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GNSS Remote Sensing.pdf
Preface
Contents
Acronyms
Part I: GNSS Theory and Delays
Chapter 1: Introduction to GNSS
1.1 GNSS History
1.1.1 GPS
1.1.2 GLONASS
1.1.3 GALILEO
1.1.4 Beidou/COMPASS
1.1.5 Other Regional Systems
1.2 GNSS Systems and Signals
1.2.1 GNSS Segments
1.2.1.1 Space Segment
1.2.1.2 Control Segment
1.2.1.3 User Segment
1.2.1.4 Augmentation Segment
1.2.2 GNSS Signals
1.3 GNSS Theory and Errors
1.3.1 GNSS Principle
1.3.2 GNSS Error Sources
1.4 GNSS Observations and Applications
1.4.1 GNSS Observation Network
1.4.2 GNSS Applications
1.4.2.1 Positioning, Navigation and Timing
1.4.2.2 GNSS Remote Sensing
References
Chapter 2: GNSS Atmospheric and Multipath Delays
2.1 Atmospheric Refractivity
2.2 GNSS Atmospheric Delays
2.2.1 Neutral Atmospheric Delays
2.2.2 Empirical Tropospheric Models
2.2.2.1 Modified Saastamoinen Model
2.2.2.2 Modified Hopfield Model
2.3 GNSS Ionospheric Delay
2.3.1 The Ionosphere
2.3.2 GNSS Ionospheric Delay
2.3.3 Empirical Ionospheric Models
2.3.3.1 Bent Model
2.3.3.2 IRI Model
2.3.3.3 Klobuchar Model
2.4 GNSS Multipath Delay
2.4.1 Multipath Effects
2.4.2 Multipath Variations
2.4.2.1 Multipath Variations with Elevation Angle
2.4.2.2 Multipath Variations with Antenna Height
2.4.3 Surface Reflection Characteristics
References
Part II: GNSS Atmospheric Sensing and Applications
Chapter 3: Ground GNSS Atmospheric Sensing
3.1 Introduction
3.2 Theory and Methods
3.2.1 Estimates of GNSS ZTD
3.2.1.1 Double Difference
3.2.1.2 Non-difference Observation
3.2.2 Mapping Functions
3.2.2.1 Herring Mapping Function
3.2.2.2 Niell Mapping Function
3.2.2.3 Vienna Mapping Functions 1 (VMF1)
3.2.2.4 Global Mapping Function
3.3 ZTD Estimate and Variations
3.3.1 ZTD Estimates from IGS Observations
3.3.2 Multi-Scale ZTD Variations
3.3.2.1 Secular ZTD Variations
3.3.2.2 Seasonal ZTD Variations
3.3.2.3 Diurnal and Semidiurnal ZTD Cycles
3.4 GNSS Precipitable Water Vapor
3.4.1 GNSS PWV Estimate
3.4.2 Comparison with Independent Observations
3.4.3 Mean PWV Characteristics
3.4.4 Seasonal PWV Variations
3.4.5 Diurnal PWV Variations
3.5 3-D Water Vapor Topography
3.6 Summary
References
Chapter 4: Ground GNSS Ionosphere Sounding
4.1 History
4.2 GNSS Ionospheric Sounding
4.2.1 DCB Determination
4.2.1.1 Test Results of Multi-stations
4.2.1.2 Test Results of Single Station
4.2.2 TEC Estimate
4.3 2-D Ionopspheric Mapping
4.3.1 Method of 2-D Ionospheric Mapping
4.3.1.1 Surface Fitting Method
4.3.1.2 Distance-Weighted Method
4.3.1.3 Hardy Function Interpolation (HFI) Method
4.3.1.4 Spherical Harmonics Functions
4.3.1.5 Triangular Grid Method
4.3.2 Applications of 2-D GNSS TEC
4.3.2.1 TEC Climatology
4.3.2.2 TEC Responses to Solar Flare and Storms
4.3.2.3 TEC Disturbances Following Earthquakes
4.4 3-D GNSS Ionospheric Mapping
4.4.1 3-D Ionospheric Topography
4.4.2 Validation of GNSS Ionospheric Tomography
4.4.3 Assessment of IRI-2001 Using GNSS Tomography
4.4.4 Ionospheric Slab Thickness
4.4.5 3-D ionospheric Behaviours to Storms
4.4.5.1 Geomagnetic Conditions
4.4.5.2 Disturbance of F2-Layer Parameters
References
Chapter 5: Theory of GNSS Radio Occultation
5.1 Introduction
5.1.1 Radio Occultation in Planetary Sciences
5.1.2 GNSS Radio Occultation in Earth Sciences
5.2 Principle of GNSS Radio Occultation
5.2.1 Atmospheric Refraction
5.2.2 Geometric Optics Approximation
5.2.3 Spherically Symmetric Atmosphere Assumption
5.2.4 Bending Angle and Refractive Index
5.3 GNSS Radio Occultation Processing
5.3.1 Calibrating and Extracting GNSS RO Observables
5.3.1.1 Precision Orbit Determination (POD) Method
5.3.1.2 Differencing Technique to Remove Clock Errors
5.3.2 Bending Angle Retrieval
5.3.2.1 Geometric Optics Method
5.3.2.2 Radio-Holographic (RH) Method
5.3.3 Ionosphere Retrieval
5.3.4 Neutral Atmosphere Retrieval
5.3.4.1 Ionospheric Calibration on Bending
5.3.4.2 Refractivity Retrieval from Abel Inversion
5.3.4.3 Quality Control
References
Chapter 6: Atmospheric Sensing Using GNSS RO
6.1 GNSS RO Atmospheric Sounding
6.1.1 Parameters Retrieval from GNSS RO
6.1.2 Dry Atmosphere Retrieval (Density, Pressure and Temperature)
6.1.3 Moist Atmosphere Retrieval
6.1.4 1D-Var (Variational Method)
6.2 Characteristics of GNSS RO Observations
6.2.1 Spatial Resolution (Vertical and Horizontal Resolution)
6.2.2 Accuracy and Precision Analysis
6.2.3 Measurement Errors
6.2.3.1 Thermal Noise
6.2.3.2 Clock Instability
6.2.3.3 Local Multipath
6.2.3.4 Receiver Tracking (Open Loop vs. Close Loop)
6.2.4 Calibration Errors
6.2.5 Retrieval Errors
6.2.5.1 Upper Boundary Condition
6.2.5.2 Spherically Symmetric Atmosphere Assumption
6.2.5.3 Atmospheric Multipath
6.2.5.4 Ducting (or Super-Refraction)
6.2.5.5 Refractivity Constant Uncertainty
6.2.5.6 Water Vapor Ambiguity
6.2.6 Experimental Validation of RO Accuracy and Precision
6.3 Dynamic Processes Studies with GNSS RO
6.3.1 Tropopause and Stratospheric Waves
6.3.2 Tropical Tidal Waves
6.3.3 Weather Front
6.3.4 Tropical Cyclones (TC)
6.3.5 Atmospheric Boundary Layer (ABL)
6.4 Weather Prediction Applications
6.4.1 GNSS RO Data Assimilation
6.4.2 Operational Assimilation of GNSS RO in NWP Models
6.5 Climate Applications
6.6 Future Application of Radio Occultation
6.6.1 Future GNSS and GNSS RO Missions
6.6.2 Airborne and Mountain-Top GNSS RO
6.6.3 LEO-to-LEO Occultation
References
Chapter 7: Ionospheric Sounding Using GNSS-RO
7.1 Introduction
7.2 Ionospheric Inversion
7.2.1 Ionosphere Inversion Based on Doppler
7.2.2 Ionosphere Inversion Based on TEC
7.2.3 Recursive Inversion of TEC
7.2.4 Amplitude Inversion
7.3 Error Analysis
7.3.1 Measurement Errors
7.3.1.1 Carrier Phase Measuring Errors
7.3.1.2 Orbit Errors
7.3.2 Data Processing Errors
7.4 Ionospheric Products
7.5 GNSS-RO Ionospheric Applications
7.5.1 Establishing Ionospheric Models
7.5.2 Ionospheric Tomography
7.5.3 Monitoring Ionospheric Anomalies
7.5.4 Ionospheric Scintillation
References
Part III: GNSS Reflectometry and Remote Sensing
Chapter 8: Theory of GNSS Reflectometry
8.1 Introduction
8.2 Multi-static System: Geometry and Coverage
8.3 Specular and Diffuse Scattering
8.4 Delay and Doppler
8.5 Reflectivity Levels and Polarization Issues
8.6 Scattering Theories
8.6.1 Kirchhoff or Tangent Plane Approximation (KA)
8.6.1.1 KA in Stationary-Phase Approximation (Kirchhoff Geometrical Optics, KGO)
8.6.1.2 KA in Physical Optics Approximation (KPO)
8.6.1.3 Alternative Formulations of KA
8.6.1.4 Validity Limits of Kirchhoff Approximations
8.6.2 Summary of Other Methods
8.6.3 Received GNSS Scattered Fields
8.6.4 The Bi-static Radar Equation for GNSS Modulated Signals
8.7 Noise and Coherence Issues
8.8 Systematic Errors
8.9 PARIS Interferometric Technique (PIT)
8.10 Observables
References
Chapter 9: Ocean Remote Sensing Using GNSS-R
9.1 Altimetry
9.1.1 Group Delay Altimetry
9.1.2 Atmospheric Corrections
9.1.3 GNSS-R Ocean Altimetric Performance
9.2 Ocean Surface Roughness
9.2.1 Surface Modelling
9.2.1.1 Ocean Wave Spectra
9.2.1.2 Surface Slopes Probability
9.2.1.3 Surface Generation
9.2.2 Retrieval Approaches
References
Chapter 10: Hydrology and Vegetation Remote Sensing
10.1 Introduction
10.2 Hydrology GNSS-Reflectometry
10.3 Hydrology Sensing from GNSS-R
10.3.1 Waveform Correlation
10.3.2 Interference Pattern Technique (IPT)
10.3.3 Hydrology Sensing from GNSS
10.3.4 GNSS-R Scattering Properties
10.3.5 GNSS-R Polarization
10.4 GNSS-R Forest Biomass Monitoring
10.5 Summary
References
Chapter 11: Cryospheric Sensing Using GNSS-R
11.1 Dry Snow Monitoring
11.1.1 Dry Snow Reflection Model: Multiple-Ray Single-Reflection
11.1.2 Dry Snow Observable: Lag-Hologram
11.2 Wet Snow Monitoring
11.2.1 Observations from Space-Borne GNSS-R
11.2.2 Observations from Ground GNSS-R
11.3 Sounding the Sea Ice Conditions
References
Chapter 12: Summary and Future Chances
12.1 Status of GNSS Remote Sensing
12.1.1 Atmospheric Sensing
12.1.2 Ocean Sensing
12.1.3 Hydrology Sensing
12.1.4 Cryosphere Mapping
12.2 Future Developments and Chances
12.2.1 More GNSS Networks and Constellations
12.2.2 Advanced GNSS Receivers
12.2.3 New Missions and Systems
12.2.4 New and Emerging Applications
12.3 Summary
References
Index
GNSS Remote Sensing
Remote Sensing and Digital Image Processing
VOLUME 19
Series Editor:
Freek D. van der Meer
Department of Earth Systems Analysis
International Institute for
Geo-Information Science and
Earth Observation (ITC)
Enchede, The Netherlands
&
Department of Physical Geography
Faculty of Geosciences
Utrecht University
The Netherlands
EARSel Series Editor:
André Marçal
Department of Mathematics
Faculty of Sciences
University of Porto
Porto, Portugal
Editorial Advisory Board:
EARSel Editorial Advisory Board:
Michael Abrams
NASA Jet Propulsion Laboratory
Pasadena, CA, U.S.A.
Paul Curran
University of Bournemouth, U.K.
Finland
Arnold Dekker
CSIRO, Land and Water Division
Canberra, Australia
Steven M. de Jong
Department of Physical Geography
Faculty of Geosciences
Utrecht University, The Netherlands
Michael Schaepman
Department of Geography
University of Zurich, Switzerland
Mario A. Gomarasca
CNR - IREA Milan, Italy
Martti Hallikainen
Helsinki University of Technology
Finland
Håkan Olsson
University of Zurich, Switzerland
Swedish University
of Agricultural Sciences
Sweden
Eberhard Parlow
University of Basel
Switzerland
Rainer Reuter
University of Oldenburg
Germany
For further volumes:
http://www.springer.com/series/6477
Shuanggen Jin Estel Cardellach Feiqin Xie
GNSS Remote Sensing
Theory, Methods and Applications
123
Estel Cardellach
Institut d’Estudis Espacials de Catalunya
(ICE/IEEC-CSIC)
Barcelona, Spain
Shuanggen Jin
Shanghai Astronomical Observatory
Chinese Academy of Sciences
Shanghai, China
People’s Republic
Feiqin Xie
Texas A&M University-Corpus Christi
Corpus Christi
TX, USA
ISSN 1567-3200
ISBN 978-94-007-7481-0
DOI 10.1007/978-94-007-7482-7
Springer Dordrecht Heidelberg New York London
ISBN 978-94-007-7482-7 (eBook)
Library of Congress Control Number: 2013950927
© Springer Science+Business Media Dordrecht 2014
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of
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Preface
The Global Navigation Satellite System (GNSS) has provided an unprecedented
high accuracy, flexibility and tremendous contribution to navigation, positioning,
timing and scientific questions related to precise positioning on Earth’s surface,
since Global Positioning System (GPS) became fully operational in 1994. Since
GNSS is characterized as a highly precise, continuous, all-weather and near-real-
time microwave (L-band) technique, additional more applications and potentials
of GNSS are being explored by scientists and engineers. When the GNSS signal
propagates through the Earth’s atmosphere, it is delayed by the atmospheric
refractivity. GNSS radio occultation together with ground GNSS have been used
to produce accurate, all-weather, global refractive index, pressure, density profiles
in the troposphere, temperature with up to the lower stratosphere (35–40 km), and
the ionospheric total electron content (TEC) as well as electron density profiles,
to improve weather analysis and forecasting, monitor climate change, and monitor
ionospheric events. Therefore, GNSS has great potentials in atmospheric sounding,
meteorology, climatology and space weather.
In addition, surface multi-path is one of main error sources for GNSS navigation
and positioning. It has recently been recognized, however, that the special kind of
GPS multi-path delay reflected from the Earth’s surface, could be used to sense
the Earth’s surface environments. A recent interesting result on fluctuations in near
surface soil moisture has been successfully retrieved from the ground GNSS multi-
path, fairly matching soil moisture fluctuations in soil measured with conventional
sensors. In addition, the space-borne GNSS received delay of the GNSS reflected
signal with respect to the rough surface could provide information on the differential
paths between direct and reflected signals. Together with information on the receiver
antenna position and the medium, the delay measurements associated with the
properties of the reflecting surface can be used to produce the surface roughness
parameters and to determine surface characteristics. The Bistatic radar using L-band
signals transmitted by GNSS can be as an ocean altimeter and scatterometer.
A number of experiments and missions using GNSS reflected signals from the ocean
v
vi
Preface
and land surface have been tested and applied, such as determining ocean surface
height, wind speed and wind direction of ocean surface, soil moisture, snow and ice
thickness.
Therefore, the refracted, reflected and scattered GNSS signals can image the
Earth’s surface environments as a new, highly precise, continuous, all-weather and
near-real-time remote sensing tool, which is expected to revolutionize various atmo-
spheric sounding, ocean remote sensing and land/hydrology mapping, especially
for various Earth’s surfaces and the atmosphere. With the development of the next
generation of multi-frequency and multi-system GNSS constellations, including the
US’s modernized GPS-IIF and planned GPS-III, Russia’s restored GLONASS, and
the coming European Union’s GALILEO system and China’s Beidou/COMPASS
system as well as a number of Space Based Augmentation Systems, such as
Japan’s Quasi-Zenith Satellite System (QZSS) and India’s Regional Navigation
Satellite Systems (IRNSS), more applications and opportunities will be exploited
and realized using new onboard GNSS receivers on future space-borne GNSS
reflectometry and refractometry missions in the near future.
GNSS Remote Sensing –Theory, Methods and Applications has been written as
a monograph and textbook that guides the reader through the theory and practice
of GNSS remote sensing and applications in the atmosphere, oceans, land and
hydrology. This book covers Chap. 1: Introduction to GNSS, Chap. 2: GNSS
Atmospheric and Multipath Delays, Chap. 3: Ground GNSS Atmospheric Sensing,
Chap. 4: Ground-Based GNSS Ionospheric Sounding, Chap. 5: Theory of GNSS
Radio Occultation, Chap. 6: Atmospheric Sensing using GNSS RO, Chap. 7: Iono-
spheric Sounding using GNSS-RO, Chap. 8: Theory of GNSS Reflectometry, Chap.
9: Ocean Remote Sensing using GNSS-R, Chap. 10: Hydrology and Vegetation
Remote Sensing, Chap. 11: Cryospheric Sensing using GNSS-R and Chap. 12:
Summary and Future Chances. Chapters 1, 2, 3, 4, 7, 10, 11 and 12 were contributed
from Prof. Shuanggen Jin, Chaps. 5 and 6 were contributed from Dr. Feiqin Xie,
Chaps. 8 and 9 and part of Chap. 11 were contributed from Dr. Estel Cardellach as
well as some contributions from Rui Jin and Xuerui Wu.
This book provides the theory, methods, and applications of GNSS Remote
Sensing for scientists and users who have basic GNSS background and experiences.
Furthermore, it is also useful for the increasing number of next generation multi-
GNSS designers, engineers and users community. We would like to thank Assistant
Editor’s help and Springer-Verlag for their cordial collaboration and help during the
process of publishing this book.
Shanghai, People’s Republic of China
Barcelona, Spain
Corpus Christi, TX, USA
Shuanggen Jin
Estel Cardellach
Feiqin Xie
Contents
Part I GNSS Theory and Delays
1
Introduction to GNSS ......................................................
GNSS History ........................................................
1.1
GPS..........................................................
1.1.1
GLONASS ..................................................
1.1.2
1.1.3
GALILEO ...................................................
Beidou/COMPASS .........................................
1.1.4
1.1.5
Other Regional Systems ....................................
GNSS Systems and Signals ..........................................
1.2.1
GNSS Segments ............................................
1.2.2
GNSS Signals ...............................................
GNSS Theory and Errors ............................................
GNSS Principle .............................................
1.3.1
1.3.2
GNSS Error Sources........................................
GNSS Observations and Applications ..............................
GNSS Observation Network ...............................
1.4.1
1.4.2
GNSS Applications .........................................
References ....................................................................
1.3
1.2
1.4
2.1
2.2
2 GNSS Atmospheric and Multipath Delays ..............................
Atmospheric Refractivity ............................................
GNSS Atmospheric Delays ..........................................
2.2.1
Neutral Atmospheric Delays ...............................
2.2.2
Empirical Tropospheric Models ...........................
GNSS Ionospheric Delay ............................................
The Ionosphere .............................................
2.3.1
2.3.2
GNSS Ionospheric Delay...................................
Empirical Ionospheric Models .............................
2.3.3
2.3
3
3
3
5
6
7
7
8
8
10
11
12
12
13
13
14
16
17
17
18
18
19
20
20
21
24
vii
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