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

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

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

Evaluation of Chinese Quad-polarization Gaofen-3 SAR Wave Mode Data for Significant Wave Height Retrieval

Our work describes the accuracy of Chinese quad-polarization Gaofen-3 (GF-3) synthetic aperture radar (SAR) wave mode data for wave retrieval and provides guidance for the operational applications of GF-3 SAR. In this study, we evaluated the accuracy of the SAR-derived significant wave height (SWH) from 10,514 GF-3 SAR images with visible wave streaks acquired in wave mode by using the existing wave retrieval algorithms, e.g., the theoretical-based algorithm parameterized first-guess spectrum method (PFSM), the empirical algorithm CSAR_WAVE2 for VV-polarization, and the algorithm for quad-polarization (Q-P). The retrieved SWHs were compared with the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis field with 0.125° grids. The root mean square error (RMSE) of the SWH is 0.57 m, found using CSAR_WAVE2, and this RMSE value was less than the RMSE values for the analysis results achieved with the PFSM and Q-P algorithms. The statistical analysis also indicated that wind speed had little impact on the bias with increasing wind speed. However, the retrieval tended to overestimate when the SWH was smaller than 2.5 m and underestimate with an increasing SWH. This behavior provides a perspective of the improvement needed for the SWH retrieval algorithm using the GF-3 SAR acquired in wave mode

Measurements of Sea Surface Currents in the Baltic Sea Region Using Spaceborne Along-Track InSAR

The main challenging problems in ocean current retrieval from along-track interferometric (ATI)-synthetic aperture radar (SAR) are phase calibration and wave bias removal. In this paper, a method based on differential InSAR (DInSAR) technique for correcting the phase offset and its variation is proposed. The wave bias removal is assessed using two different Doppler models and two different wind sources. In addition to the wind provided by an atmospheric model, the wind speed used for wave correction in this work is extracted from the calibrated SAR backscatter. This demonstrates that current retrieval from ATI-SAR can be completed independently of atmospheric models. The retrieved currents, from four TanDEM-X (TDX) acquisitions over the 6resund channel in the Baltic Sea, are compared to a regional ocean circulation model. It is shown that by applying the proposed phase correction and wave bias removal, a good agreement in spatial variation and current direction is achieved. The residual bias, between the ocean model and the current retrievals, varies between 0.013 and 0.3 m/s depending on the Doppler model and wind source used for wave correction. This paper shows that using SAR as a source of wind speed reduces the bias and root-mean-squared-error (RMSE) of the retrieved currents by 20% and 15%, respectively. Finally, the sensitivity of the sea current retrieval to Doppler model and wind errors are discussed