# GNSS Transpolar Earth Reflectometry exploring
## System (G-TERN): Mission Concept

Estel Cardellach, Jens Wickert, Rens Baggen, Javier Benito, Adriano Camps, Nuno Catarino, Bertrand Chapron, Andreas Dielacher, Fran Fabra, Greg Flato, et al.

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Estel Cardellach, Jens Wickert, Rens Baggen, Javier Benito, Adriano Camps, et al. GNSS Transpolar Earth Reflectometry exploriNg System (G-TERN): Mission Concept. IEEE Access, 2018, 6, pp.13980-14018. 10.1109/ACCESS.2018.2814072. hal-04202103

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Distributed under a Creative Commons Attribution 4.0 International License
Received December 21, 2017, accepted February 4, 2018, date of publication March 9, 2018, date of current version March 28, 2018.

Digital Object Identifier 10.1109/ACCESS.2018.2814072

# GNSS Transpolar Earth Reflectometry exploring
System (G-TERN): Mission Concept

ESTEL CARDELLACH<fnref n="1" />,<fnref n="1" />, (Member, IEEE), JENS WICKERT<fnref n="2" />, RENS BAGGEN<fnref n="3" />, JAVIER BENITO<fnref n="4" />,
ADRIANO CAMPS<fnref n="5" />, (Fellow, IEEE), NUNO CATARINO<fnref n="6" />, BERTRAND CHAPRON<fnref n="7" />,
ANDREAS DIELACHER<fnref n="8" />, FRAN FABRA<fnref n="1" />, GREG FLATO<fnref n="9" />, HEINRICH FRAGNER<fnref n="8" />,
CAROLINA GABARRÓ<fnref n="10" />, CHRISTINE GOMMENGINGER<fnref n="11" />, CHRISTIAN HAAS<fnref n="12" />, SEAN HEALY<fnref n="13" />,
MANUEL HERNANDEZ-PAJARES<fnref n="5" />, PER HØEG<fnref n="14" />, ADRIAN JÄGGI<fnref n="15" />, JUHA KAINULAINEN<fnref n="16" />,
SHFAQAT ABBAS KHAN<fnref n="17" />, NORBERT M. K. LEMKE<fnref n="18" />, WEIQIANG LI<fnref n="1" />,
SON V. NGHIEM<fnref n="19" />, (Fellow, IEEE), NAZZARENO PIERDICCA<fnref n="20" />, (Senior Member, IEEE),
MARCOS PORTABELLA<fnref n="10" />, KIMMO RAUTIAINEN<fnref n="21" />, ANTONIO RIUS<fnref n="1" />, (Member, IEEE),
INGO SASGEN<fnref n="12" />, MAXIMILIAN SEMMLING<fnref n="2" />, C. K. SHUM<fnref n="22" />, FRANÇOIS SOULAT<fnref n="23" />,
ANDREA K. STEINER<fnref n="24" />, SÉBASTIEN TAILHADES<fnref n="18" />, MAIK THOMAS<fnref n="2" />, ROGER VILASECA<fnref n="25" />,
AND CINZIA ZUFFADA<fnref n="19" />, (Member, IEEE)

Corresponding author: Estel Cardellach (estel@ice.csic.es)

This work was supported in part by the Spanish under Grant ESP2015-70014-C2-2-R and Grant ESP2015-70014-C2-1-R and in part by the Specific Expedition Budget of GFZ. The work of S. V. Ngiem was supported in part by the NASA Cryospheric Sciences Program and in part by the NASA Land Cover and Land Use Change Program.

**ABSTRACT** The global navigation satellite system (GNSS) Transpolar Earth Reflectometry exploring system (G-TERN) was proposed in response to ESA's Earth Explorer 9 revised call by a team of 33 multi-disciplinary scientists. The primary objective of the mission is to quantify at high spatio-temporal resolution crucial characteristics, processes and interactions between sea ice, and other Earth system components in order to advance the understanding and prediction of climate change and its impacts on the environment and society. The objective is articulated through three key questions. 1) In a rapidly changing Arctic regime and under the resilient Antarctic sea ice trend, how will highly dynamic forcings and couplings between the various components of the ocean, atmosphere, and cryosphere modify or influence the processes governing the characteristics of the sea ice cover (ice production, growth, deformation, and melt)? 2) What are the impacts of extreme events and feedback mechanisms on sea ice evolution? 3) What are the effects of the cryosphere behaviors, either rapidly changing or resiliently stable, on the global oceanic and atmospheric circulation and mid-latitude extreme events? To contribute answering these questions, G-TERN will measure key parameters of the sea ice, the oceans, and the atmosphere with frequent and dense coverage over polar areas, becoming a "dynamic mapper" of the

<footnote n="1">¹Institute of Space Sciences (CSIC), 08193 Cerdanyola del Vallès, Spain</footnote>

<footnote n="2">²GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany</footnote>

<footnote n="3">³IMST GmbH, 47475 Kamp-Lintfort, Germany</footnote>

<footnote n="4">⁴Airbus DS Space Systems España, 28022 Madrid, Spain</footnote>

<footnote n="5">⁵UPC/IEEC, 08034 Barcelona, Spain</footnote>

<footnote n="6">⁶DEIMOS Engenharia, 1998-023 Lisbon, Portugal</footnote>

<footnote n="7">⁷IFREMER, 29280 Plouzané, France</footnote>

<footnote n="8">⁸RUAG Space GmbH, 1120 Wien, Austria</footnote>

<footnote n="9">⁹Environment and Climate Change Canada, Victoria, BC V8P 5C2, Canada</footnote>

<footnote n="10">¹⁰Institut de Ciències del Mar and Barcelona Expert Center on Remote Sensing, 08003 Barcelona, Spain</footnote>

<footnote n="11">¹¹National Oceanography Centre, Southampton SO14 3ZH, U.K.</footnote>

<footnote n="12">¹²Alfred Wegener Institute for Polar and Marine Research, 27570 Bremerhaven, Germany</footnote>

<footnote n="13">¹³ECMWF, Reading RG2 9AX, U.K.</footnote>

<footnote n="14">¹⁴Department of Physics, University of Oslo, 0371 Oslo, Norway</footnote>

<footnote n="15">¹⁵Astronomical Institute, University of Bern, 3012 Bern, Switzerland</footnote>

<footnote n="16">¹⁶Harp Technologies Oy, 02150 Espoo, Finland</footnote>

<footnote n="17">¹⁷National Space Institute, Technical University of Denmark, 2800 Kongens Lyngby, Denmark</footnote>

<footnote n="18">¹⁸OHB System AG, 82234 Weling/Oberpfaffenhofen, Germany</footnote>

<footnote n="19">¹⁹Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91125, USA</footnote>

<footnote n="20">²⁰Department of Information, Electronics and Communications Engineering, Universita La Sapienza, 00185 Rome, Italy</footnote>

<footnote n="21">²¹Finnish Meteorological Institute, 101 Helsinki, Finland</footnote>

<footnote n="22">²²The Ohio State University, Columbus, OH 43210, USA</footnote>

<footnote n="23">²³Collecte Localisation Satellites SA, 31520 Ramonville Saint-Agne, France</footnote>

<footnote n="24">²⁴Wegener Center for Climate and Global Change, University of Graz, 8010 Graz, Austria</footnote>

<footnote n="25">²⁵TRYO Group, 08530 La Garriga, Spain</footnote>
ice conditions, the ice production, and the loss in multiple time and space scales, and surrounding environment. Over polar areas, the G-TERN will measure sea ice surface elevation (<10 cm precision), roughness, and polarimetry aspects at 30-km resolution and 3-days full coverage. G-TERN will implement the interferometric GNSS reflectometry concept, from a single satellite in near-polar orbit with capability for 12 simultaneous observations. Unlike currently orbiting GNSS reflectometry missions, the G-TERN uses the full GNSS available bandwidth to improve its ranging measurements. The lifetime would be 2025–2030 or optimally 2025–2035, covering key stages of the transition toward a nearly ice-free Arctic Ocean in summer. This paper describes the mission objectives, it reviews its measurement techniques, summarizes the suggested implementation, and finally, it estimates the expected performance.

**INDEX TERMS** Polar science, GNSS, reflectometry, GNSS-R, sea ice, altimetry, polarimetry, radio-occultation, Low Earth Orbit.

# I. INTRODUCTION

A novel remote sensing technique based on signals of the Global Navigation Satellite System (GNSS) reflected off the Earth surface, the so called GNSS reflectometry (GNSS-R), was suggested in the nineties for ocean altimetric [1] and scatterometric [2] applications. As investigations progressed, experimental campaigns, dedicated modelling activities and the analysis of actual spaceborne data sets have expanded the range of applications of the GNSS-R, which so far have generated two special issues of the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (J-STARS) [3], [4], an IEEE GRSS tutorial [5] and dedicated book chapters [6]–[8]. The cryosphere and polar areas are some of the new scientific targets of this technique.

Komjathy et al. [9] pioneered the research on GNSS-R for cryosphere information acquiring and analyzing data collected from airborne instruments. Their experimental results indicated the potential of reflected GNSS signals to provide information on the presence and condition of sea and fresh-water ice, as well as the freeze/thaw state of frozen ground. The Arctic sea ice data set was analyzed afterwards confirming its potential for ice scatterometric applications in [10], [11]. Reflected signals captured from a GNSS Radio Occultation satellite were preliminary inverted to sea ice and Greenland ice sheet altimetry under very slant geometries [12], while data obtained from a dedicated GNSS-R spaceborne experiment demonstrated the feasibility of acquiring signals reflected off sea ice from space at near nadir geometries [13], [14], even when a relatively low gain antenna was used. Dedicated coastal experiments based in Greenland [15] firstly investigated polarimetric responses of GNSS reflection off sea ice [16] and the trackability of the electromagnetic carrier phase after sea ice reflections, enabling precise phase-delay altimetry of the coastal ice [17]. Mid latitude snow properties were found to be characterized from reflected signals unintentionally captured in ground-based geodetic GNSS stations (e.g. [18]–[20]), while the interaction of GNSS signals with the dry snow in polar ice sheets was theoretically tackled in [21] and experimentally investigated [22]. Penetration depths down to a few hundred meters were reported in Antarctica ice sheet.

More recently, new sets of GNSS-R data have enabled to test some of these polar remote sensing concepts from space-borne scenarios. One of the data sets has been acquired from the Soil Moisture Active Passive (SMAP) mission, as the transmitting chain of its L-band radar failed and the receiving chain was tuned to collect GNSS reflected signals. The novelties of SMAP GNSS-R over other GNSS-R missions are the reception in two polarizations (two orthogonal linear base) and the high gain of its 6 meter antenna. These data have enabled GNSS-R to detect the land surface freeze/thaw state [23] and distinguish between ocean water and sea ice through the polarimetric response [24]. SMAP GNSS-R data were opportunistic, limited and are not available to the community, and they mostly cover continental areas (target of the SMAP mission). On the other hand, the UK TechDemoSat-1 (TDS-1) polar satellite operated a GNSS-R payload in a 2 out of 8 days cycle since July 2014 to July 2017, the data were open but the antenna was in a single polarization and of much moderate gain (13 dBi). The extensive sets of TDS-1 data over the poles have resulted in ice sheet altimetry studies [25], different algorithms to detect sea ice [26], [27], to estimate sea ice concentration [28], to perform sea ice altimetry using the group-delay of the reflected echo [29] or by using its carrier phase delay [30]. The latter reports negative correlation between the ice thickness and the altimetric solution, both presenting variations of the same order of magnitude. These findings might be an indication that the altimetric response comes from the ice-water interface (draft), which if confirmed would suppose a new and complementary way of extracting sea ice thickness.

The GNSS-R technique is proposed in a polar-science oriented mission [31], in response to the ESA EE9 Revised Call [32]. Unlike the GNSS-R spaceborne payloads deployed so far, the GNSS Transpolar Earth Reflectometry explorING system (G-TERN) proposes to implement a different acquisition technique to access the full GNSS transmitted bandwidth and a system of antennas tailored to altimetric applications. This approach follows the steps of the ESA's PAssive Reflectometry and Interferometry System In-Orbit-Demonstration (PARIS-IOD) [33] and the ESA's GNSS rEflectometry, Radio Occultation and Scatterometry on board the ISS (GEROS-ISS) [34], both missions focused on
GNSS-R altimetry and having successfully passed their irrespective industrial feasibility studies (Phase-A). G-TERN was proposed by a multidisciplinary international team of 33 scientists and engineers experts in GNSS remote sensing, polar sciences, oceanography, hydrology and space technology, to attempt to contribute solving a relevant scientific problem within the constraints of the ESA EE9 ‘Revised Call’. The call, issued in December 2016, asked for missions to address a relevant Earth scientific problem, while fitting in a reduced budget and short implementation time, using innovative techniques but based on proved concepts. Different aspects of the mission concept and suggested implementation are detailed in the following sections, together with the simulation exercises to assess the performance of the system.

## II. SCIENTIFIC OBJECTIVES

Advancing the understanding of the cryosphere in a changing climate has been identified as a ‘Grand Challenge’ by the World Climate Research Programme (WCRP). Components of the cryosphere play a central role in several processes that remain an important source of uncertainty in projections of future climate change. Examples of such processes are the prospect of an ice-free Arctic Ocean in contradiction to Antarctic sea ice increase; the role of ice-sheet dynamics in amplification of Greenlands and Antarcicas contribution to the global sea-level rise; the fate of mountain glaciers providing fresh water to hundreds of millions of people worldwide; and the strength of positive feedbacks between the warming climate and natural emissions of greenhouse gases from the thawing permafrost [35]. Furthermore, a particular issue has emerged in past Intergovernmental Panel on Climate Change (IPCC) Assessments [36] as topic of considerable uncertainty: the ability of models to simulate recent declines and future changes in sea ice. Recent studies have linked changes in snow and ice to circulation changes, weather extremes, and the obvious impacts on terrestrial and marine ecosystems, which create a great sense of urgency [37]. For the reasons discussed below, G-TERN primarily aims to contribute to understanding sea ice processes, their evolution and interactions with the rest of the climate systems.

The sea ice cover is a crucial component of the polar and global systems, influencing and influenced by changes across a wide range of temporal and spatial scales. A recent attempt to quantify the overall impact of sea ice on the current climate found that sea ice and anthropogenic greenhouse gas emissions are of similar magnitude in terms of their influence on the global heat budget [38]. Sea ice plays a number of key roles in moderating global climate, not only by influencing the planetary heat budget but also by interacting with the oceanic and atmospheric circulation systems as well as the terrestrial environment [39]–[50]. These complex feedback mechanisms link the atmosphere, sea ice, ocean, seafloor, and land, and many of them are not yet fully understood [46]. For example, winds and ocean currents can alter the distribution of sea ice. These changes

in the sea ice cover can then affect large-scale circulation patterns in the atmosphere (e.g. [41], [43]) and the ocean (e.g. [39]), which in turn may impact weather and the global climate system. Moreover, the Southern and Arctic Oceans are different dynamic systems. On one hand, surface waters in the Southern Ocean have experienced less warming than has been observed in other areas. On the other hand, the Arctic sea ice has decreased rapidly, and recent reports indicate that it could be largely free of sea ice in summer as early as the late 2030s, only two decades from now.<fnref n="1" /> Climate models face a challenging paradox when attempting to predict the evolution of the polar systems: whereas the historical trend in Arctic sea-ice extent is underestimated by the models, the simulated downward trend in Antarctic sea-ice extent is at odds with the small observed positive trend that has been further complicated by unusual weather events shrinking Antarctic sea ice in the last season. The polar sea ice paradox remains one of the most challenging science issues to be resolved regarding climate change science [51]–[54].

Arctic sea ice prediction has inherent limitations due to the stochastic nature of the climate system. These limitations are poorly understood, especially across the full range of timescales and variables of scientific and societal interest. Advances in understanding these limitation and in the seasonal-to-decadal predictive capabilities require enhancements of our theoretical, observing, and modeling capabilities [55]. The recent decline in the extent of Arctic summer sea ice has resulted in a dramatic shift in its composition, first-year sea ice become dominant over multiyear sea ice (e.g. [47], [55], [56]), which reduces its size, remains younger and thinner [57], [58]. This rapid change to a new state is likely to have important implications for sea ice variability, predictability and even Arctic halogen photochemistry [59]–[62]. In the face of this significant transition, there is the need to identify and understand whether and how key parameters are properly modeled. Currently, sea ice models’ treatment of ice dynamics and thermodynamics employs parameterizations that were often developed based on observations taken in a primarily multiyear ice regime, and they may not apply in the new state, in which the surface albedo heat balance are profoundly altered. Moreover, it is likely that if, as expected, the substantial ice retreat continues and the remaining ice transforms to a largely seasonal character, the oceanic and atmospheric circulation and thermodynamic structure will respond to the changes in the surface state, affecting large-scale patterns. The regime shift may also cause changes in physical and biochemical processes that have not been adequately accounted for in current models.

Over Antarctica, it is not yet well established quantitatively the relative contributions from multiple mechanisms to explain the observed variability and the slight increase in overall Antarctic sea ice extent, as many local,

<footnote n="1">¹AMAP Snow, Water, Ice and Permafrost. Summary for Policy-makers. This document presents the policy-relevant findings of the AMAP 2017 assessments of snow, water, ice and permafrost in the Arctic (SWIPA), 2017.</footnote>
regional, and global processes influence sea ice growth and melt. Different theories suggest different potential explanations to this phenomena, including the role of feedbacks between the ocean and sea ice; possible tropical Pacific and Atlantic teleconnections; and effects of winds and ocean currents controlled by topography and bathymetry [63]. Understanding the mechanisms and processes driving sea ice variability and trends in the Southern Ocean is limited by the lack of proper observations to quantify sea ice characteristics and processes [63], [64]. Changes in the Antarctic, where average sea ice extent is approximately 20% greater than in the Arctic [64], could result in relatively significant changes to planetary albedo. Furthermore, feedbacks between sea ice production and ocean water temperature and salinity may play a role in determining the stability of Antarcicas massive sheets of glacial ice [65]–[67]. Understanding sea ice variability and trends may thus be important for anticipating the rate of ice sheet melt and sea level rise in the coming decades. Process-based understanding is critical for improving our knowledge of the mechanisms of Antarctic sea ice variability, but they require high-resolution atmosphere and ocean products, especially for resolving some of the features such as eddies, polynyas/ice formation, and katabatic winds/cyclogenesis. These complexities demand major advances to observe the Southern Ocean.

Furthermore, extreme events such as polar lows and anomalous winds due to dipole anomalies [47] may combine with preconditioning and ice-albedo feedback to result in abrupt changes, e.g., a large decrease of sea ice in a short time [57], [68]–[71], with decadal impacts. For example, drastic loss of perennial sea ice owing to persistent wind patterns in 2005 and 2007 [42] may influence the long-term sea ice trends. Models can simulate extreme events of this type (e.g. [72]) but the accuracy of how simulated extreme events modify key parameters of the ice needs to be further assessed.

The Arctic Marginal Ice Zone (MIZ) and the Antarctic Frontal Ice Zone (FIZ) are the areas where sea ice is more exposed to weather and ocean phenomena [63], together with advection zones (AZ) in coastal areas. Moreover, near coastal areas, warm waters from river discharge can bring significant heat to melt sea ice effectively. From Arctic rivers, massive discharges carry an enormous heating power of $1.0×10^{19}$ J/yr for each $1°C$ of the warm river waters above freezing [49]. River discharges, which vary weekly, rapidly warm up sea surface temperature by more than $10°C$ at the scale of ~150 km away from the coast and $2°C$ as far as ~450 km out in the ocean [49]. These phenomena not only melt the sea ice, but also alter the air-sea interactions in the boundary layer through variations in the air-sea temperature difference that impacts the Monin-Obukhov length and the friction velocity. The ice in these areas is therefore highly dynamic, and proper understanding and quantification of its rapid response to quick evolving episodes of winds, waves, polar lows and discharge episodes would enhance our knowledge of the interactive mechanisms leading to the ice

variability (see Figure 1). This could be achieved with observations of these forcing phenomena, together and synchronized with frequent quantification of ice production and deformation processes, including divergence in polynyas near the coast, evolution of the MIZ and FIZ formations, and ice mass variations.

<figure><img src="image_2.png" /><figcaption>**FIGURE 1.** Sketch of different processes and interactions involving the cryosphere. G-TERN aims to improve understanding of the sea ice related processes and interactions at regional to climate scales, with focus on highly dynamic variations (ice production, deformation, melt), their driving forces and feedback mechanisms, including extreme events, ocean and atmospheric circulation. Other processes of the cryosphere related to sea ice snow cover and brine content, ice sheets and caps, hydrology and snow, biochemistry, and permafrost are secondary objectives of the G-TERN mission. Figure from G-TERN EE9 Proposal.</figcaption></figure>

Understanding of sea ice changes critically depends on local and regional thickness distribution. Apart from thermodynamic growth (modal growth) and melt, ice thickness and changes of sea ice extent are governed by the drift and deformation of the ice (dynamical growth). Changes of Antarctic sea ice extent critically depend on northward ice advection and the formation of sea ice in coastal polynyas. Ice thickness variations result from changes in ice convergence, which thickens ice by rafting and ridging. The latter process is also manifested by large changes of surface roughness and scattering properties. These pieces of information could contribute to resolving the Arctic-Antarctic sea ice paradox, improving the re-parametrization of the sea ice processes in climate models and therefore enhancing the short and long term predictability of sea ice trends and other valuable sea ice related parameters. This knowledge would also have potential to find proper proxies to extend the records of sea ice to before the satellite era, a key element to fully understand the trends currently observed [64]. Acquiring this knowledge requires observational capabilities to capture the highly dynamics of the sea ice, quantify their quick changes in ice production and growth, degradation and melting processes, their mass variations in rapid intervals, and the way they interact with the surrounding ocean and weather. These observational capabilities do neither exist currently nor in planned future missions.

For the reasons above, the primary objective of the G-TERN mission is to quantify crucial characteristics, processes and interactions between the cryosphere and other
Earth system components in order to advance the understanding and prediction of climate change and its impacts on the environment and society. The mission addresses the World Climate Research Programme (WCRP) Scientific Challenge on Melting Ice and Global Consequences, including the rapid transition towards an ice-free Arctic Ocean and its impact on the large-scale atmospheric circulation, extreme weather and climate conditions. G-TERN also aims to contribute resolving the challenging polar sea-ice paradox. These objectives are articulated through three key questions:

*   **MAIN OBJECTIVE, QUESTION-1:** In a rapidly changing Arctic regime and under the resilient Antarctic sea ice trend, how will highly dynamic forcings and couplings between the various components of the ocean, atmosphere and cryosphere modify or influence the processes governing the characteristics of the sea ice cover (ice production, growth, deformation and melt)?

*   **MAIN OBJECTIVE, QUESTION-2:** What are the impacts of extreme events and feedback mechanisms on sea ice evolution?

*   **MAIN OBJECTIVE, QUESTION-3:** what are the effects of the cryosphere behaviours, either rapidly changing or resiliently stable, on the global oceanic and atmospheric circulation and mid-latitude extreme events?

The secondary objectives of G-TERN address complementary cryospheric science questions as well as other climate relevant applications. The first secondary objective aims to demonstrate the suitability of the G-TERN mission technique, the reflectometry using navigation signals (GNSS-R), to sense other cryosphere products. If successful, these products would complement the investigations on the main objective with potential to become a breakthrough in other cryospheric questions.

*   **SECONDARY OBJECTIVE-1, COMPLEMENTARY CRYOSPHERE PRODUCTS:** Which is the potential of the G-TERN techniques to extract geo-physical information about

- snow cover over sea ice, its thickness and density;

- sea ice permittivity, density and/or brine content;

- sea ice surface melt onset and melt pond fraction;

- distinction between modal (thermodynamic) and dynamical (deformation) growth of the sea ice;

- ice sheets and large caps, their surface elevation changes, mass balance, run offs, melting episodes, surface and sub-surface snow properties;

- permafrost active layer changes, freeze and thaw phase, surface deformations;

- seasonal snow in mid latitudes, its thickness and snow properties; and

- glacier evolution?

Finally, the last secondary objective addresses selected contributions of the land component into the global warming scenario. In particular, G-TERN aims to contribute quantifying the biomass and its variations as well as the extension

of the flooded areas within wetlands (i.e. inundated wetland extent), including densely vegetated ones (e.g. forested swamps). Both variables play essential roles in the water and energy cycle, linking hydrological, ecological and atmospheric carbon sciences.

*   **SECONDARY OBJECTIVE-2, LAND COMPONENT:**

- How the water coverage is changing in wetland areas (particularly swamp forests) in view of the rapid rate of wetland collapse?

- What is the role of wetlands in methane emission processes, especially in view of new pathways for methane emissions that can be potentially identified with frequent observations including densely vegetated and forested regions?

- How regional conditions, especially soil moisture, impact wetland inundation dynamics and affect regional atmospheric patterns (e.g., by altering the Bowen ratio) that in turn impact the transport and distribution of methane emitted from wetlands?

## A. OBSERVATIONAL REQUIREMENTS

The observational requirements of G-TERN are driven by the primary objectives. To properly contribute answering the primary scientific questions, G-TERN will measure key parameters of the sea ice, the oceans and the atmosphere with frequent and dense coverage over polar areas, becoming a 'dynamic mapper' of the ice conditions, ice production and loss in multiple time and space scales, and surrounding environment. Frequent mapping is very important for better observing and understanding multi-scale interaction processes. For example, the causes and effects of deformation events on changes of the sea ice mass balance. Global interactions and their impacts will also be explored through generating global datasets of ocean and atmospheric observations suitable for assimilation in numerical models.

Given that at polar areas the rapid and violent weather systems have typical temporal scales of days to a week, river discharge change significantly over weekly scales, and given that these events are relevant target phenomena to be observed (QUESTION-1 and -2), their temporal scales constrain the time resolutions of G-TERN over polar areas to a few day periods. Particularly important during the spring-summer transition is the albedo switch from high to low values that crucially impact the surface heat balance and thus sea ice melt processes. Such albedo switch may occur on a weekly temporal scale [73], and thus demanding sub-weekly (~3 days) observations to account for the Nyquist temporal sampling requirement. The albedo change is dependent on different distribution of melt pond fraction over the synoptic sea ice classes including first-year (seasonal) and multi-year (perennial) sea ice in the Arctic [74], and over different Antarctic sea ice classes [63] depending on the sea ice roughness, including the FIZ with spatial scales as little as 100 km [63]. Indeed, understanding the causes and effects of deformation events on changes of the sea ice mass balance requires rapid repeat observations over the
<table><caption>TABLE 1. Observational requirements to address G-TERN's primary scientific objectives (level-3 products' requirements).</caption><thead><tr><th colspan="5">Requirements on geophysical variables for the PRIMARY OBJECTIVE (Q1-Q3).</th></tr><tr><th>Variable</th><th>Scope</th><th>Spatial resolution</th><th>Temporal resolution / coverage</th><th>Accuracy within spatio-temporal resolution</th></tr></thead><tbody><tr><td>Sea ice altimetry</td><td>P</td><td>30 km</td><td>3 days</td><td>10 cm</td></tr><tr><td>Sea ice roughness</td><td>P</td><td>30 km</td><td>3 days</td><td>10% dynamic range (0.0015 mss)</td></tr><tr><td rowspan="2">Ocean surface altimetry</td><td>P</td><td>30 km</td><td>3 days</td><td>10 cm</td></tr><tr><td>G</td><td>0.5°</td><td>10 days</td><td>10 cm</td></tr><tr><td rowspan="2">Ocean surface roughness</td><td>P</td><td>10 km along-track</td><td>1 second / 3 days</td><td>0.002 mss or 10%</td></tr><tr><td>G</td><td>10 km along-track</td><td>1 second / 10 days</td><td>0.002 mss or 10%</td></tr><tr><td>Time life</td><td colspan="4">5-10 years, launch 2025</td></tr></tbody></table>

same regions. Furthermore, these processes are the ones that determine the ice behavior on longer time scales and in larger regions. For properly mapping these small polar mesoscale storms (of 100 to 500 km longitudinal scales) as well as their effects into the sea ice, submesoscale horizontal resolutions are required. Given the various spatial scales of sea ice and ocean characteristics, a spatial resolution better than 50 km can account for the Nyquist spatial sampling requirement to address process at the 100-km scale. Moreover, on ~30 km scale, sea ice can be considered a continuous fluid and sub-scale processes will accumulate and integrate on the grid scale to cause measurable changes that can be interpreted with commonly used ice deformation rheologies. Therefore, G-TERN in polar areas shall resolve sea ice properties, ice surface elevation and the surrounding ocean surface at spatial resolutions of the order of 30 km with full polar coverage within 3 days. To distinguish between different seasonal ice the sea ice surface elevation measurements should have accuracies of the order of 10 cm within the spatio-temporal requirements.

At middle latitudes and the tropics, G-TERN should contribute to understand the effects of changes in the cryosphere on the global ocean circulation and mid-latitude extreme events (QUESTION-3). The extreme events must be studied at mesoscale, as well as the global circulation feedback mechanisms, which can involve intermediate scales. For instance, mesoscale features such as eddies mediate the transport of heat, salt and carbon by mixing densities and the connection of the surface water to the deep ocean through the thermocline (e.g. [75], [76]). Therefore, G-TERN shall resolve mesoscale ocean features globally, with spatial resolutions of 0.5 degree and full coverage within 10 days. These mesoscale ocean features have signatures at the decimeters level, reason for which the accuracy within the global spatio-temporal resolution is requested at 10 cm in sea surface elevation, too. For both surface ice and ocean roughness, the accuracy of the measurement should be at 10% level of its dynamic range. Hereafter we will express the surface roughness parameter as the mean square of the surface slopes (mss). The requirements of these geophysical variables are summarized in Table 1, where scope 'P' means polar areas and 'G' is for the rest of the globe (middle latitudes and tropics).

The G-TERN would be launched in 2025, with a nominal lifetime of 5 years and expected extension to cover 10 years

of observations. G-TERN operating for a period of five years would provide a large data set obtained during potential critical years of the evolution of the Arctic, 2025-2030, when sea ice reduction is projected towards nearly sea ice-free Summers, and it would also acquire sufficient information to enhance our understanding of the Southern Ocean trends and mechanisms. Models that best match historical trends project a nearly ice-free Arctic in the summer by the 2030s [77], [78], with some studies pointing that this might happen before 2030 (during G-TERN nominal lifetime) [79]. Other analysis predict the transition later on, from mid 2030s to before 2050 [80], [81]. Extending the mission timelife to ten years would increase the chances to explore this transition and its potential associated tipping point - a point beyond which the system abruptly changes into a different climatic state. The 'dynamic mapping' (e.g., 3 days) capabilities of G-TERN might become essential to properly quantify the changes and comprehend any new chain of interactions or evolved feedback mechanisms that might occur under the new conditions. Exploring the global responses to these changes would also benefit from an extended lifetime of the mission, as feedbacks between different spheres might include components at longer time scales.

### III. MISSION CONCEPT

The G-TERN mission is based on the combined application of the GNSS Reflectometry (R) and Radio Occultation (RO) remote sensing approach, which has been recently investigated successfully and in detail as heritage from the ESA funded GEROS-ISS mission Phase A studies [34]. The GNSS-R component will apply grazing carrier phase-delay altimetry [82] and group-delay/Doppler altimetry (e.g. [33], [83]), as well as scatterometry and polarimetry over ice, water and land surfaces using the interferometric GNSS-R (iGNSS-R) approach. An overview of the different measurement techniques is provided in Section IV with more details about the iGNSS-R concept in Section IV-A. G-TERN is proposed as a single-satellite mission in a near-polar sun-synchronous, 6a.m/6p.m. orbit at 600 km altitude. The selection of a sun-synchronous orbit is not a strong scientific requirement for the polar observations (other near polar orbits would perform as well), but it is a suitable choice to minimize the effects of the strongest ionospheric scintillations phenomena over tropical areas, and will suite the high power and heat dissipation demands (as shown in Section V). The
launch is planned in 2025 and the nominal mission duration is five years. Table 2 summarizes the main mission characteristics.

<table><caption>TABLE 2. Overview on the main G-TERN mission characteristics.</caption><tr><td>Sensor:</td><td>iGNSS-R + GNSS-RO<br>12 simultaneous beams<br>L-band, 2-frequencies<br>2-polarization</td></tr><tr><td>Orbit Orientation:</td><td>Near-polar</td></tr><tr><td>Optimal Orbit:</td><td>Sun-synchronous 6AM/6PM</td></tr><tr><td>Orbital height:</td><td>600 km</td></tr><tr><td>Time life:</td><td>2025-2030/2035</td></tr></table>

G-TERN is proposed as a box-shaped satellite with the OHB LEO platform, of around 800 kg dry mass. The payload is dominated by the beamforming capable antennas, which allow 12 simultaneous reflectometry observations at virtually any direction in two frequencies. The payload is separated from the platform by an isostatic mechanical interface. The spacecraft is compatible with a VEGA-C dual-launch and the possibility to fit as a secondary passenger within the VESPA adapter will be considered during the next phases, as soon as a detailed specification of the adapter is available. After presenting the measurement techniques (Section IV), further details on the platform and payload can be found in Section V, while the expected performance is analyzed in Section VI.

## IV. GNSS MEASUREMENT TECHNIQUES

This section presents a qualitative overview of the measurement techniques suggested for G-TERN. For detailed descriptions and formulation the readers are pointed to the available textbooks and tutorials [5]–[8]. The basic observation concept of the G-TERN mission is based on the innovative use of L-band navigation signals for remote sensing (GNSS remote sensing). Such ground and satellite GNSS based Earth observation techniques are already widely and operationally used since several years for atmosphere sounding. Outstanding example for this application is the routine assimilation of related data products to improve regional and global weather forecasts, which was started in 2006 (e.g. [84]). But the GNSS atmospheric data are also exploited by a large international community of geodesists, atmospheric scientists and climate researchers for atmospheric research including studies of the global change (e.g. [85]–[88]).

In the recent years the international research in GNSS remote sensing has focused on the GNSS reflectometry (e.g. [89]–[91]), which complements the atmosphere sounding techniques, and exploits navigation signals, reflected off the Earth to derive geophysical parameters of the water, ice and land surfaces (e.g. [1], [2], [5], [22], [27], [33], [92]–[96]). The advantages of the GNSS-R technique are (1) its synoptic and high-temporal resolution capabilities, derived from the multiplicity of GNSS transmitters which provide reflected signals simultaneously (Figure 2);

(2) the passive nature of the receiver, as the transmitting global navigation infrastructure is deployed and maintained by third parties; (3) its truly ‘all weather’ operability even in severe weather like cyclones, required for navigation purposes and at multiple L-band electromagnetic frequencies; and (4) represent a novel exploitation of signals of opportunity, with all surface and different types of climatic observations scalable by the number of ever-growing GNSS satellites. Numerous successful ground and airborne experiments demonstrated already the large and versatile potential of this innovative space geodetic technique for Earth Observation (e.g. [15], [82], [97], [126]). Recently also the first dedicated satellite based experiments are in preparation, as ESAs GEROS-ISS [34], or have already successfully been launched, as the UK TechDemoSat-1 (TDS-1) [91] and NASA’s CyGNSS [99].

The GNSS-R missions in orbit (TDS-1 and CYGNSS) operate under the acquisition technique used in most of the GNSS navigation applications: the weak GNSS signals are cross-correlated with a clean replica of the publicly available modulation codes. This approach, called hereafter ‘clean replica’ or ‘conventional’ (cGNSS-R) is cheap and easy to implement, it does not require large directive antennas but can only be applied to the publicly available codes. These codes (e.g. GPS Coarse Acquisition, C/A) are narrow band and therefore with limited ranging performance. Ranging performances are crucial in altimetric applications, and a way to increase the received bandwidth is to use the whole transmitted one, including the encrypted codes. These codes present broader bandwidths, of the order of 10 times wider than the publicly available modulations. As they are not public it is not possible to synthesize clean replicas of them and alternative approaches are needed. A possible approach is called interferometric GNSS-R (iGNSS-R), for which the reflected signals are cross-correlated against the direct ones. Because the direct signals are noisier than the idealized clean replicas, this approach increase the noise unless highly directive antennas point to each of the reflection points and to each of the transmitting sources. Highly directive antenna beams, each one pointing to a single satellite or specular point also help discriminating the different received satellite signals (traditionally discriminated and identified through the code itself). The iGNSS-R approach with high directive antennas has sufficiently large SNR to take advantage of the full bandwidth and enhances the altimetric precision figures by factors between 2 and 6 [96], [100]–[105], while its spatial resolution is enhanced from 25 km in cGNSS-R to 10 km in iGNSS-R (it depends on the geometry, observable, retrieval technique and integration time). The iGNSS-R approach was the one selected for mission proposals such as the PARIS IoD [33], the GEROS-ISS [34], and the ‘Cookies’ [106], but none of them orbited yet. GEROS-ISS primary science objective focused on altimetry, and it successfully passed two independent industrial Phase-A studies. G-TERN, like GEROS-ISS, envisages iGNSS-R altimetry both using group-delay observables (range information extracted from the
<figure><img src="image_3.png" /><figcaption>**FIGURE 2.** Top: sketch of the GNSS Reflectometry multi-static observation concept: from a single receiver it is possible to collect signals reflected from multiple GNSS transmitters simultaneously. Each transmitter-surface-receiver link results in an independent measurement of surface characteristics around its specular point. As the receiver moves, so the specular points do, each tracing a ground track of observations. G-TERN implements capabilities for 12 simultaneous reflections (Figure adapted from [7]). Bottom: examples of the specular locations of the G-TERNs 12 simultaneous reflections, at 1Hz rate, accumulated during 1 day (left), 2 days (centre) and 3 days (right) over the North pole. The distribution of reflection points does not follow a repeatable pattern, but it keeps the latitudinal statistics.</figcaption></figure>

envelope of the cross-correlation function) and phase-delay observables (carrier phase information when the scattering is essentially coherent). Neither TDS-1 nor CYGNSS have implemented these capabilities.

The primary observable of the GNSS-R is the 'delay-Doppler map' (DDM) or its central Doppler slice, called 'waveform'. In iGNSS-R the waveform is the cross-correlation, in the delay domain, between reflected and direct line-of-sight signals, after they have been aligned both in delay and frequency based on a-priori information of the transmitter, receiver locations and velocities, and the location of the reflecting surface. If the surface scatters predominantly in diffuse regime, the waveform follows the bi-static radar equation with a Woodward ambiguity function (WAF) given by the autocorrelation of the combination of the GNSS

transmitted codes and the sinc-exponential Doppler filtering [93, eq. 27]. In some Earth surfaces, the scattering diffuses the signal over wider areas, the 'glistening zone'. This zone spans across areas beyond the narrow stripe filtered in by the sinc-exponential. Therefore, the bi-static radar equation can also be evaluated for Doppler frequencies other than the one of the specular point. When the reflected energy is mapped into a suit of delay lags and Doppler frequency cells, the resulting observable is called 'delay-Doppler map' (see Figure 3). The DDMs acquired from TDS-1 GNSS-R experiment show clear distinctive features when the signals are scattered off the rough ocean or off areas covered by sea ice. These differences have been confirmed by several peer-reviewed studies [26]–[30], which indicate that GNSS reflections off sea ice mostly present features of coherent rather than diffuse
<figure><img src="image_4.png" /><figcaption>**FIGURE 3.** The primary GNSS-R observable is the delay-Doppler map (DDM) and its central frequency slice, called waveform. Figure adapted from [7].</figcaption></figure>

scattering. When the coherent scattering predominates, as it also happens over calm waters and even land, the waveform follows the criteria and models in [107]–[111]: the DDM essentially shrinks to the WAV shape. It is also important to note that when the scattering is coherent, the carrier electromagnetic signal can be tracked. Then, the time series of its phases mainly evolves with the geometry of the observation and carrier phase-delay altimetry is possible to achieve (see Section IV-B).

The resolution of the GNSS-R products depends on many factors, such as the observable being used (large portion of the DDM, or the peak power, or the leading edge of the waveform, etc), the nature of the scattering (diffuse or specular), the geometry of the bi-static observation (orbital height, incidence and azimuth angles) and even the coherent integration time. The baseline techniques to be applied in G-TERN will generally rely on the leading edge and near-peak observables, which correspond to the resolution of the specular WAF, its projection onto the surface. For typical coherent integration times of 1 milisecond, the central WAF is mostly limited by the ellipse of points on the surface suffering delays with respect to the specular point of the order of one chip of the GNSS modulation. In iGNSS-R, this is approximately a chip of 30 m range, which projects ellipses of sizes ~10 km. When the scattering is coherent (over most of sea ice), the peak of the signal comes from the 1 st Fresnel zone, projecting ellipses of a few to several hundred meters (geometry dependent). Indicative values as function of the elevation are given in Table 3 for the near-nadir field of view. At grazing angles of observation the scattering is specular, and the Fresnel zones elongates between 0.5 km to 5 km.

## A. iGNSS-R GROUP-DELAY ALTIMETRY

GNSS-R altimetry is the determination of the Earth's surface elevation using GNSS reflected signals [1]. Most of the GNSS-R altimetric developments have been done over the ocean [102], [112]–[116], but the bi-static altimetric measurement principles used over the ocean are applicable and also

<table><caption>**TABLE 3.** Approximate spatial resolutions of G-TERN baseline observations in the near-nadir field of view (incidence angles ≤ 45°), and corresponding to one snap-shot measurement.</caption><thead><tr><th rowspan="2"></th><th colspan="3">Approximate spatial resolutions of G-TERN baseline (snap-shot) observations</th></tr><tr><th>Incidence 0 deg</th><th>Incidence 30 deg</th><th>Incidence 45 deg</th></tr></thead><tbody><tr><th>Diffuse scattering:</th><td>6 km</td><td>6-7 km</td><td>7-10 km</td></tr><tr><th>Specular scattering:</th><td>340 m</td><td>370-430 m</td><td>410-580 m</td></tr></tbody></table>

valid for altimetry over any other surface that can reflect enough power to enable precise observables. For GNSS-R, these are typically ocean and also ice (e.g. [17]), although certain land surfaces, inland water bodies, or estuaries can also reflect GNSS signals strongly [109]–[111], [117]. The vertical height of the reflecting surface can be given with respect to a reference value (ellipsoid, mean topography, digital elevation/surface model, etc). The position and velocities of both the transmitting and receiving systems must be known accurately, as the vertical component of the surface location is measured from the total range of the radio-link between the transmitter, the surface and the receiver. The central point around which the signal is reflected is defined as the ‘specular point’, which follows the Snell’s laws of reflection. Potentially, a single receiver could measure the reflected range corresponding to each transmitter in view, meaning they all could be solved into the altimetric retrievals for each of their specular points (synoptic capabilities in Figure 2). The number of simultaneous GNSS transmitters in view, from Low Earth Orbiter (LEO) altitudes was of the order of 40 for GPS and GLONASS constellations as in 2012 [7]. This number can be easily doubled when Galileo and BeiDou will be fully deployed by 2020. In practice, the number of simultaneous measurements will be limited by the receiver capabilities. G-TERN implements capabilities for twelve simultaneous observations. The GNSS-R measured surface elevation is an averaged value across the measurement area, zone from which the GNSS-R observation is representative. As presented in Table 3, iGNSS-R resolutions approximately correspond to ~10 km if the scattering is diffuse.
In group-delay altimetry the observable of interest is the delay (or range) of the reflected signal. In interferometric GNSS-R technique, planned for G-TERN, the delay is understood as the time lapse between the arrival of the reflected radio link and the arrival of the line-of-sight radio link (non-reflected, also called ‘direct’ signal). Among the GNSS community it is common to work with ranges or distances rather than the time lapses needed for the signal to travel them. The term ‘delay’ is then used indistinctly for both concepts, and often expressed in units of length (as range/distance). Given that these measured ranges include systematic effects such as drifts in the clocks, atmospheric delays, or instrumental biases, they should be called pseudo-ranges. As explained before, the GNSS-R observable is the DDM or its central slice, the waveform. The determination of the arrival time of the reflected signal is equivalent to finding the point along the waveform or DDM that corresponds to the reflection off the specular point. Signals reflected off a roughness-free surface (e.g., very calm waters or smooth sea ice) present a non-distorted correlation function, and the specular delay corresponds to the delay of its peak. This is also the case in standard GNSS navigation receivers for determining the arrival time of the line-of-sight signals. In general, though, this does not apply in Earth reflec-tometry. For rough surfaces such as the ocean or rigged ice, the peak of the waveform is typically shifted from the spec-ular delay because of the surface roughness, which induces scattering off surface elements around and even away from the specular point. Then, the arrival time of the shortest-specular-delay corresponds to some point between the rising of signal power and its peak, an unknown point along the leading edge of the waveform. Several approaches have been suggested to determine this point (e.g. [102], [103], [105], [118]), among others, the peak of the first delay-derivative of the waveform, a certain fraction of its power, or fitting a theoretical model (e.g., match filter).

The group-delay altimetry has been tested from ground-based and airborne campaigns, for both conventional GNSS-R and interferometric GNSS-R. The experiments have applied the same principles, regardless of the acquisition approach (cGNSS-R vs iGNSS-R), being the main difference between them the bandwidth (thus range resolution) of the signals involved in the processing. The improvement in precision in iGNSS-R compared to cGNSS-R is in the range 2 to 6 [96], [100]–[102], [105]. Airborne iGNSS-R experiments have reported precisions in the range of 0.25 to 0.6 m in 10 seconds observations [119], largely limited by the noise of the aircraft trajectory (see Figure 4), which agrees with the precision predicted by the theoretical models evaluated at these airborne scenarios [102], [105].

Group-delay spaceborne altimetry has also been reported from TDS1 satellite, over ocean and sea ice surfaces. Because TDS-1 does not implement the interferometric capabilities, the results correspond to cGNSS-R. Over smooth sea ice in Hudson Bay the reported precision is 0.96 m in 0.5 seconds and 3.5 km sampling [29]. Over open ocean, [118] reports

<figure><img src="image_5.png" /><figcaption>**FIGURE 4.** Precision obtained in an iGNSS-R airborne campaign flying at ~3000 meter altitude over the Baltic Sea under variable wind conditions between 6 and 15 m/s, for GPS PRN1, PRN3 and Galileo PRN19 and PRN11. The values obtained from Galileo satellites around 40° are due to contamination from other satellites caused by the limitations of this particular beamformer to get a proper separability in such particular events (unpublished Figure from [119]).</figcaption></figure>

group-delay cGNSS-R precisions of the order of 7 to 8 m in 1 second observations, in agreement with precision predicted based on models tuned for the CYGNSS mission (very similar to TDS-1) [120], which is not tailored to altimetric applications.

## B. iGNSS-R PHASE-DELAY ALTIMETRY

Most GNSS reflections off the Earth surface correspond to diffuse scattering, where the phase of the carrier is too noisy to be tracked and it loses its range information. Nevertheless, at slant to grazing angles of observation (below 30° elevation) the roughness effectively becomes smoother and coherent reflections occur. Signatures of Earth surface reflection based on GPS carrier phase observations at grazing observation geometry have been reported for satellite based receivers within the GPS/MET proof-of-concept mission for GPS radio occultation [121] and aboard the German CHAMP satellite [122]. These observations were achieved with low-gain antennas and state-of-the-art GPS radio occultation receivers without any specific optimization for GNSS reflectometry. A case study done with carrier phase data from CHAMP also presented altimetric retrievals over the Greenland ice sheet and sea ice [12]. The derived surface heights had 0.7 m precision in 0.2 second averaging. Coherent phase delay GNSS-R altimetry is applied within a radio occultation like geometry and is therefore highly synergistic with GNSS-RO measurements, which in parallel provide atmospheric/ionospheric information for the offset reduction/elimination of the coherent GNSS-R measurements itself.

Phase altimetric simulations have been performed for ocean application to prepare for measurements from Low Earth Orbiting Satellites within the GEROS-ISS related scientific study GARCA [34], [123]. The simulation results
show that phase altimetric retrievals are sensitive to anomalies of the ocean topography and that an altimetric precision of 10 cm in 1 second observation is possible in this respect [124]. At angles of elevation below 10°, critical uncertainties were found to be induced by residuals of the tropospheric delay, degrading the precision to about 30 cm. In general, a limit for phase altimetry is set by the diffuse character of L-band reflections off the rough surface that impede the retrieval of coherent phase observations. However, the diffuse reflection limit depends on the surface roughness and the signal incidence/elevation angle. Coastal experiments demonstrated carrier phase delay altimetry for wind speeds up to 10m/s [125] and significant wave heights < 0.6 m [108]. Airborne experiments revealed the sensitivity of carrier phase retrievals to geoid undulation [126] sea surface topography [82] over rough open waters in the Mediterranean Sea. Figure 5 shows phase altimetric retrievals from an airship experiment. The 20 cm geoid undulation along the 15 km reflection track is resolved with 3-4 cm precision. The phase-altimetric precision relies on a model-based retracking of the signal, using geometric and atmospheric corrections. A general difficulty arises from the apriori unresolved phase ambiguity. A reference height is provided by the nearby tide gauge stations to fix the ambiguity at the crossover point. In spaceborne scenarios, crossover points with reflected GNSS signals from other transmitters and other altimetric sensors would allow to mitigate the uncertainty of the phase ambiguity. The previous coastal and airborne experiments over sea surfaces have shown that carrier phase altimetry works for reasonable range of elevation angles at the reflection point (5°-30°). At higher elevation angles coherent observations off the wind-driven sea are much less frequent due to diffuse reflection. At lower elevations the tropospheric residual usually impedes precise altimetric retrievals.

The presence of sea ice at the water surface significantly shifts the diffuse reflection limit and improves the phase coherence of L-band observations [17], [128] and phase delay altimetry was conducted with a few cm precision from a 700 m cliff in Greenland [17]. In fact, smooth carrier phase observations have even been obtained at much higher elevation angles (~50° incidence) over smooth sea ice from the TDS-1 mission [30], with preliminary analysis showing precisions of 4.7 centimetres in 20 millisecond observations. In addition to the tracks analyzed in [30], other phase delay data obtained from TDS-1 over sea ice seems to confirm the possibility of tracking the carrier phase when reflected off sea ice surfaces (see Figure 6). Also continental ice sheets yield rather distinct than diffuse reflections [22] that can be suitable for phase altimetry. The ability of phase altimetry to use data at low elevation angles increases the swath significantly compared to near-nadir configurations. An extension of the elevation range from grazing and slant observations also towards higher angles is expected for sea ice and ice sheet altimetry. The reason is the reduced roughness of some types of sea ice and ice sheet surfaces, that yields reduced diffuse scatter and coherent phase observations.

<figure><img src="image_6.png" /><figcaption>FIGURE 5. Panel (a): Example reflection track (blue) over Lake Constance obtained from a GNSS-R payload aboard a zeppelin. A crossover reference S0 is indicated which allows to solve the phase ambiguity. The reference is based on lake level estimates from the gauge stations (red circle) nearby. Panels (b) and (c) show the phase altimetric solution (gray) for right- and left-handed polarization retrievals, respectively. Due to crossover referencing the total height level H can be estimated. For comparison, the geoid undulation G along the track is plotted as blue line, taken from GCG05 model [127].</figcaption></figure>

An important question, which requires further investigations, is the L-band signal penetration into the snow cover on sea ice, sea ice itself and ice sheets. In [11] the penetration into sea ice was estimated between 30 and 70 cm, while over dry snow over ice sheets [22] reported reflections from sub-surface layers down to 200-300 meter at Concordia Station, Antarctica. In general, L-band signals are more transparent to snow than other instruments at higher frequency bands, thus representing an advantage to minimize the contamination of the retrievals induced by the snow cover (issues in Cryosat-2 and ICEsat/ICEsat-2).

### C. iGNSS-R SCATTEROMETRY

During the initial stages of the GNSS reflectometry, the target of the incoherent reflection measurements was the wind speed and wind direction (e.g. [129], [130]), when precisions of the order of 2 m/s in wind speed and 20 degrees in wind direction were reported. However, it was soon understood that the wavelengths of L-band signals were sensitive to a combination of other ocean surface parameters, such as wind, swell and wave age, reason for which the term ‘L-band roughness’ was introduced. The mean square slopes, mss-dispersion of the surface slopes-was thus the preferred parameter in some other studies (e.g. [131]–[133]). The ‘L-band roughness’ has
<figure><img src="image_7.png" /><figcaption>**FIGURE 6.** In addition to the TDS-1 phase-delay altimetry over sea ice shown in [30], other sets of data provide further evidences of the trackability of the phase in sea ice GNSS reflections. Top-left: Three GNSS reflected tracks over sea ice, acquired in raw data mode by TDS-1 on March 24th, 2015. The red segments correspond to the portions where phase-delay altimetry is applied. Top-right and bottom panels: Carrier phase altimetry obtained with the data sets, and compared to the mean sea surface (DTU13 model). TDS-1 raw data made available by SSTL and processed by W. Li (ICE-CSIC/IEEC).</figcaption></figure>

interest as complementary information required in sea surface salinity measurements performed with L-band radiometry (ESA's SMOS, NASA's Aquarius), as well as potential source of air-sea interaction and dragging, when combined with independent wind estimates.

The previous statements were first supported by a wide diversity of air-borne and stratospheric experiments performed at different altitudes, receiver speeds, instrumental equipments, and analysis techniques (e.g. [129]–[140]). At least eight different techniques were used in the listed references, of different degree of complexity and elaboration, different final product (scalar roughness, directional roughness, non-Gaussian features). Recently, intensive work has been done to extract wind and roughness information from GNSS-R spaceborne missions, such as TDS-1 and CYGNSS, mostly constraining the source of information around the peak of the DDM [90], [141], [142] or inspecting the geophysical informational content in DDM cells further away from the specular [143], [144]. In all these inversion

schemes the starting point is the bi-static radar equation from which the radar cross section or the probability density function of the slopes is inferred. Over the oceans, given the G-TERN specifications one expects similar scatterometric performance as for the CyGNSS mission, with finer spatial resolution (provided by the iGNSS-R technique).

Characterization of sea-ice has been also reported from experimental GNSS scatterometric work [11], [16]. Over ice, mss derived from the decay rate of the GNSS reflected waveforms was also reported as a valuable indicator of the ice surface roughness, as it is linearly related to the standard deviation of the surface elevation [11]. These airborne campaigns showed good agreement with the surface elevation dispersion obtained from GPS reflections and those measured with a lidar aboard the same aircraft. Similarly, an efficient permitivity of the ice, obtained from the received GNSS-R power, correlated with the ice age. A combination of both power and decay characterize the ice age or type. From the TDS-1 spaceborne platform, high accuracy in sea ice detection has
been obtained using DDM observables [26] through investigating the degree of coherence of the waveform extracted from DDM [27] or using neuronal networks [28]. Moreover, the signatures around the peak of the DDM have also been used in these neuronal networks to estimate the sea ice concentration [28], with an overall discrepancy with respect to independent concentration estimates at 1% level.

### D. iGNSS-R POLARIMETRY

Polarimetry is a powerful tool for radar remote sensing of our planet. It consists in observing the polarization properties of the electromagnetic wave scattered by the target for any polarization of the impinging wave illuminating the target. The strength of the technique stems from the capability to identify the main scattering mechanisms involved in the interaction of the signal with the target, each mechanism being characterized by its own polarization signature. A number of measurements has to be performed, which consists in observing in two orthogonal polarizations the scattered signals obtained when illuminating the target with as many polarizations of the impinging waves. Depending on the polarization base we consider, e.g., horizontal (H) and vertical (V); or right handed circular (R or RHCP) and left handed circular (L or LHCP), we have to measure HH, VV, HV and VH or RR, LL, RL, LR. Note that we have to measure not only the signal strength (i.e., its power) but also the phase difference between incidence and scattered polarization components. We can translate measurements in the circular polarization base into measurements in the linear polarization base [145]. Some of these measurements can be redundant (e.g., VH and HV in backscattering) or can bring poor information content, so that we can reduce the number of observations keeping the relevant information for target characterization.

The GNSS transmitters radiate a wave whose polarization is nominally RHCP. To carry out a fully polarimetric measurement one should measure the co-polar (R)ight but also the cross-polar (L)eft component due to transmitting antenna polarization imperfections, and then receive at the same time the Right and Left polarized scattered signals in amplitude and phase. Monostatic radars are already exploiting polarimetry from satellites, but G-TERN will provide for the first time polarimetric spaceborne measurements of the signal reflected around the specular direction, with high potential in the cryosphere domain, but also capable to fulfil many secondary objectives of the mission. A critical aspect (especially at RHCP, as it can be several dBs below LHCP) is the sensitivity required to cover the full dynamic range of the signal associated to different surface conditions. This requires a suitable gain of the system and in particular of the nadir-looking antenna. Additional critical aspects can be the effects of surface topography and land cover heterogeneity, especially if they change within the area of the first Fresnel zone. Those are challenges of GNSS-R over land that G-TERN could help to tackle and solve.

For cryosphere applications, the polarimetric response of the scattering is well recognized by the scientific

community as an essential aspect of the remote sensing of sea ice (e.g. [146]). At L-band, the Fresnel reflection coefficients of the circular polarization base show sensitivity to water-ice transition and, in lower degree, also to ice properties through its permittivity changes (e.g. brine content). At relatively low angles of elevation (large incidence) such as the geometries planned for the phase-delay altimetry, these changes affect both the ratio between the power of the two polarized scattered signals (e.g. LHCP/RHCP) as well as their phase shift (here called Polarimetric Phase Interferometry, POPI, [15], [16]). Figure 7-left shows the polarimetric ratio and POPI of sea water and sea ice as obtained from their Fresnel coefficients (from formulations in [147]). The figure clearly shows two separate regions, for sea water and for ice. Actual measurements are also affected by the textures of the roughness, the purity of the transmitted signals and the receiver instrumental response. These ideas were tested during an ESA field campaign conducted between November 2008 and May 2009 from a 700 m cliff overlooking Disko Bay, Greenland (ESA's GPS-SIDS campaign). Despite the polarimetric ports were not calibrated, signatures consistent with the sea ice concentration were found (Figure 7-right). The ideas on polarimetric response of water/ice surfaces were also tested in a shipborne experiment, conducted 2016 in Fram Strait, which provided reflectometry data during drift and fast ice periods [148] in two orthogonal polarizations for reflections at slant elevation angles (5°-30°) (Figure 8). The power loss observed in LHCP data during the transition from calm open-water to the regime of high sea ice concentration agrees with model predictions. Recently, the receiver chain of SMAPs radar, working at two linear polarizations, has been used to search for GNSS reflected signals. For the first time it has been possible to obtain from a spaceborne platform the polarimetric signatures of GNSS reflected signals. Over polar regions, the polarimetric ratio, here defined in linear base and at smaller angle of incidence (40°) has shown sensitivity to sea ice [24].

The combination of different geometries (from nadir to 45° incidence and 5° to 30° elevation) accumulated in a few days within a relatively small area, together with the polarimetric capabilities of G-TERN may have potential to discern leads and polynyas and melt onset; or to help characterizing the snow cover above the sea ice and the phase of permafrost active layer [23]. These potential products are some of the demonstration activities envisaged as secondary objective of the mission.

### E. GNSS RADIO OCCULTATION

An additional, but secondary, objective for G-TERN is GNSS based radio occultation (RO) for precise sounding of the neutral atmosphere and the ionosphere. Global and precise atmosphere sounding using GNSS radio occultation has matured in recent years from experimental proof-of-concept missions to well-established and operational applications (e.g. [149]). Outstanding examples for this progress are the results from CHAMP (e.g. [149], [150]),
<figure><img src="image_8.png" /><figcaption>**FIGURE 7.** Left: Polarimetric phase interferometry (POPI) of sea ice and sea water obtained from their Fresnel reflection coefficients. Left-bottom: The same magnitude as a function of polarimetric ratio, at 15° elevation angle, using the same examples of sea water and sea ice. The vertical gray dashed lines mark the polarimetric ratio for absolute permittivities going from 10 to 70 (left to right) in steps of 10 units. Right: ESA experimental campaign of reflectometry at 700 m cliff over Disko Bay, Greenland 2009, GPS-SIDS contract number GPS-SIDS, R21793/08/NL/ST. Top-right: the sea ice concentration values (visually inspected from site). Top-bottom: GNSS-R polarimetric ratio measured at 10 deg elevation angle, with uncalibrated ports. Figures from [16] reproduced with authorization of the authors.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**FIGURE 8.** Sea ice characterization from a shipborne GNSS-R experiment. Sea ice concentration routinely observed from the ship (upper panel). Close drift ice period B occur in central Fram Strait. Fast ice C and open drift ice D was spotted near the Greenland, western Fram Strait. Rough sea periods A without sea ice were encountered near Spitsbergen, eastern Fram Strait. Modelled power loss due to increased ice concentration (centre panel). Power estimates from the shipborne reflectometry data (lower panel).</figcaption></figure>

GRACE (e.g. [151]) and FORMOSAT-3/COSMIC satellites (e.g. [152], [153]), and their operational assimilation into the weather forecast systems since 2006 [84], [154]–[159]. The RO measurements brought significant improvements in

forecast quality [153], [160] and in atmospheric reanalyses [161], [162] because they complement the information provided by satellite radiances, as a result of superior vertical resolution, and the ability to assimilate them without bias correction. Furthermore, computations based on ensemble data assimilation techniques suggest there is a strong case for increasing the RO data numbers for NWP applications [163].

Continuous RO observations starting with CHAMP data in 2002 enabled the establishment of a meanwhile 15-year climate record with highest quality in the UTLS (Upper Troposphere and Lower Stratosphere) [86], which is of high benefit for monitoring climate variability and detecting changes in the Earth's atmospheric temperature and further relevant parameters [85], [164], [165]. RO measurements are also now considered a key observation type for climate reanalyses [161]. More generally, we expect the climate applications to increase in the coming years as the time-series lengthen. RO data are also valuable for investigating atmospheric dynamics, e.g., the planetary boundary layer [166], the tropopause region [167]–[169], wind fields [170], atmospheric tides and waves [171]–[173] and further phenomena [174].

In addition to RO-based remote sensing of the neutral atmosphere, also the ionospheric profiles of free electron number density are being consolidated as one unique source of information in terms of global coverage and accuracy. This has been motivated since the pioneering RO-mission, GPS/MET [175]–[180], to other follow-on missions like CHAMP, SAC-C and COSMIC/FORMOSAT-3 (e.g. [181], [182]), among others. Now, in parallel with the better understanding and improvement of the RO technique (see, e.g. [183]), the ionospheric electron density profiles
are widely used for space weather related but also climatological studies related to the variability of the Earth's ionosphere [153], [184]–[186]. Complementary results verified the potential, according to classical Chapman theory, to monitor climatologically parameters of the thermosphere such as the scale height by measuring the equivalent slab thickness. Recent computations based on measurements of the total electron content (TEC) and the peak electron density, have indicated a cooling of the thermosphere above northern Germany during the recent solar cycle [187]. It has been recently proven to be a much better description of the topside electron density profile in terms of a linearly varying scale height (Vary-Chap model), in agreement with the first principles prediction (based on an increasing electron temperature with height in such a region [188]). GNSS RO enables measurements all over the globe, in particular also at low latitudes where highly dynamic electron density variations and plasma turbulences occur but the data base is far from being sufficient and will profit from the G-TERN data. The impact of a better modelling of the ionospheric contribution to the bending angle is receiving as well an increasing interest [189].

GNSS RO data are currently already operationally available from several missions, e.g., Metop-A/B, GRACE, TerraSAR-X, TanDEM-X, and the dying FORMOSAT-3/COSMIC mission. Several new operational missions with GNSS RO started recently or will be realized in near future, e.g., COSMIC-2, EUMETSAT Polar System - Second generation (EPS-SG), FengYun-3 (FY3), Spire. Therefore, the need to get RO data from G-TERN seems less compelling and is regarded as mission goal with lower priority, as compared to GNSS based ice and ocean remote sensing. Nevertheless, the case for increasing the number of RO measurements is clear [163].

Moreover, there are several highly innovative aspects supporting GNSS-RO measurements within the G-TERN mission. These are:

* Exploring new capabilities: Galileo, GLONASS and BeiDou signals for RO. In addition to the new signal structured in the new GNSS constellations, G-TERN would also use the modernized GPS system. Therefore, G-TERN will provide a unique data set for scientific investigations to improve POD and RO data analysis and related product quality.

* Provision of high quality RO data in the lower troposphere due to high-gain antenna, which is not possible from current missions.

* Strong complementarity to the grazing angle GNSS reflectometry approach, the coherent reflectometry observations for altimetric measurements of ice and ocean surface topography, which are part of the primary mission goals [12], [122]. This also represents provision of important additional atmospheric (dry and wet tropospheric) and ionospheric delay information partially collocated with the coherent G-TERN GNSS-R measurements and of relevance for the analysis and

correction of the grazing reflectometry measurements for ice and ocean surface height measurements obtained aboard the G-TERN satellite.

* Omnidirectional downlooking RHCP for reflectometry allows the reception of side-looking RO events, which last significantly longer than the standard occultation data events and are not available from current and future operational RO missions. They cover larger horizontally atmospheric regions and contain more atmospheric information as the currently used RO data products. The value of these data to improve global weather forecasts would be investigated in cooperation with the leading NWP centers. Experiments for a GNSS RO based monitoring system using 12 beams in parallel could be conducted from G-TERN (see Figure 9 for example of 24 hours coverage). This would allow assessing the potential of new scientific applications in polar but also non-polar regions, e.g., 3D atmospheric reconstructions to investigate meso-scale atmospheric phenomena, as, e.g. atmospheric waves.

<figure><img src="image_10.png" /><figcaption>**FIGURE 9.** Example of geographical distribution of G-TERN RO profiles accumulated in 24 hours if all the twelve beams were devoted to capture GNSS occulting signals, enabling side-looking events. From this single receiving system it would be possible to acquire ~3000 RO profiles per day.</figcaption></figure>

## F. GNSS PRECISE ORBIT DETERMINATION

The success of G-TERN critically depends on the ability to accurately geolocate the phase centre position of the G-TERN antennas in a well-defined Earth-fixed terrestrial reference frame. For this purpose, Precise Orbit Determination (POD) using GNSS carrier phase tracking data, collected by a dedicated zenith-looking antenna, is a prerequisite and thus a key task of the G-TERN experiment. Most stringent POD accuracy requirements are needed for many satellite missions, typically demanding (1-D) position RMS errors of few centimeters, e.g., for gravity missions such as GRACE and GOCE (e.g. [190], [191]), altimetry missions such as Jason-2 (e.g. [192]), and SAR missions such as TanDEM-X and Sentinel-1 (e.g. [193]). Especially the radial component is crucial for altimetry missions to derive high quality data products. Most dynamic solutions based on a sophisticated force modeling of the underlying orbital dynamics are thus preferable for this type of orbit determination (e.g. [194]).
Off-line dynamic and reduced-dynamic POD based on dual-frequency GPS data has evolved to a mature and well established technique, offering cm-accuracies. As a prerequisite the attitude motion of the onboard GNSS receiver antennas in inertial space needs to be precisely known, e.g. from star tracker measurements, and GNSS sensor locations need to be well specified by proper calibrations on ground such that only small systematic errors remain in the data, e.g. antenna phase center variations, that may be calibrated in orbit [195]. Compared to dynamic and reduced-dynamic orbit determination only marginally worse accuracies are today achieved in the kinematic mode if the number of simultaneously and continuously tracked GPS satellites is sufficiently large.

# V. IMPLEMENTATION

## A. INSTRUMENT

The instrument concept is based in previous studies led by Airbus DS Space System España, (former EADS CASA Espacio), namely: the ESA PARIS In Orbit Demonstration (PARIS-IOD) Critical Technology-1; the ESA PARIS-IOD GNSS-R Feasibility Study; and the ESA GEROS-ISS industrial feasibility (mission's phase-A) study.

This section provides a brief overview of the main characteristics of the payload. The instrument will work in two RF frequency bands simultaneously L1 (1570.809 MHz) and L5 (1189.35 MHz) that are converted to intermediate frequency by means of a local oscillator. The bandwidths are set to 47.322 MHz and 63.9 MHz at L1 and L5 respectively. Many parameters will change from one operational observation to the next, mainly driven by the selected application (altimetry, scatterometry, grazing altimetry, radio occultation) and acquisition geometry. Even during the observation, adaptation of parameters is required, i.e. delay coefficients, beams, etc.

All these particulars prompt to plan a flexible commanding technique that is able to cope with a multitude of user demands and needs. In principle, the commanding concept provides the capability to program an operational run of the instrument in form of a series of user defined antenna modes and applications states during a swapping period. Each application state can be split into different sub-states reflecting beam pointing changes during the state. Each antenna mode, application state pair reflects the complete parameter setting for a dedicated instrument operation and selectable time duration.

These features are planned to be implemented in the G-TERN instrument through the following elements, sketched in a blocks diagram in Figure 10:

* Instrument RF Front-End including:

- 1 Double side (Up and Down) antenna Array

- 31 Calibration and Low Noise Amplifiers Modules (CAL/LNA)

- 4 Beam Forming Network Units (BFN)

* Instrument Back-end including:

- 4 Signal Processor Unit (SPU)

- 1 Instrument Control Unit (ICU)

- 1 Precision and Orbit Determination Receiver (POD)

- 1 Power Supply Unit (SPU)

For instrument time synchronization it is convenient to use the GPS/POD time as a highly accurate atomic time scale. This time scale is available in both the ground segment and the satellite, on ground by conversion of UTC time to GPS time and onboard due to the use of POD receiver. The onboard POD receiver outputs a PPS (pulse per second) time tick signal which will be used onboard as a 1 Hz synchronization signal. This synchronization signal coincides with the GPS epoch with a very high precision and fixes the exact moment of GPS time validity. Hence, any onboard event can be dated accurately in terms of GPS time by means of time measurements with respect to the PPS signal and by assigning the absolute GPS time to the relevant PPS epoch.

A set of instrument modes is introduced to ease the operation of the instrument from ground on one hand and to clearly structure the control of the instrument according to the system hierarchy on the other hand. The instrument is set into the desired mode by processing the commands from ground. The instrument control expands or converts the commands into an appropriate sequence of instrument internal commands that will be sent to other units and modules. The on-ground telecommand generation should follow a simple approach. First, the user must select the GNSS to be tracked. Depending on the desired application the instrument must point the antenna towards the direct signal and/or the reflected one. Second, the user establishes a sequence of observation states (applications) within a swapping period and some parameters that configure the selected application such as integration times. Based on the parameter information the instrument control composes and sends the required commands to the CAL/LNA, BFN and SPU units. Imaging of desired ground scenarios is planned and prepared in advance on ground. During this planning phase the desired orbit position and the related OBT time are predicted for each observation and are included in the corresponding time-tagged Configuration commands.

The instrument electrical concept is the result of a trade-off between instrument complexity and the survival of all mission applications. The Instrument Control Unit is the central element in charge of instrument operation. The front-end and back-end elements respond to ICU commands. The operational synchronization of all elements is under this unit responsibility. The SPU is based on the signal processing cores developed for PARIS-IOD and GEROS-ISS missions, the 'PARIS CORrelator' (PACO) unit [204]. The SPU control is basically the PACOs control. Each PACO has one Spacewire interface that shall be used by the ICU to control all PACO internal parameters and configurations. The same Spacewire interface is used for housekeeping and scientific telemetry.

The two G-TERN antennas are arrays of 31 patch elements (up-looking side) and 30 path elements (down-looking side) in a hexagonal array lattice with a separation of 178 mm between patches as shown in Figure 12. The down-looking side of the antenna contains Left Hand
<figure><img src="image_11.png" /><figcaption>FIGURE 10. G-TERN instrument block diagram.</figcaption></figure>

Circular Polarized (LHCP) and Right Hand Circular Polarized (RHCP) polarizations and the up-looking antenna RHCP only. The central element in the up-looking array is used for the POD receiver and the central element of the down-looking is used by a retro-reflector allowing laser ranging, so actually the array has 30 active elements. The concept for the G-TERN elementary radiator is similar to the PARIS-IOD elementary radiator, developed in the frame of ESA contracts PARIS-IOD Critical Breadboarding -1 (No.102784) and GNSS-R-Feasibility Study (Phase A) (AO/1-6576/2010/F/WE). The radiating elements of the antenna array are stacked circular patches using as substrate an air-like foam. In this configuration, an active circular patch is excited by two coaxial probes that are combined with a 90° hybrid coupler with one stage to provide the required bandwidth. Each input of the hybrid provides an orthogonal circular polarization (RHCP and LHCP). Therefore, it is possible to have access to both circular polarizations at each radiator by terminating the 90° hybrid by a connector. An array model has been developed in order to simulate the antenna performance for the different coverages. This model takes into account the coupling between elements in the array using the Finite Array Generalized Scattering Matrix

Method with spherical modes coupling. Thus, the radiation pattern of each element in the array is different due to the couplings between the adjacent elements. As an example of this effect (and the importance of considering the coupling and not just an array factor computation), Figure 11 shows the embedded radiation pattern of the central element at L1 band (1575 MHz) in directivity.

Variable antenna beam pointing can be achieved by respective control of received signal phases of each RF path within the beam-former Network. To cope with this task a number of basic one-dimensional phase settings for elevation and azimuth pointing, respectively, will be stored in separate onboard look-up tables. A particular antenna beam configuration will then be generated by means of simple onboard synthesis that combines two one-dimensional settings in elevation and azimuth to the desired phase setting of the operational beam. This procedure supposes an ideal behavior of phase shifters. Compensation of unwanted parasitic effects, which would degrade the beam steering, is another task of the instrument control by means of compensating the effect via the control of one attenuator per RF path. The pointing angles used to select azimuth and elevation indexes shall be calculated on board using the relative positions between
<figure><img src="image_12.png" /><figcaption>FIGURE 11. Embedded elementary radiator radiation pattern (central element; f = 1575 MHz).</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>FIGURE 12. G-TERN antenna array.</figcaption></figure>

G-TERN, GNSS transmitter and reflection point plus the application type. The relative angles are derived from present G-TERN position and GNSS predicted position. G-TERN position is propagated from Precise Orbit Determination data and GNSS position is propagated from TLE parameters. The beamforming network is based on 4 BFN modules, each pair devoted to one RF path at one frequency. Each BFN module delivers 12 different beam outputs, to enable the synthesis of 12 different beams simultaneously. The architecture is based on a succession of four stages: RF dividers, beam former, using variable attenuators and phase shifters, RF combiners and RF to IF down-converters. The BFN section is a 48 beams conforming network (from a 30 elements array) together with down-converting from RF to IF. Furthermore it includes DC/DC converter from primary supply bus and LO signal generation from Master Clock. The 48 beams correspond to 12 beams for each frequency (CL1 and CL5) and for each antenna (up- and down-looking).

The beams at intermediate frequency are connected to
the processing unit SPU. This module is based on ADC

chips and ASIC correlators controlled by Spacewire links,
which are able to perform different processing techniques
for GNSS reflectometry, scatterometry and radio occultation.
Each correlator can compensate the physical delay and the
Doppler frequency differences between the signals to process
(i.e. for altimetry application the up and down signals).
The coherent and non-coherent integration times are also
configurable via the Spacewire interface. Also each correlator
allows the use of clean replica codes to be processed together
with the down-looking signal for radio occultation purposes.

The main components of the board are sketched
in Figure 13. The SPU boards are clocked by the G-TERN
master clock of 153.6MHz (TBC). The clock distribution
ensures that the skew between all ADC and the PACO are so
that correct sampling of the data is guaranteed. The foreseen
ADC features a 1:2 de-multiplexed data signal, meaning that
the data rate is half of the sampling clock of 153.6 MHz,
hence 76.8 MS/s. The analogue part of the board is placed
on the left side of the board, whereas the digital interfaces
are connected to the backplane. In addition to the digital
interfaces, the power supply is also routed via the backplane.
Local Point of Load (PoL) converters are foreseen, to ensure
stable supply for all components of the SPU board. The
PACOs are placed as close as possible to the backplane,
in order to maximize the heat transfer to the frame.

The concept for the PACO processor includes the cross-
correlation of up-looking and down-looking beams' signals,
its swapping, Doppler and delay compensations, and voltage
offset compensations, among other features. The swapping
can be configured via the interface control unit (ICU).
The cross correlator is a 400 tap lag correlator, running at
76.8MHz, hence a tap spacing of 13ns. The output of the
cross correlator is then fed into a Doppler Shift circuit (called
Doppler bin), which is replicated for 5 different Doppler
frequencies (controllable via the ICU). The circuit allows to
further increase the number of Doppler frequencies up to a
factor of 3 (with reduced time resolution). PACO generates
the DDM as power and/or complex waveforms.

The Instrument Control Unit (ICU) shall be in charge of
the control of the overall instrument, data handling, data
storage and of interfacing towards the platform. It is the
operational centre of the whole instrument. So it is in charge
<figure><img src="image_14.png" /><figcaption>FIGURE 13. Signal-Processor Unit board concept.</figcaption></figure>

of the instrument preparation for an event observation and the synchronized execution of the observation. It manages the beams pointing, the CAL/LNAs switching and the operational management of the SPU, providing it with data as Doppler settings, coherent and incoherent integration parameters, desired output waveform, etc. It also generates and distributes the master clock to the whole instrument. It is basically composed of a microprocessor system, where the software runs, a big memory module, an instrument housekeeping signals acquisitions module and hardware interfaces towards the rest of the instrument modules and the satellite platform.

The ICU is based on a unit composed of two completely redundant parts assembled in a common mechanical housing hosting three different types of board per section plus two boards common to both sections: Interface Module (IM), in charge of the instruments data processing and communication with the platform and instruments down-stream units (BFNs, SPU, POD, and CAL/LNAs); Specific Interface Module (SIM), in charge of generation and acquisition of discrete standard interfaces (as thermistors, voltages, currents) needed for monitoring, reporting and control of the complete instrument; Converter Module (CM), in charge of providing Power Conversion functions; Clock Distribution Module (CDM), amplifying, splitting and combination of master clock signal to be distributed towards the G-TERN Instrument units; Backplane (MB), connecting all modules and performing the signals cross-strapping where needed.

A POD receiver subsystem is included in the instrument which: (1) provides the on-board time reference to all the instrument, including the PPS signal; (2) provides the

position of the instrument to the ICU; (3) obtains ephemerides data to estimate the reflection observation; and (4) delivers observables that are considered as Level 0 data products and it is used in the geo-location of the power-delay waveforms in the level 1B product.

Finally, Table 4 shows the main performances and budgets of instrument units and for the overall instrument. The total estimated power consumption is 461.6 W (553.92 W with 20% margin), and the total weight 266.63 kg (319.96 kg with 20% margin).

## B. PLATFORM

In order to accommodate the payload and support its operations in orbit, the OHB LEO platform has been preliminary selected for the G-TERN mission. The platform fits within the constraints coming from the above described payload characteristics and is compliant with the G-TERN mission requirements. Designed mainly for LEO Earth observation missions, it is based on EnMAP heritage, combined with experiences from Galileo and SAR-Lupe and SARah. The G-TERN platform is tailored to fly in Sun-Synchronous Orbits (SSO) between 600 and 800 km altitude and different Local Time of the Ascending Nodes (LTANs) can be attained with minimum modification of the spacecraft accommodation. Different orbits require adaptation of the platform with the magnitude of fall-outs depending on the deviation from the nominal: however the platform is again flexible to cope with these changes with, possibly, few adaptations. The platform offers a design lifetime of 10 years and it is intended for the payload ranges detailed in Table 5. A major highlight is the capability to be launched with a dual-launch
<table><caption>TABLE 4. Main budgets and performances of the G-TERN instrument.</caption><tbody><tr><td>Antenna Array Structure</td><td>1 Unit</td></tr><tr><td>Mass per unit (kg)</td><td>18.5</td></tr><tr><td>Dimension per unit (L,W,H mm)</td><td>900x1135x334</td></tr><tr><td>Radiator</td><td>31 Units (Up) + 30 Units (Down)</td></tr><tr><td>Outputs</td><td>Up: U_RHCP; Down: D_LHCP and D_RHCP</td></tr><tr><td>Mass per unit (kg)</td><td>0.24</td></tr><tr><td>Dimension per unit (L,W,H mm)</td><td>178x90x22</td></tr><tr><td>CAL/LNA</td><td>31 Units</td></tr><tr><td>Inputs</td><td>U_RHCP, D_LHCP, D_RHCP</td></tr><tr><td>Ouputs</td><td>A-CL1: U_RHCP or D_LHCP or D_RHCP; A-CL5: U_RHCP or D_LHCP or D_RHCP; B-CL1: U_RHCP or D_LHCP or D_RHCP; B-CL5: U_RHCP or D_LHCP or D_RHCP</td></tr><tr><td>Mass per unit (kg)</td><td>1.9</td></tr><tr><td>Dimension per unit (L,W,H mm)</td><td>135x94x167</td></tr><tr><td>Power per unit (W)</td><td>2.65</td></tr><tr><td>BFN</td><td>4 Units</td></tr><tr><td>Inputs</td><td>BFN_A-CL1: 30xA_CL1; BFN_A-CL5: 30xA_CL5; BFN_B-CL1: 30xB_CL1; BFN_B-CL5: 30xB_CL5</td></tr><tr><td>Outputs</td><td>Total:120 BFN_A-CL1: 12 A-beams at CL1; BFN_A-CL5: 12 A-beams at CL5; BFN_B-CL1: 12 B-beams at CL1; BFN_B-CL5: 12 B-beams at CL5</td></tr><tr><td>Mass per unit (kg)</td><td>33.7</td></tr><tr><td>Dimension per unit (L,W,H mm)</td><td>380x480x270</td></tr><tr><td>Power per unit (W)</td><td>19.8</td></tr><tr><td>SPU</td><td>4 Units</td></tr><tr><td>Inputs</td><td>SP1: 6xA_beaums_CL1 and 6xB_beaums_CL1<br>SP2: 6xA_beaums_CL1 and 6xB_beaums_CL1<br>SP3: 6xA_beaums_CL5 and 6xB_beaums_CL5<br>SP4: 6xA_beaums_CL5 and 6xB_beaums_CL5</td></tr><tr><td>Outputs</td><td>Total: 48<br>12 correlations A*B at CL1 12 correlations A*B at CL5<br>Total: 24<br>12<br>200x300x250</td></tr><tr><td>Mass per unit (Kg)</td><td>81</td></tr><tr><td>Dimension per unit (L,W,H mm)</td><td>81</td></tr><tr><td>Power per unit (W)</td><td>81</td></tr><tr><td>POD</td><td>1 Unit</td></tr><tr><td>Inputs</td><td>CL1 and CL2</td></tr><tr><td>Ouputs</td><td>ICU interface</td></tr><tr><td>Mass per unit (Kg)</td><td>7.2</td></tr><tr><td>Dimension per unit (L,W,H mm)</td><td>205x226x184</td></tr><tr><td>Power per unit (W)</td><td>24</td></tr><tr><td>ICU</td><td>1 Unit</td></tr><tr><td>Nr units</td><td>1</td></tr><tr><td>Inputs</td><td>24 correlations A*B</td></tr><tr><td>Ouputs</td><td>3072 Kbps of instrument data rate</td></tr><tr><td>Mass per unit (kg)</td><td>10</td></tr><tr><td>Dimension per unit (L,W,H mm)</td><td>200x300x230</td></tr><tr><td>Power per unit (W)</td><td>20</td></tr><tr><td>PSU</td><td>1 Unit</td></tr><tr><td>Inputs</td><td>22V-37V unregulated</td></tr><tr><td>Outputs</td><td>31x CAL/LNA: DC/DC (N&R) V1 DC/DC (N&R) V2<br>DC/DC (N&R) V3 4XSPU (N&R): 22-37V 4X BFN (N&R): 22-37V<br>1xICU (N&R): 22-37V</td></tr><tr><td>Mass per unit (Kg)</td><td>10</td></tr><tr><td>Dimension per unit (L,W,H mm)</td><td>350x230x230</td></tr><tr><td>Power per unit (W)</td><td>25</td></tr></tbody></table>

<table><caption>TABLE 5. Main characteristics of the G-TERN platform.</caption><thead><tr><td>Payload (kg)</td><td>mass</td><td>Average payload power (W)</td><td>Spacecraft mass (kg)</td><td>dry</td><td>Mission lifetime (years)</td><td>Payload downlink (Mbit/s)</td><td>data rate</td><td>Min platform dimensions (m³)</td><td>Max payload dimensions (m³)</td><td>Most constraining launcher</td></tr></thead><tbody><tr><td>&lt; 350</td><td></td><td>&lt; 600</td><td>&lt; 800</td><td></td><td>&lt; 10</td><td>≤ 150</td><td></td><td>1.5 × 1.1 × 1.1 (L x W X H)</td><td>2.0 × 1.2 × 2.0 (L x W X H)</td><td>Inside Vespa, Upper Vega (on Vespa)</td></tr></tbody></table>

on Vega by using the VESPA adapter: depending on the payload volume, the spacecraft might even fit into the lower position inside VESPA. The platform is conceived to have a

'plug and play' payload, or in other words, to be decoupled from the payload module: the payload module is separated from the platform module and the two are only connected
via power, data and mechanical interfaces. The platform uses for the most parts off-the-shelf space-qualified components with Technological Readiness Level (TRL) ≥8, while the subsystems, which require minor modifications for the specific mission needs, still reach a TRL ≥5/6. The agile 3-axis stabilized platform is able to meet the most stringent pointing requirements. Furthermore, it offers several optional features to adapt to different mission-specific and payload-specific constraints, for example in terms of power generation and storage, payload data handling and transmission. The platform can comply with both uncontrolled and controlled re-entries. Due to its cost-effectiveness and modular decoupled design, providing separation between payload and platform modules and resulting in programmatic savings, the platform is the perfect candidate for the G-TERN mission in the frame of Earth Explorer 9 programme.

The platform is designed to fit either in the lower or upper position - depending on the payload - of the ‘extended’ VESPA (+500mm) of VEGA, for dual launch. In Figure 14, the spacecraft is depicted, fitting within the useable envelope of Vega upper position. The Launch Vehicle Adapter is a band clamp with a diameter of 937mm. Given the limited information available on Vega-C and the smaller size of current Vega fairing, the conservative approach of fitting the spacecraft inside the current launcher configuration was assumed. In the next phase of the study, following the consolidation of mission, payload and system requirements as well as updated information of the VESPA adaption to Vega-C, a more detailed assessment could be performed on whether and under which conditions/configurations it would be possible to fit the spacecraft inside VESPA, in lower position.

<figure><img src="image_15.png" /><figcaption>FIGURE 14. G-TERN satellite inside VEGA Launcher.</figcaption></figure>

Figure 15 provides an overview of the G-TERN spacecraft in stowed and deployed configurations, with dimensions. It is possible to clearly identify the separation between platform module and payload modules and their isostatic mechanical interface. Figure 16 shows the internal units accommodation

of G-TERN. In Figure 17, the difference between two orbital configurations is shown: on the left, the platform is adapted to LTAN close to Noon-Midnight orbits (typical orbits for optical payloads) while on the right the solution is adapted to a Dawn-Dusk orbit. As it is possible to see, the orbit choice is flexible and minimum adaptation of configuration and internal accommodation is required (the radiator is for both configurations on the hidden side in these figures).

<figure><img src="image_16.png" /><figcaption>FIGURE 15. G-TERN satellite in stowed (left) and deployed configurations (middle).</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>FIGURE 16. Internal accommodation of G-TERN payload systems and components.</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>FIGURE 17. G-TERN solar array configuration for different orbits.</figcaption></figure>

The propulsion system is sized to allow station keeping for 10 years in conditions of maximum solar activity. The residual propellant is used to perform perigee lowering for uncontrolled re-entry in less than 25 years. It is remarked that if the orbit altitude is selected close to 600 km, the spacecraft would naturally decay in less than 25 years. If a controlled
re-entry is required, adaptation of the propulsion system is needed: in the following phase of the development, a consolidation of mission requirements and spacecraft design will allow for detailed re-entry analyses and assessment of casualty risk, to demonstrate compliance with current regulations.

The telemetry and telecommand transmission is performed via S-band while the science data are downlinked via X-band, together with telemetry data for contingency. The payload data handling and transmission subsystem has the following characteristics:

* An Isoflux antenna allows transmission to the ground station

* The high data downlink rate and memory size allow considerable memory margins, even when considering 100% duty cycle with 12 beams and 2 frequencies, i.e. 3.1 Mbit per second of science data.

A downsizing of the payload data handling and transmission subsystem could be performed, if considered necessary, to reduce the design margins in a more mature phase. The data downlink budget was analysed assuming the Kiruna 13 meters dish with 5° minimum elevation angle. As the electrical power generation and distribution system (EPS) is concerned, a solar array driving mechanism coupled with a mounting cant angle, when applicable, allows to achieve high performances by sun tracking. The spacecraft has a 28V unregulated bus with direct energy transfer distribution. The electrical power system is sized for 10 years for a 600 km dawn-dusk orbit (LTAN 06:00), where maximum eclipse reaches 20 minutes duration in winter. The sever square meter solar array is able to provide 1315 W at the power control and distribution unit. Batteries provide 57 Ah at 33.6 V. The power budget in analysed under different modes of operation: 91% of the duty cycle it would operate under nominal operation mode, while the ground station pass mode (payload operational and simultaneous downlink) would happens up to 9% of the duty cycle. This results in an average power budget of 943.8 W, which consistently accounts for the design margins.

The mass budget has been estimated considering a range of margins (from 5% to 30% depending on the subsystem) and including the propellant mass. The total spacecraft wet mass then results in 870 kg, which fits within the constraints of the launcher and the EE9 Call.

# VI. EXPECTED PERFORMANCE

The fulfillment of the required critical performances (Table 1) is evaluated by means of end-to-end simulation exercises. The exercises are limited to the altimetric performances, as they represent the most demanding application in G-TERN. The approach comprises the following blocks:

1) Generation of synthetic 1-second level-1 data according to the G-TERN orbital and instrumental characterization, as well as a limited set of sea ice conditions and geometries. These data sets must include the different noise components, in the form of a Monte Carlo like approach.

2) To apply the inversion algorithms to retrieve the group-delay altimetric products (1 Hz level-2 data) from the synthetic level-1 observables generated in block 1 above.

3) To determine the uncertainty of the retrieved 1Hz level-2 group-delay altimetric products over sea ice, by means of comparison with the well-known ground truth (simulation settings) and the dispersion obtained from the Monte Carlo set of samples. Blocks 1 to 3 are presented in Section VI-A. Given that GEROS-ISS mission went through industrial and scientific feasibility studies (Phase-A) and these sort of exercises were done and compiled for Ocean applications in [123], we limit these simulations to sea ice scattering conditions, and will use the outcome of [123] for sea surface altimetric performances.

4) To simulate phase-delay synthetic data and its retrieved altitudes to estimate the 1-second equivalent phase-delay accuracy (Section VI-B).

5) To simulate the location of the specular points that a G-TERN system would collect in 3 days, at 1 second sampling over polar areas (here defined as $|\text{lat}| > 60°$). Define a grid of cells sized 30 km × 30 km across the polar zone, and group the 1-second observations by the cell where their specular points belong.

6) With the 1-second uncertainties obtained in blocks 3 and 4 above and the number of 1-second observations within each cell obtained in block 5, compute the overall uncertainty over each cell.

7) Analyze the statistics of the obtained uncertainties at each cell within the 3 days simulation period. Blocks 5 to 7 are presented in Section VI-C.

## A. GENERATION OF 1HZ-LIKE LEVEL-1 WAVEFORMS AND DERIVED LEVEL-2 GROUP-DELAY ALTIMETRIC ACCURACIES

This section compiles blocks 1 to 3 of the end-to-end simulation description above. The simulations correspond to the G-TERN orbit and instrument (see Sections III and V respectively) in four different geometries and two rather extreme examples of sea ice, the best and worst reflectors. The best case reflector corresponds to smooth ice (low roughness) and more reflecting, i.e. saltier ice such as first-year (FY). For simplicity we will call it FY (despite FY can also be rougher). The worst reflector corresponds to ice with rough surfaces and less reflecting properties, i.e. fresher ice with less salt, such as in multi-year ice (MY), hereafter identified as MY (despite MY ice can present smooth surfaces). The smooth sea ice corresponds to the conditions found in Hudson Bay in TDS-1 TD18, 15th January 2015 [29], [30], providing highly specular reflections. The scattering regime for the MY extreme case considered here has been analyzed through TDS-1 TD51 track, 11 February 2015, from 16:55 to 16:58 UTC, for rough ice conditions. The summary of relevant parameters is given in Table 6, including orbital, instrumental, geometries and characterization of the sea
<figure><img src="image_19.png" /><figcaption>FIGURE 18. Examples of 10 synthetic level-1 waveforms generated for four scenarios (colored lines), in dB SNR. In black the noise-free or 'average' waveform. Top-left: best case (smooth first year sea ice with 4 times larger coherent scattering than diffuse scattering) observed at 10° incidence (80° elevation). Top-right: same for 45° incidence ange. Bottom-left: worst case (rough multi-year ice with diffuse only scattering) observed at 10° incidence (80° elevation). Bottom-right: same at 45° incidence. Note that the simulations included 1,000 of such noisy synthetic level-1 products, only 10 randomly selected are shown here for clarity purposes.</figcaption></figure>

ice (surface roughness and permittivity) and the scattering regime (ratio between coherent and incoherent scattering, analyzed on actual TDS-1 data following the methodology in [104]).

For each geometry and ice type, 1000 realizations of 1 second incoherent integration each have been generated with the 'wavpy' open source simulation tool [196], [197]. The coherent integration time is set to 1 ms. Within the wavpy simulator, the noise terms include thermal noise and speckle, with their corresponding distinctive delay-correlation and time correlation. The delay-correlation of the noise is relevant to the altimetric precision because the retrieval algorithm takes the derivative of the leading edge and this involves differentiating the waveform along its delay axis. Noise correlated along the delay axis tends to reduce the noise of this altimetric retrieval. As for the time-correlation of the noise, it has

a relevant role in the final determination of the dispersion of the 1-second waveforms. For example, if the model determines that the correlation time of the measurement is longer than 1 ms, then the 1000 ms integrated within one second are not fully independent and the dispersion is not reduced as $1/\sqrt{1000}$ but for the corresponding number of independent samples in one second (e.g. [96], [105]).

Figure 18 shows 10 noisy waveforms (level-1 product) out of the 1000 realizations corresponding to four of the simulated scenarios: best reflector and worst reflector for 10° and 45° incidences, the highest and lowest incidence angles simulated in the exercise. The signals reflected off rough multi-year ice, under purely diffuse regime and lower permittivity (extreme case with volumetric brine content set to zero and L-band mss=0.017), present much larger noise levels than the mostly coherent scattering off the
<table><caption>TABLE 6. Settings of the simulation to generate the level-1 observables for 1Hz group-delay altimetry over sea ice.</caption><thead><tr><th colspan="3">Near-nadir group-delay simulation of level-1 synthetic data for sea ice G-TERN reflectometry</th></tr></thead><tbody><tr><td>Orbit height:</td><td colspan="2">600 km</td></tr><tr><td>Antenna gain:</td><td colspan="2">20 dB</td></tr><tr><td>Sampling rate:</td><td colspan="2">76 Msampes/second</td></tr><tr><td>Receiver bandwidth:</td><td colspan="2">10 MHz at baseband (G-TERN has configurable filters, this number should be optimized in further phases of the mission)</td></tr><tr><td>Noise figure:</td><td colspan="2">3.5 dB</td></tr><tr><td>Incidence angles:</td><td colspan="2">10°, 20°, 30°, 45°</td></tr><tr><td rowspan="2">Sea ice characterization (extremes):</td><td colspan="2">Best case: coherent on FY</td></tr><tr><td>Roughness [11]:<br>σ<sub>z</sub>=0.1 m<br>L<sub>coh</sub>=3.3 m<br>mss=2 σ<sub>z</sub><sup>2</sup>/L<sub>coh</sub><sup>2</sup>=0.002<br>Permittivity [148]:<br>V<sub>b</sub>=70<br>ε=3.750 + j 0.390<br>Scattering regime:<br>Coherence analysis in [105] applied to TDS-1 data [30]<br>results in P<sub>coh</sub>/P<sub>diff</sub> = 4</td><td>Worst case: diffuse on MY<br>Roughness [11]:<br>σ<sub>z</sub>=0.3 m<br>L<sub>coh</sub>=3.3 m<br>mss=2 σ<sub>z</sub><sup>2</sup>/L<sub>coh</sub><sup>2</sup>=0.017<br>Permittivity [148]:<br>V<sub>b</sub>=0<br>ε=3.120 + j0.040<br>Scattering regime:<br>Coherence analysis in [105] applied to TDS-1 rough MY data<br>results in diffuse scattering</td></tr></tbody></table>

<table><caption>TABLE 7. Mean error and dispersion of the error resulting from the simulations described in this section, corresponding to 1 Hz level-2 sea ice altimetric products. Reflection off smooth salty sea ice (best reflector) and rough fresh ice (worst reflector) have been simulated at four geometries each, and 1000 times for each case following a Montecarlo-like approach.</caption><caption>Group-delay sea ice altimetric error from 1000 level-1 sea ice reflected waveforms, thermal and speckle noise terms:</caption><thead><tr><th>Case:</th><th>Mean error (bias-like):</th><th>Standard deviation σ :</th><th>Multi-Doppler processing, standard deviation σ :</th></tr></thead><tbody><tr><td>Best reflector 10° incidence:</td><td>&lt; 0.1 cm</td><td>2.5 cm</td><td>1.9 cm</td></tr><tr><td>Best reflector 20° incidence:</td><td>&lt; 0.1 cm</td><td>2.6 cm</td><td>2.0 cm</td></tr><tr><td>Best reflector 30° incidence:</td><td>&lt; 0.1 cm</td><td>2.7 cm</td><td>2.0 cm</td></tr><tr><td>Best reflector 45° incidence:</td><td>0.1 cm</td><td>3.4 cm</td><td>2.6 cm</td></tr><tr><td>Worst reflector 10° incidence:</td><td>-1.7 cm</td><td>39.7 cm</td><td>29.8 cm</td></tr><tr><td>Worst reflector 10° incidence:</td><td>-1.9 cm</td><td>39.1 cm</td><td>29.3 cm</td></tr><tr><td>Worst reflector 10° incidence:</td><td>-1.9 cm</td><td>42.6 cm</td><td>32.0 cm</td></tr><tr><td>Worst reflector 10° incidence:</td><td>-3.1 cm</td><td>58.9 cm</td><td>44.2 cm</td></tr></tbody></table>

smooth Hudson Bay first-year ice. Note that the scattering off smooth first-year ice has been set to a regime with 4 times higher coherent power than diffuse power, as deduced from TDS-1 data in [30] applying the method described in [104]. An altimetric retrieval algorithm based on the leading-edge derivative (LED) approach [94], [114], has then been applied to each of these level-1 noisy waveforms. The inversion algorithm provides an altimetric solution (level-2 product) for each of the level-1 waveforms. Given that the exact 'true' altimetric information is known (set by the simulation), we can then extract the error obtained in each case. The statistics of these errors provide indication of the overall expected bias and root mean square (RMS) dispersion (1-σ precision). Table 7 summarizes the results, which show precisions of the order of 3 cm in 1 second observation over the best reflector (smooth FY ice). On the other hand, group-delay altimetry measured with G-TERN over extreme rough and low permittivity sea ice presents worse precisions by one order of magnitude, especially at the edge of the near-nadir field of view (45° incidence). Closer to the nadir (10° incidence) the precision is 39.7 cm. Recent studies have proved both theoretically and with actual spaceborne GNSS-R TDS-1 data, that the precision can be improved by a factor of 20-30% using multi-Doppler altimetric algorithms [101], [198]. An intermediate reduction factor of 25% in the dispersion has been here assumed in the last column of Table 7.

The accuracies obtained in this exercise must be complemented with the uncertainties given by other systematic

effects, such as orbital errors, tropospheric errors and ionospheric errors. The orbital errors (precise orbit determination, POD) and tropospheric errors are obtained with the simulator developed for the GEROS-ISS mission, and for simplicity they will be here fixed to 5 cm POD and 1 cm for tropospheric residual errors [123]. Note that these values were computed for GEROS-ISS, and for this reason they represent a conservative envelope of the actual expected values: the POD error values at the ISS are larger than POD values in small satellites (larger uncertainty in ISS positioning is due to large structures with changing mass distribution, uncertain centre of mass, changes in attitude due to maneuvers, etc). As for the troposphere, the 1 cm standard deviation of the residual errors found in GEROS-ISS studies were based on actual ECMWF field values and their uncertainties across tropical areas, where the atmosphere is moister than in polar areas.

The ionospheric effects have been simulated through the SCIONAV model, developed under an ESA contract [199]. This tool was initially developed to investigate the influence of ionospheric perturbations on the performance of navigation receivers. This simulator has been later used to study the impact of ionospheric perturbations in GNSS-R scatterometry [200] and altimetry measurements [201] with the GEROS-ISS mission as example. The solar activity predicted for period when G-TERN would orbit, the 25th solar cycle (2017 to 2029), is expected to be mild (110 sunspots of of maximum [202]). Nevertheless, the simulation has used two extreme values of
<table><caption>**TABLE 8.** Total group-delay altimetry uncertainties in the near-nadir field of view (incidence ≤45°), including thermal and speckle noise, orbital, tropospheric and ionospheric errors. The noise figures for level-2 sea ice altimetric products have been obtained from a polynomial fit as a function of the incidence angle (ϑ) of the data in Table 7 for two extreme sea ice conditions (best and worst ice reflectors), and multi-Doppler processing is assumed. For sea surface altimetry, the noise terms have been extracted from the GEROS-ISS studies [123]. POD effects are all set to 5 cm level. Tropospheric effects all set to 1 cm level. Ionospheric effect at polar areas are negligible while 15 cm residual dispersion is assumed in the ionospheric-free GNSS combination at non-polar regions. All units in cm.</caption><thead><tr><th colspan="2">Final 1-second equivalent level-2 group-delay uncertainty (noise and systematic effects)</th></tr><tr><th></th><th>Total precision (cm)</th></tr></thead><tbody><tr><td>Best reflector sea ice</td><td>a+b*θ+c*θ²+d*θ³,<br/>a=5.4,<br/>b=0.017,<br/>c=-0.0008,<br/>d=1.3e-05<br/>(mean ~5.5 cm)</td></tr><tr><td>Worst reflector sea ice</td><td>a+b*θ+c*θ²+d*θ³,<br/>a=32.5,<br/>b=-0.2861,<br/>c=0.0035,<br/>d=1.94e-04<br/>(30 to 45 cm)</td></tr><tr><td>Rough waters, polar zones</td><td>30.4</td></tr><tr><td>Rough water, non-polar zones</td><td>34<br/>(30.4 to 44.9 cm)</td></tr></tbody></table>

solar activity for illustration purposes. Solar flux data from http://www.sws.bom.gov.au/Solar/1/6 has been used, assuming a minimum solar flux SF = 68.2 SFU (corresponding to January 2009) and a maximum SF = 223 SFU (April 2000). Based on these numbers, the Global Ionospheric Scintillation Model (GISM) has been used to compute the corresponding scintillation indexes S4 and σφ at L1, L2 and L5 GNSS frequency bands. Over polar regions, 'diffractive scintillation' has not been found. This type of scintillation cannot be corrected using the ionospheric-free combination of GNSS signals. Therefore, only 'refractive scintillation' would be produced, which can be fully corrected with the ionospheric-free combination. Nevertheless, to be on the conservative side, we have generated the residual delay errors after ionospheric-free combination for the worst possible case, that is 0° longitude and 20° latitude at 20:00 UTC, which corresponds to the maximum perturbation. We will therefore consider that the G-TERN polar observations are not affected by the scintillation, but the non-polar ones would be affected, especially around the Tropics. The selection of a Sun-synchronous orbit 6AM-6PM would minimize this effect, avoiding the maximum activity. To provide a single number for the non-Polar areas, we simply take the average of the Tropical 8PM effect between a minimum solar activity (≪1 mm bias, 0.62 cm dispersion) and a maximum solar activity (23 cm bias, 33 cm dispersion). The resulting value, ~15 cm dispersion is rather conservative for the rest of the Globe, especially in a 6AM-6PM Sun-synchronous orbit.

To provide sets of overall errors induced by these combined effects, the uncertainties in last column of Table 7 are first expressed through a polynomial fit as function of the incidence angle, and then added quadratically to the remaining

<figure><img src="image_20.png" /><figcaption>**FIGURE 19.** Actual spaceborne GNSS-R reflected signals captured aboard TDS-1 across sea ice in the Southern Ocean, surrounding Antarctica. The SNR of the signals reflected off the sea ice are higher or equal to the SNR of signals reflected off the ocean. Unpublished figure from [203], reproduced here with authorization of the authors.</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>**FIGURE 20.** Simulation of phase delay observables and their altimetric retrievals in grazing angle geometry (varying around 10° elevation in this case). The surface height is perturbed with a 30 cm step-like feature, and the signal is received at 18 dB SNR. The phase obtained can be tracked, and it presents noise levels between 0.4 to 0.5 rad (top). Bottom: after unwrapping the phase and applying the altimetric inversion, the altimetric profile is recovered at around ~4-5 cm precision (better precisions achieved at slightly higher elevation angles and/or higher SNR).</figcaption></figure>

sources of error (POD, troposphere and ionosphere). The resulting 1 Hz level-2 precisions, with all terms accounted for, are summarized in Table 8.

## B. PHASE DELAY LEVEL-2 ALTIMETRIC ACCURACY

As already described in Sections IV-B, the phase delay observables are either very precise or they are not possible to track. The possibility of tracking the phase and the noise level of the phase itself are both driven by the SNR of the signal. On the other hand, as is shown in Figure 19, actual spaceborne data obtained with TDS-1 generally present higher levels of
<figure><img src="image_22.png" /><figcaption>**FIGURE 21.** Top row: number of 1-second observations obtained at each cell of 30 km × 30 km resolution during 3 days in Scenario 1, north pole and south pole (right-left). Central row: same for Scenario-2. Bottom row: number of 1-second observations obtained at each cell of 0.5° × 0.5° resolution cells during 10 days in Scenario-3.</figcaption></figure>

SNR over sea ice than over ocean waters. The worst case SNR over sea ice corresponds the old sea ice, with SNR values similar to those of ocean waters. The simulation exercise

presented here correspond to SNR values compatible with this worst-case scenario. Any other sea ice phase delay altimetric retrieval will present lower levels of noise, thus better
<figure><img src="image_23.png" /><figcaption>**FIGURE 22.** Top row: particular realization of the geographical distribution of level-3 altimetric accuracy at each cell of 30 km × 30 km resolution during 3 days in Scenario 1, north pole and south pole (right-left). Central row: same for Scenario-2. In both cases best ice reflector has been assigned to first year ice and worst case reflector to multi-year ice. The FY and MY contours have been approximated from Nimbus-7 SMMR and DMSP SSM/I-SSMIS ice concentrations provided by NSIDC, considering MY the extension during an arbitrary date of the local summer and FY the extension of another arbitrary date in local winter (dates: 01/08/2015 and 01/01/2015). GNSS-R does not follow a repeatable pattern, therefore the actual distribution of observations will change daily, but keeping the latitudinal statistics. Bottom row: geographical distribution of the accuracy of scenario-3 over 0.5° × 0.5° cells in 10 days accumulated data, for −70° ≤ lat ≤ 70°. The group delay level-2 altimetric product has been assumed at 34 cm accuracy in 1 Hz [123].</figcaption></figure>

precision. For example, the aforementioned ~3-cm RMS precision at 50Hz rate reported for phase-delay altimetry from TDS-1 data in [30].

Simulations have been run following the approach in [123] and [124]. SNR values set at 18 dB have been used to simulate the coherent scattering along a surface which presents
a step-like height increase of 30 cm. These simulations include the tropospheric, ionospheric and POD systematic effects [123]. After applying the phase delay retrieval algorithms, it is first possible to connect and nearly stop the phases (Figure 20-top). These residual phases are later resolved as height anomalies, recovering the original 30 cm step in the altimetric profile (Figure 20-bottom). The precision of these phase delay measurements are between 0.4 and 0.5 rad, which maps into uncertainties between 1 and 8 cm in 1 second (changing with the geometry, between 60° and 85° incidence). Similar performances are found with shorter surface height steps (20 cm). The performance improves also when higher SNR are assumed. Hereafter we will continue the simulations assuming an equivalent 1 Hz error of ~5 cm in the phase delay altimetric retrievals.

### C. FULFILLMENT OF THE MISSION REQUIREMENTS

The distributions of 1-second observations obtained for the G-TERN system in a particular set of 3 subsequent days (polar areas) and 10 days (globally) have been simulated. The simulations correspond to three scenarios:

* Scenario-1: Availability of up to 12 simultaneous beams pointing within the grazing angle field of view (5° to 30° elevation) over extended polar areas ($|lat| > 60°$). This means that grazing angle GNSS-R phase-delay altimetry could be done in up to 12 different specular points simultaneously.

* Scenario-2: Availability of a combination of up to 6 grazing angle and up to 6 near-nadir (incidences smaller than 45°) simultaneous reflections over the extended polar areas ($|lat| > 60°$).

* Scenario-3: Availability of up to 12 simultaneous beams pointing to reflections within the near-nadir field of view (incidences smaller than 45°) over the non-polar areas (here defined as $|lat| < 70°$).

The distributions of 1-Hz measurement points for each of these scenarios correspond to those shown in Figure 21. We remind here that GNSS-R does not follow a repeatable pattern, therefore the actual distribution of observations will change daily, but keeping the latitudinal statistics. At this step of the simulations we have considered that all the 1-second observations are incorrelated. This assumption is too strong, as some of the errors do present spatial or temporal correlations. Nevertheless, this approach permits a quick implementation accounting for all systematic effects without need of simulating natural runs fed by actual tropospheric and ionospheric fields nor POD errors. Therefore, these results might have slightly overestimated the accuracy (underestimate the sigmas), to be partially compensated by certain values of the errors taken on the conservative side.

Using all the 12 G-TERN beams to point at grazing angles of observation, and assuming that the final accuracy of the 1-Hz phase delay observations is at the level of 5 cm (Section VI-B), scenario-1 results in accuracies over 30 km × 30 km cells in 3 days accumulation that fulfills the mission requirements in 99.1% of the cells.

<figure><img src="image_24.png" /><figcaption>**FIGURE 23.** Histograms of level-3 altimetric performances over the cells in Figure 22. Top to bottom for scenarios-1 to -3.</figcaption></figure>

In fact, the average precision across the $|lat| > 60°$ regions is 1.6 cm.

For scenario-2, where measurements at near-nadir using group-delay altimetry are combined with phase delay measurements at grazing angles, the simulation has used the following accuracies: measurements done with phase delay observables (Section VI-B): 5 cm at 1 Hz; measurements done with group-delay observables over first year sea ice are considered to correspond to best reflector sea ice reflections, therefore (Table 8-top): 5.5 cm at 1 Hz; measurements done with group-delay observables over multi-year sea ice are
considered to correspond to worst reflector sea ice reflectoins, therefore (Table 8-2nd row): $32.5 - 0.29\theta + 3.5E - 3\theta^2 + 1.9E - 4\theta^3$ cm at 1 Hz, ranging from ~30 cm at nadir to ~44 cm at 45° incidence; measurements done with group-delay observables over ocean waters (Table 8-3rd row) and ice sheets: 30.4 cm. The overall results of combining these 1-second accuracies in 30 km × 30 km cells during 3 days of accumulated data shows that scenario-2 fulfills the altimetric requirements of the mission in a large extent, with 95.5% of the cells performing better than the mission requirements, and an average accuracy of 2.7 cm over regions with $|lat| > 60°$.

Finally, the scenario-3, over global waters (here defined as $-70° \le lat \le 70°$) and $0.5°×0.5°$ cells accumulated in 10 days, results in similar numbers: 97.1% of the cells present accuracies below 10 cm (requirement) while the average accuracy over the cells is 5.3 cm. Figure 22 shows the geographic distributions of the resulting level-3 altimetric accuracies for each scenario, while Figure 23 displays their histogram. The optimal combination of grazing angle phase delay measurements (finer precision) and near nadir group delay measurements (better roughness estimates) would be investigated in future stages of the mission.

## VII. CONCLUSIONS

This study summarizes the main aspects of the GNSS Transpolar Earth Reflectometry exploriNg system (G-TERN), a mission proposal submitted in 2017 in response to the ESA Earth Explorer 9 (Revised Call). The mission is foreseen to implement the interferometric GNSS reflectometry technique to address key scientific questions on the inter-relationship between the cryosphere and other main components of the climate system, in view of the global warming. The main focus of G-TERN is set on the sea ice, its dynamic variations and how they both module and are modulated by its surrounding environment, the global atmospheric and ocean circulations as well as extreme weather systems.

The G-TERN satellite should provide altimetric, scatterometric and polarimetric GNSS-Reflectometry based geophysical data products, characterizing the sea ice, oceans, ice sheets and land surface, covering the poles in grids of 30 km × 30 km cells in just 3 days, and the rest of the globe in 10 days over grids of $0.5° × 0.5°$ cells. The foreseen observation techniques of G-TERN and their preliminary implementation have been introduced. The technical concept is substantially different from other recent GNSS-R missions and includes several novelties and innovation aspects. We highlight in this context: (1) interferometric GNSS reflectometry from space, which provides finer horizontal resolution and higher altimetric accuracy; (2) parallel provision of altimetric, scatterometric and polarimetric GNSS-R data products; (3) twelve simultaneous GNSS-R high-gain beams electronically synthesized and steered to enable observations with unprecedented coverage; (4) combination of slant phase-delay observations and near-nadir group-delay measurements for ice/ocean altimetry with high accuracy; and (5) symbiotic use of GNSS

reflectometry and radio-occultation for combined monitoring of the Earth surface and atmosphere/ionosphere.

The G-TERN spacecraft is based on a modernized platform of space-proven components. The main payload, the combined GNSS-R/RO instrument, has strong heritage from two ESA mission studies: the PARIS-IOD and GEROS-ISS concepts. The proposed orbit is near-polar at 600 km altitude, optimally Sun-synchronous at 6AM/6PM.

A set of specific mission simulations was conducted during the proposal preparation to provide first estimates of the altimetric performance of G-TERN over sea ice and oceans. The required geophysical observational needs are essentially met according to the results of these calculations. Accuracies were obtained, better or equal to 10 cm in more than 95% of the sea ice cells in the polar grid in three days integration, and in more than 97% of the global ocean cells in ten days integration. The G-TERN measurements are also expected to prove a set of secondary mission goals, which include the provision of currently not available innovative cryosphere and wetland related data products. These observations would represent a breakthrough in their irrespective science fields. The G-TERN, with its versatile mission scope and unique payload may act as a forerunner for a potential next generation of ‘low cost’ Earth Observation Systems.

## ACKNOWLEDGMENTS

The authors would like to thank SSTL for providing GNSS-R data acquired from the TDS-1 mission that have been used in this study.

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**ESTEL CARDELLACH** (M'10) received the Ph.D. degree in physics from the Polytechnic University of Catalonia, Barcelona, Spain, in 2002. She has been involved in scientific applications of global navigation satellite systems (GNSS) for remote sensing of the Earth, such as extraction of geophysical information of the GNSS reflected signals, radio occultation, and geodetic techniques. She held a post-doctoral position at NASA/Jet Propulsion Laboratory, Pasadena, CA, USA, from 2002 to 2003, and a post-doctoral research position at the Harvard Smithsonian Center for Astrophysics, Cambridge, MA, USA, from 2003 to 2005. Since 2005, she has been with the Institute of Space Sciences (ICE-CSIC/IEEC). She is the Principal Investigator of the space-borne experiment radio-occultation and heavy precipitation aboard the PAZ low earth orbiter. She was a recipient of the National Research Council Award.

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**JENS WICKERT** received the Graduate degree in physics from the Technical University Dresden, Dresden, Germany, in 1989, and the Ph.D. degree in geophysics/meteorology from the Karl-Franzens-University Graz, Austria, in 2002. He was with several German geoscience research institutes. He currently holds a joint professorship of GFZ with the Technical University of Berlin on global navigation satellite systems (GNSS) remote sensing, navigation and positioning. He is also the Deputy GFZ Section Head Space Geodetic Techniques and the GFZ Speaker of the Atmosphere and Climate Research Programme of the German Helmholtz Association. He has authored/co-authored over 160 ISI listed publications on GNSS Earth observation. He was a Principal Investigator of the pioneering GPS radio occultation experiment aboard the German CHAMP satellite and was coordinating the GEROS-ISS proposal to ESA. He is the Chair of the Science Advisory Group of the GEROS-ISS mission.

**RENS BAGGEN** was born in Geelen, The Netherlands. He received the M.Sc. degree from the Technical University of Eindhoven in 1992, with a focus on antennas and propagation. He was involved in the fields of avionics and quality management with the Dutch Aerospace Laboratory (NLR), Amsterdam, in 1993. In 1995, he joined the Department of Antennas and EM-Modeling, IMST, and specialised in antenna theory, simulation methods/tools, waveguide antennas, metamaterials, and measurement techniques. He was the Coordinator of the FP7-Project MEMS-4-MMIC. He has been involved in many research and development projects in FP5, FP6, and FP7 and is currently involved in H2020 projects. Phased array technology is one of his main fields of expertise. He has authored and co-authored over 50 peer-reviewed publications in conferences and journals. His current activities involve acquisition and coordination/management of long term EU and ESA projects.

**JAVIER BENITO** received the degree in telecommunication engineer from the Universidad Politécnica de Madrid, Madrid, Spain, in 1989. Since 1989, he has been with EADS CASA Espacio, where he is currently a part of Airbus Defence and Space, where he was involved in technological areas and departments of the company. From 1990 to 1992, he was with MATRA-Marconi, Toulouse, France, where he was involved in the integration and test campaign of the telecommunication satellite (HISPASAT 1A) and also with Kourou, (French Guiana), where he was involved in the launch campaign. He has been participating as a Project/Study Manager in multitude of ESA and Spanish projects related with space payloads and satellites MINISAT, COPLAS (communication payload for a LEO satellite), CESAR&MINISOB (observation small satellites), SMOS, SMOSops, SuperMIRAS, PARIS, or GEROS-ISS. He is also a Project Manager of PROBA-3 Project in Airbus.

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**ADRIANO CAMPS** (F'90) He is currently a Full Professor with the Technical University of Catalonia-BarcelonaTech, Spain. In 1999, he was on sabbatical leave at the Microwave Remote Sensing Laboratory, University of Massachusetts, Amherst. Since 1993, he has been involved in the European Space Agency SMOS Earth Explorer Mission, from the instrument and algorithmic points of view, performing field experiments. Since 2001, he has been studying the use of GNSS-R techniques to perform the sea state correction needed to retrieve salinity from L-band radiometric observations. He is currently the Co-Leader of the Remote Sensing Laboratory and the UPC NanoSat Laboratory. He is responsible of the first four UPC nano-satellites 3Cat-1, a 1U CubeSat with seven small technology demonstrators and scientific payloads, and 3Cat-2, a 6U CubeSat with the first dual-frequency dual-polarization GNSS-R payload, 3Cat-4 a 1U Cubesat with a GNSS-R Payload selected in the 2016 ESA Fly your satellite call, and 3Cat-5A/B a tandem 6U satellite mission winner of the Copernicus masters ESA Sentinel Small Satellite Challenge. He has authored over 170 papers in peer-reviewed journals and over 350 international conference presentations. He holds 10 patents and has advised 22 Ph.D. thesis students, over 130 final projects, and M.Sc. theses. His research interests are focused on in microwave remote sensing, with special emphasis in microwave radiometry by aperture synthesis techniques and remote sensing using signals of opportunity (GNSS-R). Since 2007, he has been focusing on the use of small satellites to test innovative Earth observation sensors. His publications have received over 5900 citations and his h-index is 38.

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**NUNO CATARINO** received the degree in physics engineering from the Instituto Superior Tecnico, Lisbon, Portugal, in 2000, and the Ph.D. degree in applied mathematics from the University of Warwick, Coventry, U.K., in 2004. He is currently the Head of the PDGS Division, Ground Segment Systems Business Unit, DEIMOS Engenharia, Lisboa, Portugal. He has led many projects for ESA and other clients, namely, the GNSS-R projects PARIS IOD and GEROS-ISS Phase A studies, the GARCA scientific studies, the GEAF intelligent Ground Segment orchestrator, and the Sentinel 3 Optical Ground Processor Level 1 Prototype at DEIMOS Engenharia, Lisboa. He has also involved in the Phases 3 and 4 of the SMOS L1 Processor Prototype, including the Image Validation Campaign at ESRIN and the re-design and implementation of the image reconstruction module.

**BERTRAND CHAPRON** received the Ph.D. degree in fluid mechanics from Aix-Marseille II, Marseille, France, in 1988. He is currently the Head of the Space Oceanography Laboratory, Institut Franais de Recherche Pour l'exploitation de la Mer, Paris, France, where he is responsible for the Centre Exploitation and Research for Satellite Data. He has broad background in the use of satellite data for ocean remote sensing and oceanography in general and participates in numerous related national and international satellite and research projects. He has authored over 100 papers in refereed journals on applied mathematics, physical oceanography, electromagnetic wave theory, and its applications to ocean remote sensing, data processing, and management.
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**ANDREAS DIELACHER** received the M.Sc. degree in computer engineering from Technical University, Vienna, Austria, in 2010. He is currently pursuing the Ph.D. degree with the Austrian CubeSat Project—Passive Reflectometer and DosimeTrY, RUAG Space GmbH, Vienna. He is assigned as Project Engineer for the Passive Reflectometer payload. He is with the Projects Engineering Group, RUAG Space GmbH. He was involved in various designs,

including a GPS L2C receiver and has led the passive reflectometry projects, namely, PARIS Correlator and GEROS-ISS and the PACO for CubeSat study at RUAG Space GmbH.

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**FRAN FABRA** received the M.Sc. degree in telecommunication engineering in 2007, the master's degree in information and communication technologies from the Universitat Politecnica de Catalunya, Barcelona, Spain, in 2007, and the Ph.D. degree from the Universitat Politecnica de Catalunya in 2013. His master's thesis was on security in aeronautical mobile networks at TriaGnoSys GmbH, Munich, Germany. His Ph.D. dissertation was dedicated to

research of the GNSS-R concept toward remote sensing of the cryosphere. He is currently with the Earth Observation Research Group, Institute of Space Sciences (ICE-CSIC/IEEC), Barcelona, where he is involved in the applications of global navigation satellite systems to Earth science.

**GREG FLATO** is currently a Senior Research Scientist with the Environment Canada's Climate Research Division, who specializes in sea ice and global Earth System modeling. He has authored over 60 peer-reviewed papers and has was involved in the development of Environment Canada's series of global and regional climate models; these models are highly regarded internationally and are used in a wide range of climate research and impact assessment studies. He is also involved in the ongoing development of Environment Canada's seasonal to interannual climate prediction system, and the development of the Canadian Climate Data and Scenarios website. He has extensive experience in the IPCC assessment process. He served as a Lead Author on the Working Group I contribution to the Fourth Assessment Report (2007) and a Coordinating Lead Author on the Fifth Assessment Report (2014). He has also served for a number of international scientific committees, including the World Climate Research Program's Joint Scientific Committee. He is currently a Co-Chair of the WCRP's Climate and Cryosphere core project.

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**HEINRICH FRAGNER** received the Ph.D. degree in physics from the University of Vienna, in 2005, and the M.Sc. degree in computer engineering from the Technical University of Vienna, in 1997. He is currently a Project Manager with RUAG Space GmbH. In particular, he lead the projects which were covering the definition, design, development, and validation of an ASIC-based signal processing core for passive reflectometry. He is also assigned as the Project Manager for the devel-

opment of the latest space-born GNSS receiver generation of RUAGs product line.

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**CAROLINA GABARRÓ** received the B.Eng. degree in telecommunications engineering and the Ph.D. degree in ocean science from the Universitat Politècnica de Catalunya, Barcelona, in 1998 and 2004, respectively. From 1997 to 1999, she was with the European Space Research and Technology Centre, European Space Agency, Noordwijk, The Netherlands, where she was involved in ocean-color remote sensing (for the ENVISAT mission). Since 2000, she has been with the Phys-

ical Oceanography Department, Instituto de Ciencias del Mar, Consejo Superior de Investigaciones Científicas, Barcelona, Spain, where she has been involved in the Soil Moisture and Ocean Salinity (SMOS) mission. She is currently the Executive Director of the SMOS Barcelona Expert Centre on Remote Sensing. Her work is focused on cryospheric applications using satellite radiometry.

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**CHRISTINE GOMMENGINGER** received the Diplôme d'Etudes Approfondies degree in electromagnetics, telecommunications, and remote sensing from the University of Toulon, La Garde, France—University of Nice Sophia Antipolis, Nice, France, and the Ph.D. degree from the University of Southampton, Southampton, U.K., with a focus on microwave radar remote sensing of the ocean at low grazing angles. She has been involved with the National Oceanography Centre for over 20 years.

Her work includes research in altimetry for sea state, along-track interferometric SAR for currents, global navigation satellite systems reflectometry for surface winds and sea state, SAR altimetry, salinity from space with SMOS, and wide-swath ocean altimetry. Her research interests include active and passive microwave remote sensing of the ocean, understanding interactions of microwave signals with the ocean surface, remote sensing of ocean wind and waves, and developing new Earth observation technologies and applications.

**CHRISTIAN HAAS** received the B.Sc. degree from the Technische Universität Clausthal, in 1989, and the M.Sc. degree from the Christian Albrechts Universität zu Kiel, in 1993, and the Ph.D. degree from the Universität Bremen, in 1996. He was with the University of Alberta and York University, Canada. He is currently a Professor with the University of Bremen, and the Head of the Sea Ice Geophysics Section, Alfred Wegener Institut for Polar and Marine Research, Germany. His research interests focus on the role of Arctic and Antarctic sea ice for the climate and ecosystem and for humans, on atmosphere-ice-ocean-biology interactions, ice thickness measurements, and remote sensing.

**SEAN HEALY** has been involved in the applications of GNSS radio occultation (GNSS-RO) measurements since 1996, starting with the validation of GPS-MET temperature retrievals. He subsequently co-developed the first "1D-Var" retrieval codes for GNSS-RO measurements and performed early information content studies. He designed and implemented the GNSS-RO refractivity assimilation system used operationally for NWP at the Met Office until 2010 and the bending angle assimilation system used operationally at ECMWF. He was involved in assimilating GNSS-RO measurements in the ERA-interim reanalysis, climate applications of GNSS-RO measurements, the sensitivity to the empirical refractivity coefficients, and the retrieval of surface pressure information from GNSS-RO. He has contributed to numerous successful projects for ESA and EUMETSAT. He has authored/co-authored 31 journal papers in this area since 2000. He is a member of the EUMETSAT/ESA's GRAS Scientific Advisory Group. He organized the ECMWF/GRAS SAF Workshop on applications of GPS radio occultation measurements in 2008.