Ocean Remote Sensing Technologies: High frequency, marine and GNSS-based radar

Description

A vast array of ocean instrumentation has been developed for research purposes since the middle of the twentieth century, among which remote sensing technologies have become increasingly important. Within this class of instruments, high frequency (HF) surface and skywave radar, microwave marine radar and global navigation satellite systems (GNSS)-based radar have been successfully implemented in gathering information on large tracts of the ocean surface. This book provides a systematic introduction to the principles, state-of-the-art methods and applications of HF surface and sky wave radar, microwave marine radar and GNSS-based radar, as well as an exploration of ongoing challenges in the field. Ocean Remote Sensing Technologies: High frequency, marine and GNSS-based radar includes 23 chapters that are organized into three parts, mainly according to sensor types. The first part covers work related to HF radar, the second focusses on microwave marine radar, and the third concentrates on GNSS-based radar. Each part consists of an introductory chapter that provides an overview of the corresponding sensor, followed by chapters focussing on fundamental theory, specific applications, or advanced algorithm development. Each of the chapters is self-contained and readers should be aware that there may be across-chapter differences in symbols used for various parameters. The book is intended for a variety of readers in the radar and remotes sensing communities, and content has been selected with a range of interests and backgrounds in mind. Halftitle Page Series Page Title Page Copyright Contents About the Editors Preface 1 HF radar in a maritime environment 1.1 HF radar as an ocean remote sensor – introduction 1.1.1 A few fundamentals 1.1.2 Common classes and properties of ocean-mapping HFSWR 1.2 A brief historical perspective on relevant theory and technology 1.2.1 Relevant propagation and scattering theory 1.2.2 Technological advances 1.3 RCSs of the ocean 1.3.1 A technique for developing an RCS of the ocean 1.3.2 Other cross-section results 1.3.3 RCS depictions and discussion Acknowledgment References 2 Oceanographic applications of high-­frequency (HF) radar backscatter 2.1 Factors influencing HF backscatter 2.1.1 The electromagnetic spectrum and the speed of light 2.1.2 Factors related to the use of HF transmissions 2.1.3 Impacts of noise and averaging 2.1.4 Relevant time and space scales 2.1.5 Depths observed by HF radar 2.2 Real-time applications of HF radar backscatter 2.2.1 Considerations of real-time applications 2.2.2 Examples of real-time applications 2.3 Example of an intermediate-scale observation 2.4 Process studies using HF radar backscatter 2.5 Conclusions References 3 Symbiosis of remote sensing and ocean surveillance missions of HF skywave radar 3.1 Modelling the radar observation process 3.1.1 The radar process model 3.1.2 Calibration 3.1.3 Sea clutter modelling I: the direct problem 3.1.4 Sea clutter modelling II: the inverse problem 3.2 Characteristics of OTHR radar missions 3.3 Remote sensing information for enhanced surveillance 3.3.1 Detection 3.3.2 Location 3.3.3 Target classification 3.3.4 Resource management 3.3.5 Tactical intelligence 3.4 Summary References 4 Sea surface current mapping with HF radar – a primer 4.1 Introduction 4.2 Theory behind radial and vector current derivation from HF radar Doppler spectrum 4.3 Factors affecting current measurements 4.3.1 HF radar system types 4.3.2 Range resolution 4.3.3 Geometrical dilution of precision 4.3.4 Signal propagation and sea state 4.4 HF radar current observations on the West Florida Shelf 4.5 Ongoing HF radar investigations on the West Florida Shelf 4.5.1 An event of offshore working range drop 4.5.2 Average background noise and RFI effect 4.5.3 Atmospheric radio Refractivity ( 4.5.4 Wind speed effect 4.6 Summary Acknowledgment References 5 An initial evaluation of high-­frequency radar radial currents in the Straits of Florida in comparison with altimetry and model products 5.1 Introduction 5.2 Data sets 5.2.1 High-frequency radar current data and post-processing 5.2.2 Satellite altimetry-derived current products 5.2.3 Numerical model output 5.3 Evaluation metrics 5.4 Comparison with geostrophic currents derived from along-track altimetry 5.5 Comparison with geostrophic currents derived from gridded altimetry 5.6 Comparison with data assimilative model output 5.7 Summary and discussion Acknowledgment References 6 Ocean wave measurement 6.1 Introduction to ocean waves 6.2 Waves in the Doppler spectrum 6.2.1 First order 6.2.2 Second order 6.3 Inversion 6.3.1 Approximations and empirical methods 6.3.2 Integral inversion 6.3.3 The constrained iteration method 6.4 Examples and validations 6.4.1 Time series 6.4.2 Statistics 6.4.3 Spatio-temporal wave development 6.5 Sources of error and limitations 6.5.1 Radar data quality 6.5.2 Averaging 6.5.3 The scattering model 6.5.4 Numerical methods 6.6 Summary Acknowledgment References 7 A non-­linear method to estimate the wave directional spectrum by HF radar 7.1 Introduction 7.2 Equations of radar cross sections 7.3 Discretization of the integral equation 7.4 Other constraints 7.5 Algorithm 7.6 Procedure of wave spectrum estimation 7.7 Example of wave estimation and issues to be addressed References 8 HF radar observation of nearshore winds 8.1 Introduction 8.2 Background 8.2.1 Early studies 8.2.2 Wind direction via wave spreading models 8.2.3 Wind speed 8.3 Winds from second-order wave estimates 8.4 Winds from first order 8.5 Discussion 8.5.1 Trade off between first- and second-order wind sensing 8.5.2 Further radar noise issues 8.5.3 Propagation losses 8.5.4 Future directions 8.6 Summary Acknowledgment References 9 HF radar in tsunami detection 9.1 The underlying physics 9.2 Observation of surface currents 9.3 Tsunami characteristics 9.3.1 Physics of tsunamis 9.4 HF ocean radar detection of tsunamis 9.4.1 Crossed-loop HF radar systems 9.4.2 Phased-array HF radars 9.5 Definition of a hazardous tsunami 9.6 Discussion and summary 9.6.1 Oblique tsunamis 9.6.2 Maximising the alert period 9.6.3 Achieving surface current resolution 9.7 Conclusion Acknowledgment References 10 High-­frequency surface wave radar for target detection 10.1 Introduction to high-frequency surface wave radar basics 10.2 HFSWR system configurations 10.2.1 Bistatic c2onfiguration 10.2.2 Monostatic 10.3 HFSWR for target detection 10.4 Radar power budget 10.4.1 Radar range equation for a noise-limited environment 10.4.2 Radar range equation for an ocean clutter limited environment 10.5 Ocean clutter 10.6 Surface wave propagation 10.7 Maximum detection range 10.8 External noise 10.8.1 Manmade noise 10.8.2 Atmospheric noise level 10.8.3 Galactic noise 10.9 Interference and clutter 10.9.1 External interference 10.9.2 Self interference (clutter) 10.9.3 Ionospheric clutter 10.9.4 Ionospheric clutter scattering modes 10.9.5 Range wrap clutter mitigation 10.9.6 Meteor clutter 10.10 Radar cross section at HF 10.10.1 Definition of RCS at HF 10.10.2 RCS aspect angle dependency 10.10.3 RCS sea state dependency 10.10.4 RCS and stealth 10.10.5 Modelling radar cross section of vessels 10.10.6 RCS of large vessel 10.10.7 RCS of medium vessel 10.10.8 RCS of small vessel 10.10.9 RCS of very small vessels 10.11 Resolution 10.12 Accuracy of estimates 10.13 HFSWR and cognitive sensing 10.14 Challenges and ongoing research References 11 Introduction to ocean remote sensing with marine radars 11.1 Marine radar ocean observing instrumentation 11.1.1 Hardware 11.1.2 Software 11.2 Applications 11.2.1 Waves 11.2.2 Currents 11.2.3 Bathymetry 11.2.4 Winds 11.3 Recent developments included in this book 11.3.1 Chapter 12: Observation of sea surface waves by noncoherent X-band marine radar 11.3.2 Chapter 13: Wavelet-based methods to invert sea surfaces and bathymetries from X-band radar images 11.3.3 Chapter 14: Wave field reconstruction using orthogonal decomposition of Doppler velocities 11.3.4 Chapter 15: Current mapping from the wave spectrum 11.3.5 Chapter 16: Bathymetry (and current) retrieval: phase-based method 11.3.6 Chapter 17: Wind parameter measurement using X-band marine radar images References 12 Observation of sea surface waves by noncoherent X-­band marine radar 12.1 Introduction 12.2 FFT-based algorithms 12.2.1 Retrieval of wave spectrum 12.2.2 Estimation of wave parameters 12.2.3 Modulation transfer function 12.2.4 Example 12.3 The algorithm based on EOF analysis 12.3.1 EOF decomposition 12.3.2 Estimation of wave parameters 12.3.3 Physical interpretation of modes 12.3.4 Discussion of mode choice for SWH estimation 12.3.5 Example and validation 12.4 Summary References 13 Wavelet-­based methods to invert sea surfaces and bathymetries from X-­band radar images 13.1 Simulation of the sea surface elevation and radar images over a laterally uniform bottom profile 13.2 Direct and inverse 2D Continuous Wavelet Transform 13.3 The 2D Wavelet-based Surface Reconstruction method 13.4 Bathymetry reconstruction technique 13.5 Conclusions Acknowledgment References 14 Wave field reconstruction using orthogonal decomposition of Doppler velocities 14.1 Potential limitations of the FFT-based wave field processing 14.2 Proper orthogonal decomposition for wave field reconstruction 14.2.1 Data 14.2.2 Proper orthogonal decomposition 14.2.3 Mode selection and physical significance of the POD modes 14.3 Evaluation of POD-based wave field reconstructions 14.3.1 Wave field statistics 14.3.2 Phase resolved wave field comparisons 14.4 Summary and limitations of pod-based wave field reconstructions References 15 Current mapping from the wave spectrum 15.1 Wave propagation atop background currents 15.2 Appearance of the linear dispersion relation in the spectrum 15.2.1 Practical considerations 15.3 Extracting currents from the spectrum 15.3.1 Least squares method 15.3.2 Normalized scalar product method 15.3.3 Polar current shell method 15.3.4 Algorithm comparison 15.4 Reconstructing depth-dependent flows 15.4.1 Effective depth method 15.4.2 Ha-Campana method 15.4.3 Polynomial effective depth method 15.5 Challenges and further work 15.5.1 Validation 15.5.2 Interpretation of the currents: Stokes drift 15.6 Summary References 16 Bathymetry (and current) retrieval: phase-­based method 16.1 Introduction 16.2 Brief overview 16.3 Frequency and wavenumber estimates 16.3.1 Fourier series representation of the imaged wave field 16.3.2 Compute the temporal discrete Fourier transform 16.3.3 Compute the cross-spectral coherence spectrum 16.3.4 Extract the dominant cross-spectral eigenvector 16.3.5 Minimize a cost function to estimate wavenumber 16.3.6 Wavenumber estimate quality metrics 16.4 Depth inversion 16.4.1 Problem formulation 16.4.2 Remove temporal water level trends 16.5 Temporal updates 16.5.1 Kalman filter 16.5.2 Moving average 16.6 Revisit current estimation 16.7 Performance 16.8 Summary and future work References 17 Wind parameter measurement using X-­band marine radar images 17.1 Wind streaks/wind gusts based methods 17.1.1 Local gradient based method 17.1.2 Optical flow based method for wind vector retrieval 17.2 Intensity information and curve fitting based methods 17.2.1 Single curve fitting based algorithm 17.2.2 Two-model curve fitting for rain mitigation 17.2.3 Dual curve fitting for low sea state cases 17.2.4 Significant wave height incorporated curve fitting 17.2.5 Intensity level selection algorithms 17.2.6 Modified ILS 17.2.7 Texture analysis incorporated ILS 17.3 Transform domain and curve fitting based methods 17.3.1 Spectral noise based algorithm 17.3.2 Spectral integration based algorithm 17.3.3 Ensemble empirical mode decomposition based methods 17.4 Nonparametric regression based methods 17.4.1 Neural network based method 17.4.2 Support vector regression based method 17.4.3 Gaussian process regression based method 17.5 Error mitigation 17.6 Conclusions and outlook References 18 Introduction to remote sensing using GNSS signals of opportunity 18.1 A quick historical review of GNSS-R 18.2 Basic concepts on GNSS 18.2.1 Measurement principle 18.2.2 Structure of the GNSS signals 18.2.3 Received power of the GNSS signals 18.2.4 Atmospheric and ionospheric effects 18.2.5 Satellite navigation systems 18.3 GNSS-R 18.3.1 Spatial resolution 18.3.2 Received power: coherent and incoherent components 18.3.3 The Woodward ambiguity function 18.3.4 GNSS-R observables and techniques 18.3.5 SNR computation 18.4 GNSS-R ocean applications 18.4.1 Ocean Scatterometry 18.4.2 Ocean altimetry 18.4.3 Ocean imaging 18.5. Conclusions Acknowledgment References 19 Modeling and simulation of GNSS-­R delay-­Doppler maps over the ocean 19.1 Introduction 19.2 Observation geometry and GNSS signal propagation 19.2.1 Ionospheric delay 19.2.2 Tropospheric delay 19.3 Statistically rough surfaces 19.4 Scattering models for GNSS-R signal simulation 19.4.1 Facet approach 19.4.2 Zavorotny-Voronovich bistatic equation model 19.4.3 Statistical scattering model 19.4.4 Comments to modeling equations 19.5 Simulation of the GNSS-R signal 19.5.1 Observation geometry and specular point calculation 19.5.2 Surface gridding 19.5.3 Simulation with a facet scattering model 19.5.4 Simulation based on the Zavorotny-Voronovich equation 19.5.5 Simulation based on a stochastic model 19.6 Conclusions References 20 Wind estimation 20.1 Modelling ocean-reflected GNSS signals 20.1.1 Woodward’s Ambiguity Function 20.1.2 The Delay Doppler Map 20.1.3 The Bistatic Radar Equation 20.1.4 Electromagnetic scattering model 20.1.5 Sea surface models 20.2 Processing delay Doppler maps 20.2.1 Feature extraction 20.2.2 Calibration 20.3 Retrieval techniques 20.3.1 Empirical wind speed estimation 20.3.2 Machine Learning 20.3.3 Stare processing 20.4 Summary 20.5 Future challenges References 21 GNSS-­R ocean altimetry 21.1 Historical overview: technical relevant aspects 21.2 Altimetric tracking point 21.3 Height precision 21.4 Impact of GNSS odes on altimetric performance 21.5 Experimental field campaigns 21.5.1 Ground-based 21.5.2 Air-borne 21.6 Space-borne missions 21.6.1 PARIS IoD 21.6.2 Space-borne Imaging Radar-C 21.6.3 UK TDS- 21.6.4 CYGNSS 21.7 Conclusions Acknowledgment References 22 Sea ice sensing using the GNSS-­R technique 22.1 Background and overview 22.2 Sea ice detection 22.2.1 DDM observable based method 22.2.2 Scattering coefficient retrieval based method 22.2.3 Machine learning based method 22.2.4 Empirical model based method 22.3 SIC estimation 22.4 SIT retrieval 22.4.1 Three-layer model 22.4.2 Empirical SIT estimation model 22.4.3 Phase altimetry based SIT retrieval 22.5 Ice altimetry techniques 22.5.1 Waveform based method 22.5.2 Phase based method 22.6 Other applications 22.6.1 Sea ice classification 22.6.2 Sea ice permittivity and roughness retrieval 22.7 Conclusions References 23 Triton – GNSS Reflectometry Mission in Taiwan 23.1 Introduction 23.2 Triton satellite mission 23.3 GPSR development 23.4 GNSS reflectometry mission payload 23.5 GNSS-R payload validation 23.6 Wind speed retrieval algorithm 23.6.1 The MSS observation principle of the miniature buoy 23.7 Summary References Appendix: List of Reviewers Index Back Cover