Ocean surface wave measurements using SAR wave mode data

Over the ocean, the SAR and ASAR instruments onboard ESA’s ERS and ENVISAT satellites are operated in wave mode whenever no other operation is requested. In wave mode, SAR collects data to form small images of 10 km x 5 km size every 200 or 100 km along the satellite’s orbit. Ocean wave parameters can be retrieved from these SAR/ASAR wave mode data over the global ocean with high quality. The wave parameters can be used for validation of numerical wave model forecasts and hindcasts, assimilation of models, observations and forecast of extreme ocean weather, as well as for global wave climate analysis. The main focus of the thesis is ocean wave information retrieval from SAR and ASAR wave mode data. This includes validation of published schemes for retrieving two-dimensional ocean wave spectra and development of the new empirical algorithm CWAVE_ENV for the retrieval of integral wave parameters directly from ASAR wave mode data without using other input as the first guess. Three months of ASAR wave mode data acquired globally from December 2006 to February 2007 are used to validate the algorithms of the nonlinear PARSA (Partition Rescaling and Shift Algorithm) and the quasi-linear WVW (used by ESA for Level 2 ASAR Wave Mode Wave Spectra) by comparing them to collocated in situ buoy measurements and numerical wave model results. The PARSA algorithm needs the SAR look cross spectra and first guess spectra from numerical wave model as input. The algorithm can yield the full two-dimensional ocean wave spectrum and the retrieved integral wave parameters agree with buoy measurements with a bias of only 0.09 m and a scatter index of 21%. The comparison with the forecast wave model of DWD is even better with a bias of -0.01 m and a scatter index of 16%. The quasi-linear ESA algorithm WVW has the advantage of not needing any priori. However, the retrieved wave spectra are limited to the domain of long wavelengths, mainly swell. Therefore the significant wave height (SWH) integrated from the WVW spectra has a higher bias of -0.19 m and a larger scatter index of 36% when compared to in situ buoy measurements. Furthermore, the underestimation of SWH increases with sea state. Around 25% ASAR wave mode cross spectra cannot be converted successfully by using the algorithm, probably because of the low signal to noise ratio. Based on the empirical algorithm CWAVE_ERS developed for reprocessed ERS-2 SAR wave mode data, the CWAVE_ENV algorithm is proposed in this thesis and implemented for the ASAR wave mode data. Using the same three months ASAR wave mode data and the collocated dataset, the empirical algorithm is validated. Validation, particularly compared to independent datasets, i.e., in situ buoy measurements and radar altimeters, proves that reliable and accurate sea state measurements can be achieved. The bias is only 0.06 m and the scatter index 24%, compared to the buoy measurements over deep water. The respective bias is -0.11 m and -0.13 m and the scatter index 13% and 17% when compared to the crossover measurements of the spaceborne radar altimeters on GFO and JASON, respectively. For a full year dataset, from June 2006 to May 2007, ASAR wave mode data were processed using the CWAVE_ENV algorithm leading to a global sea state analysis. Global 10-year returned extreme SWH is estimated to be 23.4 m using a lognormal probability density function (pdf) as the best fit for high sea state. Seasonal and annual maps for SWH, mean wave period, and wave steepness are compiled. In the winter season, the fetch-limit effects of the North Atlantic lead to high wave build up continuously from west to east, causing the gradual growth of swell. Compared to the results of reanalyzed wave model ERA-40 during 1971 - 2000, the annual mean wave height derived from ASAR wave mode data shows a similar pattern of high waves in the North Pacific, North Atlantic and the Southern Hemisphere. However, in the Northwestern Indian, a much stronger monsoon signal is observed in the ASAR results than the model results. With respect to the mean wave period, extreme swell is observed in the open sea south of Australia, which is around 1 s higher than the model results for the mean value. The SAR wave mode data are useful for global wave studies, while in the coastal regions, SAR data with higher resolution as well as larger coverage are required for investigating spatial changes of sea state. Wave refraction and diffraction around the Terceira island (located in the North Atlantic) is analyzed using the new high resolution TerraSAR-X data. Variations of wave height, peak wavelength and wave direction in the coastal wave processes are identified using the two-dimensional SAR image spectra

Comparing wavelengths simulated by the coastal wave model CWAM and TerraSAR-X satellite data

The accuracy of the high resolution coastal wave forecast model CWAM is validated on the basis of sea state information from satellite images of TerraSAR-X (TS-X). Employing 2-dimensional Fast Fourier Transformation, image spectra are derived from TS-X and locally varying patterns of the peak wavelengths are provided. Subsequently, wavelength comparisons are performed between a typical set of TS-X scenes acquired in December 2013 over the German Bight and the model hindcasts. This results mostly in reasonable agreement. Potential wave modelling errors are discussed as well

Investigating High-Resolution Spatial Wave Patterns on the Canadian Beaufort Shelf Using Synthetic Aperture Radar Imagery at Herschel Island, Qikiqtaruk, Yukon, Canada

The Arctic is experiencing the greatest increase in air temperature on Earth. This significant climatic change is leading to a significant positive trend of increasing wave heights and greater coastal erosion. This in turn effects local economies and ecosystems. Increasing wave energy is one of the main drivers of this alarming trend. However, the data on spatial and temporal patterns of wave heights in the Arctic are either coarse, interpolated or limited to point measurements. The aim of this study is to overcome this shortcoming by using remote sensing data. In this study, the Synthetic Aperture Radar (SAR) satellite TerraSAR-X (TS-X) and TanDEM-X (TD-X) imagery are used to obtain sea state information with a high spatial resolution in Arctic nearshore waters in the Canadian Beaufort Sea. From the entire archive of the TS-X/TD-X StripMap mode with coverage around 30 km × 50 km acquired between 2009 and 2020 around Herschel Island, Qikiqtaruk (HIQ), all the ice-free scenes were processed. The resulting dataset of 175 collocated scenes was used to map the significant wave height (����) and to link spatial and temporal patterns to local coastal processes. Sea state parameters are estimated in raster format with a 600 m step using the empirical algorithm CWAVE_EX. The statistics of the ���� were aggregated according to spatial variability, seasonality and wind conditions. The results show that the spatial wave climate is clearly related to the dominant wind regime and seasonality. For instance, the aggregation of all the scenes recorded in July between 2009 and 2020 results in an average of 0.82 m ����, while in October the average ���� is almost 0.40 m higher. The analysis by wind direction shows that fetch length and wind speed are likely the most important variables influencing the spatial variability. A larger fetch under NW conditions results in a mean wave height of 0.92 m, while waves generated under ESE conditions are lower at 0.81 m on average

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

Meteo-Marine Parameters from Sentinel-1 SAR Imagery: Towards Near Real-Time Services for the Baltic Sea

A method for estimating meteo-marine parameters from satellite Synthetic Aperture Radar (SAR) data, intended for near-real-time (NRT) service over the Baltic Sea, is presented and validated. Total significant wave height data are retrieved with an empirical function CWAVE_S1-IW, which combines spectral analysis of Sentinel-1A/B Interferometric Wide swath (IW) subscenes with wind data derived with common C-Band Geophysical Model Functions (GMFs). In total, 15 Sentinel-1A/B scenes (116 acquisitions) over the Baltic Sea were processed for comparison with off-shore sea state measurements (52 collocations) and coastal wind measurements (357 colocations). Sentinel-1 wave height was spatially compared with WAM wave model results (Copernicus Marine Environment Monitoring Service (CMEMS). The comparison of SAR-derived wave heights shows good agreement with measured wave heights correlation r of 0.88 and with WAM model (r = 0.85). The wind speed estimated from SAR images yields good agreement with in situ data (r = 0.91). The study demonstrates that the wave retrievals from Sentinel-1 IW data provide valuable information for operational and statistical monitoring of wave conditions in the Baltic Sea. The data is valuable for model validation and interpretation in regions where, and during periods when, in situ measurements are missing. The Sentinel-1 A/B wave retrievals provide more detailed information about spatial variability of the wave field in the coastal zone compared to in situ measurements, altimetry wave products and model forecast. Thus, SAR data enables estimation of storm locations and areal coverage. Methods shown in the study are implemented in NRT service in German Aerospace Center’s (DLR) ground station Neustrelitz

Empirical Relationship Between the Doppler Centroid Derived From X-Band Spaceborne InSAR Data and Wind Vectors

One of the challenges in ocean surface current retrieval from synthetic aperture radar (SAR) data is the estimation and removal of the wave-induced Doppler centroid (DC). This article demonstrates empirically the relationship between the dc derived from spaceborne X-band InSAR data and the ocean surface wind and waves. In this study, we analyzed over 300 TanDEM-X image pairs. It is found that the general characteristics of the estimated dc follow the theoretically expected variation with incidence angle, wind speed, and wind direction. An empirical geophysical model function (GMF) is fit to the estimated dc and compared to existing models and previous experiments. Our GMF is in good agreement (within 0.2 m/s) with other models and data sets. It is found that the wind-induced Doppler velocity contributes to the total Doppler velocity with about 15% of the radial wind speed. This is much larger than the sum of the contributions from the Bragg waves (~0.2 m/s) and the wind-induced drift current (~3% of wind speed). This indicates a significant (dominant) contribution of the long wind waves to the SAR dc. Moreover, analysis of dual-polarized data shows that the backscatter polarization ratio (PR=σ⁰VV/σ⁰HH) and the dc polarization difference (PD=|dcVV|-|dcHH|) are systematically larger than 1 and smaller than 0 Hz, respectively, and both increase in magnitude with incidence angle. The estimated PR and PD are compared to other theoretical and empirical models. The Bragg scattering theory-based (pure Bragg and composite surface) models overestimate both PR and PD, suggesting that other scattering mechanisms, e.g., wave breaking, are involved. In general, it is found that empirical models are more consistent with both backscatter and Doppler data than theory-based models. This motivates a further improvement of SAR dc GMFs

A Variational Stereo Method for the Three-Dimensional Reconstruction of Ocean Waves

We develop a novel remote sensing technique for the observation of waves on the ocean surface. Our method infers the 3-D waveform and radiance of oceanic sea states via a variational stereo imagery formulation. In this setting, the shape and radiance of the wave surface are given by minimizers of a composite energy functional that combines a photometric matching term along with regularization terms involving the smoothness of the unknowns. The desired ocean surface shape and radiance are the solution of a system of coupled partial differential equations derived from the optimality conditions of the energy functional. The proposed method is naturally extended to study the spatiotemporal dynamics of ocean waves and applied to three sets of stereo video data. Statistical and spectral analysis are carried out. Our results provide evidence that the observed omnidirectional wavenumber spectrum S(k) decays as k-2.5 is in agreement with Zakharov's theory (1999). Furthermore, the 3-D spectrum of the reconstructed wave surface is exploited to estimate wave dispersion and currents

Oil spill and ship detection using high resolution polarimetric X-band SAR data

Among illegal human activities, marine pollution and target detection are the key concern of Maritime Security and Safety. This thesis deals with oil spill and ship detection using high resolution X-band polarimetric SAR (PolSAR). Polarimetry aims at analysing the polarization state of a wave field, in order to obtain physical information from the observed object. In this dissertation PolSAR techniques are suggested as improvement of the current State-of-the-Art of SAR marine pollution and target detection, by examining in depth Near Real Time suitability

Nearshore Bathymetry Retrieval from Wave-Based Inversion for Video Imagery

A wavelet-based method for bathymetry retrieval using a sequence of static images of the surface wave field, as obtained from video imagery, is proposed. Synthetic images of the water surface are generated from a numerical Boussinesq type model simulating the propagation of irregular waves. The spectral analysis is used to retrieve both wave periods and wavelengths by evaluating the spectral peaks in the time and spatial domains, respectively. The water depths are estimated using the linear dispersion relation and the results are validated with the model’s bathymetry. To verify the proposed methodology, 2D and 3D simulations considering effects of wave shoaling and refraction were performed for different sea conditions over different seafloors. The method’s ability to reproduce the original bathymetry is shown to be robust in intermediate and shallow waters, being also validated with a real case with images obtained with a shore-based video station. The main improvements of the new method compared to the consideration of a single image, as often used in Satellite Derived Bathymetry, is that the use of successive images enables the consideration of different wave periods, improving depth estimations and not requiring the use of subdomains or filters. This image processing methodology shows very positive results to provide bathymetry maps for shallow marine environments and can be useful to monitor the nearshore with high time- and space-resolution at low cost.This research was funded by Direção Geral da Política do Mar, through projectNAVSAFETY of the Fundo Azul program. Thanks are due to FCT/MCTES (PT) for the financial support to CESAM (UIDP/50017/2020 + UIDB/50017/2020 + LA/P/0094/2020), through national funds and to the project Space for Shore funded through EOEP 5 Coastal Erosion Program (ESA/AO/1-9219/18/I-LG).info:eu-repo/semantics/publishedVersio