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

2018 ◽  
Vol 10 (9) ◽  
pp. 1367 ◽  
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.

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

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.

Extreme Waves Detected by Satellite Borne Synthetic Aperture Radar

2002 ◽  
Author(s):  
Susanne Lehner ◽  
Johannes Schulz-Stellenfleth ◽  
Andreas Niedermeier ◽  
Jochen Horstmann ◽  
Wolfgang Rosenthal
Keyword(s):  
Synthetic Aperture Radar ◽  
Wave Height ◽  
Sampling Rate ◽  
Ocean Wave ◽  
Synthetic Aperture ◽  
Extreme Waves ◽  
Sar Images ◽  
Data Set ◽  
Sar Data ◽  
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.

scholarly journals Deep Learning for Predicting Significant Wave Height From Synthetic Aperture Radar

2020 ◽  
pp. 1-9
Author(s):  
Brandon Quach ◽  
Yannik Glaser ◽  
Justin Edward Stopa ◽  
Alexis Aurelien Mouche ◽  
Peter Sadowski
Keyword(s):  
Deep Learning ◽  
Synthetic Aperture Radar ◽  
Wave Height ◽  
Significant Wave Height ◽  
Synthetic Aperture ◽  
Significant Wave ◽  
Aperture Radar

scholarly journals Synthetic aperture radar detection of the snowline on Commonwealth and Howard Glaciers, Taylor Valley, Antarctica

2002 ◽  
Vol 34 ◽  
pp. 177-183 ◽  
Author(s):  
Patrick Bardel ◽  
Andrew G. Fountain ◽  
Dorothy K. Hall ◽  
Ron Kwok
Keyword(s):  
Synthetic Aperture Radar ◽  
Optical Depth ◽  
Synthetic Aperture ◽  
Polar Regions ◽  
Sar Images ◽  
Reflection And Refraction ◽  
Taylor Valley ◽  
Sar Imagery ◽  
Radar Wave ◽  
Aperture Radar

AbstractSynthetic aperture radar (SAR) images of Taylor Valley, Antarctica, were acquired in January 1999 in coordination with ground-based measurements to assess SAR detection of the snowline on dry polar glaciers. We expected significant penetration of the radar wave resulting in an offset of the SAR-detected snowline relative to the true snowline. Results indicated no detectable displacement of the SAR snowline. Snow depths of 15 cm over ice can be detected on the imagery. We hypothesize that the optical depth of thin snowpacks is enhanced by reflection and refraction of the radar beam by internal snow layers. The enhanced optical depth increases the volume scattering, and thereby enhances backscatter sufficiently to be detected by the SAR. Consequently, SAR imagery may be used directly to image the position of transient snowlines in dry polar regions.

scholarly journals Evaluation of the Significant Wave Height Data Quality for the Sentinel-3 Synthetic Aperture Radar Altimeter

2020 ◽  
Vol 12 (18) ◽  
pp. 3107
Author(s):  
Yong Wan ◽  
Rongjuan Zhang ◽  
Xiaodong Pan ◽  
Chenqing Fan ◽  
Yongshou Dai
Keyword(s):  
Synthetic Aperture Radar ◽  
Data Quality ◽  
Wave Height ◽  
Water Depth ◽  
Deep Water ◽  
Significant Wave Height ◽  
Synthetic Aperture ◽  
Radar Altimeter ◽  
Significant Wave ◽  
Water Depths

Synthetic aperture radar (SAR) altimeters represent a new method of microwave remote sensing for ocean wave observations. The adoption of SAR technology in the azimuthal direction has the advantage of a high resolution. The Sentinel-3 altimeter is the first radar altimeter to acquire global observations in SAR mode; hence, the data quality needs to be assessed before extensively applying these data. The European Space Agency (ESA) evaluates the Sentinel-3 accuracy on a global scale but has yet to perform a detailed analysis in terms of different offshore distances and different water depths. In this paper, Sentinel-3 and Jason-2 significant wave height (SWH) data are matched in both time and space with buoy data from the United States East and West Coasts and the Central Pacific Ocean. The Sentinel-3 SWH data quality is evaluated according to different offshore distances and water depths in comparison with Jason-2 SWH data. In areas more than 50 km offshore, the Sentinel-3 SWH accuracy is generally high and less affected by the water depth and sea conditions (root-mean-square error of 0.28 m and correlation coefficient of 0.98); in areas less than 50 km offshore, the SWH data accuracy is slightly affected by water depth and sea conditions (especially the former). Compared with Jason-2, the observation ability of the Sentinel-3 altimeter in nearshore areas with water depths of 0 m-500 m is greatly improved, but in some deep water areas with stable sea conditions, the Jason-2 SWH data accuracy is higher than that of Sentinel-3. This work provides a reference for the refined application of Sentinel-3 SWH data in offshore deep water areas and nearshore shallow water areas.

scholarly journals Automated global water mapping based on wide-swath orbital synthetic aperture radar

2012 ◽  
Vol 9 (6) ◽  
pp. 7801-7834
Author(s):  
R. S. Westerhoff ◽  
M. P. H. Kleuskens ◽  
H. C. Winsemius ◽  
H. J. Huizinga ◽  
G. R. Brakenridge
Keyword(s):  
Synthetic Aperture Radar ◽  
Real Time ◽  
Data Dissemination ◽  
Incidence Angle ◽  
Probability Distributions ◽  
Open Data ◽  
Synthetic Aperture ◽  
Training Dataset ◽  
Sar Imagery ◽  
Aperture Radar

Abstract. This paper presents an automated technique, embedded in an online service, which ingests orbital synthetic aperture radar (SAR) imagery and outputs surface water maps in near real time and on a global scale. The service anticipates future open data dissemination of water extent information using the European Space Agency's Sentinel-1 data. The classification methods used are innovative but practical and different per 1 × 1 degree tile. For each tile, a probability distribution function of a pixel, being covered with water or being dry is established based on a long SAR training dataset. These probability distributions are conditional on the backscatter and the incidence angle. In classification mode the probability of water coverage is calculated, conditional on the current backscatter – incidence angle combination. The overlap between the probability distributions of a pixel being wet or dry is used as a proxy for the quality of our classification. The service has multiple uses, e.g. for water body dynamics in times of drought or for urgent inundation extent determination during floods. The service generates data systematically: it is not an on-demand service activated only for emergency response, but instead is always up-to-date and available. We demonstrate its use in flood situations using Envisat ASAR information during the 2011 Thailand floods. A first merge with a NASA near real time water product based on MODIS optical satellite imagery shows excellent agreement between these independent satellite-based water products.

scholarly journals Automated global water mapping based on wide-swath orbital synthetic-aperture radar

2013 ◽  
Vol 17 (2) ◽  
pp. 651-663 ◽  
Author(s):  
R. S. Westerhoff ◽  
M. P. H. Kleuskens ◽  
H. C. Winsemius ◽  
H. J. Huizinga ◽  
G. R. Brakenridge ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Real Time ◽  
Incidence Angle ◽  
Probability Distributions ◽  
Open Data ◽  
Synthetic Aperture ◽  
Training Dataset ◽  
Local Conditions ◽  
Sar Imagery ◽  
Aperture Radar

Abstract. This paper presents an automated technique which ingests orbital synthetic-aperture radar (SAR) imagery and outputs surface water maps in near real time and on a global scale. The service anticipates future open data dissemination of water extent information using the European Space Agency's Sentinel-1 data. The classification methods used are innovative and practical and automatically calibrated to local conditions per 1 × 1° tile. For each tile, a probability distribution function in the range between being covered with water or being dry is established based on a long-term SAR training dataset. These probability distributions are conditional on the backscatter and the incidence angle. In classification mode, the probability of water coverage per pixel of 1 km × 1 km is calculated with the input of the current backscatter – incidence angle combination. The overlap between the probability distributions of a pixel being wet or dry is used as a proxy for the quality of our classification. The service has multiple uses, e.g. for water body dynamics in times of drought or for urgent inundation extent determination during floods. The service generates data systematically: it is not an on-demand service activated only for emergency response, but instead is always up-to-date and available. We validate its use in flood situations using Envisat ASAR information during the 2011 Thailand floods and the Pakistan 2010 floods and perform a first merge with a NASA near real time water product based on MODIS optical satellite imagery. This merge shows good agreement between these independent satellite-based water products.

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