Earth Observation with GNSS Reflections
E-GEM – GNSS-R Earth Monitoring
D4.1 State of the Art Description Document
13 - Jan 15
Prepared by: Estel Cardellach (ICE-CSIC/IEEC), Sections 1, 2, 3, 4, 5 Tiago Peres and Rita Castro, Nuno Catarino (DEIMOS), Section 2.3 Nilda Sanchez (USAL), Maria Piles, Adriano Camps (UPC) section 5.4.4 Leila Guerriero (TOV-DICII), review of Sections 5.4 and 5.5 Nazzareno Pierdicca (DIET), review of Sections 5.4 and 5.5 Jorge Bandeiras (DEIMOS), document formatting andApproved review by: E-GEM Steering Committee
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Table of Contents
1 Introduction ...... 6
1.1 Basics of GNSS-Reflectometry ...... 6
2 Constellations and Signals ...... 9
2.1 Systems and Constellations ...... 9
2.2 Spatial Coverage ...... 10
2.3 GNSS Signals ...... 11
2.3.1 Definition ...... 11
2.3.2 Signals Description ...... 12
3 GNSS-R Observables and Modelling ...... 16
3.1 Basic GNSS-R Observables ...... 16
3.2 Electromagnetic Scattering Models ...... 18
4 Receiver-level Data Acquisition ...... 21
4.1 Existing GNSS-R Receivers ...... 24
5 Scientific Applications and Requirements ...... 27
5.1 Ocean: Altimetry ...... 28
5.1.1 GNSS-R Status on Altimetric Applications and Retrieval Algorithms ...... 29
5.1.2 GNSS-R Altimetric Missions ...... 33
5.1.3 Other Related Techniques ...... 33
5.1.4 E-GEM Applicability ...... 33
5.2 Ocean: Surface Roughness, Wind and Tropical Storms/Cyclones ...... 34
5.2.1 GNSS-R Status on Ocean Scatterometric Applications and Retrieval Algorithms ...... 38
5.2.2 GNSS-R Scatterometric Missions ...... 41
5.2.3 Other Related Techniques ...... 41
5.2.4 E-GEM Applicability ...... 42
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5.3 Ocean: Salinity ...... 42
5.3.1 GNSS-R Status on Sea Surface Salinity Applications and Retrieval Algorithms ...... 43
5.3.2 GNSS-R Sea Surface Salinity Missions ...... 43
5.3.3 Other Related Techniques ...... 43
5.3.4 E-GEM Applicability ...... 44
5.4 Land: Soil Moisture ...... 44
5.4.1 GNSS-R Status on Soil Moisture Applications and Retrieval Algorithms ...... 45
5.4.2 GNSS-R Soil-Moisture Missions ...... 46
5.4.3 Other Related Techniques ...... 47
5.4.4 E-GEM Applicability ...... 48
5.5 Land: Vegetation and Biomass ...... 50
5.5.1 GNSS-R Status on Vegetation Applications and Retrieval Algorithms ...... 51
5.5.2 GNSS-R VEGETATION Missions ...... 52
5.5.3 Other Related Techniques ...... 52
5.5.4 E-EGM Applicablility ...... 53
5.6 Hydrology: Inland-water Bodies ...... 54
5.7 Cryosphere: Snow ...... 54
5.7.1 GNSS-R Status on Snow Applications and Retrieval Algorithms ...... 55
5.7.2 GNSS-R Snow Missions: ...... 56
5.7.3 Other Related Techniques: ...... 56
5.7.4 E-GEM Aplicabillity ...... 57
5.8 Cryosphere: Sea Ice ...... 57
5.8.1 GNSS-R Status on Sea-Ice Applications and Retrieval Algorithms: ...... 58
5.8.2 GNSS-R Sea-Ice Missions ...... 60
5.8.3 Other Related Techniques ...... 60
5.8.4 E-GEM Applicability ...... 61
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5.9 Cryosphere: Glaciers ...... 62
5.10 Atmosphere ...... 62
5.11 Civilian Applications: Ship Detection ...... 63
5.12 Civilian Applications: Buried Metallic Bodies ...... 64
6 REFERENCES ...... 65
7 ACRONYMS ...... 82
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Scope
State of the Art Description Document: will contain the output of Task T4.1. D4.1 will be delivered at CDR (T6). [month 6]
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Earth Observation with GNSS Reflections
1 Introduction
The scope of this document is to present in a comprehensive way the output of E-GEM Task T4.1, under which a review of the state-of-the art on GNSS-Reflectometry (GNSS-R) techniques, applications and implementations is performed. The goal of the task is to assist the first iteration of the high-level design of the GNSS-R systems to be implemented in E- GEM, in such a way that the optimal architectures and technologies selected can meet the user requirements while respecting the constraints of the platforms under consideration.
The document is structured as follows:
• This Section 1 gives a brief introduction to the GNSS-R concept.
• An overall review of the GNSS constellations and geographical coverage, current and future signals is given in Section 2.
• Sections 3 to 5 focus on GNSS-R modelling, techniques and scientific applications respectively. The applications are linked to their user requirements, analysis techniques and processing strategies. Some of the analysis techniques have potential to be applied to any of the three E-GEM systems (ground-based, airborne and space-borne platforms), whereas some of them can only be applied from certain platforms or altitudes. This information is clearly indicated.
1.1 Basics of GNSS-Reflectometry
The GNSS-Reflectometry concept was conceived in early 90ies [Martín-Neira, 1993] to densify the Earth observations in a low cost effective way. The GNSS-reflectometry works as a bi-static radar: a system in which the transmitter and the receiver are separated by a significant distance, comparable to the expected distance to the target. This definition can be extended to a system in which a single receiver can simultaneously track a diversity of bi-statically scattered signals, from a diversity of different transmitting sources. Then we call it multi-static. Section 2.2 gives more details on multi- static nature of the GNSS-R concept and its current and expected spatio-temporal coverage. The electromagnetic field at the receiver site has contribution from several GNSS sources (transmitting satellites). Different GNSS transmitters can be identified and separated from the rest of transmitters being received simultaneously by the modulation applied to each GNSS. These contributions correspond to signals that have propagated directly from the source to the receiver, crossing the atmosphere; as well as signals that have propagated down to the Earth surface, scattered off its surface, and up to the receiver coordinates. In principle, these two sort of contributions can be separated using two different antennas, one pointing to the transmitters to gather direct rays, and the other to the surface, to collect Earth-surface scattered signals. However, in some applications the geophysical information is extracted from the interference produced by direct and reflected signals. Then, a single antenna pointing towards the horizon, the Earth limb, or at certain slant orientation is used to collect them both. If the receiver is at air-borne or at higher altitudes, the delay and Doppler information can be used to separate both radio-links. Note that other contributions to the receiver electromagnetic field are also possible, such as those coming from atmospheric ducting (atmospheric multipath), or from reflection off other objects surrounding the receiver or along the propagation path. These other contributions are in general source of noise and systematic effects that need to be corrected or mitigated.
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Figure 1.1a: Sketch of the GNSS-R concept as a multi-static system of Earth observations. Figure extracted from [Jin et al., 2014] with permission of the author.
The electromagnetic scattering is a complex process involving surface dielectric properties and topographic features as a whole system. The dielectric properties of the surface have direct impact on the reflected power. Two limit-conditions are typically distinguished and contrasted as topographic features: specular or mirror-like reflection vs. diffuse scattering. In most of the cases, the scattering process contains both types of contribution, that is, the scenarios do not present either specular-only or diffuse-only scattering, but both of them in different proportions.
The specular reflection corresponds to scattering processes in which waves from a single direction are reflected into a single reflected direction. On the opposite side, in diffuse scattering the incoming waves are reflected in a broad range of directions. The specular-to-diffuse regime is determined by the roughness structures of the surface topography, rather than its dielectric properties. Scattering with dominant specular component occurs in smooth surfaces, where the surface topography/roughness has not significant features of spatial scales similar to the electromagnetic wavelength.
The diffuse scattering can be approximated by reflections off surface facets. “Facets” are here defined as surface patches of size and curvature of the order of or higher than a few electromagnetic wavelengths. Because of the roughness, different facets are oriented towards different directions. Incident rays reflect off the facets, each facet producing a mirror like reflection, which forwards the reflected rays towards a direction determined by the facet’s normal vector and the incident ray direction. In bi-static geometric conditions, the receiver only collects those rays reflected off facets with the appropriate tilt. The glistening zone is then defined as the area from where well-oriented facets might exist above a probability threshold. The glistening zone corresponds to the deterioration of the specular image. Note that surface coordinates away from the nominal specular point require higher slopes of the facet to forward the signal towards the receiver. Note also that the rougher the surface the higher the probability of largely tilted facets, meaning higher probability of well-oriented facets at coordinates far away from the nominal specular point. Therefore, the rougher the surface the largest the resulting glistening zone.
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Figure 1.1b: Example of delay and Doppler values across the reflecting surface given in the form of iso-value lines, for a receiver at 10 km altitude and reflection geometry at 30 degrees elevation angle. Red iso-delay lines spaced ~1 microseconds (GPS C/A code chip). Black for iso-Doppler lines at 100 Hz spacing. Green for reflection iso-power lines at -3dB and -10dB from peak power (glistening zone). From [Cardellach 2002].
The total optical path traveled by the signals reflected at surface points away from the specular point are longer than the path traveled by the specular one, the farther away from the specular reflection the longer the reflected optical path. Therefore, large glistening zones (rough conditions) result in longer tails in the reflected echo. Similarly, the Doppler effects differ across the reflecting surface, resulting in spread frequency responses as the reflection occurs over large glistening zones (rough surface conditions). The shape and power distribution of the echo along the delay- Doppler domain (called waveform and delay-Doppler map) is thus representative of the reflecting surface conditions: its dielectric properties and roughness state. These are the primary GNSS-R observables, further discussed in Section 3.1.
Brief descriptions of the GNSS-R techniques and applications are given in Sections 5.1 to 5.12 of this document, while further details can be found in, e.g., Chapters 8 to 11 of the textbook [Jin et al., 2014].
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2 Constellations and Signals
2.1 Systems and Constellations
The E-GEM projects aims to extract geophysical parameters from signals of the Global Navigation Satellite Systems (GNSS) reflected off the Earth surface. The navigation systems represent a source of freely available signals covering the entire Globe. In this section we review the status of potential sources of signals for the purposes of E-GEM.
GNSS, together with regional navigation satellite systems, consist of constellations or series of artificial satellites providing highly precise, continuous, all-weather and near-real-time microwave L-band signals designed for navigation, that is, to solve the time-position coordinates of a device near the Earth surface, capable of receiving these signals.
The North American Global Positioning System (GPS) is one of the most widely used systems of this sort. It was designed in the 70ies, and implemented through different phases from 1973 to 1994, when it became fully operational. Since then, the maintenance of the system and its modernization has permitted to gradually upgrade the broadcast signals and the satellite platforms. Another operational constellation is the Russian GLONASS, of similar characteristics (but slightly different signal structure and discrimination technique, as it will be described below). These operational constellations currently contain 32 and 28 Medium Earth Orbiters (MEO, orbital altitudes ~20000 km), respectively.
Another two Global systems are being developed. The European Galileo, currently with 2 prototypes and 4 in-orbit- validation satellites orbiting and transmitting navigation signals, and the Chinese Beidou-2/Compass. The latter is the replacement of the Chinese regional navigation system (Beidou-1) of geostationary satellites. Both new global systems plan to achieve complete deployment of 30 and 27 MEO respectively each by 2020. In addition Beidou-2 will keep some geostationary and geosynchronous inclined orbit transmitters for regional augmentation. Currently, Beidou-2 has 5 MEO orbiting and transmitting, and several geostationary and geosynchronous ones.
Other augmentation and regional navigation systems are also available and transmitting signals similar to those transmitted by the GNSS. Generally, standard GNSS receivers are capable of tracking and using these signals, complementing GNSS constellations. These systems tend to orbit in geostationary and geosynchronous inclined orbits. The regional and augmentation systems currently actively transmitting signals are the WAAS, EGNOS, IRNSS, and QZSS.
System: #MEO: #GEO: #IGSO: Carrier bands: Multiple Access: Modulations:
GPS 24 (30) 0 0 L1, L2, L5 CDMA BPSK, BOC
GLONASS 24 (24) 0 0 L1, L2, L3, L5 FDMA and CDMA BPSK, BOC
GALILEO 30 (4 IOV) 0 0 E1, E5, E6 CDMA BPSK, BOC, MBOC, AltBOC
BEIDOU-2 27 (5) 5 (5) 3 (5) B1, B2, B3, L5 CDMA QPSK, BOC, MBOC
EGNOS 0 (6) 0
WAAS 0 (5) 0
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IRNSS 0 7 (2) 0 L5, S
QZSS 0 0 4 (1) L1, L2, L5 CDMA BPSK, BOC
Table 2.1a: Summary of GNSS constellations. Green numbers for current (April 2014) values, otherwise nominal.
2.2 Spatial Coverage
The constellations described above cover the entire Globe in an attempt to provide a sufficiently large number of simultaneously visible transmitters from any point on or near the Earth. At mid latitudes, the GPS system alone typically provides 9-13 signals from different transmitters. It is easily doubled when the GLONASS system is also considered.
For reflectometry applications the receiver is supposed to be above the Earth surface. Then, each one of the visible transmitters can potentially be captured both by a direct-looking antenna (line-of-sight signal) and by an antenna looking at the surface (reflected signal). The GPS+GLONASS constellations, as in March 2012, were large enough to guarantee 30 to 40 simultaneous reflection points from a receiver orbiting at 800 km altitude, as pictured in Figure 2.2a [Jin et al., 2014]. The corresponding reflection ground-tracks for a 24-hours scenario is given in Figure 2.2b.
Figure 2.2a: Number of simultaneously reflected GPS+GLONASS satellites as a function of the latitude-coordinate of their specular point on the Earth surface, as computed from a receiver orbiting at 800 km altitude and 72⁰ inclination (GPS and GLONASS constellation as in March 18, 2012). Statistic of 24 hours. Black for all reflected signals, red, green and blue after applying an elevation cut-off at 30⁰, 45⁰ and 60⁰ respectively. Figure from [Jin et al., 2014] with permission of the author.
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Figure 2.2b: Location of the GPS+GLONASS reflections' specular points assuming a reflectometry receiver orbiting at 800 km altitude and 72 deg. inclination (GPS and GLONASS constellations as in March 18, 2012). Figure extracted from [Jin et al., 2014] with permission of the author.
2.3 GNSS Signals
2.3.1 Definition
The definitions of the specific terms used in this document are listed below:
[RF] Carrier: The RF carrier (also referred to as, simply, carrier) is the unmodulated centre frequency of a given frequency band (see Frequency Band). A carrier component is denoted as “X carrier”, where X can be L1, L2, or L5, for GPS, or E1, E6, E5, E5a, or E5b, for Galileo.
Band: A band (also known as frequency band or frequency channel) is the transmission band covered by a navigation signal (see Navigation Signal) including all its components (see Channel). A band is denoted as “X band” (where X can be L1, L2, or L5, for GPS, or E1, E6, E5, E5a, or E5b, for Galileo).
Carrier Component: A carrier component is the in-phase or quadrature modulation of a navigation signal (see Navigation Signal).
[Navigation] Signal: A navigation signal (also referred to as, simply, signal) is a nominally modulated carrier (see RF Carrier). A navigation signal may have several signal components, or channels (see Channel). A navigation signal X is denoted as “X navigation signal” or “X signal” (where X can be L1, L2, or L5, for GPS, or E1, E6, E5, E5a, or E5b, for Galileo).
Channel: A channel (also referred to as navigation signal component or just signal component) is one of the spreading sequences modulated onto one common carrier (see RF Carrier). Each channel has its own spreading code and can carry its own data modulation. A channel carrying data modulation can be denoted as data channel. Channels that do not contain data modulation can be denoted as pilot channels. A channel is denoted as “X-Y channel”, “X-Y [navigation] signal component”, or “X-Y [signal] component”, where X can be L1, L2, or L5, for GPS, or E1, E6, E5, E5a, or E5b, for
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Galileo, and Y can be one of I, Q, A, B, C, C/A, P, Y, L, M, etc. The I, Q notation is usually applied if only two signal components are multiplexed onto one carrier. The A, B, and C notation is usually applied for Galileo signals with three signal components multiplexed onto one carrier. C/A is usually applied for the coarse acquisition component of GPS signals.
2.3.2 Signals Description
There are (or will be in the future) several GNSS systems (GPS, Galileo, Glonass, Beidou) transmitting signals in several frequency (GNSS bands). Here we will focus on the signals broadcasted by GPS and Galileo systems. The GPS and Galileo systems transmit (or will transmit in the future) navigation signals over several bands, as illustrated in the following figure. The modernized GPS shall transmit signals over the L1, L2 and L5 bands, while the Galileo system shall use the E1, E6, and E5 bands.
All GPS and Galileo signals are Code Division Multiple Access (CDMA) spread spectrum signals resulting from the modulation of an RF carrier with a Pseudo-Random Noise (PRN) sequence (different for each satellite), a data bit stream, and, in some cases, a sub-carrier and/or a secondary code.
The following subsections will describe the GPS and Galileo channels.
Figure 2.3a: Spectra of the GNSS, including GPS, Galileo, Glonass and intended Beidou-2/Compass (Figure extracted from Navipedia)
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2.3.2.1 GPS L1 C/A Channel
The GPS L1 signal Navstar GPS Space Segment / Navigation User Interfaces includes two components: the L1 C/A (for Coarse Acquisition) and the L1 P(Y) (for Precise positioning) each with 2/3 and 1/3 of the total transmitted power, respectively. The L1 P(Y) has a 7 day period and will not be supported by SARGO, hence it will not be further addressed in this discussion.
The L1 C/A component is BPSK(1) modulated by the C/A code and navigation data. The C/A code is a LFSR based code with a length of 1023 chips, yielding a chip rate of 1.023Mcps and a code period of 1ms. The reference bandwidth defined in Navstar GPS Space Segment / Navigation User Interfaces is 20*1.023MHz (to take the P(Y) component into account). The C/A code is modulated with the NAV navigation message at a rate of 50bps (with no encoding).
2.3.2.2 GPS L2 C (CM+CL) Channel
The signal transmitted in the GPS L2 band shall include Navstar GPS Space Segment / Navigation User Interfaces (according to the modernization plan for GPS, scheduled for 2013) three components: a Civil component, C, the current Precision component, P(Y), and a Military component, M. The P(Y) component, similar to the L1 P(Y) component, will not supported by SARGO. The M component is for military use only and, thus, also falls outside the scope of this analysis. The L2 C component is BPSK modulated with the result from the time multiplexing of two codes: the CM (M for “moderate” length) and CL (L for “long”). Each of these codes has a 0.5115Mcps rate and for each chip, the combined C code has the CM value in the first half of the chip and the CL value in the second half of the chip, leading to an overall chip rate of 1.023Mcps. The reference bandwidth, defined in Navstar GPS Space Segment / Navigation User Interfaces, is 20*1.023MHz (to take the P(Y) component into account). The CM and CL codes are LFSR based and have lengths of 10230 and 767250, respectively. Both are generated with a 27-stage LFSR which is reset after the last chip is generated and whose initial state depends on the PRN number of the code to be generated. The CM code is further modulated by a 50sps train resulting from the ½ rate convolutional encoding of a 25bps navigation data bit train containing the CNAV navigation message. The power of the CM and CL components is the same.
2.3.2.3 GPS L5 I and Q Channels
The GPS L5 signal Navstar GPS Space Segment / User Segment L5 Interfaces includes two components, I and Q, each with half the total transmitted power. Both are BPSK(10) modulated (yielding a chip rate of 10.23Mcps) and their spreading sequences consist of a combination of a primary and a secondary codes. The primary codes (LFSR based) have a length of 10230 chips (yielding a primary code period of 1ms) and the secondary codes have lengths of 10 and 20 chips, for the I and Q components, respectively. The reference bandwidth, defined in Navstar GPS Space Segment / User Segment L5 Interfaces, is approximately 24*1.023MHz. The Q channel is a pilot channel and the I channel is a data channel, being also modulated by a 100sps train resulting from the ½ rate convolutional encoding of a 50bps navigation data bit train containing the L5 CNAV navigation data.
The L5 signal uses a QPSK multiplexing scheme, where the I and Q components are in phase quadrature.
2.3.2.4 Galileo E1 B and C Channels
The Galileo E1 signal (Galileo ICD, Ávila-Rodríguez, et al. 2007) shall include three components, A, B, and C, among which are distributed 44%, 22%, and 22% of the total transmitted power, respectively The three components shall be combined using Interplex multiplexing scheme, in which the A component is modulated onto the signal's real part and the B and C components are modulated onto its imaginary part. The A channel is not an OS channel, thus being out of the scope of this analysis. Both B and C components shall be CBOC(6,1,1/11) modulated (yielding a chip rate of
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1.023Mchip/s and subcarrier rates of 1.023Mslot/s and 6.138Mslot/s with 10/11 of the power assigned to the lower frequency sub-carrier component). The demodulation of the B and C channels can be made assuming BOC(1,1) modulation at the expense of some power loss. The reference bandwidth, defined in Galileo ICD, is 24*1.023MHz. The B and C primary codes are both memory codes of length 4092, a chip rate of 1.023Mchip/s, and period of 4ms. The B channel is a data channel, being further modulated by a 250sps train resulting from the ½ rate convolutional encoding of a 125bps navigation data bit train containing the I/NAV navigation data. The C channel is a pilot channel, being further modulated by a 25-chip secondary code.
2.3.2.5 Galileo E5a and E5b Sub-Bands
The Galileo E5 signal (Galileo ICD) shall use AltBOC(15,10) modulation/multiplexing. The E5 band can be separated into two sub-bands, E5a and E5b, which can be processed independently. One possible approach [N.C. Shivaramaiah, A.G. Dempster, “An Analysis of Galileo E5 Signal Acquisition Strategies”, ENC GNSS 2008, Toulouse, France, April 2008] is called Single Side-Band (SSB) processing and results in two “equivalent” sub bands with BPSK(10) modulation. Each of the E5a and E5b sub-bands has two components, I (data) and Q (pilot), each of the components having 21% of the total transmitted power in the E5 band. Given that the Galileo E5a sub-band and the GPS L5 band share the same RF carrier, the E5a and E5b signals were deliberately designed as to maximize compatibility with the GPS L5 signals (in terms of modulation and signal structure, with a data channel in the I component and a pilot channel in the Q component, in quadrature phase). In fact, if SSB is used, the E5a or E5b sub-signals have a structure and modulation identical to those of the GPS L5 signal: 10230-chip length LFSR-based primary codes, BPSK(10) modulation, and secondary codes in both data (I) and pilot (Q) components. The differences are in the secondary code lengths: 100 for the pilot components (both E5a-Q and E5b-Q), 20 for the E5a-I component, and 4 for the E5b-I component. The reference bandwidth, defined in (Galileo ICD), is 20*1.023MHz.
The E5a (E5b) data channel, shall be further modulated by a 50sps (250sps) train resulting from the ½ rate convolutional encoding of a 25bps (125bps) navigation data bit train containing the F/NAV (I/NAV) navigation data.
2.3.2.6 Other Sources of Opportunity
The interferometric approach suggested in [Martin-Neira, et al., 2011], explained in Section 4, permits to obtain reflectometry signals independently of the knowledge of their modulations. Given that the technique cross-correlates the signals received along the line-of-sight against those along the reflection path, it is suitable to be applied to any other source of microwave signals.
A few studies have been conducted to investigate the potential of the reflectometry using other (non-GNSS) signals. In particular, to use the large amount of communications and digital broadcasting satellites. Reflectomety using these signals can potentially provide a wide range of applications, especially when using the interferometric technique (see Chaper 4) as it poses few requirements to the transmitted signals. First, the use of different frequency bands enables us to improve delay estimates from a space-borne platform due to the reduced effect of the ionosphere on the signals. While at L-band (GNSS) the ionospheric delay is roughly 200 cm, at X-band, it reduces to about 4 cm [Laxon and Roca, 2002]. In scatterometric applications, the combination of measurements at different bands provides various roughness metrics, thus improving wind estimation over the ocean and allowing us to separate its effects from other roughness parameters (e.g., wave age). Technological aspects such as antenna size and transmitted power need to be also considered. The power of GNSS signals is relatively weak, compared to broadcast TV or radio signals. The combination between the higher power of these transmissions and their higher frequency allows to improve the signal-to-noise ratio (SNR) with the same antenna size or to use smaller receiver antennas without reducing the SNR. This can be of great importance for space-borne instruments, where size and mass restrictions are important. Studies to evaluate the
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suitability of this concept at higher frequency bands have been carried out by different authors in very basic or preliminary phases:
• [Torres and Lawrence, 2008] prepared a simple setup at Ku-band (12 GHz) to measure the intensity of reflected direct broadcast satellite (DBS) signals.
• [Shah, Garrison, Grant, 2012] used broadcast S-band (2.3 GHz) radio signals from geostationary satellites to perform a set of experiments using the interferometric technique. The instrument was a double down-conversion chains and recorder system, with post-processing interferometric technique (software receiver).
• [Ribó et al., 2014] presented the first real-time DBS-reflectometry hardware receiver, using the interferometric technique. The group-delay altimetry precision of the Ocean reflected signals, obtained from a ~105 meter cliff experiment, was ~7 cm in 10 seconds integration using X-band ASTRA DBS signals.
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3 GNSS-R Observables and Modelling
3.1 Basic GNSS-R Observables
We define the observables as measurable objects (correlation counts, voltages, power...) from which geo-physical information can be derived. Different receiving instruments produce different types of observables, but most of the dedicated GNSS-R receivers are able to provide the returned pulse as a funcion of its delay and delay-Doppler parameters. These parameters were described in Section 1.1. These basic observables are called delay-waveform and delay-Doppler maps respectively. Sometimes the instruments yield power waveforms only, and some others their in- phase and quadrature (I/Q) components (thus providing phase information of the received field). We consider here the delay and delay-Doppler waveforms as the primary observables, from where the rest of them can be defined.
The power waveforms are modelled using the bi-static radar equation. The main reference for GNSS-R bi-static radar equation was given in [Zavorotny and Voronovich, 2000], publication in where the equation was comprehensively deduced. That derivation in [Zavorotny and Voronovich, 2000] considered Gaussian surface statistics and assumed the Kirchhoff geometrical optics scattering approach (KGO), which essentially is to assume the electromagnetic propagation as “rays” and each contribution to the scattering as locally specular. This and other scattering model approximations will be briefly discussed in Section 3.2.
Figure 3.1a: Generation of a couple of frequency-slices of a DDM. Contribution to each frequency-slice comes from a Doppler belt (red lines). Contributions to the delay-map (from white iso-delay zones) are indicated by the black arrows. Figure from [Cardellach et al., 2011].
The formulation of the [Zavorotny and Voronovich, 2000] presented below follows [Cardellach, 2002]:
[Eq 3.1a]
where PT , GT , and GR are the transmitted power, transmitter’s antenna gain, and receiver’s antenna gain respectively; λ is the electromagnetic wavelength; τi the integration time; r' any point on the surface where to integrate the functions;
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τ the delay at which the correlation function is being evaluated; and fc the central correlation frequency; R(a, b) the distance between points a and b; τ (r') is the delay of the ray-path from the transmitter to the surface point r' and from 0 there to the receiver; and fD(r') its Doppler frequency; σ is the bi-static scattering coefficient, defined as the fraction of incident power that can be scattered into certain direction and polarization state pq, normalized by the incident power density and area. Note that other sources of power attenuation or loss might also be introduced, such as atmospheric attenuation; cabling/instrumental loss; quantification (number of bit sampling) loss... All these factors would simply multiply the right-hand side of the Equation.
The bi-static scattering coefficient for KGO is [e.g. Ulaby et al., 1982]
[Eq 3.1b] where k is the electromagnetic wavenumber; Rpq the scattering coefficients; and PDF(Zx , Zy) is the 2-D Probability Density Function of the surface slopes Z (along the x-direction Zx, and y-direction Zy). Note that in GNSS-R system, the incident polarization is Right-Hand Circular, typically switching to Left-Hand Circular after reflection (except around and below the Brewster angle). The Fresnel scattering coefficients corresponding to circular polarization are a linear combination of the linear ones: RRL=1/2(Rparallel-Rperp) and RRR=1/2(Rparallel+Rperp). These coefficients are a function of the dielectric properties of the surface. Different types of surface (salty ocean, dry soil, wet soil,...) have different permettivities, and thus different values of the scattering parameters. The permittivity of different Earth surface materials can be found in e.g. [Ulaby et al., 1982, 1986]. [Blanch and Aguasca, 2004] and [Vall-llosera et al., 2005] present updated models for the sea water permittivity. Permittivity models of the sea-ice can be found in e.g. [Carsey, 1992; Winebrenner et al., 1989].
The modelling of the surface roughness is also embedded in σ0 through the sea surface slopes' PDF. This function can be extracted from spectral representations of the waves (ocean wave spectrum), such as e.g [Apel, 1994; Elfouhaily et al., 1997] among others. In particular, it is possible to obtain the statistics of the surface slopes (e.g. the mean squared slopes, MSS). The slopes' distribution can then be assumed as Gaussian, bivariate normal distributed, or introducing non-Gaussian features by means of Gram-Charlier distributions [Cox and Munk, 1954].
The expression of σ0 given above corresponds to the KGO model (see Section 3.2). However, it is possible to extend the bi-static radar equation to other electromagnetic scattering models by replacing σ0in Equation [Eq 3.1a] by its corresponding expression.
As explained before, the transmitted fields are modulated by a set of phase-shifts, and the receiving process requires the cross-correlation against template replicas of the code modulations, at different delays τ and frequencies f (matched filter technique). As it will be explained in Section 4, these templates can either be well-known replicas of the code modulations, or other branches of the signals. For simplicity we here assume that the modulation corresponds to a BKPS code (such as GPS’ C/A, L2C, or P), trains of chips, each chip being τc long. The autocorrelation function is then the triangle function, Λ(δτ), that appeared in Equation [Eq 3.1a].
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[Eq 3.1c]
and τi is the integration time.
i2πf t The template, r, of the signal is mounted on a carrier or intermediate frequency phasor: r(t, fc) = c(t)e c , where fc is the central correlation frequency, or frequency at which the scattered signal is assumed to reach the receiver. The cross-correlation of the signal against this template acts thus as a frequency filter and it is sensitive to residual components of the frequency. This is given by the sinc-exponential function S, as it appeared in Equation [Eq 3.1a].
[Eq 3.1d]
The product S² × Lambda² is also called the Woodward Ambiguity Function (WAF). Note that the physical meaning of the WAF is the impulse response of scattering from a single delay-Doppler cell on the surface (see Section 1.1, Figure 1.1b).
Noise and speckle aspects of the GNSS-R observables have been modelled in (e.g. [Cardellach et al., 2013; Park et al., 2012; IEEC-UPC, 2012; IEEC-UPC, 2013]); while modelling some of the terms in Equation [Eq 3.1a] require good knowledge of the instrument and the transmitted signal, respectively. Modelled instrument topology can be found in [e.g. Camps et al., 2010] and operations, including tracking/retracking in [e.g. Park et al., 2011; Park et al., 2013, Martín- Neira et al., 2014]. Recent papers present modeling analysis of both the slow time (waveform to waveform) and fast time (sample to sample) correlation properties of GNSS reflected signals, and their statistical properties [Martín et al., 2014a, 2014b].
3.2 Electromagnetic Scattering Models
The study and modelling of the interaction of electromagnetic waves with random rough surfaces (such as most of Earth's surfaces) is a broad topic, vast enough to fill entire books [e.g. Bass and Fuks, 1979; Beckmann and Spizzichino, 1987], or complete chapters [e.g. Ishimaru, 1978][Chap.21], or [e.g. Ulaby et al., 1982, Chap.12], or [e.g. Zhurbenko Ed. , 2011, Chap.10]. [Elfouhaily and Guérin, 2004] listed more than thirty different approaches and methods that have been reported to deal with electromagnetic scattering off rough surfaces. That reference performs an exhaustive review of several aspects of them all. This Section aims to qualitatively describe a set of different, most used, approaches and approximations, and help understanding their limitations. Table 3.2 summarizes them.
In the Kirchhoff or Tangent Plane Approximation (KA), the total fields (incident plus scattered) at any point on the surface are approximated by those that would be present on an infinitely extended tangent plane at the surface integration point. That is, each contribution to the scattering is considered to be locally specular and it depends only on the Fresnel reflection coefficients at the facet plane on each surface point. Note that this approximation is a local approximation: the supposed field at a point on the surface does not depend on the surface elsewhere. For this approximation to be valid, every point on the scattering surface should present a large radius of curvature (compared to the electromagnetic wavelength).
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The Kirchhoff Geometrical Optics (KGO) is a limit of the Kirchhoff approximation, and one of the most implemented approaches in GNSS-R studies. The physical meaning of the assumptions behind this limit is to constraint the reflection process to those areas in the surface from where the received phase is nearly constant. It also corresponds to those areas in where the surface is locally oriented such that its local normal corresponds to the bisector angle between the incidence direction and the direction pointing from that particular surface point towards the receiver.
The KA in physical optics approximation (KPO), unlike the Geometrical Optics solution, accounts for contributions of the scattered field over the entire rough surface, not only well-oriented facets. However, this analysis is limited to surfaces with small slopes.
The Small Perturbation Method (SPM) tries to find a solution to the partial differential boundary equation by expanding the field in a perturbation series of the slopes of the surface [Rice , 1951, 1963]. The SMP is a good model for small slopes statistics (both standard deviation of the sea surface height and correlation length below the electromagnetic wavelength), it is the most appropriate for Bragg scattering issues, and to assess polarimetric performances.
The Two-Scale Composite Model (2SCM) sums the contribution of the large scale roughness and the small scale effect to the scattered field. While the large scale contribution is modeled through the KGO, the small roughness contribution is the SPM solution averaged over the statistics of the tilt of the large scale sea surface characterization [Bass and Fuks , 1979; Valenzuela , 1978]. This method permits to account for scattering mechanisms such as diffraction and Bragg resonance, which are produced by small scales of the sea surface roughness and when the radius of curvature is smaller than the electromagnetic wavelength. Given that most of the Earth reflecting surfaces present a continuous roughness spectra, the main problem of the 2SCM is to define the limit between large and small scales in which to apply KGO and SPM respectively.
The Integral Equation Method (IEM) is a unifying theory suggested in late 1980’s to bridge the gap between KA and SPM, and thus it covers all roughness scales [Fung and Pan , 1986; Fung, Li, and Chen , 1992; Fung, 1994]. The integral equations of the electromagnetic fields are solved iteratively from the charges and electric currents on the sea surface. In the first iteration only the induced currents are used (Kirchhoff approximation). The second iteration in the small slope statistics leads to the SPM. The IEM is computationally expensive, but quite accurate, reason why it is specially useful to serve as reference to compare with the previous models.
The Small Slope Approximation (SSA) was also suggested in mid 1980’s to unify KA and SPM [Voronovich , 1985, 1994a,b]. The SSA is applicable irrespective of the wavelength of radiation, provided that the slopes of the roughness are small compared to the angles of incidence and scattering. [Zavorotny and Voronovich, 1999] reported significant differences between KGO and SSA models of normalized bi-static cross-section at GPS L1 frequency, especially at off- specular angles of the co-polar component of the scattering.
Method: Bibliography: Limitations:
KA [Ulaby et al., 1982; • surface correlation length larger than the electromagnetic wavelength, and
Beckmann and • surface mean radius of curvature larger than the electromagnetic wavelength Spizzichino, 1987;
Treuhaft et al., 2011]
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KGO [Ulaby et al., 1982] • large standard deviation of the surface height compared to the electromagnetic wavelength (high-frequency limit)
KPO [Ulaby et al., 1982] • small vertical -scale roughness and
• small slope statistics
SPM [Rice , 1951, 1963] • Standard deviation of the sea surface heigh smaller than electromagnetic wavelength, and
• surface correlation length smaller than electromagnetic wavelength
2SCM [Bass and Fuks , 1979; • Difficulty to define the limit between large and small scales
Valenzuela , 1978]
IEM [Fung and Pan , 1986; • Computationally expensive
Fung, Li, and Chen , 1992;
Fung, 1994]
SSA [Voronovich , 1985, • Slopes of the roughness small compared to the incidence and scattering angles 1994a,b]
Table 3.2: Summary of the limitations and applicability of different scattering models and some bibliographical sources.
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4 Receiver-level Data Acquisition
In this Section we compile information on different data acquisition techniques at the receiver level. Those have direct impact on the system design. cGNSS-R: CLEAN-REPLICA or CONVENTIONAL: The conventional approach, consisting of the cross-correlation of the received reflected signal against clean models (also called replicas) of the signal. These replicas must compensate for Doppler and delay effects, and they must reproduce the codes that modulate the GNSS signals. GNSS signals are modulated by multiple codes, at different time-resolution. Unfortunately, the civil available ones have coarse resolution, of the order of one microsecond, or equivalently, 300 meters range in its effective pulse. This approach is the one used in all the GNSS-R experiments until 2010, and most of the campaigns between 2010 and 2013. It is also the receiver signal-processing approach to be implemented at the NASA recently approved CYGNSS Mission.
This technique has been quite limiting in the past, but it could be promising in the future, when precise public codes will be widely available (Galileo E5, GPS L5). However, to be able to perform certain applications at high precision (e.g. altimetry), these public precise codes would be needed at two frequencies for ionospheric corrections. Until this does not happen, cGNSS-R will have limited performance for altimetric applications. iGNSS-R: INTERFEROMETRIC or PARIS: The second approach is called interferometric, because it does not require modelling/replicas of the codes, but the reflected signals are cross-correlated against the signals received through the line-of-sight radio-link (direct propagation from the transmitter to the receiver without any reflection off the Earth surface). This approach requires higher antenna gains to compensate for extra noise, but it has the advantage that all the bandwidth of the transmitted signals can be captured (i.e. all the codes are implicitly available, even the encrypted ones). The resulting waveform, equivalent to the pulse or echo in general radar altimeter context, has improved time- resolution by an order of magnitude (waveform width reduction in the time-domain). Consortium members have designed and manufactured the only existing HW GNSS-R interferometric receiver, and three experimental campaigns have been conducted, a ground-based and two air-borne experiments, in the frame of European Space Agency (ESA) contracts.
The term “interferometric” can be confusing, since some post-processing techniques applied to standard data obtained through the CLEAN-REPLICA approach can make use of interferometric fringes between direct and reflected signals. These latter techniques will be called multipath-reflectometry (GNSS-MR) or Interference Patter Technique (IPT) hereafter to distinguish to the PARIS or INTERFEROMETRIC receiver-level processing approach. The GNSS-MR/IPT techniques will be reported in Section 5, together with other retrieval algorithms.
PARTIAL INTERFEROMETRIC: A variation of the iGNSS-R was suggested in [Li et al., 2013]. The principle of this approach is to use the well-known codes to enhance the interferometric processing, in such a way that at last only the precise codes contribute to the waveform.
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Figure 4a: Normalized waveforms from measured data acquired over the Baltic Sea at ~3 km altitude using two receivers simultaneously. (Left) data from a cGNSS-R receiver working with C/A code (GOLD-RTR), and (right) data from a iGNSS-R receiver for a satellite transmitting C/A+P(Y)+M codes (PIR/A). Coherent integration time of 1 ms and incoherent integration time of Tin = 10 s have been used in these examples. Here, the zero delay is set ad hoc at the peak power, and the delay axis τ is given in units of length (in meters). From [Cardellach et al., 2013]. rGNSS-R: RECONSTRUCTED-CODE: we will call rGNSS-R the acquisition approaches that make use of semi-codeless techniques to deal with the encrypted codes [Woo, 1999]. These techniques were designed and are typically applied to navigation applications, that is, onto the line-of-sight signal, to partially solve for the encrypted codes. We will distinguish between (a) semi-codeless applied to the reflected signals, and (b) semi-codeless applied to direct signals to use the resulting information for modelling the reflected signals:
SEMI-CODELESS ON REFLECTED SIGNALS (rGNSS-Ra): In this approach the receiver applies the semi-codeless techniques directly to the reflected signals, although the reflection-channels act as slave of the line-of-sight, master, channels. This is the approach behind E-GEM's space-borne system–PYCARO receiver [Carreño-Luengo et al., 2013].
SEMI-CODELESS ENCRYPTED CODES FROM DIRECT SIGNALS (rGNSS-Rb): This approach consists of capturing direct signals with a high gain antenna to identify the code chip transitions and thus recover a significant part of it. This is done by means of semi-codeless techniques. Once the semi-codeless algorithm has been applied to the direct signals, a reconstructed-code replica is modelled and cross-correlated against the reflected signal. The signal processing chain applied to the line-of-sight signals is thus different from the one applied to the reflected signals. This is the main difference between this rGNSS-Rb technique and the rGNSS-Ra above. In rGNSS-Ra both line-of-sight and reflected signals go through the same processing chain–except for the master/slave parameters.
A possible disadvantage of the iGNSS-R technique is its higher noise figures, introduced by the fact that the cross- correlation is made between two sequences of signals rather than signal against clean-replicas. Coastal and airborne experimental work has been done to check the performance of this technique. A static and an air-borne GNSS-R interferometric receiver was successfully tested in one ground-based and two air-borne campaigns [Rius et al., 2011, Cardellach et al., 2013]. Some studies have evaluated the improvement in altimetric precision between the cGNSS-R technique using publicly available codes and the one obtained with the interferometric approach. Both experimental and theoretical studies agree that the improvement is at least a factor of 2 [D'Addio and Martín-Neira, 2013, Cardellach et al., 2013, Camps et al., 2014].
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A comparison was made between iGNSS-R and rGNSS-Rb in [Lowe et al, 2014]. The results shown that the SNR performances were better for the rGNSS-Rb than the iGNSS-R approach. Only SNRs were evaluated, while the altimetric performance was neither tested nor compared. None of these studies have analyzed the four techniques (cGNSS-R, iGNSS-R, rGNSS-Ra, rGNSS-Rb) using data from the same data recorder. Neither of them have tackled the synoptic capabilities of the techniques nor their sensitivity to the electronic beam-forming characteristics of the system.
Figure 4b: Comparison between the result of processing the same raw data set (10 seconds) with the iGNSS-R approach (black lines) and the rGNSS-Rb approach—only PRN25 (red line). The interferometric one is noisier and the waveform embeds all the transmitted codes (C/A+P(Y) in these cases). Waveforms obtained from re-processed raw data acquired in an old air-borne experiment (Monte Rey 2003). Figure from [Lowe et al., 2014].
The conclusions of this topic are:
• New data acquisition techniques at the receiver-level are being suggested, they have been tested during a few experimental field campaigns and also by means of simulated and theoretical analysis
• In general, the best SNR performance (which could turn into best altimetric performance) is the one given by rGNSS-R; there is agreement about the improvement factor between the altimetric performance of the iGNSS-R technique with respect to the one achieved by cGNSS-R (C/A code): it is at least a factor of 2
• The synoptic capabilities of these new techniques, and their dependences on beam-forming characteristics have not been explored
• A fair inter-comparison between the three techniques with data from the same front-end system does not exist yet
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E-GEM ground-based system will work using the conventional approach (cGNSS-R), while ³CAT-2 (E-GEM space-borne system) will embark a semi-codeless receiver (rGNSS-R), also working under the conventional approach. The air-borne E-GEM system will provide raw data (before any correlation) to permit applying any of the the above techniques to the same set of data. This will facilitate inter-technique comparisons and better understand the strengths and weaknesses of each of them.
E-GEM systems and data acquisition at receiver-level
E-GEM system: Acquisition Technique:
GROUND-BASED cGNSS-R
AIR-BORNE ALL possible (RAW DATA)
SPACE-BORNE rGNSS-Ra, cGNSS-R, iGNSS-R (but not full bandwidth)
Table 4a: Summary of the E-GEM systems regarding their data acquisition techniques.
4.1 Existing GNSS-R Receivers
The table below compiles a summary of some available information on all existing GNSS-R receivers to our knowledge. This information has been shared across the GNSS-R community by means of a mailing list (latest update April 2014). At the bottom in green, the systems developed or to be developed for the E-GEM project: space-borne, air-borne, and ground-based. First iterations on each of these systems are given in Sections 8, 7 and 6 respectively.
BB SAMPLING OUTPUT Number FREQUENCY RECEIVER GNSS ID: HW/SW: BANDWIDTH RATE RATE of RF BANDS: TECHNIQUE: CONSTELLATIONS: ports: (MHz): (MHz): (Hz):
GOLD-RTR HW 3 L1 8 20 1000 cGNSS-R GPS
(C/A)
PIR/A HW 3 L1 12 80 1000 iGNSS-R ANY at L1
GORS-1(2) HW 2(4) L1+L2 CGNSS-R GPS, Galileo
(C/A, L2C)
TR SW 2 L1+L2 RAW GPS
BJ SW 4 L1+L2 18 20 20MHz RAW GPS
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TriG HW 8 (16) Any 4 within 2 to 40 20/40 0.1- ANY: GPS, Glonass (extended) 1000 FDMA, L-band configurable SW configurable Galileo, other 1-2 GHz
OceanPal/SAM SW 2 L1 4 16.367 1000 RAW GPS
PAD HW
OpenGPS HW 2 L1 5.7 <100 cGNSS-R (C/A) GPS
COMNAV SW 1 L1 5.7 RAW GPS
NordNAV SW 1(4) L1 2 16.4 RAW GPS
R30(Quad)
GRAS HW 3 L1+L2 20 28.25 1000 cGNSS-R GPS
(C/A,
P-semi- codeless)
DMR HW L1+L2 GPS
POLITO-GNSS- SW 1 L1 8.1838 RAW GPS R
SPIR SW 16 L1 80 40 40MHz RAW ANY at L1
Ublox LEA-4T HW 1 L1 2 4 cGNSS-R (C/A) GPS
(gri)PAU HW (1)2 L1 2.2 (5.745) cGNSS-R (C/A) GPS 16.384
SMIGOL HW 1 L1 2.2 5.745 1 cGNSS-R (C/A) GPS
PYCARO¹ HW 2 L1+L2 20 20 cGNSS-R GPS
(C/A),
rGNSS-R
(P-semi-
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codeless)
iGNSS-R
(not full bandwidth)
SPIR-UAV² SW 8 L1 80 40 40MHz RAW ANY at L1
GRIP-SARGO³ HW 2 L1+L5,E1+E5 52 <=150 1 cGNSS-R GPS and Galileo
¹ ² ³ E-GEM systems for ¹space-, ²air-borne, and ³ground-based systems respectively
Table 4.1a: Summary of some available information on all the existing GNSS-R instruments (to our knowledge).
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5 Scientific Applications and Requirements
This section reviews the scientific and civilian applications of the GNSS reflectometry, as identified and referenced in the literature, together with the link to their users' requirements. In terms of user applications, the objectives of the E-GEM project are [E-GEM Team, 2012]:
• to advance towards high-precision altimetry with GNSS-R techniques;
• to consolidate sea wave height and surface wind speed determination with GNSS-R techniques;
• to advance the capabilities of vegetation and cryosphere monitoring with GNSS-R techniques.
Most of the applications and algorithms have been confronted to experimental work, as the GNSS-R community has conducted field campaigns since the 90s all over the world: more than 250 air-borne experiments have been conducted from ~100 metres altitude up to ~14 km; more than 20 ground-based campaigns, recording data for more than 19 months, from surface level to ~800 m altitude; and even a few stratospheric flights. In addition, there have been two examples to date of GNSS-R data acquired from space-borne altitude: (a) in 2000, for the first time from space, on- board the Space shuttle SIR-C, at a relatively low altitude of about 200 km [Lowe et al., 2002b]; and (b) in 2004, with an experimental GNSS-R payload on-board the United Kingdom Disaster Monitoring Constellation (UK-DMC) satellite, operating in a ~680 km sun-synchronous orbit, built and launched by Surrey Satellite Technology Ltd ([Gleason et al., 2005, Gleason, 2006]).
This extended field campaign work has permitted to test a variety of GNSS-R applications, such as:
• sea surface altimetry using group delay information [e.g. Martín-Neira et al., 2001, Lowe et al., 2002, Ruffini et al., 2004, Rius et al., 2010, Rius et al., 2011, Carreño-Luengo et al., 2012, Cardellach et al., 2013],
• sea surface altimetry using phase-delay information [e.g. Treuhaft et al., 2001, Martín-Neira et al., 2002, Cardellach et al., 2004, Helm et al., 2004, Semmling et al., 2012]
• sea surface scatterometry for wind or surface roughness information [e.g. Armatys 2001, Garrison et al., 2002, Germain et al., 2004, Komjathy et al., 2004, Gleason et al., 2005, Cardellach and Rius, 2008, Clarizia et al., 2009, Valencia et al., 2014, Clarizia et al., 2014],
• hurricane extreme events [e.g. Katzberg et al., 2001, 2006, Katzberg and Dunion, 2009],
• synergies with L-band radiometric missions [e.g. Valencia et al., 2011, 2011b],
• sea-ice altimetry and characterization [e.g. Komjathy et al., 2000, Belmonte et al., 2009, Gleason 2010, Semmling et al., 2011, Fabra et al., 2011],
• Antarctic dry snow sub-surface structures [Cardellach et al., 2012],
• soil moisture [e.g. Rodríguez-Alvarez et al., 2009, Alonso-Arroyo et al., 2013],
• vegetation [e.g. Rodríguez-Alvarez et al., 2012], or
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• a number of local (a few meters coverage) applications from geodetic GNSS stations, such as tide gauge [e.g. Larson et al., 2013], snow depth monitoring [e.g. Nievinski, 2013, Nievinski and Larson, 2014a, 2014b], soil moisture [e.g. Larson et al., 2010, Zavorotny et al., 2010] and vegetation [e.g. Small et al., 2010].
More detailed descriptions of these applications are given in Sections 5.1 to 5.12. Some of the applications might be restricted to some of the systems. The limitations might be originated by the technique itself, or by the particular characteristic of the receiving system (e.g. polarimetric capabilities might be required for certain applications). The table below indicates the capabilities of the planned E-GEM systems to address different applications. More detailed tables to summarize the applicability of different retrieval algorithms at different scenarios can be found at the end of each application Section: 5.1 to 5.12, and a final summary table is given in Section 5.13.
Application: GROUND-BASED AIR-BORNE SPACE-BORNE
Ocean altimetry APPLICABLE APPLICABLE APPLICABLE
Ocean roughness/wind APPLICABLE APPLICABLE APPLICABLE
Ocean permittivity NOT APPLICABLE NOT APPLICABLE UNCERTAIN
Soil moisture APPLICABLE APPLICABLE UNCERTAIN
Vegetation and bio-mass APPLICABLE NOT APPLICABLE NOT APPLICABLE
Snow APPLICABLE NOT APPLICABLE NOT APPLICABLE
Sea-ice APPLICABLE APPLICABLE APPLICABLE
Glaciers APPLICABLE APPLICABLE UNCERTAIN
Atmosphere NOT APPLICABLE NOT APPLICABLE APPLICABLE
Ship detection NOT APPLICABLE APPLICABLE UNCERTAIN
Buried objects APPLICABLE NOT APPLICABLE NOT APPLICABLE
Table 5a: Summary of the Capabilities of the planned E-GEM systems to address different applications. Green cells indicate that the appliaction can be implemented, red cells indicate that cannot, white cells for uncertain cases (TBC).
5.1 Ocean: Altimetry
A major challenge for physical oceanography today is to better map the complex mesoscale structure (10-100km or longer) of the ocean circulation in the open ocean and in the coastal regions. The need for observations has further amplified over the past decade. Mesoscale altimetry mission concepts, such as the wide swath ocean altimetry mission [Fu et al., 2009] have long been proposed (and have now been accepted) to address the shortcomings of nadir-pointing pulse-limited altimeters in terms of their narrow swaths and relatively large cross-track separation. The GAMBLE project [Cotton et al., 2004] reviewed various mission concepts to address this issue, putting forward recommendations for
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future missions. Ten years on, with operational ocean models now operating routinely on global 1/12th degree resolution grids [Bahurel, 2011], and the operating NASA/CNES Surface Water and Ocean Topography (OSTST) Jason-n series of nadir altimeters following TOPEX/POSEIDON, the need for finer, more frequent monitoring of ocean surface height field has become ever more pressing. There is also a growing evidence from scientific research quantifying the essential contribution by the oceanic mesoscale variability to global oceanic circulation and transports and atmosphere/ocean exchanges.
The potential of GNSS-R for ocean altimetry was identified about 20 years ago [Martin-Neira, 1993]. GNSS-R presents several unique features, which complement traditional nadir-pointing radar altimetry. Depending on the characteristics of the GNSS-R receiver and antenna, one GNSS-R receiver can potentially track up to 40 separate GNSS reflections (GPS+GLONASS constellatinos as in April 2012, e.g. [Jin et al., 2014]) to provide wide-swath sampling. Therefore the spatio-temporal resolution compared to nadir-looking satellites can be significantly improved. In contrast to traditional altimetry, obtaining sea surface height measurements with GNSS-R with sufficient precision poses serious technological challenges, but the averaging of abundant and overlapping observations could enable the reduction of errors in the sea surface height measurements. Moreover, being at L-band, the GNSS-R observations, as compared to Ku-band altimeter systems, will not be affected by heavy rains and will thus provide a unique data set of sea surface topography heights under all weather conditions. This is of interest in particular in the tropical areas.
Altimetry under extremes is still quite unknown, at best observed with large temporal lags, and not during the real action. Depending on size (typically from 60 km to 500 km) and intensity, translation speed, and ocean upper layer stratification, tropical extreme events leave impressing trenches ahead in their wakes with sea surface height anomalies that can often reach 0.5 m and more, thus requiring a sea surface height retrieval accuracy of at least 50 cm (goal: 20 cm). Decorrelation times of these phenomena are a few days. Given the ISS orbit (tropical coverage) and with the expected improved temporal sampling and mapping (4 days or less), it should thus be possible to assess – for the first time - in more details the time evolution of the storm-induced displacements that control the intensification of these extreme events.
5.1.1 GNSS-R Status on Altimetric Applications and Retrieval Algorithms
The altimetric applications are among the most challenging to the GNSS-R concept. In most ocean situations the reflection has little specular component, becoming essentially diffuse. As a consequence, the carrier phase of the reflected signals cannot be locked because it suffers random jumps. Therefore, unlike the precise carrier-phase delay navigation GNSS observables, the altimetric applications essentially rely on group-delay measurements. The transmitted signals were not optimized to provide highly precise group-delay measurements, as it is in monostatic dedicated radar altimeter missions. In addition to this constraint, the bi-static nature of the GNSS-R observations adds complexity to the geometric retrieval. Overall, GNSS-R altimetry presents lower single-shot performance that dedicated altimeters, and the challenge is on one hand to obtain the best single-shot performance within these constraints, and on the other hand to prove the impact of its highly dense time-space coverage into ocean observational and modeling systems.
Different receiver-level data acquisition architectures are nowadays being discussed to better address the single-shot performance of the GNSS-R group-delay altimetry. The conventional architecture (cGNSS-R), used since 90s, relies on publicly available GNSS modulation codes, such as the GPS C/A, of narrow bandwidth (large uncertainty in the time- domain). The encrypted codes typically have 10-times wider bandwidths (greater precisions in the time-domain). To overcome this limitation, [Martín-Neira et al., 2011] suggested the interferometric technique (iGNSS-R), for which the full transmitted bandwidth is used despite some of the codes are no accessible. This architecture has been tested from a ground-based and two airborne experiments, proving two-fold enhanced single-shot altimetric performances with
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respect to the conventional ones. This technique and these instruments are the background experience for the E-GEM airborne system (UAV-SPIR receiver). A third architecture has been recently suggested and experimentally tested in [Lowe et al., 2014], applying semi-codeless techniques to the line-of-sight data to facilitate the modeling of the reflected encrypted ones (rGNSS-R). The SNR values seem to indicate better remote sensing capabilities for this latter approach. A similar technique is applied directly onto the reflected signals in [Carreño-Luengo et al., 2014], here identified as rGNSS- R, too. This is the one to be implemented in the E-GEM space-borne system (³CAT-2 PYCARO receiver). The different data-acquisition architectures are detailed in Section 4.
The retrieval algorithms developed so far to infer altimetric information from GNSS-R observables are listed below. It also indicates the range of applicability within the E-GEM systems. An identifier is given to each retrieval algorithm for internal use in this document [RA-A#]:
• [RA-A1] Peak-Delay: the altimetric range is obtained from the delay of the peak. This only applies for reflections off smooth surfaces (little diffuse component), such as in [Martín-Neira et al., 2001] or [Rius et al., 2011]. This approach does not work in Ocean standard conditions, when the peak delay is driven by the surface roughness [Rius et al, 2002]. E-GEM applicability: surface dependent.
• [RA-A2] Model-Fitting delay: which consists on fitting a theoretical model to the data. The best-fit model implicitly indicates the delay-location where the specular point lies. Examples can be found in e.g. [Lowe et al., 2002]. E-GEM applicability: all systems.
• [RA-A3] Peak-Derivative delay: [Hajj and Zuffada, 2003] suggested and [Rius et al., 2010] shown that the maximum of the derivative of the waveform’s leading edge corresponds to the specular ray-path delay (except for filtering effects of the limited bandwidth). E-GEM applicability: all systems.
• [RA-A4] Power-ratio based delay: [Yu et al, 2014] presents a methodology to find the specular-delay within the delay-axis of the DM based on the relationship between the correlation-power at a given and peak correlation- power. E-GEM applicability: to be determined.
[Cardellach et al., 2013] also played with the approach taken in the monostatic Radar-Altimeters, which is to associate the delay of the echo at the mean sea surface level as the delay of the half-power point along the leading edge. However, this ad-hoc selection is in GNSS-R strongly dependent on the surface roughness, as it is the peak delay (see above).
Recently, two novel approaches have been suggested to increase the precision and/or the sampling density of the GNSS-R:
• [RA-A5] DDM multi-look: this new technique proposed in [D'Addio et al., 2014], is based on the acquisition of the full DDM as a way to perform multi-look altimetry beyond the typical pulse-limited region. The authors claim an altimetry performance improvement of at least 25% to 30% with respect to altimetry done in the central-frequency slice of the DDM (standard approach). E-GEM applicability: air-borne and space-borne systems.
• [RA-A6] Mosaic-altimetry: this unpublished approach attempts to perform altimetry at off-specular directions, taking advantage of the highly directive antennas. This represents higher density of independent observations/specular ground-tracks, or equivalently, larger number of observations within the user spatio- temporal resolution. Strong degradation of a single-shot performance is expected by severe decrease of the SNR. It
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needs to be check whether the increased number of observations compensates its lower single-shot performance. E-GEM applicability: to be determined.
In addition to the group-delay techniques discussed above it is still possible to apply carrier-phase delay measurements under certain conditions: when the signal reaches the receiver with long coherence time, that is, when the phase can be tracked and a few phase-jumps or shifts are experienced solely. However, scattering process off rough surfaces yields diffuse scattering, with loss of coherence and therefore difficulties or impossibility to connect the phases. For this reason, GNSS-R altimetry using phase-delay observations has been only reported from experimental fields over calm waters [Treuhaft et al., 2001, Martín-Neira et al., 2002, Helm et al., 2004], or in very slant observations. The first examples of grazing angle phase-delay altimetry using reflected GNSS signals was conducted with data from a GNSS Radio-Occultation space-borne mission [Cardellach et al., 2004].
• [RA-A7] More recently, improved altimetric techniques based on phase observations [Semmling et al., 2012] have been tested from an aircraft [Semmling et al., 2014] and a Zeppelin-Airship [Semmling et al., 2013]. The results, at geometries up to ~30 degrees elevation, show altimetric precisions comparable to nadir-looking group-delay GNSS- R over open sea waters. E-GEM applicability: potentially all systems.
• [RA-A8] A complementary technique for ground-based GNSS-R altimetry is the one called GNSS-MR, multi-path reflectometry, or Interference Pattern Technique (IPT). These techniques are sometimes called “interferometric” because they infer the geophysical information from the interferometric patterns observed in the measured SNR, resulting from superposition of direct line-of-sight signals and those reflected in near-by surfaces. We prefer to use the “multi-path reflectometry” or IPT terms to avoid confusion with the PARIS or interferometric receiver-level processing approach described in Section 4. This technique has been successfully applied to geodetic ground-based GNSS stations to infer water-level [e.g. Larson et al., 2013, Löfgren, 2014] and snow-depth measurements [e.g. Rodriguez-Alvarez et al., 2011; Nievinski, 2013]. The altimetric information is extracted from the frequency of the interference fringes. This frequency, in units of cycles per sin(elevation angle) is proportional to the receiver altitude above the reflecting surface (horizontal planar approximation). The same technique is also applied to monitor snow depth, see Section 5.7. E-GEM applicability: ground-based system.
The table below compiles results obtained in the different altimetric GNSS-R experiments found in the literature. The table was originally presented in [Jin et al., 2014], here extended to include the most recent bibliographic findings, and all altimetric precisions given in their single-measurement 1-second equivalent. When the original sources do not provide the 1-second precision the conversion has been approximated to σ1-second=σN-seconds sqrt(N).
Data Acquisition Receiver Dynamic/Static Equivalent Reference Surface type Architecture altitude (m) platform 1-second σH (m)
PHASE-DELAY ALTIMETRY
[Treuhaft et al., 2001] cGNSS-R 480 Static Lake 0.02
[Martín-Neira et al., 2002] cGNSS-R 8 Static Pond 0.003
[Cardellach et al., 2004] cGNSS-R 400000 LEO Ice 0.10
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[Helm et al., 2004] cGNSS-R ~1000 Static Lake 0.02
[Semmling et al, 2012] cGNSS-R 700 Static Ocean 0.5
[Semmling et al., 2013, 2014] cGNSS-R 3500 Airborne Ocean >= 0.8
(elevation dependent)
GROUP-DELAY ALTIMETRY
[Martín-Neira et al., 2001] cGNSS-R 20 Static Estuary 7
[Lowe et al., 2002] CGNSS-R 1500-3000 Airborne Ocean 0.07
using P(Y)
[Ruffini et al., 2004] cGNSS-R 1000 Airborne Ocean 1.5
[Rius et al., 2010] cGNSS-R 3000 Airborne Ocean 1.4
[Rius et al., 2011] iGNSS-R 18 Static Estuary 0.08
[Cardellach et al., 2013] iGNSS-R 3000 Airborne Ocean 0.58
cGNSS-R 1.21
[Lowe et al., 2014] rGNSS-R Airborne Ocean N/A better SNR
than iGNSS-R
[Carreño-Luengo et al., 2014] cGNSS-R 65 Static Pond 0.08
rGNSS-R 0.04
cGNSS-R 4.76 Ocean 0.45
rGNSS-R 0.20
[Yu et al., 2014] cGNSS-R ~330 Airborne Ocean ~1
Table 5.1a: Examples of GNSS-R altimetry experiments and performances.
One of the processing details with direct impact on the altimetric performance is the re-tracking. A recent special issue of IEEE-JSTARS on GNSS reflectometry presents two papers relevant to this matter. Both look at the re-tracking strategies as a way to address the effect of the delay (and Doppler) drifts suffered by the altimetric observables during the integration period. These papers analyze how to mitigate this effect and the final residual impact on the altimetric solution. In [Martín-Neira et al., 2014] it is found that, for space-borne scenarios, both the coherent time and the
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refreshing period are relevant parameters to control the re-tracking performance: shorter coherent times are better to reduce the negative effect of the drift, but it is possible to preserve precision in longer integrations times by refreshing the delay compensation at every correlation period. The analysis yields to similar conclusion and it is extended to the effects of the Doppler dynamic variations within integration in [Park et al., 2014].
5.1.2 GNSS-R Altimetric Missions
Currently, there are three GNSS-R altimetric space-borne missions in different stages of development:
• ESA's PARIS IOD, which concluded Phase-A studies
• GEROS-ISS, which will enter into Phase-A studies during 2014
• E-GEM space-borne system: ³CAT-2.
PARIS IOD is based on the interferometric concept, while ³CAT-2 will use semi-codeless techniques to measure delays using the P(Y) modulation. GEROS-ISS, the instrument and technique aboard the International Space Station has not been decided yet.
5.1.3 Other Related Techniques
GNSS-R topography was shown to be an attractive complement to high-precision radar altimetry systems such as Sentinel-class missions [Le Traon et al., 2002, Lee et al., 2013]. The very high density of measurements provided by GNSS-R was shown to balance the higher level of errors of this measurement system.
The current programmatic baseline (Jason-CS/Sentinel-6, Sentinel-3A/B) shows that minimal observation needed by Operational Oceanography (e.g. GODAE models, and the COPERNICUS Marine Service) can be met by radar altimetry missions [Le Traon et al., 2014]. However, [Escudier and Fellous, 2008] showed that a higher density of topography measurements remains beneficial to resolve smaller structures and/or to provide a faster and denser regional coverage for marine applications. Furthermore, [Dibarboure and Lambin, 2014] reported that the anticipated altimeter constellation remains very fragile in 2017-2019 and 2024+ because any altimeter launch delay, or on-board anomaly, would result in a direct degradation of the MyOcean sea-level products. To that extent, GNSS-R has the unique potential to allow the COPERNICUS Marine Service to develop better and higher resolution products, as well as to significantly increase its operational resilience as a complementary dataset of opportunity (like ESA’s ice mission Cryosat-2).
5.1.4 E-GEM Applicability
The table below lists the GNSS-R retrieval algorithms for altimetric applications, and identifies the scenarios from which these algorithms can be applied using green or red background color, white cells for uncertain cases (TBC). “E-GEM” in red characters indicates that despite the technique can in general be applied to that scenario, the E-GEM system particularities will hinder it.
Retrieval algorithm ID GROUND-BASED AIR-BORNE SPACE-BORNE
RA-A1: Peak delay APPLICABLE, Surface dependent (calm waters)
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RA-A2: Model fit delay APPLICABLE
RA-A3: Peak-derivative APPLICABLE
RA-A4: Power-ratio UNCERTAIN
RA-A5: DDM multi-look NOT APPLICABLE APPLICABLE
RA-A6: Mosaic UNCERTAIN
RA-A7: Phase-delay APPLICABLE APPLICABLE but not for -GEM¹
RA-A8: GNSS-MR/IPT APPLICABLE NOT APPLICABLE
¹ In principle, the E-GEM space-borne system will not down-link I/Q samples, only integrated products, which do not contain phase-information.
Table 5.1b: Summary of applicability of the GNSS-R altimetric retrieval algorithms.
5.2 Ocean: Surface Roughness, Wind and Tropical Storms/Cyclones
GNSS reflectometry, like scatterometers, measure surface roughness, not wind speed directly, and it is generally assumed that surface roughness is more closely correlated with the wind stress τ on the sea-surface rather than with a wind speed measured at some elevation above the ocean surface (typically at 10 m). For this reason, scatterometer wind retrievals are usually defined as the 10-m equivalent neutral wind, called U10EN, rather than the actual wind at 10 m. The relationship between U10EN and τ is driven by the air density and the neutral stability drag coefficient at a height of 10 m, which in turn is also a function of U10EN. Many ocean applications require τ, while the starting point for meteorological applications is often U10EN. The relationship of τ and U10EN (i.e. the drag coefficient) is currently focus of intense research activity (e.g. there is a dedicated working group within the Ocean Vector Wind Science Team—OVWST [IOVWST, 2014]). The contribution of L-band bi-static measurements into this topic is still unknown, but there are chances that it could complement the information retrieved by mono-static wind scatterometers: GNSS-R works at a different electromagnetic frequency than most of them, less affected by rain, relatively insensitive to Bragg scattering, and under different geometry.
The benefits of accurate knowledge of the ocean surface roughness impacts both for operational services and basic scientific research. The primary operational benefits of satellite sea roughness and wind observations are the improvements of weather forecasting and warnings. In addition, knowledge of the winds and waves over the ocean is also essential for the maritime transportation, fishing, and oil production industries, as well as for search and rescue efforts, and the accurate tracking and management of marine hazards such as oil spills [Bourassa et al., 2009]. This same reference lists the progress and impact of ocean winds measurements into operational services:
• Impact on Numerical Weather Prediction (NWP) Winds: dramatically improve the forecasts of tropical cyclones; larger impact in the storm track regions, where there is relatively large and rapid variability in the winds and in the southern hemisphere, where much fewer in-situ surface data are available.
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• Impact on Surge Forecasting: Knowledge of the current and past wind (or stress) fields is essential for surge forecasting. The winds used in surge models are forecast winds, which are greatly improved by observations from the recent past and the environment about the storm.
• Impact on Marine Nowcasting: Since QuikSCAT winds have been available in near real-time on analysts’ workstations, the number of short-term wind warnings issued by forecasters for the mid-latitude high seas waters have dramatically increased. In particular, hurricane force warnings were not issued for extra-tropical regions prior to QuikSCAT observations.
• Impact on Tropical Cyclone Forecasting: The use of a satellite-based active microwave scatterometer, with QuikSCAT-like sampling is considered (in some forecast offices) essential to the analysis and understanding of the near ocean surface wind field about tropical cyclones (TCs). Near real-time knowledge of both wind speed and direction offers the regional tropical cyclone forecaster the ability to more accurately anticipate TC genesis, see the development of the inner and outer core winds or structure, and determine a ‘minimum estimate’ for a TC’s maximum sustained winds. In fact, TC track forecasts have improved in accuracy by ~50% since 1990, largely as a result of improved mesoscale and synoptic modeling and data assimilation. In that same period, there has been essentially no improvement in the accuracy of intensity forecasts. The principal deficiency with current TC intensity forecasts may lay in inadequate observations and modeling of the storm inner core. The inadequacy in observations results from two causes: (a) Much of the inner core ocean surface is obscured from conventional remote sensing instruments by intense precipitation in the eye wall and inner rain bands. (b) The rapidly evolving (genesis and intensification) stages of the TC life cycle are poorly sampled in time by conventional observational systems.
• Ocean Model Forcing
• Currents
[Bourassa et al., 2009] also lists the scientific topics related to ocean surface roughness and winds. The list includes several topics that might be applicable to E-GEM systems and/or other GNSS-R missions. Among others:
• Air/Sea Surface Fluxes: great importance of winds on fluxes of energy, moisture, momentum, and gases.
• High Winds: play a disproportionately large role in Earth's climate. Mid and high latitude, high wind events (cold air outbreaks) lasting several days, can remove what at typical wind speeds would be a month’s worth of the ocean’s heat and moisture, leading to the formation of "deep water" that helps drive global ocean circulation patterns. High winds also help exchange disproportionately large amounts of carbon dioxide.
• Near Coastal Processes: Synoptic scale winds are very important for transporting riverine water from coastal shelves to the open ocean. These findings suggest a link between the transport of nutrients and the finfish and shellfish life cycles and population. The upwelling associated with coastal wind variability also appears to be a very important part of the coastal ecosystem.
• ENSO and Atlantic Niño and Decadal Variability.
The operational use of these type of data have severe requirements, currently not fully fulfilled [Chang and Jelenak, 2006]. Neither of the currently operational ocean surface vector wind sensors satisfies the new operational requirements. [Chang and Jelenak, 2006] lists the limitations of current ocean surface vector wind missions, and the requirements for future missions. Some of them are listed below:
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• The inability to resolve maximum winds in the inner core of most hurricanes. It is necessary to have the capability to accurately measure all sustained wind speeds encountered in tropical cyclones, from zero up to 165 kts.
• The inability to resolve maximum winds in extra-tropical storms. We do not know how strong the maximum winds that occur in winter ocean storms are. We only know that hurricane force conditions exist. Ocean waves respond to the square of the wind speed, therefore knowledge of the maximum wind speed (and direction) is needed for accurate wave predictions.
• Rain contamination and the resulting biases in retrieved wind speeds. It is desired to provide reliable wind speed and direction retrievals regardless of rain rate (no rain, light rain, or heavy rain).
• The long intervals between repeat passes of any single satellite—even the broad swath QuikSCAT—over any given region.
• The time lag between the satellite overpass and data receipt. Reduce time of data receipt to, at most, a few minutes following the time of data collection by the satellite.
• Data limited by their spatial resolution.
• The unavailability of near-shore data. Coastal regions that are the responsibilities of many weather forecast offices are the “area where most lives are lost”. With greater temporal/spatial resolution and more accurate wind speed and direction information, advisory or near-advisory conditions would be forecast with greater certainty and provide greater safety for boaters.
Similarly, [Bourassa et al., 2009] described the main challenges to satellite ocean wind measurement as:
• availability of data (preferably in near real time),
• inter-calibration of wind (vector and scalar) sensors,
• insufficient sampling of natural variability (e.g., diurnal and inertial cycles), particularly for vector winds,
• insufficient resolution and near coastal data for non-SAR instruments,
• rain contamination (all weather retrievals), and
• accuracy for high wind speeds (>20ms-1).
Climate studies also require very small calibration drift; otherwise the challenges are similar for science and operations. E-GEM system could potentially shed some light into the GNSS-R possibilities of improving the data sampling and rain contamination issues.
The operational requirements for satellite ocean surface vector wind measurements were defined in the Integrated Operational Requirements Document II [IORD II, 2001], later re-defined during the NOAA Operational Satellite Ocean Surface Vector Winds Requirements Workshop [Chang and Jelenak, 2006]. Two tables below summarize both IORD-II requirements, the updated ones, and how they compare with different scatterometric mission performances.
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Table 5.2a: IORD-II and newest requirements on ocean surface winds for operational services, and how they compare to Quicksat and Windsat mission performances. From [Chang and Jelenak, 2006].
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Table 5.2b: IORD-II and newest requirements on ocean surface winds for operational services, and how they compare to ASCAT and future mission performances. From [Chang and Jelenak, 2006].
5.2.1 GNSS-R Status on Ocean Scatterometric Applications and Retrieval Algorithms
The GNSS, L-band signals, have electromagnetic carrier wavelengths longer than the fine surface ripples generated by instantaneous winds. In principle, only surface features of typical length longer than the electromagnetic carrier wavelength can be sensed, meaning that L-band signals are not in an optimal frequency for wind monitoring. However, as the wind blows, it transfers energy to the ocean, increasing the waves’ height and length. One of the discussions
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among the GNSS-R community is the strength of the link between GNSS-R observations and wind speed. Some studies adjusted or calibrated the apparent ocean surface slopes at L-band, in the form of a modified relationship between the variance of the slopes and the wind [Katzberg et al., 2006, Eq.3], and valid for a wide range of wind speeds. Some others present L-band roughness parameters as a product by itself [e.g. Cardellach et al. , 2003; Germain et al. , 2004]. Based on [Elfouhaily et al. 1997] sea waves' spectrum, there are different relationships between the variance of the surface slopes (mean square slope, MSS) and the wind, which correspond to different stages of development of the sea. On the other hand, the drag coefficient is a relevant parameter to model momentum exchanges between the sea waves and the atmosphere. It can be a function of both the wind speed and the wave age [e.g. Nordeng , 1991; Makin et al., 1995]. This opens potential inversion schemes, closer to data assimilation approaches, in which independent wind information could be combined with GNSS-R observations of the L-band roughness to infer information about wave age or dragging- related parameters.
The L-band radiometric measurements of the surface salinity have a major systematic effect induced by the surface roughness, in particular, to the portion of the spectrum to which L-band signals are sensitive. [Marchan-Hernandez et al. 2008]; [Valencia et al. 2009]; and [Camps et al. 2011] suggested and experimentally checked the potential use of GNSS-R derived L-band roughness parameters to provide roughness corrections to L-band radiometric missions for improving their sea surface salinity measurements.
S. Katzberg has conducted intensive work on wind retrieval under hurricane-like conditions [Katzberg et al., 2001, 2006, Katzberg and Dunion 2009, Katzberg et al., 2013]. Despite empirical models and corrections to the data, the experience seems to indicate that GNSS-R can achieve ~4 m/s precision in wind retrievals under high-wind conditions, poorer that the operational precision-requirements given above [Chang and Jelenak, 2006].
In a lower range of wind speeds, [Clarizia et al., 2014] presents a wind retrieval that combines five different GNSS-R observables, it applies it to UK-DMC low earth orbiter GNSS-R data, and compares to collocated buoy information. It results in 1.65 m/s error in the range of winds from 2.4 to 10.7 m/s. This represents the upper bound of the formal wind-speed uncertainties found in [Cardellach et al., 2003], using stratospheric GNSS-R data (wind uncertainty reported from 0.1 to 2 m/s in the range 1 to 8 m/s), and similar to the findings in [Garrison et al., 2002], achieving precision of the order of 1 m/s from aircraf altitudes.
Anisotropy and wind-direction issues have been tackled in several studies. The general agreement was that GNSS-R was sensitive to anisotropies and wind direction with 180⁰ ambiguity [Armatys 2001; Cardellach 2002; Komjathy et al., 2004; Germain et al., 2004]. However, more recent data analysis strategies permitted to infer non-Gaussian features of the surface slopes statistics, including the sense (up- or down-wind) direction, and breaking the 180⁰ ambiguity [Cardellach and Rius, 2008].
Several algorithms have been implemented to extract ocean surface roughness and wind state from the GNSS-R observables. The list below is a summary of the algorithms and techniques found in the literature. The summary list has been extracted from [Cardellach et al., 2011] and [Jin et al., 2014], here complemented with more recent bibliographical findings and indicators of applicability in the E-GEM systems and retrieval algorithm identified [RA-S#]:
• [RA-S1] DM-fit: After re-normalizing and re-aligning the delay-waveform, the best fit against a theoretical model gives the best estimate for the geophysical and instrumental-correction parameters. Depending on the model used for the fit, the geophysical parameters can be 10-meter altitude wind speed, or sea surface slopes’ variance (mean square slopes–MSS). Some of the works done with this methodology are: [e.g. Katzberg et al., 2001; Garrison et al., 2002; Cardellach et al., 2003; Komjathy et al., 2004]. E-GEM applicability: air- and space-borne systems.
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• [RA-S2] Multiple-satellite DM-fit: extends the DM-fit inversion to several simultaneous satellite reflection observations, which resolves the anisotropy (wind direction or directional roughness). It was suggested in tested in air-borne campaigns in [Armatys 2001; Komjathy et al., 2004]. The technique could also be applied from space- borne altitudes, but the combined estimated would be representative to the total area that includes all satellite reflections. This area is much wider than any user requirement, and large variations are expected in sea surface roughness across its extension. E-GEM applicability: air-borne system.
• [RA-S3] DDM-fit: The fit is performed on a delay-Doppler map [Germain et al., 2004, Clarizia et al., 2009]. In this way, anisotropic information can be obtained from a single satellite observation. E-GEM applicability: air-borne and space-borne systems.
• [RA-S4] Trailing-edge: As suggested from theoretical models in [Zavorotny and Voronovich, 2000], [Garrison et al., 2002] implements in real data a technique in which the fit is performed on the slope of the trailing edge, given in dB. E-GEM applicability: air-borne and space-borne systems.
• [RA-S5] Delay and Doppler spread: [Elfouhaily et al., 2002] developed a stochastic theory that results in two algorithms to relate the sea roughness conditions with the Doppler spread and the delay spread of the reflected signals. The technique was applied to LEO-based GNSS-R observations taken from one of the UK-DMC satellites [Gleason, 2006], where 5 GNSS-R measured Doppler spreads correlated with the MSS records taken by nearby Buoys. E-GEM applicability: air-borne and space-borne systems.
• [RA-S6] Scatterometric-delay: For a given geometry, the delay between the range of the specular point and the range of the peak of the reflected delay-waveform is nearly linear with MSS [Rius et al., 2002]. This fact is applied to air-borne acquired data to retrieve MSS [Nogués-Correig et al., 2007; Rius et al., 2010]. Only high altitude ground- based experiment could respond to this technique (E-GEM system expected to be installed at low altitude). At space-borne altitudes it is expected to saturate. E-GEM applicability: ground-based (if high enough above the surface) and air-borne systems.
• [RA-S7] DDM Area/Volume: Simulation work in [Marchan-Hernandez et al., 2008] indicates that the volume under the normalized DDM or the area under the normalized waveform up to a predetermined threshold are due to the changes in the surface roughness, signals which in turn are also captured in the brightness temperature of the ocean L-band emission. The hypothesis has been experimentally confirmed in [Valencia et al., 2011]. This approach might be valuable for potential use of GNSS-R observations in support to Oceanic L-band radiometric missions, such as SMOS, as proposed in [Camps et al., 2006]. E-GEM applicability: air-borne and space-borne systems.
• [RA-S8] Discrete-PDF: When the bi-static radar equation for GNSS signals is re-organized in a series of terms, each depending on the surface’s slope Z , the system is linear with respect to the Probability Density Function (PDF) of the slopes. Discrete values of the PDF(Z’) are therefore obtained. This retrieval does not require an analytical model for the PDF (no particular statistics assumed). When the technique is applied on delay-Doppler-maps, is it possible to obtain the directional roughness, together with other non-Gaussian features of the PDF (such as up/down-wind separation [Cardellach and Rius, 2008]). E-GEM applicability: air-borne and space-borne systems.
• [RA-S9] NRCS inversion: [Valencia et al., 2011b, 2013, Schiavulli et al., 2014] present numerically efficient methods for inverting the delay-Doppler map (DDM) to produce a 2-D mapping of the normalized radar cross section (NRCS) over the glistening zone. E-GEM applicability: air-borne system and space-borne systems.
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• [RA-S10] Coherence-time: Finally, when the specular component of the scattering is significant (very low altitude observations, very slant geometries, or relatively calm waters), the coherence-time of the interferometric complex field depends on the sea state. It is then possible to develop the algorithms to retrieve significant wave height [Soulat et al., 2004; Valencia et al., 2010]. E-GEM applicability: ground-based system.
5.2.2 GNSS-R Scatterometric Missions
Currently, there are two GNSS-R scatterometric space-borne missions in different stages of development:
• UK Tech-Demo-Sat-1, ready for launch, which will test the new SSTL GNSS-R receiver, and
• NASA's CYGNSS, a constellation of 8 nano-satellites in equatorial orbits to monitor tropical cyclones.
In addition, the altimetric GNSS-R missions listed in Section 5.1 can also infer roughness information as secondary mission objectives.
5.2.3 Other Related Techniques
There are (and have been) other wind vector sensor technologies aboard space platforms. [Bourassa et al., 2009] lists:
• Microwave scatterometers, typically at Ku- and C-band of the electromagnetic spectrum, such as Seasat, ERS1 and ERS2, NSCAT, SeaWinds on QuikSCAT and ADEOS2, ASCAT-1, ASCAT-2, and at the L-band of the spectrum: Aquarius and SMAP. They provide accurate winds in rain-free conditions at in-swath grid spacing on scales of typically 25km (with special products at fine spacing, such as 2.5km). The main weaknesses of scatterometers are rain contamination for some rain conditions (far more so for Ku-band than C-band), a lack of data near land (15km for QuikSCAT; 30km for ASCAT), and temporal sampling.
• Passive polarimetric sensors: WindSat, launched in January 2003, is the sole instrument using passive polarimetric techniques for estimating ocean surface vector winds. In clear skies and winds in the range of 6m/s to 20m/s, its products are of comparable quality to scatterometry but there is significantly larger wind direction uncertainty in WindSat retrievals at typical wind speed. Furthermore, different versions of WindSat wind speeds can be biased either high or low in high wind speed conditions such as tropical or extra-tropical cyclones. WindSat wind vector retrievals are much more susceptible to error in cloudy and rainy conditions, which are often associated with extreme weather events.
• Synthetic Aperture Radar (SAR): C-band and L-band SAR systems have been used to retrieve surface winds on ERS1, ERS2, Envisat, RADARSAT1, ALOS, and RADARSAT2. Also X-band SAR algorithms are being developed to retrieve winds on COSMO-SkyMed and TerraSAR-X. SAR has the advantage of being able to generate images on a much finer spatial scale (as small as <10 m). The directional dependence of SAR-derived vector winds is much less certain than for scatterometers.
• Scalar Wind Sensors: surface wind speeds (at 10 m height, without directions) are routinely estimated from passive microwave radiometers (SSM/I, AMSR-E, TMI, SSMIS) on a spatial scale of roughly 25 km. These instruments are quite accurate (rms differences <1m/s relative to buoys) under typical ocean conditions, but do not retrieve winds in rain. Excellent agreement is found between passive radiometer winds and vector winds from scatterometers despite different measuring methods, with the exception of a few small regions of bias. Altimeters can also accurately estimate wind speed on a smaller spatial scale. However, the sampling from current altimeters is very sparse.
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5.2.4 E-GEM Applicability
The table below lists the GNSS-R retrieval algorithms for ocean scatterometric applications, and identifies the scenarios from which these algorithms can be applied using green or red background color.
Retrieval algorithm ID GROUND-BASED AIR-BORNE SPACE-BORNE
RA-S1: DM-fit NOT APPLICABLE APPLICABLE APPLICABLE
RA-S2: Multiple DM-fit NOT APPLICABLE APPLICABLE NOT APPLICABLE
RA-S3: DDM-fit NOT APPLICABLE APPLICABLE APPLICABLE
RA-S4: Trailing edge NOT APPLICABLE APPLICABLE APPLICABLE
RA-S5: Spreads NOT APPLICABLE APPLICABLE APPLICABLE
RA-S6: Scatt. delay NOT APPLICABLE APPLICABLE NOT APPLICABLE
RA-S7: DDM Area/Vol. NOT APPLICABLE APPLICABLE APPLICABLE
RA-S8: Discrete-PDF NOT APPLICABLE APPLICABLE APPLICABLE
RA-S9: NRCS NOT APPLICABLE APPLICABLE APPLICABLE
RA-S10: Coherence T APPLICABLE NOT APPLICABLE NOT APPLICABLE
Table 5.2c: Summary of the applicability of GNSS-R ocean scatterometry retrieval algorithms.
5.3 Ocean: Salinity
The ocean surface salinity relates to the concentration of dissolved salts in the upper layers of the sea water. These salts have been delivered into the oceans by the weathering of rocks throughout Earth's history. At short time scales, its variations mostly depend on the addition or removal of fresh water by different mechanisms: evaporation, precipitation of rain and snow, melting and freezing of the sea ice, or input of fresh water from rivers. The ocean plays a pivotal role in the global water cycle: about 85% of the evaporation and 77% of the precipitation occurs over the ocean [Rhein et al., 2013]. The horizontal salinity distribution of the upper ocean largely reflects this exchange of freshwater, with high surface salinity generally found in regions where evaporation exceeds precipitation, and low salinity found in regions of excess precipitation and runoff.
The salinity and temperature influence the density of seawater, variations of which have large effects on the water cycle and ocean circulation and stratification patterns, impacting ocean's capacity to store heat and carbon as well as to change biological productivity. The ocean circulation patterns moderate climate by bringing warm surface waters to higher latitudes and cool deeper waters back to equatorial regions. Because of its relevance to the climate, ocean salinity is addressed in the Assessment Reports (AR) of the Intergovernmental Panel on Climate Change (IPCC). The last, fifth report, AR5 [Rhein et al., 2013], states that it is very likely that regional trends have enhanced the mean geographical contrasts in sea surface salinity since the 1950s: saline surface waters in the mid-latitudes (evaporation-
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dominated) have become more saline, while relatively fresh surface waters in rainfall-dominated tropical and polar regions have become fresher. The mean contrast between high- and low-salinity regions increased by 0.13 psu from 1950 to 2008. Similar conclusions were reached in AR4 [Bindoff et al., 2007], in which surface and subsurface salinity changes consistent with a warmer climate were highlighted, based on linear trends for the period between 1955 and 1998. Recent studies based on expanded data sets and new analysis approaches provide high confidence in the assessment of trends in ocean salinity.
Ocean surface salinity, together with the temperature, are the main parameters driving its dielectric properties. Weather satellites have been available to determine sea surface temperature information since 1967, however, salinity has remained poorly observed. L-band is sensitive to changes in water surface permittivity, with relatively little contamination by other parameters such as surface roughness. For this reason two L-band radiometers aboard Low Earth Orbiters, after correcting for temperature and roughness contributions, are providing global measurements of the sea surface salinity: ESA's SMOS and NASA's Aquarius.
5.3.1 GNSS-R Status on Sea Surface Salinity Applications and Retrieval Algorithms
Little work has been done in the field of the ocean salinity applications using GNSS reflectometry. [Zavorotny and Voronovich, 1999] detected that [RA-OS1] the co-polar normalized bi-scattering cross-section at off-nadir directions presents sensitivity to permittivity changes in the water. The study was based on the small slope approximation (SSA) scattering model. This was never confirmed by experimental evidence, neither further developed. E-GEM applicability: ground-based and air-borne system if they were polarimetric (they are not) and space-borne system.
[Cardellach et al., 2006] suggested a technique based on [RA-OS2] the polarimetric phase-interferometry (POPI), or phase-shift between the circular co-polar and cross-polar components of the reflected signal. The complex Fresnel coefficients at L-band, in a circular base of polarization, present a phase-shift between polarimetric components rather independent of the incidence angle. This makes this observables less affected by geometry and surface roughness. The technique was tested with data from an experimental airborne field campaign. The resulting complex polarimetric interferometric field was largely coherent, despite each of the polarimetric field components were highly non-coherent. That is, the very frequent random phase jumps induced by the roughness were essentially the same on both polarimetric fields, so the polarimetric-interferometric one was essentially coherent. The resulting phase has a smooth and slowly changing evolution, essentially given by the phase wind-up effects (geometry) of the observations. Proper modelling of these geometric effects, plus instrumental ones (antenna phase patterns, etc) would be required to correct them and extract the dielectric properties of the surface water. E-GEM applicability: ground-based and air-borne system if they were polarimetric (they are not) and space-borne system.
5.3.2 GNSS-R Sea Surface Salinity Missions
ESA's GEROS-ISS is the only GNSS-R mission among those planned or under study that considers acquiring GNSS reflections at dual-polarization [ESA, 2013].
5.3.3 Other Related Techniques
The main providers of globally distributed data sets of sea surface salinity are the L-band radiometers aboard ESA's SMOS [Font et al., 2010] and NASA's Aquarius [Lagerloef, 2012] satellites. These instruments measure the emissivity of L-band radiation by the ocean surface, and after correcting several terms, such as water temperature, roughness, or ionospheric effects, estimates of the SSS are obtained.
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Besides these two dedicated space-borne instruments, some other techniques have permitted to measure SSS from other non-dedicated and existing data sets. For example, [Reul et al. 2009] demonstrated that Sea Surface Salinity (SSS) in the Amazon Plume area can be already retrieved from Space combining the vertically polarized C and X-bands brightness temperature (Tbs) data from the Advanced Microwave Scanning Radiometer -Earth Observing System (AMSR-E) satellite. Other algorithms have also been used to extract SSS from data acquired with the Moderate Resolution Imaging Spectroradiometer (MODIS) [e.g. Marghany and Hashim, 2011].
5.3.4 E-GEM Applicability
The table below lists the GNSS-R retrieval algorithms for sea surface salinity applications, and identifies the scenarios from which these algorithms can be applied using green or red background color. Red “E-GEM” characters indicate that despite it is possible to use the technique from this scenario, the E-GEM system has no capabilities to apply it. For these particular set of applications, note that neither of the algorithms have been proved with experimental data (white background = TBC).
Retrieval algorithm ID GROUND-BASED AIR-BORNE SPACE-BORNE
RA-OS1: co-polar off- UNCERTAIN but not applicable UNCERTAIN but not applicable UNCERTAIN specular for E-GEM¹ for E-GEM¹
RA-OS2: POPI UNCERTAIN but not applicable UNCERTAIN but not applicable UNCERTAIN but not applicable for E-GEM¹ for E-GEM¹ for E-GEM²
¹ E-GEM ground-based and air-borne systems have not polarimetric capabilities.
² E-GEM space-borne system will not provide phase-information, only non-coherently integrated values.
Table 5.3a: Summary of the applicability of GNSS-R SSS retrieval algorithms.
5.4 Land: Soil Moisture
Soil moisture is usually defined as the water present in the unsaturated part of the soil profile, i.e. between the soil surface and the ground water level. It can be expressed in different units. The most common definition is total volumetric soil moisture, expressed as the depth of a column of water contained in a given depth of soil (in mm or cm), or as the volumetric percent of water in a given soil depth (in percent or m³/m³). A fraction of soil consists of pores that can be filled with air or water. This fraction is called the “porosity”. If this fraction were completely filled with water, the soil would reach its maximum soil moisture content or saturation. Hence, soil moisture can also be expressed as fraction of saturation, between 0 and 1. A similar definition may refer to weight instead of volume, that is the gravimetric soil moisture is defined as percent of water mass for a given bulk soil mass. This is the parameter measured by gravimetric techniques, i.e. measuring soil sample weight before and after a drying period. Furthermore, using the so-called field capacity and permanent wilting point, a further soil moisture definition is sometimes encountered, usually termed soil moisture index (SMI). This is a measure of soil moisture content as ratio of the total storage available to plants (varying between 0 and 1). While above definitions express soil moisture in relative terms, i.e. as ratio of a given soil volume or water storage [m3/m3] or [mm/mm], soil moisture can also be defined in absolute terms (water depth [mm] or mass [kg]). Among all these units and definitions, [ESA EOP-SE, 2011] requires the soil moisture essential climate variable (ECV) to be expressed in a volumetric ratio unit.
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[ESA EOP-SE, 2011] reports how changes in soil moisture are an important component of the global climate change, and the reasons behind this statement. Most significantly, soil moisture is responsible for partitioning the outgoing convective fluxes from land surfaces between sensible and latent heat flux. Changes in the balance between the two types of fluxes have an immediate and strong effect on the resulting surface temperature. Furthermore, soil moisture in itself represents the main source of natural water resources for agriculture and vegetation growth in general. Apart from affecting the vertical fluxes of energy and water at the land-atmosphere boundary, the spatial distribution of soil moisture also influences the horizontal fluxes (runoff). Moreover, soil moisture is also one of the most important components of meteorological memory for the land climate system, soil moisture anomalies (together with presence of snow cover) being an important initial condition for seasonal forecasts. In terms of dynamics, soil moisture presents the same high spatio-temporal variability as the other main hydrological parameters over land (precipitation, evaporation, runoff). An adequate monitoring of this parameter for climate purposes is thus crucial.
ESA's SMOS mission contributes filling this gap by providing a global image of surface-soil moisture every three days. This information, along with numerical modelling techniques, results in a better estimation of the water content in soil down to a depth of 1-2 m, which is referred to as the ‘root zone’. Estimation of soil moisture in the root zone is important for improving short- and medium-term meteorological forecasting, hydrological modelling, monitoring photosynthesis and plant growth, and estimating the terrestrial carbon cycle. Timely estimates of soil moisture are also important for contributing to the forecasting of hazardous events such as floods, droughts and heat waves.
[Ochsner et al., 2013] reviews the state-of-the-art on large-scale soil moisture monitoring. It identifies the strengths and weaknesses of the current observational system. It reports that large-scale soil moisture monitoring has advanced in recent years, creating opportunities to transform scientific understanding of soil moisture and related processes. These advances are being driven by researchers from a broad range of disciplines, but this complicates collaboration and communication; and, for some applications, the science required to utilize large-scale soil moisture data is poorly developed.
5.4.1 GNSS-R Status on Soil Moisture Applications and Retrieval Algorithms
The possibility of using GNSS reflectometry for soil moisture monitoring was initially suggested in [Kavak et al., 1996] and [Kavak et al., 1998] looking at multipath behaviour of GNSS stations. [Zavorotny and Voronovich, 2000b] later suggested to use linear-polarized observations. A few years later, a simpler approach was being tested in several campaigns, for which only the circular cross-polar component of the reflected field was acquired [Masters et al., 2004; Katzberg et al., 2006b], often normalized by the direct co-polar one. Later on, several studies further developed each of these techniques, either by inspecting features of the linearly polarized reflected signals around interferometric patterns (notches in the V-pol interferometric pattern technique) [Rodriguez-Alvarez et al., 2009], amplitude of the interference pattern [Larson et al., 2010], or extending the cross-polar ratio technique to both circular ones [Egido et al., 2012]. Recently, the relative contribution of the coherent and incoherent scattering has been studied using the SAVERS Simulator [Pierdicca et al., 2014]. The latter includes the Advanced Integral Equation Model in the simulations of Soil Moisture and Roughness effects on the GNSS reflected signal at both Left and Right Circular polarization.
Several techniques to extract soil moisture information contents can be found in the literature. They are mostly sensitive to the 1–2 cm upper layer [Katzberg et al., 2005]. A summary of the GNSS-R retrieval algorithms for soil moisture are listed below (labeled [RA-M#]):
• [RA-M1] Normalized linearly polarized reflected field, and its ratios: [Zavorotny and Voronovich, 2000b] suggested a method that assumes that the received signal power is proportional to the product of two factors: a polarization
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sensitive factor dependent on the soil dielectric properties and a polarization insensitive factor that depends on the surface roughness. Therefore, the ratio of the two orthogonal polarizations excludes the roughness term and retains the dielectric effects. This approach was tested with the first-order small slope approximation model in [Zavorotny and Voronovich, 2000b] and later checked experimentally in [Zavorotny et al., 2003]. This latter reference note that real data did not support this hypothesis. Some of the assumptions might be too crude, and better modeling is required. E-GEM applicability: TBC.
• [RA-M2] Circular cross-polar field: this technique simply uses the LHCP SNR as the observable, from which the surface reflectivity is extracted. It can be normalized by the direct power level or even calibrated with observations over smooth water bodies. It was first used in [Masters et al. 2004], and later in other studies [e.g Katzberg et al., 2006b]. E-GEM applicability: ground-based and air-borne, space-borne TBC.
• [RA-M3] Circularly polarized Interferometric Pattern Technique: [Kavak et al., 1996, 1998] showed results on dielectric properties of soils from inspecting the power fluctuations of the interference of the direct and the reflected electric fields as the GNSS transmitter satellite moves. E-GEM applicability: ground-based.
• [RA-M4] Linearly polarized Interferometric Pattern Technique at 1 notch: the previous method was switched to receiving linear polarizations (V-pol in [ Rodriguez-Alvarez et al., 2009] and both H- and V-pol in [Rodriguez-Alvarez et al., 2011]). In this new basis of polarization the V-polarization can easily capture the null reflectivity at the Brewster’s angle (otherwise masked by the H-pol when using circularly polarized antennas). This null reflectivity results in a 'notch' or angle at which the interferometric amplitude oscillations are minimum (null). As the Brewster angle changes with soil moisture content, so it does the elevation angle at which the resulting notch appears. E- GEM applicability: ground-based if it were polarimetric (it is not).
• [RA-M5] Amplitude of the multipath interference (GNSS-MR): if the former technique looks at the location of the interferometric notches, this technique simply relates the amplitude of the oscillations to soil moisture variations. It has been used in several studies [e.g. Larson et al., 2010] and currently is operationally implemented and providing data at the PBO H2O project (http://xenon.colorado.edu/portal/). E-GEM applicability: ground-based.
• [RA-M6] Circular polarimetric Interferometric Complex Field measurements (pol-ICF): [Egido et al., 2014] separated the coherent-scattered part of the signal from the ICF (ratio between the direct and reflected waveform’s peak) by subtracting the variance of the ICF. Both co- and cross-polar ICF showed sensitivity to soil moisture changes. It was also observed that changes in the surface roughness caused strong variations on the signals. If the signal were completely coherent, this problem could be essentially solved using the ratio between cross-polar ICF and co-polar ICF, which is rather independent of the surface roughness. However, in the case of high surface roughness, the incoherent components predominates, so that a long coherent integration time should be used to isolate the coherent component. Furthermore, the circular co-polar component of the reflected signal is very low and its detection requires a very sensitive instrumentation. E-GEM applicability: ground-based and air- borne if they were polarimetric (they are not).
5.4.2 GNSS-R Soil-Moisture Missions
In principle any of the missions listed in Sections 5.1 and 5.2 will over-pass continental areas. However, the reflectivity of land reflections may be very low, and the performance of space-borne GNSS-R for soil moisture applications is uncertain. Among the planned/developing GNSS-R missions, those with higher-gain antennas have larger chances of providing soil moisture measurements: ESA PARIS-IOD and ESA GEROS-ISS.
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5.4.3 Other Related Techniques
As explained in [Ochsner et al., 2013], remote sensing approaches for soil moisture monitoring have been investigated since the 1970s, although the first dedicated soil moisture mission, ESA's SMOS, was not launched until 2009. However, soil moisture estimates are also being retrieved from satellite instruments not specifically designed for sensing soil moisture, most notably from microwave sensors operating at suboptimal frequencies. The Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) instrument was carried into orbit aboard the NASA Aqua satellite in 2002 and provided passive measurements at six dual-polarized frequencies until October 2011, when a problem with the rotation of the antenna ended the data stream. Soil moisture information is also being retrieved from active microwave sensors, specifically from ESA's Advanced Scatterometer (ASCAT), which was launched in 2006 aboard the MetOp-A meteorological satellite (and before that from ASCAT's predecessors, the European Remote Sensing (ERS) satellites) The ERS and ASCAT instruments are C-band radar scatterometers designed for measuring wind speed; however, soil moisture retrievals have also been developed. An operationally supported, remotely sensed soil moisture product derived from the ASCAT instrument is currently available. The recent launch of Sentinel-1 can provide, at the end of the commissioning phase, another source of soil moisture information as the short revisit time of the C-band radar, when both satellites (A and B) will be operating , offer the opportunity to retrieve frequent soil moisture maps at relatively high resolution. Finally in November 2014 it is planned the launch of the NASA mission SMAP (Soil Moisture Active Passive) which will carry on board an L-band radiometer and a radar jointly working to provide better soil moisture maps at different scales (36, 9 and 3 km).
The GNSS-R technique, collecting mainly signal scattered around the specular direction, could provide independent information with respect to monostatic radars and radiometers, and this synergy could be exploited through proper data integration approaches.
[ESA EOP-SE, 2011] also lists possible sources of global soil moisture data suitable as essential climate variables and their current performance. The primary sources would be:
• AMSR-E 0.050-0.148 m³/m³
• WindSat 4%
• TMI 2.5%
• SSM/I 5.49%
• SMMR N/A
• ASCAT-A 0.035–0.060 m³/m³
• ERS Scatterometer: 0.022–0.084 m³/m³
A list of secondary sources is also given, and it includes several SAR and Radar Altimeter missions.
Current products coming from dedicated soil moisture space missions are delivered (or are planned to be delivered) in a daily basis:
• The ESA’s SMOS mission, launched in November 2010, has a 3-day global coverage, with ascending and descending passes at 6am/6pm, respectively (Kerr et al. 2010, Font el at. 2010). It provides an L2 product, surface soil moisture
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at ~43 km spatial resolution (delivered in ISEA4h9 discrete global grid, 15km inter-cell distance). Target accuracy: 0.04 m3 m-3. Format: binary (dbl+hdr)..
• The NASA’s SMAP mission, to be launched in November 2014, will have a 3-day global coverage, with ascending and descending passes at 6 am/6 pm. (Entekhabi et al. 2010). It will provide a L2P surface soil moisture product at 40 km spatial resolution from radiometer measurements. Target accuracy: 0.04 m3 m-3 conditioned to Vegetation Water Content (VWC) <5 kg m-2. SMAP plans to provide a 10-km resolution soil moisture product (L2A/P) using an optimal algorithm combining the SMAP radar (3-km resolution) and radiometer (40-km resolution) observations. The desired accuracy of the 10-km soil moisture product is 0.04 m3 m-3. SMAP also plans to provide a 3-km product (L2A) from radar observations only with a relaxed target accuracy of 0.06 m3 m-3. Products will be provided in netCDF format and EASE grids of 3 km (L2A), 9 km (L2A/P) and 36 km (L2P).
5.4.4 E-GEM Applicability
The retrieval of soil moisture from GNSS reflectometers is an emerging field; E-GEM is a pioneer project in this direction, aiming at doing fundamental research for setting the bases of future space programs. Key aspects to be considered in soil moisture retrievals from GNSS-R:
• Possibility of higher spatial resolutions than microwave radiometers
• Less accuracy (to be confirmed) than microwave radiometers due to increased speckle noise (to be reduced by incoherent averaging, at the expense of poorer spatial resolution).
• Geometry of bistatic radar systems , speckle noise effects.
• From the soil bistatic scattering coefficient (depending on the incidence angle, θ), the surface dielectric constant can be –in principle- estimated and then the SM retrieved
• The footprint of the observations is the first Fresnel Zone that depends on the receiver height (that could be on a plane or a satellite), the GPS constellation and the frequency, as follows:
[Eq.2.4a]
Fr = The Fresnel Zone radius in metres
R1 = The distance from the specular reflection point on the surface to the receiver in meters (satellite or airborne)
R2 = The distance from the specular reflection point on the surface to the emitter in meters (GPS constellation)
λ= The wavelength of the transmitted signal in metres (e.g. l=0.19 m at L1: 1575.42 MHz)
R1 ,R2 depending on the incidence angle (θ). For a flat Earth (approximation valid only for low heights R(1,2,)=H(1,2)/cos(θ) ).
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Considering H1 height of the receiver (~630 km for our nanosatellite and 300 m for airplane), H2 height of the GPS satellites (~22000 km), a Fresnel Zone radius of 400 m and 8 m is obtained for the satellite and airborne configurations, respectively. Note these numbers are only indicative and should be confirmed with experimental data.
• Measurements should be integrated in time (amount of incoherent averaging to be confirmed) to obtain accurate soil moisture estimates. The spatial coverage of the observations after the time integration as well as the optimal method to combine all GNSS-R measurements into soil moisture maps should be studied to set up grids and spatial resolutions.
• The penetration depth of the signal and therefore the soil moisture sensing depth should be evaluated. Using radiometry at L-band, it is ~5 cm but could increase up to meters in very dry conditions. Since GNSS-R is an active system, the penetration depth may differ.
Figure 2.4a: Observations of soil moisture from the Light Airborne Reflectometerfor GNSS-R Observations (LARGO) Instrument (Alonso-Arroyo et al., 2013)
Figure 2.4a shows soil moisture retrievals from an airborne field experiment over Eastern Australia using the Light Airborne Reflectometer for GNSS-R Observations (LARGO) Instrument. By comparing with the overlapped aerial photography, LARGO observations seem to have a high sensitivity to the presence of water bodies (bottom left of the image), and to changes in land cover. Further experiments with ground measurements are needed to quantify the goodness of the estimates.
The table below lists the GNSS-R retrieval algorithms for soil moisture applications, and identifies the scenarios from which these algorithms can be applied using green or red background color. White background identifies uncertain cases (TBC). Because only the space-borne system will work at 2-polarizations, some of these techniques, with potential
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to be applied to ground-based and air-borne system, cannot be applied to the particular E-GEM systems (indicated with red “E-GEM” characters on green background).
SPACE-BORNE Retrieval algorithm ID GROUND-BASED AIR-BORNE
RA-M1: Lin-pol ratio UNCERTAIN
RA-M2: Circ. cross-pol APPLICABLE APPLICABLE UNCERTAIN depending on SNR levels?
RA-M3: Circ-pol IPT APPLICABLE NOT APPLICABLE NOT APPLICABLE
RA-M4: Lin-pol IPT APPLICABLE but not for E-GEM¹ NOT APPLICABLE NOT APPLICABLE
RA-M5: SNR GNSS-MR APPLICABLE NOT APPLICABLE NOT APPLICABLE
RA-M6: pol-ICF APPLICABLE APPLICABLE UNCERTAIN, to be verified
¹ The ground-based E-GEM system has not dual polarization capabilities, so this technique, despite generally applicable from ground-based scenarios, cannot be applied in the particular E-GEM ground-based system.
² The air-borne E-GEM system has not dual polarization capabilities, so this technique, despite generally applicable from air-borne scenarios, cannot be applied in the particular E-GEM air-borne system.
Table 5.4a: Summary of the applicability of GNSS-R soil moisture retrieval algorithms.
5.5 Land: Vegetation and Biomass
Measurements of vegetation state are required for climate and hydrologic modeling applications, validation of satellite estimates of land surface conditions, and testing of ecohydrological hypotheses. With increasing temperatures and amplified drought conditions expected in the long term, it is necessary to understand how water is used by vegetation before characterizing climatic and soil–water interactions at regional and global areas. The vegetation water content is usually given in kg/m².
Phenology, the study of the timing of biological events, integrates climate–biosphere relationships and is used to evaluate the effects of climate change. Understanding the timing, rate, and duration of vegetation growth is key in the study of global change and the carbon cycle. The timing of vegetation growth controls photosynthesis, carbon sequestration, and land–atmosphere water and energy exchange. Optical measurement of the normalized difference vegetation index (NDVI) are commonly used for these purposes.
In particular, information on forest biomass, its height and disturbance patterns is urgently needed to improve our understanding of the global carbon cycle and to reduce uncertainties in the calculations of carbon stocks and fluxes associated with the terrestrial biosphere [Biomass MAG, 2012]. The emission of carbon dioxide to the atmosphere by human activity has been recognised as the major driver in climate change. Terrestrial ecosystems play an important role, both in the release of carbon through land use and deforestation and in the sequestration of carbon through vegetation growth processes. There is strong evidence that the terrestrial biosphere has acted as a net carbon sink over the last 30 years, removing from the atmosphere approximately one third of the carbon dioxide emitted from the
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combustion of fossil fuel. Nevertheless, terrestrial ecosystems are the largest source of uncertainty in the global carbon budget. Uncertainties lie in the spatial distribution of carbon stocks and carbon exchange, and in the estimates of carbon emissions resulting from human activity and natural processes. A central parameter in the terrestrial carbon budget is forest biomass, which is a proxy for carbon. Despite its crucial role in the terrestrial carbon budget, forest biomass in most parts of the world is poorly quantified owing to the difficulties in taking measurements from the ground and the lack in consistency when aggregating measurements across scales.
Biomass of low vegetation, such as grass and agricultural crops, is measured through the vegetation (or plant) water content, and it is usually given in kg/m². Large scale values of forest biomass are usually described in metric gigatonnes of carbon (GtC). Small scale values are usually quoted in terms of metric tonnes per hectare (t ha⁻¹), where 1 ha=10⁴ m², though the carbon modelling community often works in gC m⁻².
5.5.1 GNSS-R Status on Vegetation Applications and Retrieval Algorithms
The potential of GNSS-R to monitor vegetation variables has been addressed by diverse publications e.g. [Masters et al., 2004; Pierdicca et al., 2007; Rodríguez-Álvarez et al., 2010]. It has been observed that the presence of vegetation attenuates and scatters the GNSS signal before it impinges on the ground and after it is reflected to the receiver [e.g. Katzberg et al, 2006b; Grant et al., 2007]. Vegetation leaves its imprint on the waveform, whose parameters can therefore be used for vegetation monitoring: the attenuation effect of vegetation modifies the GNSS-R waveform peak, while incoherent scattering, when present, may alter its width. Also theoretical studies [Ferrazzoli et al., 2010] predict that at L-band, where penetration into vegetation cover is high, coherent specular reflection from soil is not masked by vegetation and, since the magnitude of the reflected signal is dependent on the attenuation of the canopy, it is sensitive to the vegetation biomass. More recently, (Pierdicca et al., 2014), an end to end simulator has been developed and validated. It allows to show how the geophysical properties of the land surfaces affect the magnitude of the reflected navigation signals, and to interpret the experimental data.
It is well known that backscattering from vegetated soils is not correlated to a single variable, but it is influenced by complex interactions among soil scattering, vegetation attenuation and vegetation scattering. In natural environment soil and vegetation variables evolve simultaneously, producing effects that can add or subtract to each other. This gives rise to the so called “saturation limit” of monostatic radar. On its side, the bi-static scattering around the specular direction is essentially influenced by vegetation attenuation [Ferrazzoli et al., 2000], so that it decreases with increasing plant biomass. It thus provides a statistically independent piece of information able to improve the solution of the inverse retrieval problem with respect to considering existing monostatic radar only. These considerations suggest, for instance, the complementarity of the GNSS-R technique with the Biomass candidate Earth Explorer mission, foreseeing P-band radar data, as well as with Sentinel-1 dual pol measurements, thus improving the retrieval accuracy of many geophysical parameters.
The retrieval algorithms found in the literature are (labeled as [RA-V#]:
• [RA-V1]: H/V linear polarized, multiple-notch Interferometric Pattern Technique [Rodríguez-Álvarez et al., 2011] is a technique based on H/V linearly polarized GNSS-IPT (see Section 5.4, [RA-M4]), but where more than one notch is observed and from which the vegetation information is obtained. By simple models it can be proved that when a vegetation layer with a finite thickness is considered between the air and the soil layers, more than one notch appears and the number of them depends on the thickness of this layer. One of the notches observed is due to the Brewster’s angle and the rest of notches are due to the oscillations in the reflectivity caused by multiple reflections in the vegetation layer. If the vegetation layer thickness is increased up to 3 m, soil layer effects are negligible, and
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the equivalent model air + vegetation + soil model is transformed into air + vegetation model. E-GEM applicability: ground-based system if it were polarized (it is not).
• [RA-V2] GNSS-MR Normalized Microwave Vegetation Index (NMRI): this technique isolates the multipath signatures in data from the geodetic GNSS stations to generate a new observable, related to the RMS variation of the multi-path amplitude. A normalization to scale this RMS by its maximum value is performed to remove the first- order terrain effect. This new observable is called Normalized Microwave Reflection Index (NMRI) [Larson and Small, 2014]. [Small et al., 2014] shown a consistent relationship between this NMRI and vegetation water content (VWC) and a consistent linear relationship between NMRI and independent optically obtained normalized difference vegetation index (NDVI) cross several grassland sites. The amplitudes of the SNR GNSS-MR show a nearly linear relationship to the water content in grasses (0–0.5 kg/m²) and wheat crops (0–0.9 kg/m²), however the simple linear relationship breaks down in vegetation with heavy water content, such as alfalfa [Wei et al, 2014]. This technique is currently operationally implemented and providing data at the PBO H2O project (http://xenon.colorado.edu/portal/). E-GEM applicability: ground-based system.
• [RA-V3] Coherent polarimetry from Interferometric Complex Field measurements (pol-ICF): [Egido et al., 2012] separated the coherent-scattered part of the signal from the ICF (ratio between the direct and reflected waveform’s peak) by applying appropriate integration schemes. [Egido et al., 2014] did it by subtracting the variance of the ICF. This observable, at cross-polar polarization, showed sensitivity to above ground biomass in ground-based experiments (LEiMON campaign) and air-borne campaigns (GRASS campaign) [Guerriero et al., 2013]. In particular, the cross-polar coherent ICF experiences a monotonic decrease with increasing above ground biomass, which holds for up to more than 300 t/ha. The calculated sensitivity yields 1.5 dB/(100 t/ha ). The fact that the measured reflection coefficient does not saturate with biomass is a remarkable result, since conventional monostatic L-band radars saturate for biomass values above 150 t/ha. This study confirmed that the most significant information content of the GNSS-R signal is held by its coherent component and that efforts are needed for the identification of the coherent integration processing suitable to the extraction of the coherent component from higher platforms. E-GEM applicability: ground-based and air-borne systems, space-borne TBC.
5.5.2 GNSS-R VEGETATION Missions
In principle any of the missions listed in Sections 5.1 and 5.2 will over-pass continental areas. However, the reflectivity of land reflections are in general very low, and the performance of space-borne GNSS-R for vegetation applications is uncertain. Among the planned/developing GNSS-R missions, those with higher-gain antennas have larger chances of providing vegetation measurements: ESA PARIS-IOD and ESA GEROS-ISS.
5.5.3 Other Related Techniques
The normalized difference vegetation index (NDVI) is one of the most widely used vegetation remote sensing methods. It is calculated as the difference between the near-infrared (NIR) and red portion of visible (VIS) reflectance values normalized over the sum of the two. NDVI is a good indicator of the ability of plant matter to absorb photosynthetically active radiation, therefore NDVI is often used to estimate green biomass or phytomass. NDVI is also used to estimate other vegetation properties, including leaf area index evapotranspiration, and primary productivity.
However, NDVI has a variety of shortcomings, including: problems with background effects from soil, atmospheric effects, smoke and aerosol contamination, cloud cover, complex terrain, weather, and interruption of signals at high latitudes. Standard NDVI products are derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and other satellites.
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Other spectral vegetation indices such as soil-adjusted vegetation indices (SAVI) include soil-line parameters, but it is not as commonly used as NDVI. Compared with NDVI, SAVI considerably reduces influences from soil and surface roughness resulting in a lowered vegetation index signal. Although SAVI reduces soil effects, it still yields imprecise vegetation estimates, particularly when there is limited vegetation cover. The normalized difference water index (NDWI), another optical remote-sensing method, utilizes a water absorption band at 1.24 μm. However, NDWI is not a better predictor of vegetation water content than NDVI, especially at sites with soil background reflectance effects.
Unlike the optical methods listed above, microwave radar measurements are not hindered by cloud cover or time of day. In the microwave wavelengths, radar signals are sensitive to surface roughness and the water content of vegetation and surface soil [Ulaby et at, 1986]. Therefore, the primary challenge when using microwave data for vegetation studies is removing the effects of soil moisture and surface roughness. Vegetation mapping via Synthetic Aperture Radar (SAR), at L- and C bands, is similarly complicated by the effects of soil moisture and surface roughness. Although active microwave sensing can be used to estimate biophysical parameters, this type of data is not currenly used to monitor changes in vegetation status at high frequencies (i.e., daily). Space-bome SAR is used for one-time surveys or multi-temporal analyses with repeat times of months or longer.
P-band instruments are more suitable for direct detection of the biomass. Because of its wavelength, much longer than L- and C-band instruments:
• P-band backscatter has the highest sensitivity to biomass compared to all other frequencies that can be exploited from space.
• P-band displays high temporal coherence over repeat passes separated by several weeks, even in dense forest, allowing the use of PolInSAR to retrieve forest height and, forest vertical structure from space in tomographic mode.
• P-band is highly sensitive to disturbances and temporal change of biomass.
A polarimetric P-band SAR will be the payload of ESA Biomass mission [Biomass MAG, 2012].
5.5.4 E-EGM Applicablility
The table below lists the GNSS-R retrieval algorithms for vegetation and biomass applications, and identifies the scenarios from which these algorithms can be applied using green or red background color. “E-GEM” in red characters when the E-GEM system particularities hinders the applicability.
Retrieval algorithm ID GROUND-BASED AIR-BORNE SPACE-BORNE
RA-V1: Lin-pol IPT APPLICABLE but not for E-GEM¹ NOT APPLICABLE NOT APPLICABLE
RA-V2: GNSS-MR NMRI APPLICABLE NOT APPLICABLE NOT APPLICABLE
RA-V3: pol-ICF APPLICABLE APPLICABLE UNCERTAIN, to be verified
¹ The ground-based E-GEM system is has not dual-polarization capabilities.
Table 5.5a: Summary of applicability of the GNSS-R vegetation/biomass retrieval algorithms.
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5.6 Hydrology: Inland-water Bodies
Water-level monitoring of lakes, rivers and other inland water bodies is a particular application of the algorithms detailed in Section 5.1. Some of the techniques and experiments detailed in Section 5.1 refer to GNSS-R studies made over lakes and rivers.
5.7 Cryosphere: Snow
Climate scientists have known for quite some time that polar areas experienced an enhanced response to any change in climate as a consequence of a number of positive feedbacks (e.g. sea ice albedo) operating in the region. At the same time polar regions are also thought to be an important component of the climate system.
[Lemke et al., 2007] stated that, due to the extreme polar environmental conditions, the surface mass balance and its most important parameter, the snow accumulation, are poorly retrieved. Moreover, the European Commission, through the GMES Bureau, has identified a set of Essential Climate Variables (ECV) the provision of which needs to be secured at European and global scale. The snow cover is among them [Uppala et al., 2011; Stitt et al., 2011].
Besides its relevance for global climate studies, the snow is also an important component of the regional climate systems, as well as a critical storage component in the hydrologic cycle. Snow water equivalence (SWE), is the most important parameter for hydrological study because it represents the amount of water potentially available for runoff [Larson et al., 2009]. Management of water supply and flood control systems requires measurement of the amount of water stored in the snowpack and forecasting the rate of melt are thus essential. Typically, snow data such as SWE and snow depth are often available in considerable temporal detail from a single point (e.g. from snowpack telemetry networks), but the spatial resolution of snow property data is poor.
The use of space-based systems for tracking the Polar regions started approximately in the late 70’s. Since 1978, a wide base of knowledge about microwave and optical signatures has been acquired, initially focused on sea-ice applications. The different techniques employed are mainly based on radar backscattering or radiometric measurements, including combinations of multiple sensors. These techniques were later adapted to characterize the snow cover over large areas [Drinkwater et al.,
2001; Markus et al., 2006]. Despite these emerging techniques, snow cover as an ECV presents data gaps. For instance, snow cover data from many sources need to be blended to obtain globally applicable data [Fabra, 2013]. Standard methods are needed to validate and quantify the accuracy of satellite-based passive microwave retrieval algorithms. Snow-cloud discrimination needs to be improved while avoiding sensor saturation. Errors associated with not detecting snow cover under forest canopy need to be quantified and techniques developed to adjust for these errors [Stitt et al., 2011].
Continental snow is monitored for hydrological reasons in a limited number of sites by dedicated snowpack measurement networks. However, their resolution and coverage is not sufficient [Molotch and Bales, 2006]. Remote sensing instruments on airborne platforms are an alternative to ground-based measurements of snow properties. Optical sensors provide important information on snow-covered area, but cannot provide information about snow depth, density, or SWE. SWE can also be measured with passive microwave instruments [e.g. Chang et al., 1982], resulting in valuable estimates of the SWE spatial distribution on a coarse grid (25 km), when terrain is gentle and over high latitudes. However, the technique is prone to errors over mountain areas. SAR and Lidar represent new techniques for snow characterization, both promising to achieve fine spatial resolutions.
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As stated in [Scherer et al., 2005], unfortunately not all of the snow variables can be retrieved with sufficient accuracy at the spatial and temporal scales provided by current spaceborne systems. This holds true particularly for SWE, which is one of the most important snow variables in hydrology. Future research and technological developments with respec to remote sensing of snow cover from space should set emphasis on SWE determination at higher spatial resolutions without decreasing temporal resolution. Multiscale approaches by different sensors on the same satellite platform can be highly interesting, since down-scaling approaches for different snow properties may be tested on optimally suited data sets.
The E-GEM project migh have potential to contribute to this observational system.
5.7.1 GNSS-R Status on Snow Applications and Retrieval Algorithms
Low density snow, dry snow with little wet content, is rather transparent to L-band signals. The monitoring of large ice sheet extensions, such the Antarctic plateau, might benefit from the transparency of snow to L-band GNSS-R signals. This property could be employed to retrieve the internal layering of large ice sheets extensions, which is related to the accumulation rate [Eisen et al., 2008]. Theoretical models developed by [Wiehl et al., 2003], represented the first study on GNSS-R over thick –several meters– dry snow masses.
These studies, based on modelling work, suggest the potential of inferring snow surface roughness and firnpack parameters like accumulation rates from GNSS-R measurements. Other works employing GNSS signals for snow observation exploit the interference pattern experienced by the direct signal’s power along different elevation angles, that can be measured with geodetic GPS receivers located near the ground level (GNSS-MR). In [Larson et al. 2009], this pattern is modeled by the impact of a signal reflected off a snow cover, which is a function of the vertical distance between the receiver and the surface point of reflection. The thickness of the snow layer is then retrieved from the estimated height variations during snowy seasons (at the order of several centimeters). Similarly, [Jacobson, 2010] studies the impact produced by a signal reflected off a soil surface beneath a snow cover, which is a function of this layer’s thickness and the dielectric characteristics of the different mediums involved, to retrieve snow depth and snow water equivalent from this single and thin –several centimeters–snow layer. Similar results are also obtained in [Rodriguez-Alvarez et al., 2011] with a dedicated GNSS-R receiver that works with linear polarizations and exploiting the same type of approach.
Experimental GNSS-R work at Concordia Station (Dome-C, Antarctica), presented highly coherent reflections off its dry- snow, but strongly and systematically distorted. In order to explain these observational facts [Cardellach et al., 2012; Fabra 2013] developed a model of GNSS reflections off multiple layers of dry-snow, down to ~300 meters depth. The interferometric patterns resulting from the coherent sum of all these external and internal interfaces had to be captured using spectral analysis (radio-holographic approach). Because some of the reflections were produced down to ~300 deep layers of the snow, the delay-filtering associated to the C/A code modulation impeded to capture them. The radio-holographic approach was then extended to the entire waveform, to be able to capture the spectral signatures of the most deep reflections (lag-hologram). The frequency bands appearing in the lag-hologram could then be related to different depths of the reflecting layer.
The GNSS-R retrieval algorithms for snow applications are listed below (labelled [RA-Sn#]):
• [RA-Sn1] Frequency GNSS-MR, [Larson et al., 2009], using the same principle as in [RA-A8] for water altimetry using the multipath reflectometry or IPT. E-GEM applicability: ground-based.
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• [RA-Sn2] Frequency and amplitude GNSS-MR: [Jacobson, 2010] measures snow thickness variations (at the order of several centimeters) from the interference pattern measured with a geodetic GPS receiver on ground. Similarly to [RA-Sn1], this approach models the pattern by the contribution of a signal reflected off the snow surface level, but now taking into account both snow depth and snow water equivalence, which in turn can be retrieved from the interference pattern measured with a geodetic GPS receiver on ground. The approach models the pattern by the contribution of a signal reflected off a soil surface beneath a thin –several centimeters– and single snow layer. E- GEM applicability: ground-based.
• [RA-Sn3] linear-pol IPT: similarly to the soil moisture and vegetation applications, the IPT is here used to infer snow thickness retrieved from the interference pattern measured with a dedicated GNSS-R receiver located near the ground level [Rodriguez-Alvarez et al., 2011]. The approach models the pattern by the contribution of a signal reflected off the snow surface considering the internal properties of a thin –several centimeters– and single snow layer. E-GEM applicability: ground-based if it were polarimetric (it is not).
• [RA-Sn4] lag-holograms: GNSS-R reflections off deep sheets of dry-snow (e.g. Antarctica) produce a complex interference patters induced by the multiple reflections occurring at different layer-interfaces of the sheet, down to ~300 meters depth. Radio-holographic techniques are used on each lag of the delay waveform to identify the spectral content of the signal, and to identify each frequency-component to different snow depths. E-GEM applicability: ground-based.
5.7.2 GNSS-R Snow Missions:
In principle, some of the missions listed in Sections 5.1 and 5.2 will over-pass polar and continental snow areas, which could be used to investigate the potential use of space-based GNSS-R for snow retrievals. At the moment, however, the performance of this technique for snow characterization from the Space is still unclear. The planned/under study GNSS- R missions expected to be allocated in polar orbit are: UK-TDS1, E-GEM's ³CAT-2, and ESA PARIS-IOD.
5.7.3 Other Related Techniques:
The table below, with information partially extracted from [Fabra, 2013], summarizes the different remote sensing approaches to sense the snow properties.
SENSOR TYPE: SNOW PROPERTY SENSED: REFERENCES:
SCATTEROMETERS Snow accumulation [Drinkwater et al., 2001]
SYNTHETIC APERTURE Snow mapping [Koskinen et al., 1997;
RADAR Nagler and Rott, 2000]
MICROWAVE SWE [Chang et al., 1982]
RADIOMETERS Snow mapping [Amlien, 2008]
Snow depth and SWE [Amlien, 2008]
OPTICAL/NEAR-INFRARED Snow mapping [Hall et al., 2002]
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RADIOMETERS Snow grain size [Lyapustin et al., 2009]
Table 5.7a: Some of the remote sensing approaches used to sense snow properties. Partially extracted from [Fabra, 2013].
5.7.4 E-GEM Aplicabillity
The table below lists the GNSS-R retrieval algorithms for snow applications, and identifies the scenarios from which these algorithms can be applied using green or red background color. “E-GEM” in red characters indicates that despite the technique can in general be applied to this scenario, E-GEM particularities hinder it.
Retrieval algorithm ID GROUND-BASED AIR-BORNE SPACE-BORNE
RA-Sn1: GNSS-MR Freq APPLICABLE NOT APPLICABLE NOT APPLICABLE
RA-Sn2: GNSS-MR Freq/Ampl APPLICABLE NOT APPLICABLE NOT APPLICABLE
RA-Sn3: Lin-pol IPT APPLICABLE but no to E-GEM¹ NOT APPLICABLE NOT APPLICABLE
RA-Sn4: Lag-hologram APPLICABLE NOT APPLICABLE NOT APPLICABLE
¹ The ground-based E-GEM system has not dual-polarization capabilities.
Table 5.7b: Summary of applicability of GNSS-R snow-parameters retrieval algorithms.
5.8 Cryosphere: Sea Ice
The fourth assessment report (4AR) by the Intergovernmental Panel on Climate Change (IPCC) put climate change on the international agenda as one of the most important issue the world is currently facing [Lemke et al., 2007]. It states that the heat capacity of the cryosphere is the second largest component of the climate system (after the ocean). The latest assessment report, 5AR [Vaughan, 2013], confirms the trends reported in the 4AR, with annual Arctic sea ice extent decreased over the period 1979-2012. Given the societal importance of global warming an unprecedented effort has been put in trying to understand the processes responsible for the observed changes. Similar effort has been put in building new data-sets needed for assessing the skills of the models to reproduce current climate.
Climate scientists have known for quite some time that polar areas experienced an enhanced response to any change in climate as a consequence of a number of positive feedbacks (e.g. sea ice albedo) operating in the region. Sea ice is a part of the cryosphere that interacts continuously with the underlying oceans and the overlaying atmosphere. The growth and decay of sea ice occur on a seasonal cycle at the surface of the ocean at high latitudes. As much as 30 million km² of the Earth’s surface can be covered by sea ice.
Sea ice is a sensitive climate indicator, and plays an important role in exploration and exploitation of marine resources. Sea ice has many roles in the global climate system:
• Sea ice acts as an effective insulator between the ocean and the atmosphere, restricting exchange of heat, mass, momentum and chemical constituents (such as water vapour and CO2). In winter time, with large temperature
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differences between the cold atmosphere and the relatively warm ocean surface, ocean-to-atmosphere heat transfer is essentially limited to areas of open water and thin ice within the pack. The winter flux of oceanic heat to the atmosphere from open water can be two orders of magnitude larger than the heat flux through an adjacent thick ice cover [Sandven and Johannessen, 2006]. As a result, the distribution of open water and thin ice is particularly important to the regional heat balance. The transfer of momentum between atmosphere and ice is substantially larger over a rough surface compared to a smooth surface. The properties of the sea-ice surface therefore influence the dynamical and thermal structure of the atmospheric boundary layer. To understand the transfer of momentum, an in-depth understanding of the sea ice surface roughness is required.
• Sea ice affects the surface albedo: Ice-free ocean generally has an albedo below 10–15%, whereas snow-covered sea ice albedos average about 80%.
• Sea ice affects oceanic circulation directly by the rejection of salt to the underlying ocean during ice growth, which makes in turn increase the density of the water layers directly under the ice. This induces convection processes that tends to deepen the mixed layer. This convection contributes to driving the thermohaline circulation of the ocean.
• Sea ice is also a major component of polar ecosystems: plants and animals at all trophic levels find a habitat in, or are associated with, sea ice.
General circulation models predict enhanced climatic warming in polar areas, which could reduce the sea ice area as well as the mean sea ice thickness [Johannessen et al. 2004]. Only a satellite-borne method can achieve the required coverage to monitor this change in time and space without prohibitive costs. One of the key objectives in sea ice science is to achieve the capability of synoptically measuring sea ice thickness in both hemispheres. Data on ice thickness are very sparse, especially in the Antarctic. Present estimates of sea ice volume, mainly based on model results due to lack of data, can have errors of 50%.
Sea ice research and monitoring is also important for many countries at high latitudes, and to those who operate in Antarctica. Sea ice imposes severe restrictions on ship traffic in the Arctic, where it represents a major limitation for ships and offshore operations. The sea ice, which is on average 2–3 m thick, can only be penetrated by ice-strengthened vessels or icebreakers with a sufficient ice class. When the ice concentration is 100% the ice pressure can be high enough to hinder the operations of most powerful icebreakers. Similarly, offshore platforms for ice-covered areas must have much stronger construction than is required in ice-free waters, and harbors and loading terminals on the coast require stronger construction in areas of sea ice. In such areas, it is therefore of primary importance to monitor the sea ice daily and produce ice forecasts to assist ship traffic, fisheries and other marine operations.
5.8.1 GNSS-R Status on Sea-Ice Applications and Retrieval Algorithms:
[Komjathy et al., 2000a] first showed correlation between the peak power of GPS returns and RADARSAT backscattered measurements over this type of surfaces. More recently, similar results have been achieved from space [Gleason, 2010]. In [Belmonte et al., 2009], permittivity and roughness retrievals are obtained from the analysis of the shape of GPS waveforms reflected off different types of sea ice. These measurements were compared against polarimetric microwave emissions, RADARSAT backscatter, MODIS imagery and a LIDAR profiler. The results obtained concluded that GPS-R retrievals (and thus GNSS-R) are helpful in the interpretation of signatures observed by the more traditional sensors, in particular, for the detection of surface glaze effects in microwave emission and the breaking of the salinity/roughness ambiguity in radar backscatter. In addition, the large GPS wavelength avoids volume effects from snow and ice internal inhomogeneities. This property is also related to ice thickness retrieval, which is one of the most important features in the determination of sea ice development stage. This parameter can be estimated from the measurement of the normal
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distance between the floating line and the ice surface (freeboard level) with accurate laser altimetry [Zwally et al., 2008]. However, the snow loading plays a key role in this estimation and the accuracy of its determination with other instruments affects the final result. The use of L-band GNSS-R signals for precise altimetry, with snow penetration depths ranging from ~1 meter to more than 100 meters (as shown in Section 5.7), would overcome this limitation, providing additional means and knowledge towards a better sea ice classification.
GNSS-R experiments on Disko Bay, Greenland, acquired polarimetric GNSS-R data for 8 months from 700 m altitude [Fabra, 2013]. During this period, the formation, evolution, and melting of sea ice could be monitored [Fabra et al., 2011]. The sea ice parameters inferred were: ice altimetry (linked to thickness) [Semmling et al., 2011; Fabra et al., 2011], ice surface roughness, and variations in its permittivity [Fabra et al., 2011].
The GNSS-R retrieval algorithms found in the literature for sea ice characterization are listed below (labelled [RA-I#]). This list is partially extracted from [Cardellach et al., 2011]:
• [RA-I1] Phase-delay altimetry: certain sea ice surfaces are smooth enough to permit phase-delay observations [Semmling et al., 2011; Fabra et al., 2011]. [Semmling et al., 2011; Fabra et al. 2011] monitored the tidal signatures of floating sea ice in Greenland. [Fabra et al., 2011; Fabra, 2013] also measured the sea ice altimetry for several months in Greenland to find how its freeboard level anti-correlated with the temperature (growing and melting processes). The technique is essentially the same as [RA-A7]. E-GEM applicability: ground-based and air-borne systems, space-borne system TBC.
• [RA-I2] Permittivity by peak-power: this method obtains the effective dielectric constant empirically, as a function of the peak power [e.g. Komjathy et al., 2000]. The empirical model was generated after comparing the peak power of GPS reflections received by airborne instruments with RADARSAT backscattered peak power. It was also applied to space-borne UK-DMC data and compared to ice concentration measurements obtained with AMSR-E and ice charts [Gleason, 2010]. However, this observable can be strongly affected by the sea ice surface roughness. E-GEM applicability: ground-based, air-borne and space-borne systems.
• [RA-I3] Permittivity by polarimetric ratio: the ratio between the amplitudes of both circular (cross- and co-) polarizations relates to variations in the permittivity of the sea ice (temperature and brine), especially at relatively low elevation angles of observation, around the Brewster angle [Cardellach et al., 2011; Fabra, 2013]. This method is in principle less affected by the sea ice surface roughness, although some remaining effect has been reported. E- GEM applicability: ground-based and air-borne systems if they were polarimetric (they are not), and space-borne system (it is polarimetric).
• [RA-I4] Permittivity by linear-polarimetric phase-shift (l-POPI): for vertical and the horizontal polarizations, [Zavorotny and Zuffada, 2002] suggested inferring the first-year thickness from the phase difference between the vertical and the horizontal polarized components. E-GEM applicability: ground-based and air-borne systems if they were polarimetric (they are not), and space-borne system (it is polarimetric).
• [RA-I5] Permittivity by circular polarimetric phase-shift (c-POPI): the technique is the same as in [RA-OS1], it uses the phase difference between the co-polar and cross-polar circular polarized components. It was applied in Greenland GNSS-R data sets, and the signatures correlated with those found in [RA-I3] [Fabra, 2013]. E-GEM applicability: ground-based and air-borne systems if they were polarimetric (they are not), and space-borne system (it is polarimetric).
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• [RA-I6] Sea ice roughness and permittivity by DM-fit: [Belmonte, 2007; Belmonte et al., 2009] obtained the sea ice roughness by fitting the waveform shape. The method showed potential for characterization of the different stages of sea ice, after comparison with other remote sensing techniques. E-GEM applicability: ground-based, air-borne and space-borne systems.
• [RA-I7] Sea ice roughness by scatterometric fit: [Fabra, 2013] applies the algorithm [RA-S6] to infer the mean squared slopes (MSS) of the sea ice surface.Although this techniques was applied from a ground-based experiment, the conditions do apply for the E-GEM ground-based case (very low altitude). At space-borne altitudes, and for roughness scales typical of the open ocean, this delay tends to quickly saturate. However, it could work around gentle roughness scales in your sea ice (TBC). E-GEM applicability: ground-based (if receiver high enough over the surface), air-borne systems, space-borne system TBC.
• [RA-I8] Sea ice roughness by coherence time/phase dispersion: similarly to [RA-S10] [Semmling et al., 2011] finds correlation between the coherence time of the reflected signals and the wind over the zone. Similarly, [Fabra, 2013] looks at the RMS dispersion of the interferometric phase to link it to RMS of the surface heights. Both tend to saturate, essentially because the phase-related methods are constraint by the electromagnetic wavelength (~19 cm for GPS L1). E-GEM applicability: ground-based.
5.8.2 GNSS-R Sea-Ice Missions
In principle, some of the missions listed in Sections 5.1 and 5.2 will over-pass polar areas, which could be used to investigate the potential use of space-based GNSS-R for sea-ice retrievals. The planned/under study GNSS-R missions expected to be allocated in polar orbit are: UK-TDS1, E-GEM's ³CAT-2, and ESA PARIS-IOD.
5.8.3 Other Related Techniques
The table below, with information extracted from [Fabra, 2013], summarizes different remote sensing approaches to sense the sea-ice properties.
SENSOR TYPE: ICE PROPERTY SENSED: REFERENCES:
RADAR ALTIMETERS Sea ice type and concentration [Fetterer et al., 1992]
Sea ice thickness [Zwally et al., 2008]
Ice sheet mass balance [Rémy and Parouty, 2009]
SCATTEROMETERS Sea ice mapping [Onstott, 1992; Remund and Long, 1999;
Anderson and Long, 2005;
Belmonte-Rivas and Stoffelen, 2011]
Sea ice classification [Onstott, 1992]
SYNTHETIC APERTURE Sea ice concentration [Onstott, 1992;
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RADAR Onstott and Shuchman, 2004]
Sea ice classification [Onstott, 1992;
Onstott and Shuchman, 2004;
Partington et al.,2010;
Ochilov and Clausi, 2012]
Sea ice thickness [Onstott, 1992;
Onstott and Shuchman, 2004]
Ice sheet dynamics [Rignot et al., 1995;
Shuchman et al., 2004;
Mouginot et al., 2012]
MICROWAVE Sea ice concentration [Eppler et al., 1992; Kwok, 2002;
RADIOMETERS Comiso et al., 2003]
Sea ice classification [Eppler et al., 1992]
Thin sea ice thickness [Kaleschke et al., 2012]
OPTICAL/NEAR-INFRARED Sea ice surface temperature [Key and Haefliger, 1992;
RADIOMETERS Hall et al., 2004]
Sea ice concentration [Burns et al., 1992;
Drüe and Heinemann, 2004]
Sea ice thickness [Yu and Rothrock, 1999]
Table 5.8a: Some remote sensing approaches for sea-ice monitoring. From [Fabra, 2013].
5.8.4 E-GEM Applicability
The table below lists the GNSS-R retrieval algorithms for sea ice applications, and identifies the scenarios from which these algorithms can be applied using green or red background color. White background for uncertain cases (TBC). “E- GEM” characters in red/white indicate that despite the technique can in general be applied to this scenario, E-GEM system particularities will hinder it (red) or it is uncertain (white).
Retrieval algorithm ID GROUND-BASED AIR-BORNE SPACE-BORNE
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RA-I1: Altim. phase-delay APPLICABLE APPLICABLE UNCERTAIN but not E-GEM³
RA-I2: Permitt. peak APPLICABLE APPLICABLE APPLICABLE
RA-I3: Permitt. pol-ratio E-GEM¹ E-GEM¹ APPLICABLE
RA-I4: Permitt. lin-POPI E-GEM¹ E-GEM¹ APPLICABLE but uncertain for E-GEM²
RA-I5: Permitt. circ-POPI E-GEM¹ E-GEM¹ APPLICABLE
RA-I6: Permitt.+MSS DM-fit APPLICABLE APPLICABLE APPLICABLE
RA-I7: MSS scatt. delay NOT APPLICABLE APPLICABLE UNCERTAIN depending on saturation?
RA-I8: Rough. coh-time APPLICABLE NOT APPLICABLE NOT APPLICABLE
¹ The ground-based and air-borne E-GEM systems have not dual-polarization capabilities.
² The space-borne E-GEM system have 2-pol circular capabilities. Extraction of linear-pol observables TBC.
³ The space-borne E-GEM system will not provide phase-information.
Table 5.8b: Summary of applicability of the GNSS-R sea ice retrieval algorithms.
5.9 Cryosphere: Glaciers
Monitoring glaciers with GNSS-R techniques can potentially be done using the same algorithms detailed in Section 5.8.
5.10 Atmosphere
At very slant observation geometries, such as in GNSS radio-occultation (RO) observations, the reflected signals signal- path cross a large portion of the atmosphere. In these cases, the reflected signal can be a significant source of information about the atmosphere. When compared to the direct signal, the delay of the reflected one is more influenced by the troposphere than by the altimetric signature (Cardellach personal communication). [Cardellach et al., 2008] showed that standard RO measurements (by means of direct line-of-sight signals) of the troposphere better compared to ECMWF background profiles when the same RO observation captured reflected signals.
The improvement in the RMS variation was up to a factor of two. Later on, [Boniface, et al., 2011] presented a method [RA-At1] to extract the refractivity profiles of the lower troposphere from the interferometric delay (delay of the reflected signal with respect to the direct one). These delays were obtained following [Cardellach et al., 2004]. The retrievals in [Boniface et al., 2008] have potential to refine the standard RO measurements, often biased in the lowest layers of the troposphere.
At higher elevation angles, the bi-static path of double-frequency GNSS-R reflected signals could be used to [RA-At2] complement ionospheric tomography. The ionospheric double-path slant delay data (biTEC) would be its main observable [Ruffini et al., 2001]. This concept was further investigated by means of simulated work in [Pallares, et al., 2005].
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The table below lists the GNSS-R retrieval algorithms for atmospheric applications, and identifies the scenarios from which these algorithms can be applied using green or red background color. “E-GEM” white characters indicate that despite the technique can in general be applied to this scenario, it is uncertain because of E-GEM system particularities.
GROUND-BASED Retrieval algorithm ID AIR-BORNE SPACE-BORNE
RA-At1: Troposphere NOT APPLICABLE NOT APPLICABLE APPLICABLE, but uncertain for E-GEM¹
RA-At2: Ionosphere NOT APPLICABLE NOT APPLICABLE APPLICABLE
¹ Only possible when the E-GEM space-borne system points to the limb (maneuvring...).
Table 5.10a: Applicability of the GNSS-R atmospheric retrieval algorithms.
5.11 Civilian Applications: Ship Detection
The possibility of using GNSS reflected signals to detect vessels in the ocean was first proposed during the GNSS-R 2010 workshop [Soulat, et al., 2010] and CCT Space Reflectometry-2010 [Soulat, 2010] where a feasibility study was presented. [RA-Sh1] The algorithm was based on tracking the ship features in the delay and Doppler dimensions through DDMs. The studies were complemented with air-borne experimental data in [Soulat et al., 2012].
An independent study [Carrie et al., 2011] concluded that the ships at ~20⁰ around the specular could not be detected, because of the large masking effect of the sea surface scattering around the specular. Their simulations, corresponding to an air-borne scenario with 4 visible satellites and L2C signals only, resulted in 3D-RMS localization errors between a few and 200 meters, with a detection range of up to 13 km for large vessels.
Recently, these techniques have been also studied by a Chinese group [Liu et al, 2014], analyzing the feasibility of the concept from space-borne platforms. [RA-Sh2] The suggested approach uses DDM to analyze both power and phase features.
The table below lists the GNSS-R retrieval algorithms to detect and localize vessels, and identifies the scenarios from which these algorithms can be applied using green or red background color. “E-GEM” in red characters indicate that despite the technique can in general be applied to this scenario, E-GEM system particularities will hinder it.
GROUND-BASED Retrieval algorithm ID AIR-BORNE SPACE-BORNE
RA-Sh1: DDM power NOT APPLICABLE APPLICABLE APPLICABLE
RA-Sh2: DDM complex NOT APPLICABLE APPLICABLE APPLICABLE but not for E-GEM¹
¹ E-GEM space-borne system will not provide phase-information.
Table 5.11a: Applicability of the GNSS-R ship detection algorithms.
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5.12 Civilian Applications: Buried Metallic Bodies
The potential use of GNSS-R to detect ground-buried metallic bodies was suggested in [Notarpietro et al., 2014]. Although the penetration depth of GNSS signals into the ground is not optimal and depends on the soil moisture, GNSS signals can likely interact with the first few cm of the ground, where typically personal mines are located. Therefore GNSS signals could be reflected back by any metallic object buried on the first terrain layer. The method suggested [RA- B1] uses sharp variations of the SNR to identify the location of the metallic plates, when slowly overpassing the area at very low altitude (a few meters).
The table below lists the GNSS-R retrieval algorithms to localize buried metallic bodies, and identifies the scenarios from which these algorithms can be applied using green or red background color.
Retrieval algorithm ID GROUND-BASED AIR-BORNE SPACE-BORNE
RA-B1: SNR variations APPLICABLE NOT APPLICABLE NOT APPLICABLE
Table 5.12a: Applicability of the GNSS-R algorithms for detection of buried metallic objects.
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7 ACRONYMS
2-D Two-Dimensional
2SCM Two-Scale Composite Model
³CAT-2 CubeCat-2
ADCS Attitude Determination and Control System
ADEOS Advanced Earth Observing Satellite
AMSR-E Advanced Microwave Scanning Radiometer - Earth Observing System
AOCS Attitude and Orbit Control System
AR Assessment Report
ASCAT Advanced SCATterometer
BOC Binary Offset Carrier
BPSK Binari Phase Shift Keying
C/A Civil Available
CDMA Code Division Multiple Access
CDS Cubesat stanDard Specficiations cGNSS-R conventional GNSS-R
CHAMP CHAllenging Minisatellite Payload
CNES Centre National d'Études Spatiales
COTS Commercial Off-The-Shelf
CSIC Consejo Superior de Investigaciones Cientificas (Spanish research agency)
CYGNSS Cyclone Global Navigation Satellite System
DBS Digital Broadcast Satellites
DDM Delay Doppler Map
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DM Delay Map
DMR Delay Mapping Receiver
ECI Earth Centered Inertial
ECV Essential Climate Variable
E-GEM European GNSS-R Environment Monitoring
EGNOS European Geostationary Navigation Overlay System
ENSO El Niño Southern Oscillation
EOL End Of Life
EPS Electrical Power System
ERS European Remote Sensing
ESA European Space Agency
EUMETSAT European Organisation for the Exploitation of Meteorological Satellites
FDMA Frequency Division Multiple Access
GAMBLE Global Altimeter Measurements by Leading Europeans
GCOS Global Climate Observing System
GEROS-ISS GNSS REflectometry, Radio Occultation and Scatterometry onboard International Space Station
GLONASS Global'naya Navigatsionnaya Sputnikovaya Sistema
GNSS Global Navigation Satellite System
GNSS-MR GNSS Multipath-Reflectometry
GNSS-R Global Navigation Satellite System Reflectometry
GODAE Global Ocean Data Assimilation Experiment
GOLD-RTR GPS Open-Loop Differential Real-Time Receiver
GPS Global Positioning System
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HDD Hard Disk Device
HW HardWare
ICD Interface Control Document
ICE Institut de Ciencies de l'Espai
ICF Interferometric Complex Field
IEEC Institut d'Estudis Espacials de Catalunya
IEM Integral Equation Method iGNSS-R Interferometric GNSS-R
IORD Integrated Operational Requirements Document
IPCC Intergovernmental Panel on Climate Change
IPT Interferometric Pattern Technique
I/Q In phase / Quadrature
IRNSS Indian Regional Navigation Satellite System
ISS International Space Station
KA Kirchhoff Approximation kbps Kilo-bit per second
KGO Kirchhoff Geometrical Optics
KPO Kirchhoff Physical Optics
LEO Low Earth Orbiter
LHCP Left-Hand Circular Polarization
MB Meba Byte
MCU Microcontroller Unit
MEO Medium Earth Orbiter
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MODIS MOderate Range Imaging Spectoradiometer
MSS Mean Square Slopes
N/A Not Available
NASA National Aeronautics and Space Administration
NDVI Normalized Difference Vegetation Index
NDWI Normalized Difference Water Index
NIR Near InfraRed
NMRI Normalized Microwave Reflection Index
NOAA National Oceanic and Atmospheric Administration
NSCAT NASA Scatterometer
NWP Numerical Weather Prediction
OBC On Board Computer
OSTST Ocean Surface Topography Science Team
OVWST Ocean Vector Wind Science Team
PARIS PAssive Reflectometric and Interferometric System
PARIS-IOD PARIS In Orbit Demonstrator
PDF Probability Density Function
PIR/A PARIS Interferometric Receiver/Airborne version
PolInSAR Polarimetric Interferometric SAR
POPI POlarimetric Phase Interferometry
PRN Pseudo-Random Noise
PYCARO P(Y) C/A ReflectOmeter
QZSS Quasi-Zenith Satellite System
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RF Radio Frequency rGNSS-R reconstructed GNSS-R (or semi-codeless)
RHCP Right-Hand Circular Polarization
RMS Root Mean Square
RO Radi-Occultation
SAR Synthetic Aperture Radar
SAVI Soil-Adjusted Vegetation Index
SCA Snow Cover Area
SIR-C Spaceborne Imaging Radar
SMAP Soil Moisture Active-Passive mission
SMC Soil Moisture Content
SMI Soil Moisture Index
SMOS Soil Moisture Ocean Salinity mission
SNR Signal-to-Noise Ratio
SPIR Software PARIS Interferometric Receiver
SPM Small Perturbation Method
SSA Small Slope Approximation
SSM/I Special Sensor Microwave/Imager
SSMIS Special Sensor Microwave Imager Sounder
SSS Sea Surface Salinity
SSTL Surrey Satellite Technology Ltd.
SW SoftWare
SWE Snow Water Equivalence
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SWOT Surface Water Ocean Topography
TBC To Be Confirmed
TC Tropical Cyclone
TT&C Telemetry Tracking and Command
UAV Unmanned Aerial Vehicle
UHF Ultra High Frequency
UK-DMC United Kingdom Disaster Monitoring Constellation
UK-TDS1 United Kingdom Technology Demonstration Satellite 1
UPC Universitat Politècnica de Catalunya
VHF Very High Frequency
VIS Visible
WAAS Wide Area Augmentation System
WAF Woodward Ambiguity Function
WMO World Meteorological Organization
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End of Document
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