# First Polarimetric GNSS-R Measurements from a Stratospheric Flight over Boreal Forests

^{*}

## Abstract

**:**

_{rl}/Γ

_{rr}) maps over boreal forests are shown to be in the range 2–16 dB for the different GNSS codes. This result suggests that the scattering is taking place not only over the soil, but over the different forests elements as well. Additionally to the interpretation of the experimental results a theoretical investigation of the different contributions to the total reflectivity over boreal forests is performed using a bistatic scattering model. The simulated cross- (reflected Left Hand Circular Polarization LHCP) and co-polar (reflected Right Hand Circular Polarization RHCP) reflectivities are evaluated for the soil, the canopy, and the canopy–soil interactions for three different biomass densities: 725 trees/ha, 150 trees/ha and 72 trees/ha. For elevation angles larger than the Brewster angle, it is found that the cross-polar signal is dominant when just single reflections over the forests are evaluated, while in the case of multiple reflections the co-polar signal becomes the largest one.

## 1. Introduction

^{3}Cat-2 6U CubeSat [14].

_{e}= (50°, 80°). On the other side, the cross-polar component is shown to be reduced to approximately 5 dB. Recently, a different approach has proposed that the forward scattering coefficient is governed by the scattering properties of the vegetation elements and the soil surface, as well as by the interaction between the canopy and the soil, and the soil with the trunks [18]. In this study, the total cross- and the co-polar scattering coefficients are shown to be, respectively, approximate −8 to 8 dB, and approximate −2 to −15 dB for elevation angles in the range θ

_{e}= (50°, 80°).

## 2. Experimental Set-Up and Scenario

**Figure 1.**Configuration of the BEXUS 19 stratospheric balloon at Esrange Space Center: 12,000 m

^{3}balloon, valve, cutter, parachute, Esrange Balloon Service System (EBASS), flight train, Argos GPS and Air Traffic Control (ATC) Transponder (AGT), strobe light, radar reflector, and gondola. Total length of the system is up to 75 m [21]. Photo credits: European Space Agency (ESA).

## 3. Results of a Stratospheric Balloon Experiment over Boreal Forests

_{e}= 70° is: GPS L1 Coarse/Acquisition C/A (74 m), GPS L2 Precise/Secure P(Y) (83 m), GPS L1 P(Y) (74 m), GPS L2 Civilian C (83 m), GLONASS L1 C/A (74 m), GLONASS L2 C/A (83 m), GLONASS L2 Precise P (83 m) and Galileo E1 BC (74 m). Additionally, the difference of the unwrapped phases of the complex waveforms peak at LHCP ${\text{\Psi}}_{\text{n}}^{\text{l}}$ and RHCP ${\text{\Psi}}_{\text{n}}^{\text{r}}$ can be used. This phase has two terms; one induced during the scattering process, which is roughly constant at high elevation angles (e.g., θ

_{e}≥ 60°), and another one due to the propagation. As compared to the polarimetric ratio the main advantage is that the phase difference between the RHCP and LHCP signals can be modeled independently of the elevation angle [22].

_{n}can be used to infer the geometric delay difference ${\text{\rho}}_{\text{geo},\text{n}}$ as:

_{n}of the center of phase of the scatterers at LHCP and RHCP are related to the geometric delay difference ${\text{\rho}}_{\text{geo},\text{n}}$ as:

_{e}= 70° for GPS and GLONASS, and θ

_{e}= 60° for Galileo. This selection was made because for lower elevation angles the performance of the technique was degraded due to the high directivity of the down-looking antenna array. The maps correspond to different ground tracks. The polarimetric ratio values at the specular reflection points were geolocated and represented over the Earth’s surface using Google Maps for simpler interpretation. The ephemerides as provided by an on-board positioning receiver were used to derive the orbit parameters of the GNSS satellites, while the PYCARO trajectory was measured by the on-board receiver. cGNSS-R was used for data acquisition of GPS L1 C/A (Figure 2a), GPS L2 C (Figure 2b), GLONASS L1 C/A (Figure 3a), GLONASS L2 C/A (Figure 3b), GLONASS L2 P (Figure 3c) and Galileo E1 BC (Figure 3d) signals, while rGNSS-R for GPS L1 P(Y) (Figure 2c) and GPS L2 P(Y) (Figure 2d). The mean polarimetric ratio (PR) for GPS L1 C/A signals is ~8 dB and ~4.2 dB over lakes and boreal forests, respectively (Table 1). Additionally, it is found that for the so-called data-less signal GPS L2 C the ratio is, respectively, ~12.7 dB and ~8.1 dB over lakes and boreal forests. The reason that explains the higher values of PR of GPS L2 C as compared to GPS L1 C/A signals is that the depolarization of the direct signal (Table 2) is higher for L1 C/A than for L2 C signals ${\text{SNR}}_{\text{GPS},\text{L}1\text{C}/\text{A},\text{l}}$ = 13 dB and ${\text{SNR}}_{\text{GPS},\text{L}2\text{C},\text{l}}$ = 3 dB). The Signal-to-Noise Ratio (SNR) values of the direct signals are higher for L1 than for L2, as can be appreciated in Table 2. This is empirical evidence showing that the degree of depolarization is lower for GPS L2 C signals.

**Figure 2.**Measured polarimetric ratios for a flight height of 27,000 m and an elevation angle θ

_{e}= 70° for (

**a**) GPS L1 C/A, (

**b**) GPS L2 C, (

**c**) GPS L1 P (Y), (

**d**) GPS L2 P (Y).

**Figure 3.**Measured polarimetric ratios for a flight height of 27,000 m and an elevation angle θ

_{e}= 70° for (

**a**) GLONASS L1 C/A, (

**b**) GLONASS L2 C/A, (

**c**) GLONASS L2 P; and for an elevation angle θ

_{e}= 60° for (

**d**) Galileo E1 BC.

**Table 1.**Mean polarimetric ratio over forests and lakes for GPS (L1 C/A, L2 C, L1 P (Y) and L2 P (Y)), GLONASS (L1 C/A, L2 C/A and L2 P) and Galileo (E1 BC) signals during the float phase of BEXUS 19 flight.

GNSS Code | Elevation Angle | PR (dB) (Forests) | PR (dB) (Lakes) |
---|---|---|---|

GPS L1 C/A | θ_{e} ~ 70° | 4.2 | 8 |

GPS L2 P(Y) | θ_{e} ~ 70° | 14.6 | 20.4 |

GPS L1 P(Y) | θ_{e} ~ 70° | 14.6 | 20.4 |

GPS L2 C | θ_{e} ~ 70° | 8.1 | 12.7 |

GLONASS L1 C/A | θ_{e} ~ 70° | 6.7 | 8.2 |

GLONASS L2 C/A | θ_{e} ~ 70° | 6.3 | - |

GLONASS L2 P | θ_{e} ~ 70° | 6.3 | - |

Galileo E1 BC | θ_{e} ~ 60° | 4.1 | - |

**Table 2.**Signal-to-Noise Ratio at RHCP y LHCP of the direct GPS (L1 C/A, L2 C, L1 P (Y) and L2 P (Y)), GLONASS (L1 C/A, L2 C/A and L2 P) and Galileo (E1 BC) signals as function of the elevation angle during the float phase of BEXUS 19 flight.

GNSS Code | SNR_{r} (dB)θ _{e}~ 70° | SNR_{l} (dB)θ _{e}~ 70° | SNR_{r} (dB)θ _{e}~ 60° | SNR_{l} (dB)θ _{e}~ 60° | SNR_{r} (dB)θ _{e}~ 50° | SNR_{l} (dB)θ _{e}~ 50° | SNR_{r} (dB)θ _{e}~ 40° | SNR_{l} (dB)θ _{e}~ 40° |
---|---|---|---|---|---|---|---|---|

GPS L1 C/A | 34 | 13 | 33 | 23 | 32 | 23 | 30 | 23 |

GPS L2 P(Y) | 19 | - | 16 | - | 13 | - | 10 | - |

GPS L1 P(Y) | 19 | - | 16 | - | 13 | - | 10 | - |

GPS L2 C | 28 | 3 | 25 | 3 | 23 | 8 | 21 | 8 |

GLONASS L1 C/A | 31 | 12 | 31 | 18 | 29 | 25 | 21 | 15 |

GLONASS L2 C/A | 16 | - | 16 | - | 20 | - | 20 | - |

GLONASS L2 P | 12 | - | 12 | - | 16 | - | 16 | - |

Galileo E1 BC | 15 | - | 14 | - | 13 | - | 11 | - |

## 4. Final Discussions

^{3}Cat-2 mission. The scientific evaluation of this dataset offers the opportunity to evaluate the feasibility of the GNSS-R to perform biomass monitoring, which is a key-factor for analyzing the carbon cycle. The main added value is the measurement of polarimetric signatures, which shows sensitivity over forests and lakes. Additionally, different data acquisition techniques have been used: cGNSS-R for the open-source codes and the novel rGNSS-R for the encrypted P(Y) GPS code. The scattering of the GNSS-R signals takes place over the soil and the canopy but also through multiple reflections involving canopy-soil and soil-branches interactions. This is an important issue that has to be considered to perform biomass monitoring, since the vegetation provides a scattered field additionally to the effect of the attenuation of the signals reflected over the soil. Future work should include a study of: (a) The potential advantages of the synergy between both data access techniques, and (b) scattering over different types of vegetated soils.

## 5. Conclusions

_{e}= 70° for GPS and GLONASS and θ

_{e}= 60° for Galileo vary in the ranges from approximate 2 to 16 dB and from approximate −1.4 to −9.6 m, respectively. This is due to the effect of different tracks, periods of signal acquisition, levels of depolarization of the direct signals and because of the squaring losses of the rGNSS-R. The polarimetric phase is found to be negative, which means that the center of phase of the reflected signals at LHCP is higher in the vertical profile of the forests as compared with RHCP signals. As the main conclusion, GNSS-R has been shown to have sensitivity to perform polarimetric measurements over lakes and boreal forests from a stratospheric balloon flight with an apogee of 27,000 m using dual-band multi-constellation signals.

_{e}= (10°, 80°). On the other side, attenuation due to signal propagation through forests leads to lower reflectivity values over soil as lower is the elevation angle, independently of the polarization. However, the polarization is found to be an important parameter that determines the reflectivity levels of canopy-soil interactions. Increments of canopy-soil cross-polar reflectivity values have a similar trend than those corresponding to soil scattering. Nonetheless, for elevation angles larger than ~30°, the former scattering mechanism shows a higher increment of reflectivity as compared to scattering only over the soil. Additionally, it is found that leaves–soil co-polar reflectivity levels reduces from 10 dB to 0 dB for elevation angles in the range θ

_{e}= (10°, 80°), while for branches–soil interactions is roughly constant around zero.

## Acknowledgments

## Author Contributions

## Appendix

## Part I: Simulations of the Reflectivity over Boreal Forests

**Figure A1.**(

**a**) Reference system following the Forward Scattering Alignment (FSA) criterion. (

**b**) Sample 3-D structure of the tree-type used along the simulations performed to estimate the reflectivity.

**Figure A2.**Simulated reflectivity for a biomass density of 725 tress/ha over leaves: (

**a**) Rayleigh approximation ideal needle, (

**b**) Rayleigh approximation needle, (

**c**) Generalized Rayleigh Gans approximation needle; and leaves-soil interactions: (

**d**) Rayleigh approximation ideal needle-Choudhury, (

**e**) Rayleigh approximation needle-Choudhury, and (

**f**) Generalized Rayleigh Gans approximation needle-Choudhury.

_{e}~ 10°, to ~20 dB for high elevation angles, θ

_{e}~ 80°). On the other side, the co-polar reflectivity levels are under-estimated ~20 dB using the Rayleigh approximation for an ideal needle for low elevation angles, θ

_{e}~ 10°.

_{e}= (10°, 80°) for three different biomass densities 725 trees/ha (Figure A3a, single reflections; and Figure A3d, multiple reflections), 150 trees/ha (Figure A3b, single reflections; and Figure A3e, multiple reflections) and 72 trees/ha (Figure A3c, single reflections; and Figure A3f, multiple reflections). The co-polar component over soil-surface is dominant for low elevation angles up to θ

_{e}~ 30°, while the cross-polar is the highest component for larger elevation angles. Actually, this corresponds to the Brewster angle, which is a property of the reflector type and indicates the change of polarity of the vertical component of incident electromagnetic field after being reflected [36]. A common characteristic of the full ranges of biomass densities and elevation angles under study is the order of reflectivity levels. First of all, the soil surface (on average ~10 dB more than other contributions), which is followed by reflectivity levels over branches and leaves.

**Figure A3.**Cross- and co-polar reflectivity simulations over forests: (

**a**–

**c**) direct scattering (canopy, soil) and (

**d**–

**f**) multiple scattering (canopy–soil interactions), for a biomass density of (

**a**,

**d**) 725 trees/ha, (

**b**,

**e**) 150 trees/ha, and (

**c**,

**f**) 72 trees/ha.

_{e}~ 30°, while the co-polar one is the highest component for higher elevation angles. This inversed-behavior as compared with single reflections is due the double polarization changes induced by, first from RHCP to LHCP, and then from LHCP to RHCP.

## Part II: Theoretical Electromagnetic Model

^{th}scatterer above the soil corresponding to a forest element, ${\text{\varphi}}_{\text{n}}$ is a phase compensation term accounting for the shift of the phase reference from the local coordinate system of the n

^{th}scatterer to the global coordinate phase reference, and ${\overline{\text{E}}}_{0}^{\text{i},\text{element}}$ is the amplitude of the incident electromagnetic wave. Denoting the position of the n

^{th}scatterer in the global coordinate system by ${\overline{\text{r}}}_{\text{n}}$, ${\text{\varphi}}_{\text{n}}$ is given by [26]:

_{i}and τ

_{s}account for the extra path lengths of the image excitation and the image scattered waves, respectively, and ${\overline{\text{k}}}_{\text{i}}$, ${\overline{\text{k}}}_{\text{s}}$ , ${\overline{\text{k}}}_{\text{gi}}$ , ${\overline{\text{k}}}_{\text{gs}}$ and ${\overline{\text{n}}}_{\text{g}}$ are defined in Figure A4.

**Figure A4.**(

**a**) Soil scattering. (

**b**) Direct scattering over canopy. (

**c**) Multiple scattering involving soil and canopy. (

**d**) Multiple scattering involving both soil and branches.

## Conflicts of Interest

## References

- Martín-Neira, M. A PAssive Reflectometry and Interferometry System (PARIS): Application to ocean altimetry. ESA J.
**1993**, 17, 331–355. [Google Scholar] - Garrison, J.L.; Katzberg, S.J.; Hill, M.I. Effect of sea roughness on bistatically scattered range coded signals from the global positioning system. Geophys. Res. Lett.
**1998**, 25, 2257–2260. [Google Scholar] [CrossRef] - Fabra, F. GNSS-R as A Source of Opportunity for Remote Sensing of the Cryosphere. Ph.D. Thesis, Universitat Politècnica de Catalunya, Barcelona, Spain, 2014. [Google Scholar]
- Rodriguez-Alvarez, N. Contributions to Earth Observation Using GNSS-R Opportunity Signals. Ph.D. Thesis, Universitat Politècnica de Catalunya, Barcelona, Spain, 2011. [Google Scholar]
- Zavorotny, V.U.; Gleason, S.; Cardellach, E.; Camps, A. Tutorial on remote sensing using GNSS bistatic radar of opportunity. IEEE Geosci. Remote Sens. Mag.
**2014**, 2, 8–45. [Google Scholar] [CrossRef] - Garrison, J.L.; Komjathy, A.; Zavorotny, V.; Katzberg, S.J. Wind speed measurement using forward scattered GPS signals. IEEE Trans. Geosci. Remote Sens.
**2002**, 40, 50–65. [Google Scholar] [CrossRef] - Cardellach, E.; Rius, A.; Martín-Neira, M.; Fabra, F.; Ribó, S.; Kainulainen, J.; Camps, A.; D’Addio, S. Consolidating the precision of interferometric GNSS-R ocean altimetry using airborne experimental data. IEEE Trans. Geosci. Remote Sens.
**2014**, 52, 4992–5004. [Google Scholar] [CrossRef] - Carreno-Luengo, H.; Camps, A.; Ramos-Pérez, I.; Rius, A. Experimental evaluation of GNSS-reflectometry altimetric precision using the P (Y) and C/A signals. IEEE Sel. Top. Appl. Earth Obs. Remote Sens.
**2014**, 7, 1493–1500. [Google Scholar] [CrossRef] - Unwin, M.; Jales, P.; Curiel, A.S.; Brenchley, M.; Sweeting, M.; Gommenginger, C.; Roselló, J. Sea state determination with GNSS reflectometry on TechDemoSat-1. In Proceedings of the 2014 ESA Small Satellites, Systems and Services Symposium, Mallorca, Spain, 26–30 May 2014.
- Unwin, M. TechDemoSat-1 and the GNSS reflectometry experiment. Available online http://www.merrbys.co.uk:8080/CatalogueData/Documents/TDS-1%20SGR-ReSI%20Experiment.pdf (accessed on 5 June 2015).
- Rose, R.; Wells, W.; Rose, D.; Ruf, C.; Ridley, A.; Nave, K. Nanosat technology and managed risk: An update of the CYGNSS microsatellite constellation mission development. In Proceedings of the 28th AIAA/USU Conference on Small Satellites, Logan, UT, USA, 4–7 August 2014; pp. 1–12.
- Wickert, J.; Beyerle, G.; Cardellach, E.; Förste, C.; Gruber, T.; Helm, A.; Hess, M.P.; Hoeg, P.; Jakowski, N.; Kern, M.; et al. GEROS-ISS-GNSS rEflectometry, Radio Occultation and Scatterometry on-board the International Space Station. In Proceedings of 4th International Colloquium Scientific and Fundamental Aspects of the Galileo Programme, Espoo, Finland, 28–31 October 2013.
- Martín-Neira, M.; D’Addio, S.; Buck, C.; Floury, N.; Prieto-Cerdeira, R. The PARIS ocean altimeter in-orbit demonstrator. IEEE Trans. Geosci. Remote Sens.
**2011**, 49, 2209–2237. [Google Scholar] [CrossRef] - Carreno-Luengo, H.; Camps, A.; Jové, R.; Alonso, A.; Olivé, R.; Amèzaga, A.; Vidal, D.; Munoz, J.F. The 3Cat-2 project: GNSS-R In-Orbit demonstrator for earth observation. In Proceedings of the 2014 ESA Small Satellites, Systems and Services Symposium, Mallorca, Spain, 26–30 May 2014.
- Zavorotny, V.U.; Voronovich, A.G. Bistatic GPS signal reflections at various polarizations from rough land surface with moisture content. In Proceedings of the 2000 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, Hawaii, USA, 24–28 July 2000; pp. 2852–2854.
- Ferrazzoli, P.; Guerriero, L.; Pierdicca, N.; Rahmoune, R. Forest biomass monitoring with GNSS-R: Theoretical simulations. Adv. Space Res.
**2011**, 47, 1823–1832. [Google Scholar] [CrossRef] - Egido, A.; Paloscia, S.; Motte, E.; Guerriero, L.; Pierdicca, N.; Caparrini, M.; Santi, E.; Fontanelli, G.; Floury, N. Airborne GNSS-R soil moisture and above ground biomass observations. IEEE Sel. Top. Appl. Earth Obs. Remote Sens.
**2014**, 7, 1522–1532. [Google Scholar] [CrossRef] - Wu, X.R.; Jin, S.G. GPS-Reflectometry: Forest canopies polarization scattering properties and modelling. Adv. Space Res.
**2014**, 54, 863–870. [Google Scholar] [CrossRef] - Martínez-Vazquez, A. Emisividad Polarimétrica del Terreno Efecto de la Vegetación. Master’s Thesis, Universitat Politècnica de Catalunya, Barcelona, Spain, 2001. [Google Scholar]
- Ledesma-Galera, I. Estudio Experimental del Comportamiento Radiométrico de LAS Superfícies naturales. Master’s Thesis, Universitat Politècnica de Catalunya, Barcelona, Spain, 2002. [Google Scholar]
- Kinnaird, A. BEXUS User Manual. Available online: http://www.rexusbexus.net/index.php?option=com_content&view=article&id=51&Itemid=63 (accessed on 5 December 2014).
- Cardellach, E.; Ribó, S.; Rius, A. Technical Note on Polarimetric Phase Interferometry (POPI). Available online: http://arxiv.org/pdf/physics/0606099.pdf (accessed on 1 March 2015).
- Beckmann, P.; Spizzichino, A. The Scattering of Electromagnetic Waves from Rough Surfaces; Artech House Inc.: Norwood, MA, USA, 1963; p. 125. [Google Scholar]
- Smyrnaios, M.; Schon, S. Multipath Propagation, Characterization and Modelling in GNSS. Available online: http://cdn.intechopen.com/pdfs-wm/43710.pdf (accessed on 9 September 2015).
- Woo, T.K. Optimum semi-codeless carrier phase tracking of L2. In Proceedings the 12th International Technical Meeting of the Satellite Division of the Institute of Navigation, Nashville, TN, USA, 14–17 September 1999; pp. 82–99.
- Yi-Cheng, L.; Sarabandi, K. A Monte Carlo coherent scattering model for forest canopies using fractal-generated trees. IEEE Trans. Geosci. Remote Sens.
**1999**, 37, 440–451. [Google Scholar] [CrossRef] - Prusinkiewicz, P.; Lindenmayer, A. The Algorithmic Beauty of Plants; Springer-Verlag: New York, NY, USA, 1990. [Google Scholar]
- Tsang, L.; Kong, J.A.; Shin, R.T. Theory of Microwave Remote Sensing; Wiley Interscience: New York, NY, USA, 1985. [Google Scholar]
- Martínez-Vazquez, I.; Camps, A.; Lopez-Sanchez, J.M.; Vall-llosera, M.; Monerris, A. Numerical simulation of the full-polarimetric emissivity of vines and comparison with experimental data. Remote Sens.
**2009**, 1, 300–317. [Google Scholar] [CrossRef][Green Version] - Caicoya, A.T.; Kugler, F.; Hajnsek, I.; Papathanassiou, K. Boreal forest biomass classification with TANDEM-X. In Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22–27 July 2012; pp. 3439–3442.
- Global map of forest height produced from NASA’s ICESAT/GLAS, MODIS and TRMM sensors. Available online: http://www.nasa.gov/topics/earth/features/earth20120217map.html (accessed on 1 March 2015).
- Imhoff, M.L. Radar backscattering and biomass saturation: Ramifications for global biomass inventory. IEEE Trans. Geosci. Remote Sens.
**1995**, 33, 511–518. [Google Scholar] [CrossRef] - Lin, Y.-C.; Sarabandi, K. Electromagnetic scattering model for a tree trunk above a tilted ground plane. IEEE Trans. Geosci. Remote Sens.
**1995**, 33, 1063–170. [Google Scholar] - Karam, M.A.; Fung, A.K.; Antar, Y.M.M. Electromagnetic wave scattering from some vegetation samples. IEEE Trans. Geosci. Remote Sens.
**1998**, 26, 799–808. [Google Scholar] [CrossRef] - Choudhury, B.J.; Schmugge, T.J.; Chang, A.; Newton, R.W. Effect of surface roughness on the microwave emission from soils. J. Geophys. Res.
**1979**, 84, 5699–5706. [Google Scholar] [CrossRef] - Rodriguez-Alvarez, N.; Camps, A.; Vall-llosera, M.; Bosch-Lluis, X.; Monerris, A.; Ramos-Pérez, I.; Valencia, E.; Marchán-Hernandez, J.F.; Martinez-Fernandez, J.; Baroncini-Turricchia, G.; et al. Land geophysical parameters retrieval using the interference pattern GNSS-R technique. IEEE Trans. Geosci. Remote Sens.
**2011**, 49, 71–84. [Google Scholar] [CrossRef] - Ulaby, F.T.; Moore, R.K.; Fung, A.K. Radar remote sensing and surface scattering and emission theory. In Microwave Remote Sensing: Active and Passive; Addison-Wesley: Reading, MA, USA, 1982; Volume II, pp. 1540–1541. [Google Scholar]
- Lindermayer, A. Developmental algorithms for multicellular organisms: A survey of L-systems. J. Theor. Bio.
**1975**, 54, 3–22. [Google Scholar] [CrossRef] - Ulaby, F.T.; Moore, R.K.; Fung, A.K. Microwave remote sensing fundamentals and radiometry. In Microwave Remote Sensing: Active and Passive; Addison-Wesley: Reading, MA, USA, 1982; Volume I, pp. 249–251. [Google Scholar]

© 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Carreno-Luengo, H.; Amèzaga, A.; Vidal, D.; Olivé, R.; Munoz, J.F.; Camps, A. First Polarimetric GNSS-R Measurements from a Stratospheric Flight over Boreal Forests. *Remote Sens.* **2015**, *7*, 13120-13138.
https://doi.org/10.3390/rs71013120

**AMA Style**

Carreno-Luengo H, Amèzaga A, Vidal D, Olivé R, Munoz JF, Camps A. First Polarimetric GNSS-R Measurements from a Stratospheric Flight over Boreal Forests. *Remote Sensing*. 2015; 7(10):13120-13138.
https://doi.org/10.3390/rs71013120

**Chicago/Turabian Style**

Carreno-Luengo, Hugo, Adriá Amèzaga, David Vidal, Roger Olivé, Juan Fran Munoz, and Adriano Camps. 2015. "First Polarimetric GNSS-R Measurements from a Stratospheric Flight over Boreal Forests" *Remote Sensing* 7, no. 10: 13120-13138.
https://doi.org/10.3390/rs71013120