monitor land deformation from a stack of interferometric SAR images. This work concerns X-band PSI and, in particular, PSI based on very high resolution (VHR) StripMap CosmoSkyMed and TerraSAR-X SAR imagery. In fact, it mainly focuses on the technical aspects of deformation measurement and monitoring over urban areas. A key technical aspect analysed in this paper is the thermal expansion component of PSI observations, which is a result of temperature differences in the imaged area between SAR acquisitions. This component of PSI observations is particularly important in the urban environment. This is an interesting feature of PSI, which can be surely used to illustrate the high sensitivity of X-band PSI to very subtle displacements. Thermal expansion can have a strong impact on the PSI products, especially on the deformation velocity maps and deformation time series, if not properly handled during the PSI data processing and analysis, and a comprehensive discussion of this aspect will be provided in this paper. The importance of thermal expansion is related to the fact that the PSI analyses are often performed using limited stacks of images, which may cover a limited time period, e.g. several months only. These two factors (limited number of images and short period) make the impact of a non-modelled thermal expansion particularly critical. This issue will be illustrated considering different case studies based on TerraSAR-X and CosmoSkyMed PSI data. Besides, an extended PSI model which alleviates this problem will be described and case studies from the Barcelona metropolitan area will demonstrate the effectiveness of the proposed strategy.

SAR Image Quality Assessment and Indicators for Vessel and Oil Spill Detection

Satellite-based synthetic aperture radar (SAR) is progressively becoming an operational asset for maritime monitoring applications. The services based on this technology rely on a level of image quality that, if not entirely fulfilled, may result in compromising the performance and accuracy of the intended application. Nonetheless, it is not always clear how to quantitatively measure the SAR image quality level from the delivered products. This paper discusses today's most relevant quality issues of satellite SAR images related to maritime applications. It introduces a set of quantitative measures that can be estimated from satellite images used in operational applications, not only to verify the conformity of the delivered image with respect to product specifications but also to assess how well the image can serve the application. To that end, the concept of “application suitability” is introduced, defined in relation to the specific application of interest. The focus is on oil spill detection and ship detection.

Remote Sensing of Ocean Oil-Spill Pollution

Oil spills on the sea surface are observed relatively often. Pollution due to either accidents or deliberate oily discharges from ships represents a serious threat to the marine environment. Operational oil spill monitoring is currently done using a combination of satellite monitoring and aircraft surveillance. The combined use of satellite-based synthetic aperture radar (SAR) images and aircraft surveillance flights is a cost-effective way to monitor oil spills in large ocean areas and catch the polluters. SAR images enable covering large areas, but aircraft observations are needed to prosecute the polluter, and in certain cases to verity the oil spill. Traditionally, oil spill detection is based on single polarization SAR images. Oil spills can be discriminated from look-alikes based on a set of features describing the contrast, shape, homogeneity, source, and surroundings of the slick. Good performance is reported for single-polarization oil spill detection, but in certain cases the oil slicks cannot be discriminated from biogenic films. In the recent years, a number of studies have shown that polarimetric SAR can improve the discrimination between oil slicks and biogenic films. Several features computed from dual-pol or quad-pol images have been proposed. These include both quad-pol features like polarimetric entropy and anisotropy, mean scattering angle, polarimetric span, conformity coefficient, as well as the dual-pol features standard deviation of the copolarized phase difference and the copolarized correlation coefficient. As dual-pol SAR imagery is now available on a regular basis from Cosmo Skymed and TerraSAR-X, and quad-pol data are available from RADARSAT-2, polarimetric SAR can now be utilized on a more regular basis. Optical data from sensors like Aqua MODIS and ENVISAT MERIS can be a useful supplement under certain cloud-free conditions.

A Novel Algorithm for Ship Detection in SAR Imagery Based on the Wavelet Transform

Carrying out an effective control of fishing activities is essential to guarantee a sustainable exploitation of sea resources. Nevertheless, as the regulated areas are extended, they are difficult and time consuming to monitor by means of traditional reconnaissance methods such as planes and patrol vessels. On the contrary, satellite-based synthetic aperture radar (SAR) provides a powerful surveillance capability allowing the observation of broad expanses, independently from weather effects and from the day and night cycle. Unfortunately, the automatic interpretation of SAR images is often complicated, even though undetected targets are sometimes visible by eye. Attending to these particular circumstances, a novel approach for ship detection is proposed based on the analysis of SAR images by means of the discrete wavelet transform. The exposed method takes advantage of the difference of statistical behavior among the ships and the surrounding sea, interpreting the information through the wavelet coefficients in order to provide a more reliable detection. The analysis of the detection performance over both simulated and real images confirms the robustness of the proposed algorithm.