GNSS-R for detection of extreme hydrological events: Red River Delta and Mekong Delta (Vietnam)

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GNSS-R atmospheric studies

The traditional atmospheric observing instruments, such as the water vapor radiometer (WVR), ionosonde, incoherent scatter radars (ISR), topside sounders onboard satellites, in situ rocket and satellite observations (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation well known as CALIPSO), are expensive and also partly restricted to either the bottom side ionosphere or the lower part of the topside ionosphere. While GPS satellites in high altitude orbits (∼20,200 km) are capable of providing details on the structure of the entire ionosphere, even the plasma-sphere. Therefore, GPS has been widely applied in atmospheric sounding, meteorology, climatology and space weather.
Figure I.12 – Illustration of the geometry of the direct, reflected and refracted GNSS signals and their use for different applications.
Due to the atmospheric refraction, GPS signals propagate through the Earth atmo-sphere along a slightly curved path and with slightly retarded speeds. For a long time, the delay of GNSS signals in the troposphere and ionosphere was considered as a nuisance, an error source, and now, with the use of GNSS receivers installed on low Earth orbit (LEO) satellites (Figure I.12) to measure the GNSS-RO signals coupled with ground-based atmo-spheric sounding techniques that use Continuously Operating Reference Station (CORS) networks, GNSS signals has been used to determine the useful atmospheric parameters. These parameters including tropospheric water vapor, temperature and pressure, and ionospheric total electron content (TEC) and electron density profile which were consis-tent with traditional instruments observations at comparable accuracies (Schmidt et al. (2005); Schmidt et al. (2008); Jin et al. (2006); Jin et al. (2007)). Nowadays, a num-ber of GNSS-RO missions have been successfully launched for atmospheric, ionospheric detection and climate change/water cycle related studies, such as:
• The US/ Argentina SAC-C, German CHAMP (CHAllenging Minisatellite Payload): with the RO on measurements onboard the spacecraft and the infrastructure de-veloped on ground, CHAMP had become a pilot mission for the pre-operational use of space-borne GPS observations for atmospheric and ionospheric research and applications in weather prediction and space weather monitoring;
Figure I.13 – CYGNSS coverage (From Calculating Coverage Statistics with CYGNSS – Earth observatory – NASA).

GNSS-R for water cycle studies

• Climate Community Initiative for Continuing Earth Radio Occultation (CICERO) is a follow-on mission to the COSMIC as a self-supporting enterprise for the greater GNSS-RO science and wider user communities who will share in its design, evolu-tion, and success ( The CICERO project changes the way to collect and disseminate Earth observational data with 100 microsatellites in Low-Earth Orbit (LEO) performing GNSS atmospheric radio occultation (GNSS-RO) and GNSS Surface Reflection (GNSS-SR) measurements. The CICERO constella-tion is designed with lower cost of acquiring data essential to understand our planet and expands the possibilities for obtaining new types of data from space. The plan is to initially launch 20 satellites with follow-on launches to reach a sustained array of 100 spacecrafts. The full CICERO constellations are expected to deliver nearly 100,000 atmospheric profiles per a day (Thomas Yunck and Lenz, 2007). Mean-while, since CICERO has GNSS Surface Reflection (GNSS-SR), it is expected to detect more detailed Earth’s surface characteristics and time-varying evolutions;
• NASA’s Cyclone Global Navigation Satellite System (CYGNSS): CYGNSS was launched into low Earth orbit on December 15, 2016 (Fig. I.13) and it’s a con-stellation of eight micro-satellites designed to measure surface winds in and near the inner core of hurricanes, including regions beneath the eye wall and intense inner rain bands that could not previously be measured from space. The CYGNSS-measured wind fields, when combined with precipitation fields (e.g., produced by the Global Precipitation Measurement [GPM] core satellite and its constellation of precipitation imagers – including the upcoming NASA TROPICS Mission), will pro-vide coupled observations of moist atmospheric thermodynamics and ocean surface response, enabling new insights into hurricane inner core dynamics and energetics.
The main objective of the RO technique (Fig. I.12) for retrieval of atmospheric profiles provided by these missions is to improve numerical weather prediction (NWP) models, especially over polar and oceanic regions where data coverage is crude.

GNSS-R ocean studies

In the recent, the exploitation of Global Navigation Satellite Systems (GNSS) signals for purposes other than navigation and positioning has been conceived and assessed both theoretically and experimentally (Zavorotny et al., 2014). GNSS-Reflectometry (GNSS-R) is a promising remote sensing tool that fulfills the requirements for high spatial coverage, short temporal revisit time, and low cost and low weight as GNSS-R sensors are passive systems equipped with very simple and low-cost instrumentation. The GNSS satellites are constantly broadcasting radio signals to the Earth. However, part of the signals is reflected back from the rough Earth’s surface. The delay of the GPS 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. For example, the measurements of GPS reflected signals from the ocean surface could retrieve the ocean surface height, wind speed, wind direction, and even sea ice conditions.


Determining ocean surface height

More than 20 years ago Martin-Neira (1993) have proposed the new opportunist applica-tion GNSS-R for altimetry. A large amount of studies has been carrying out like Fabra et al. (2012). In altimetry, the main parameter is the vertical height of the reflecting surface, either in absolute terms (e.g., with respect to the center of the Earth) or in rela-tive terms (e.g., with respect to the ellipsoid). Given that the GNSS-R measured surface height will be an averaged value across this area, area which increased with the antenna height (Löfgren et al., 2015; Liu et al., 2017). But one GNSS-R receiver can potentially track up to 40 reflections regarding only GPS and GLONASS constellations (D’Addio et al., 2014). Therefore, the spatio-temporal resolution compared, to others altimetric remote sensing techniques, is significantly improved. The altimetric retrieval techniques are presented in Yu et al. (2014) and Hajj and Zuffada (2003), whereas Yu et al. (2014) suggests to perform altimetry on multiple waveforms at different Doppler frequencies of the full DMM and Hajj and Zuffada (2003) presents an approach that requires neither surface roughness information nor models. The new signals like Galileo signal (E1, E5, and E6) have also been tested for altimetric applications and compared to GPS L1 and L5 signals (Pascual et al., 2014a,b).
Sea surface height (SSH) is constantly changing. Throughout earth’s history the sea surface height varied drastically and this ongoing process has a significant impact on life in coastal areas. Therefore, there is a need for constant monitoring of those fluctuations. Nowadays there are primary two approaches for monitoring and measuring sea surface height, mareographs and satellite altimetry. There are many forms of mareographs: float-ing mareographs, acoustic mareographs, radar mareographs and pressure-based systems, each of these systems carries out the measuring process in a slightly different manner. The mareographs provide data with high accuracy though they suffer from the disadvan-tage that the punctual measurements do only allow drawing conclusions about the sea surface height in the near vicinity of the tide graph.

Table of contents :

I.1 Why studies of the water cycle are important?
I.1.1 Water cycle
I.1.2 Factors Affecting Water Cycle
I.2 Tools permitting the analysis of the water cycle
I.2.1 Ocean Water cycle
I.2.2 Continental Water Cycle
I.2.3 Existing tools for water cycle research
I.3 GNSS-R for water cycle studies
I.3.1 GNSS-R atmospheric studies
I.3.2 GNSS-R ocean studies
I.3.3 GNSS land/hydrology studies
I.4 Organization of the manuscript
II.1 What is GNSS
II.1.1 Principle of GNSS
II.1.2 Description and structure of the GPS system
II.1.3 Description and structure of the GALILIO system
II.1.4 Description and structure of the GLONASS system
II.1.5 Other constellations
II.1.6 The Positioning measurement
II.1.7 Augmentation systems
II.1.8 Perspective
II.2 Reflection of GNSS signals
II.2.1 Multipath
II.2.2 Specular and diffuse reflection
II.3 GNSS Reflectometry (GNSS-R)
II.3.1 GNSS-R Measurement Techniques
II.3.2 Opportunity of the signal reflectometry
II.3.3 Observable obtained from airborne platforms
II.3.4 Interference Pattern Technical – Reflectometer with single antenna
II.3.5 Platforms and constraints
II.4 Efficiency of GNSS-R
II.5 Conclusions
III GNSS-R for soil moisture estimation using Unwrapping SNR phase for sandy soil 
III.1 Introduction
III.2 Objective
III.3 GNSS R – SNR data for detecting soil moisture
III.3.1 Observation Sites
III.3.2 Soil moisture retrieval
III.3.3 Relation between soil moisture and vegetation height retrieval
III.4 Article – Monitoring of soil moisture dynamics in sandy areas using the nwrapping phase of GNSS-R
IV GNSS-R for detection of extreme hydrological events: Red River Delta and Mekong Delta (Vietnam) 
IV.1 Introduction
IV.2 Methodology
IV.2.1 SNR-Least Square Method for the estimation of the continental water level
IV.3 Mekong Delta experiment (Vietnam)
IV.3.1 Presentation of the measurement site and experimental conditions
IV.3.2 Parameters for SNR data analysing
IV.3.3 Comparison between the height derived from GNSS-R and in-situ gauge records
IV.4 Red River Delta experiment (Vietnam)
IV.4.1 Presentation of the study area and datasets available
IV.4.2 Parameters for SNR data analysing
IV.4.3 Results
IV.5 Conclusion
V Conclusion and perspectives 
V.1 Conclusion
V.2 Perspectives


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