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

High-resolution polar low winds obtained from unsupervised sar wind retrieval

High-resolution sea surface observations by spaceborne synthetic aperture radar (SAR) instruments are sorely neglected resources for meteorological applications in polar regions. Such radar observations provide information about wind speed and direction based on wind-induced roughness of the sea surface. The increasing coverage of SAR observations in polar regions calls for the development of SAR-specific applications that make use of the full information content of this valuable resource. Here we provide examples of the potential of SAR observations to provide details of the complex, mesoscale wind structure during polar low events, and examine the performance of two current wind retrieval methods. Furthermore, we suggest a new approach towards accurate wind vector retrieval of complex wind fields from SAR observations that does not require a priori wind direction input that the most common retrieval methods are dependent on. This approach has the potential to be particularly beneficial for numerical forecasting of weather systems with strong wind gradients, such as polar lows

Space-based Global Maritime Surveillance. Part I: Satellite Technologies

Maritime surveillance (MS) is crucial for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies. Since the early days of seafaring, MS has been a critical task for providing security in human coexistence. Several generations of sensors providing detailed maritime information have become available for large offshore areas in real time: maritime radar sensors in the 1950s and the automatic identification system (AIS) in the 1990s among them. However, ground-based maritime radars and AIS data do not always provide a comprehensive and seamless coverage of the entire maritime space. Therefore, the exploitation of space-based sensor technologies installed on satellites orbiting around the Earth, such as satellite AIS data, synthetic aperture radar, optical sensors, and global navigation satellite systems reflectometry, becomes crucial for MS and to complement the existing terrestrial technologies. In the first part of this work, we provide an overview of the main available space-based sensors technologies and present the advantages and limitations of each technology in the scope of MS. The second part, related to artificial intelligence, signal processing and data fusion techniques, is provided in a companion paper, titled: "Space-based Global Maritime Surveillance. Part II: Artificial Intelligence and Data Fusion Techniques" [1].Comment: This paper has been submitted to IEEE Aerospace and Electronic Systems Magazin

A Marine Radar Wind Sensor

A new method for retrieving the wind vector from radar-image sequences is presented. This method, called WiRAR, uses a marine X-band radar to analyze the backscatter of the ocean surface in space and time with respect to surface winds. Wind direction is found using wind-induced streaks, which are very well aligned with the mean surface wind direction and have a typical spacing above 50 m. Wind speeds are derived using a neural network by parameterizing the relationship between the wind vector and the normalized radar cross section (NRCS). To improve performance, it is also considered how the NRCS depends on sea state and atmospheric parameters such as air–sea temperature and humidity. Since the signal-to-noise ratio in the radar sequences is directly related to the significant wave height, this ratio is used to obtain sea state parameters. All radar datasets were acquired in the German Bight of the North Sea from the research platform FINO-I, which provides environmental data such as wind measurements at different heights, sea state, air–sea temperatures, humidity, and other meteorological and oceanographic parameters. The radar-image sequences were recorded by a marine X-band radar installed aboard FINO-I, which operates at grazing incidence and horizontal polarization in transmit and receive. For validation WiRAR is applied to the radar data and compared to the in situ wind measurements from FINO-I. The comparison of wind directions resulted in a correlation coefficient of 0.99 with a standard deviation of 12.8°, and that of wind speeds resulted in a correlation coefficient of 0.99 with a standard deviation of 0.41 m s^−1. In contrast to traditional offshore wind sensors, the retrieval of the wind vector from the NRCS of the ocean surface makes the system independent of the sensors’ motion and installation height as well as the effects due to platform-induced turbulence

Operational Use and Impact of Satellite Remotely Sensed Ocean Surface Vector Winds in the Marine Warning and Forecasting Environment

Rhodesia, Corn MillColorVolume 81, Page

Applicability of Synthetic Aperture Radar Wind Retrievals on Offshore Wind Resources Assessment in Hangzhou Bay, China

In view of the high cost and sparse spatial resolution of offshore meteorological observations, ocean winds retrieved from satellites are valuable in offshore wind resource assessment as a supplement to in situ measurements. This study examines satellite synthetic aperture radar (SAR) images from ENVISAT advanced SAR (ASAR) for mapping wind resources with high spatial resolution. Around 181 collected pairs of wind data from SAR wind maps and from 13 meteorological stations in Hangzhou Bay are compared. The statistical results comparing in situ wind speed and SAR-based wind speed show a standard deviation (SD) of 1.99 m/s and correlation coefficient of R = 0.67. The model wind directions, which are used as input for the SAR wind speed retrieval, show a high correlation coefficient (R = 0.89) but a large standard deviation (SD = 42.3°) compared to in situ observations. The Weibull probability density functions are compared at one meteorological station. The SAR-based results appear not to estimate the mean wind speed, Weibull scale and shape parameters and wind power density from the full in situ data set so well due to the lower number of satellite samples. Distributions calculated from the concurrent 81 SAR and in situ samples agree well

Design Options For Low Cost, Low Power Microsatellite Based SAR.

This research aims at providing a system design that reduces the mass and cost of spaceborne Synthetic Aperture Radar (SAR) missions by a factor of two compared to current (TecSAR - 300 kg, ~ £ 127 M) or planned (NovaSAR-S — 400 kg, ~ £ 50 M) mission. This would enable the cost of a SAR constellation to approach that of the current optical constellation such as Disaster Monitoring Constellation (DMC). This research has identified that the mission cost can be reduced significantly by: focusing on a narrow range of applications (forestry and disasters monitoring); ensuring the final design has a compact stowage volume, which facilitates a shared launch; and building the payload around available platforms, rather than the platform around the payload. The central idea of the research has been to operate the SAR at a low instantaneous power level—a practical proposition for a micro-satellite based SAR. The use of a simple parabolic reflector with a single horn at L-band means that a single, reliable and efficient Solid State Power Amplifier (SSPA) can be used to lower the overall system cost, and to minimise the impact on the spacecraft power system. A detailed analysis of basic pulsed (~ 5 - 10 % duty cycle) and Continuous Wave (CW) SAR (100 % duty cycle) payloads has shown their inability to fit directly into existing microsatellite buses without involving major changes, or employing more than one platform. To circumvent the problems of pulsed and CW techniques, two approaches have been formulated. The first shows that a CW SAR can be implemented in a mono-static way with a single antenna on a single platform. In this technique, the SAR works in an Interrupted CW (ICW) mode, but these interruptions introduce periodic gaps in the raw data. On processing, these gapped data result in artefacts in the reconstructed images. By applying data based statistical estimation techniques to “fill in the gaps” in the simulated raw SAR data, this research has shown the possibility of minimising the effects of these artefacts. However, once the same techniques are applied to the real SAR data (in this case derived from RADARSAT-1), the artefacts are shown to be problematic. Because of this the ICW SAR design technique it is—set aside. The second shows that an extended chirp mode pulsed (ECMP) SAR (~ 20 - 54 % duty cycle) can be designed with a lowered peak power level which enables a single SSPA to feed a parabolic Cassegrain antenna. The detailed analysis shows the feasibility of developing a microsatellite based SAR design at a comparable price to those of optical missions

Assessment and Analysis of QuikSCAT and COAMPS Model Vector Wind Products for the Gulf of Mexico: A Long-Term and Hurricane Perspective

Global weather changes have become a matter of grave concern in hurricane prone areas as intensities of hurricanes are observed to be increasing every year, necessitating improved monitoring capabilities. NASA’s QuikSCAT satellite sensor has provided significant support in analyzing and forecasting winds for the past 8 years. In this study, the performance of QuikSCAT products, including JPL’s latest L2B 12.5km swath winds, was evaluated against buoy-measured winds in the Gulf of Mexico. The long-term study period was 1/2005 – 2/2007. The Coupled Ocean/Atmospheric Mesoscale Prediction System (COAMPS) was also assessed. The regression analyses showed very good results for QuikSCAT products, with the best results obtained from L2B winds. R2 values for moderate wind speeds were 0.75 and 0.89, 0.88 and 0.93, 0.66 and 0.77 for speed and direction and for L3, L2B and COAMPS respectively. The National Weather Product (NWP) model winds provided in the L2B dataset were also studied. Hurricanes that took place from 2002 to 2006 were studied individually to obtain regressions of QuikSCAT and COAMPS versus buoys for those events. The correlations were very high indicating that QuikSCAT is at par with buoys during hurricanes. These measurements were compared with the NHC best track analyses to determine the accuracy and found to be almost half those obtained by NHC, possibly due to rain contamination. Sea Surface Height Anomaly (SSHA) measurements by Jason-1 and sea surface temperature (SST) measurements by the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and GOES-12 (Geostationary) were compared with wind fields during hurricanes to study the effects of the Loop Current and Warm Core Rings on the intensification of the hurricanes. A preliminary study was conducted in which the regions of enhanced wind speeds were observed by studying the longitudinal and latitudinal transects across the hurricane for two hurricanes, namely Hurricanes Ivan and Katrina. This study would act as a precursor to further analysis of the radius of maximum wind and critical wind radii