Retrieval of Ocean Surface Currents and Winds Using Satellite SAR backscatter and Doppler frequency shift

Ocean surface winds and currents play an important role for weather, climate, marine life, ship navigation, oil spill drift and search and rescue. In-situ observations of the ocean are sparse and costly. Satellites provide a useful complement to these observations. Synthetic aperture radar (SAR) is particularly attractive due to its high spatial resolution and its capability to extract both sea surface winds and currents day and night and almost independent of weather.The work in this thesis involves processing of along-track interferometric SAR (ATI-SAR) data, analysis of the backscatter and Doppler frequency shift, and development of wind and current retrieval algorithms. Analysis of the Doppler frequency shift showed a systematic bias. A calibration method was proposed and implemented to correct for this bias. Doppler analysis also showed that the wave contribution to the SAR Doppler centroid often dominates over the current contribution. This wave contribution is estimated using existing theoretical and empirical Doppler models. For wind and current retrieval, two methods were developed and implemented.The first method, called the direct method, consists of retrieval of the wind speed from SAR backscatter using an empirical backscatter model. In order to retrieve the radial current, the retrieved wind speed is used to correct for the wave contribution. The current retrieval was assessed using two different (theoretical and empirical) Doppler models and wind inputs (model and SAR-derived). It was found that the results obtained by combining the Doppler empirical model with the SAR-derived wind speed were more consistent with ocean models.The second method, called Bayesian method, consists of blending the SAR observables (backscatter and Doppler shift) with an atmospheric and an oceanic model to retrieve the total wind and current vector fields. It was shown that this method yields more accurate estimates, i.e. reduces the models biases against in-situ measurements. Moreover, the method introduces small scale features, e.g. fronts and meandering, which are weakly resolved by the models.The correlation between the surface wind vectors and the SAR Doppler shift was demonstrated empirically using the Doppler shift estimated from over 300 TanDEM-X interferograms and ECMWF reanalysis wind vectors. Analysis of polarimetric data showed that theoretical models such as Bragg and composite surface models over-estimate the backscatter polarization ratio and Doppler shift polarization difference. A combination of a theoretical Doppler model and an empirical modulation transfer function was proposed. It was found that this model is more consistent with the analyzed data than the pure theoretical models.The results of this thesis will be useful for integrating SAR retrievals in ocean current products and assimilating SAR observables in the atmospheric, oceanic or coupled models. The results are also relevant for preparation studies of future satellite missions

Remote sensing of the surface wind field over the coastal ocean via direct calibration of HF radar backscatter power

Author Posting. © American Meteorological Society, 2016. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Atmospheric and Oceanic Technology 33 (2016): 1377-1392, doi:10.1175/JTECH-D-15-0242.1.The calibration and validation of a novel approach to remotely sense surface winds using land-based high-frequency (HF) radar systems are described. Potentially available on time scales of tens of minutes and spatial scales of 2–3 km for wide swaths of the coastal ocean, HF radar–based surface wind observations would greatly aid coastal ocean planners, researchers, and operational stakeholders by providing detailed real-time estimates and climatologies of coastal winds, as well as enabling higher-quality short-term forecasts of the spatially dependent wind field. Such observations are particularly critical for the developing offshore wind energy community. An autonomous surface vehicle was deployed within the Massachusetts Wind Energy Area, located south of Martha’s Vineyard, Massachusetts, for one month, collecting wind observations that were used to test models of wind-wave spreading and HF radar energy loss, thereby empirically relating radar-measured power to surface winds. HF radar–based extractions of the remote wind speed had accuracies of 1.4 m s−1 for winds less than 7 m s−1, within the optimal range of the radar frequency used. Accuracies degraded at higher winds due to low signal-to-noise ratios in the returned power and poor resolution of the model. Pairing radar systems with a range of transmit frequencies with adjustments of the extraction model for additional power and environmental factors would resolve many of the errors observed.This analysis was supported by the Massachusetts Clean Energy Center. The HF radar data used were obtained during projects supported by the National Science Foundation, the NOAA Integrated Ocean Observing System (IOOS), and internal funds from the Woods Hole Oceanographic Institution.2016-12-2

Remote Sensing with Shipborne High-Frequency Surface-Wave Radar

High-frequency surface-wave radar (HFSWR) has been successfully applied for moving target detection and remote sensing of ocean surface dynamic parameters for decades. Compared with conventional instruments such as buoys, anemometers, and microwave radars, HFSWR can be employed to an all-weather and all-time surveillance far beyond the visible horizon. Moreover, based on agility and maneuverability, shipborne HFSWR can not only enhance the survivability in complex ocean environment but also enlarge the detection distance on open sea, which will gradually become a popular deployment situation. In this chapter, ocean surface cross sections for shipborne HFSWR with linear platform motion and sway motion are derived theoretically. Then, the methods for ocean surface wind direction, wind field, and current extraction are presented. The computer simulations and experimental results of the real data are given to verify the detection accuracy and the distance limit of the abovementioned methods

SAR (Synthetic Aperture Radar). Earth observing system. Volume 2F: Instrument panel report

The scientific and engineering requirements for the Earth Observing System (EOS) imaging radar are provided. The radar is based on Shuttle Imaging Radar-C (SIR-C), and would include three frequencies: 1.25 GHz, 5.3 GHz, and 9.6 GHz; selectable polarizations for both transmit and receive channels; and selectable incidence angles from 15 to 55 deg. There would be three main viewing modes: a local high-resolution mode with typically 25 m resolution and 50 km swath width; a regional mapping mode with 100 m resolution and up to 200 km swath width; and a global mapping mode with typically 500 m resolution and up to 700 km swath width. The last mode allows global coverage in three days. The EOS SAR will be the first orbital imaging radar to provide multifrequency, multipolarization, multiple incidence angle observations of the entire Earth. Combined with Canadian and Japanese satellites, continuous radar observation capability will be possible. Major applications in the areas of glaciology, hydrology, vegetation science, oceanography, geology, and data and information systems are described

Introduction to High-Frequency Radar: Reality and Myth

The article of record as published may be found at https://www.jstor.org/stable/43924791Purpose – The purpose of this paper is to help optimize sustainment logistics for US Army brigade combat teams, which may face challenges in transporting their assigned assets. Design/methodology/approach – This paper develops a simulation framework with an integrated integer programming optimization model. The integer-programming model optimizes sustainment outcomes of supported battalions on a daily basis, whereas the simulation framework analyzes risk associated with shortfalls that may arise over the entire duration of a conflict. Findings – This work presents a scenario reflecting the steady resupply of an infantry brigade combat team during combat operations and presents an in-depth risk analysis for possible fleet compositions. Originality/value – The risk curves obtained allow decision-makers and commanders to optimize vehicle fleet design in advance of a conflict.Office of Naval ResearchThe editors of this special issue on High Frequency Radar Remote Sensing gratefully acknowledge the continued support of the Office of Naval Research through grants N00014-91-J-1775 (HIRES), 92-J-1807 (REINAS), 94-1-1016 (DUCK94), 95-3-0022 (MRY BA Y), 96-1-1065 (COPE), and 97-1-0348 (SHOALING WAVES).The editors of this special issue on High Frequency Radar Remote Sensing gratefully acknowledge the continued support of the Office of Naval Research through grants N00014-91-J-1775 (HIRES), 92-J-1807 (REINAS), 94-1-1016 (DUCK94), 95-3-0022 (MRY BA Y), 96-1-1065 (COPE), and 97-1-0348 (SHOALING WAVES)

Wavelet Analysis for Wind Fields Estimation

Wind field analysis from synthetic aperture radar images allows the estimation of wind direction and speed based on image descriptors. In this paper, we propose a framework to automate wind direction retrieval based on wavelet decomposition associated with spectral processing. We extend existing undecimated wavelet transform approaches, by including à trous with B3 spline scaling function, in addition to other wavelet bases as Gabor and Mexican-hat. The purpose is to extract more reliable directional information, when wind speed values range from 5 to 10 ms−1. Using C-band empirical models, associated with the estimated directional information, we calculate local wind speed values and compare our results with QuikSCAT scatterometer data. The proposed approach has potential application in the evaluation of oil spills and wind farms

Sea Surface Current Measurements Using Along-Track Interferometric SAR

Ocean currents affect the weather, the climate and the marine ecosystem. Observing ocean currents is important for understanding the upper-ocean layer dynamics and its interaction with the other components of the climate system. In-situ measurements are sparse and their deployment and maintenance is costly. Satellite remote sensing with large spatial coverage offers a good complement to the in-situ observations. In this work we have studied the spaceborne Along-Track Interferometric SAR (ATI-SAR) for measuring sea surface currents. The measurement principle is based on the fact that the phase difference between two SAR acquisitions is directly related to radial (line-of-sight) velocity of the illuminated surface. Previous studies based on similar systems were carried out in areas with well defined and strong tidal currents ( ~1 - 3 m/s). In this work we demonstrate thecapability of ATI-SAR, through several study cases, in areas with weak currents ( <0.5 m /s). This is challenging for the satellite measurements of surface currents because it requires very accurate processing and retrieval algorithms. In addition, it has been found that wave motion contribution, systematically dominates the measured ATI-SAR radial velocity in these weak current areas. Estimation of the wave motion contribution relies on high-resolution and accurate wind data. Thus, a wind speed retrieval algorithm from SAR is needed to support the ATI-SAR current retrieval. We have shown that with an appropriate processing of the ATI-SAR phase and with applying the necessary corrections to the measured velocity a good agreement with ocean circulation models is achieved (rmse =0.1 m /s). These corrections include phase calibration and wind compensation to correct for instrument and geophysical systematic errors, respectively. Finally, a novel method for removing the wind direction ambiguity, based on the ATI-SAR phase, is presented. In previous methods, the wind ambiguity removal was based on external information, e.g. an atmospheric model or on visual observation of wind shadows

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