Extreme Waves Detected by Satellite Borne Synthetic Aperture Radar

Within the last 20 years at least 200 supercarriers have been lost, due to severe weather conditions. In many cases the cause of accidents is believed to be ‘rouge waves’, which are individual waves of exceptional wave height or abnormal shape. I situ measurements of extreme waves are scarce and most observations are reported by ship masters after the encounter. In this paper a global set of synthetic aperture radar (SAR) images is used to detect extreme ocean wave events. The data were acquired aboard the European remote sensing satellite ERS-2 every 200 km along the track. As the data are not available as a standard product of the Europea Space Agency (ESA), the radar raw data were focused to complex SAR images using the processor BSAR developed by the German Aerospace Center. The entire SAR data set covers 27 days representing 34000 SAR imagettes with a size of 5km×10km. Complex SAR data contain information on ocean wave height, propagation direction and grouping as well as on ocean surface winds. Combining all of this information allows to extract and locate extreme waves from complex SAR images on a global basis. Special algorithms have been developed to retrieve the following parameters from the SAR data: Wind speed and direction, significant wave height, wave direction, wave groups and their individual heights. The satellite ENVISAT launched in March 2002 acquires SAR data with an even higher sampling rate (every 100 km). It is expected that a long-term analysis of ERS and ENVISAT data will give new insight into the physical processes responsible for rogue wave generation. Furthermore, the identification of hot spots will contribute to the optimization of ship routes.

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An Empirical Algorithm to Retrieve Significant Wave Height from Sentinel-1 Synthetic Aperture Radar Imagery Collected under Cyclonic Conditions

Remote Sensing ◽

10.3390/rs10091367 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1367 ◽

Cited By ~ 9

Author(s):

Weizeng Shao ◽

Yuyi Hu ◽

Jingsong Yang ◽

Ferdinando Nunziata ◽

Jian Sun ◽

...

Keyword(s):

Synthetic Aperture Radar ◽

Wave Height ◽

Significant Wave Height ◽

Synthetic Aperture ◽

Training Dataset ◽

Sar Images ◽

Data Set ◽

Significant Wave ◽

Sar Imagery ◽

Aperture Radar

In this study, an empirical algorithm is proposed to retrieve significant wave height (SWH) from dual-polarization Sentinel-1 (S-1) synthetic aperture radar (SAR) imagery collected under cyclonic conditions. The retrieval scheme is based on the well-known CWAVE empirical function that is here updated to deal with multi-polarization S-1 SAR measurements collected using the interferometric wide (IW) and the Extra Wide-Swath (EW) imaging modes, under cyclonic conditions. First, a training dataset that consists of six S-1 SAR images collected under cyclonic conditions is exploited to both tune the retrieval function and to check the soundness of the retrievals against the co-located WAVEWATCH-III (WW3) numerical simulations. The comparison of simulation from the WW3 model and measurements from altimeter Jason-2 shows a 0.29m root mean square error (RMSE) of significant wave height (SWH). Then, a testing data-set that consists of two S-1 SAR images is exploited to provide a preliminary validation. The results, verified against both WW3 and European Centre for Medium-Range Weather Forecasts (ECMWF) data, show the soundness of the herein approach.

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ACFNet: A Feature Fusion Network for Glacial Lake Extraction Based on Optical and Synthetic Aperture Radar Images

Remote Sensing ◽

10.3390/rs13245091 ◽

2021 ◽

Vol 13 (24) ◽

pp. 5091

Author(s):

Jinxiao Wang ◽

Fang Chen ◽

Meimei Zhang ◽

Bo Yu

Keyword(s):

Synthetic Aperture Radar ◽

Synthetic Aperture ◽

Glacial Lake ◽

Landsat 8 ◽

Glacial Lakes ◽

Sar Images ◽

Radar Images ◽

Optical Images ◽

Sar Data ◽

Aperture Radar

Glacial lake extraction is essential for studying the response of glacial lakes to climate change and assessing the risks of glacial lake outburst floods. Most methods for glacial lake extraction are based on either optical images or synthetic aperture radar (SAR) images. Although deep learning methods can extract features of optical and SAR images well, efficiently fusing two modality features for glacial lake extraction with high accuracy is challenging. In this study, to make full use of the spectral characteristics of optical images and the geometric characteristics of SAR images, we propose an atrous convolution fusion network (ACFNet) to extract glacial lakes based on Landsat 8 optical images and Sentinel-1 SAR images. ACFNet adequately fuses high-level features of optical and SAR data in different receptive fields using atrous convolution. Compared with four fusion models in which data fusion occurs at the input, encoder, decoder, and output stages, two classical semantic segmentation models (SegNet and DeepLabV3+), and a recently proposed model based on U-Net, our model achieves the best results with an intersection-over-union of 0.8278. The experiments show that fully extracting the characteristics of optical and SAR data and appropriately fusing them are vital steps in a network’s performance of glacial lake extraction.

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Theory for synthetic aperture radar imaging of the ocean surface: With application to the Tower Ocean Wave and Radar Dependence Experiment on focus, resolution, and wave height spectra

Journal of Geophysical Research Atmospheres ◽

10.1029/jc093ic11p13837 ◽

1988 ◽

Vol 93 (C11) ◽

pp. 13837 ◽

Cited By ~ 47

Author(s):

Dayalan P. Kasilingam ◽

Omar H. Shemdin

Keyword(s):

Synthetic Aperture Radar ◽

Wave Height ◽

Ocean Surface ◽

Radar Imaging ◽

Ocean Wave ◽

Synthetic Aperture ◽

Aperture Radar

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On the Performance of the MPI Algorithm for the Retrieval of Significant Wave Height From ERS Synthetic Aperture Radar

Volume 2: Safety and Reliability; Pipeline Technology ◽

10.1115/omae2003-37070 ◽

2003 ◽

Author(s):

Nelson Violante-Carvalho ◽

Ian S. Robinson

Keyword(s):

Synthetic Aperture Radar ◽

Wave Height ◽

Significant Wave Height ◽

Synthetic Aperture ◽

Wave Spectra ◽

Sar Images ◽

Significant Wave ◽

Directional Wave ◽

Better Than ◽

Aperture Radar

Spaceborne Synthetic Aperture Radar (SAR) is to date the only source of two dimensional directional wave spectra with continuous and global coverage when operated in the so-called SAR Wave Mode (SWM). Since the launch in 1991 of the first European Remote Sensing Satellite ERS-1 and more recently with ENVISAT millions of SWM imagettes containing detailed spectral information are now available in quasi-real time. This huge amount of directional wave data has opened up many exciting possibilities for the improvement of our knowledge of the dynamics of ocean waves. However the retrieval of wave spectra from SAR images is not a trivial exercise due to the nonlinearities involved in the mapping mechanism. The Max-Planck Institut (MPI) scheme was the first ever proposed and most widely used algorithm to retrieve directional wave spectra from SAR images. In this work significant wave height retrieved from SAR images using the MPI scheme are compared against one year of directional buoy measurements obtained in deep water and against WAM spectra. Our results show that for periods shorter than 12 seconds the WAM model performs better than the MPI method, even considering the fact that the model is used as first guess to the MPI scheme. However, for periods longer than 12 seconds (the part of the spectrum directly observed by SAR) the MPI method performs better than WAM. This is in contrast with the results obtained by Voorrips et al. (2001), who found that the performance of the WAM model is superior even when only the low wavenumber part of the spectrum is considered.

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Integration of Automatic Identification System (AIS) Data and Single-Channel Synthetic Aperture Radar (SAR) Images by SAR-Based Ship Velocity Estimation for Maritime Situational Awareness

Remote Sensing ◽

10.3390/rs11192196 ◽

2019 ◽

Vol 11 (19) ◽

pp. 2196 ◽

Cited By ~ 6

Author(s):

Maria Daniela Graziano ◽

Alfredo Renga ◽

Antonio Moccia

Keyword(s):

Synthetic Aperture Radar ◽

Velocity Estimation ◽

Synthetic Aperture ◽

Automatic Identification ◽

Identification System ◽

Sar Images ◽

Automatic Identification System ◽

Route Information ◽

Sar Data ◽

Aperture Radar

The synergic utilization of data from different sources, either ground-based or spaceborne, can lead to effective monitoring of maritime activities. To this end, the integration of synthetic aperture radar (SAR) images with data reported by the automatic identification system (AIS) is of high interest. Accurate matching of ships detected in SAR images with AIS data requires compensation of the azimuth offset, which depends on the ship’s velocity. The existing procedures interpolate the route information gathered by AIS to estimate the ship’s velocity at the epoch of the SAR data, to remove the offset. Matching accuracy is limited by interpolation errors and AIS route information unavailability or uncertainties. This paper proposes the use of SAR-based ship velocity estimations to improve the integration of AIS and SAR data. A case study has been analyzed, in which the method has been tested on TerraSAR-X images collected over the Gulf of Naples, Italy. Presented results show that the matching is improved with respect to standard procedures. The proposed method limits the distance between the AIS report and the SAR-based detection to less than 150 m, which is in line with maritime surveillance needs.

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