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FR4.TO5.3.pdf FR4.TO5.3.pdf Presentation Transcript

  • Introduction Model Results obtained Conclusions An empirical approach towards characterization of Dry Snow layers using GNSS-R Fran Fabra1 , E. Cardellach1 , O. Nogu´s-Correig1 , S. Oliveras1 , e S. Rib´1 , A. Rius1 , G. Macelloni2 , S. Pettinato2 and S. o D’Addio3 1 ICE-CSIC/IEEC,Spain 2 IFAC/CNR,Italy 3 ESA-ESTEC, Netherlands IEEE International Geoscience and Remote Sensing Symposium July 24-29, 2011, Vancouver, Canada
  • Introduction Model Results obtained ConclusionsOutline 1 Introduction 2 Model 3 Results obtained 4 Conclusions
  • Introduction Model Results obtained ConclusionsOutline 1 Introduction 2 Model 3 Results obtained 4 Conclusions
  • Introduction Model Results obtained ConclusionsThe frame of this work GPS-SIDS project: Sea Ice - Dry Snow GOAL: to investigate the use of reflected GPS signals to study sea ice and dry snow properties from Space METHOD: to collect long term data sets from fixed platforms (2 field campaigns) and then extrapolate the results CHALLENGE: experimental campaigns under polar environmental conditions Many institutions involved: ICE-CSIC/IEEC, GFZ and IFAC/CNR (funded by ESA)
  • Introduction Model Results obtained ConclusionsExperimental campaign Location Dome Concordia (Antarctica)
  • Introduction Model Results obtained ConclusionsExperimental campaign Location Dome Concordia (Antarctica): American tower
  • Introduction Model Results obtained ConclusionsExperimental campaign
  • Introduction Model Results obtained ConclusionsExperimental campaign Main aspects I Instrument controlled in-situ by IFAC in coordination with IEEC. Short campaign due to stability (DOMEX experiment [Macelloni et al., 2006]) of the dry snow cover: 10th to 21st December 2009 Antennas on top of American tower with a height of 46m. Not a single surface: reflected signal as a contribution from different layers.
  • Introduction Model Results obtained ConclusionsExperimental campaign Main aspects II Maximum elevation achievable through a GPS reflection of 65◦ due to limitations of the satellites constellation at these Latitudes. Minimum elevation of 5◦ given by GOLD-RTR’s internal GPS receiver. Monitorization area of ∼400m x 400m: pristine snow. 0˚ 345˚ 15˚ 32 ˚ 30 330 ˚ 0˚ 28 10 5˚ ˚ 31 24 20 ˚ 30 0˚ ˚ 20 30 40 PRN ˚ 16 50 ˚ 285˚ 60 ˚ 12 70 ˚ 8 270˚ 4 255˚
  • Introduction Model Results obtained ConclusionsThe instrument employed: GOLD-RTR GNSSR dedicated hardware receiver GPS L1 (1575.45 MHz) C/A code (Public and with simple autocorrelation) 10 channels compute cross-correlations (waveforms) of 64 lags every millisecond 50 ns lagspacing ⇒∼ 15 meters Scan the delay- and/or Doppler-space 3 radio front-ends One Up-looking antenna for reference signal (internal GPS receiver) A dual polarization (LHCP and RHCP) Down-looking antenna for reflected signals Designed, manufactured, and tested at the ICE-CSIC/IEEC
  • Introduction Model Results obtained ConclusionsOutline 1 Introduction 2 Model 3 Results obtained 4 Conclusions
  • Introduction Model Results obtained ConclusionsMotivation Existing Model [Wiehl et al., 2003]: sub-surface contribution essentially given by volumetric scattering. But we consider volume scattering negligible compared to absorption loss. Motivation: data shows clear interference fringes, better explained by multiple-layer reflections. 20 Amplitude (a.u.) 15 10 ⇒ Need to develop our own model 5 0 44.00 44.25 44.50 44.75 45.00 Elevation angle (deg)
  • Introduction Model Results obtained Conclusions Multi-layer Single-reflection model: MLSR Multiple infinite parallel layers 1 single reflection per layer is considered Only LHCP reflections reach the receiver Expressions k=i k=i Hk ρi = ρ0 + 2nk −( Dk )sin(θ0 ) k=1 cos(θk ) k=1 Dk = 2Hk tan(θk ) i=k −2αdi Uk = Rk,k+1 Πi=1 Ti−1i Tii−1 e 1 Tco = (T + T⊥ ) 2 1 Rcross = (R − R⊥ ) 2 2π √ α= |Ima{ }| λ
  • Introduction Model Results obtained ConclusionsMethodology Input of the forward model (MLSR) Depths and permittivity of the dry snow layers are needed Retrieved from in situ measurements of snow density
  • Introduction Model Results obtained ConclusionsMethodology Complex waveform generated Incident signal at surface with A=1 Direct signal set to lag 22 (RHCP to LHCP leakage with A=0.1) Frequency of direct signal as a reference Several contributions
  • Introduction Model Results obtained ConclusionsMethodology Complex waveform generated Incident signal at surface with A=1 Example: elevation from 44.884 to 44.955 Direct signal set to lag 22 (RHCP to deg (128 samples) LHCP leakage with A=0.1) Frequency of direct signal as a reference Several contributions
  • Introduction Model Results obtained ConclusionsMethodology Comparison of the modeled waveforms with real data
  • Introduction Model Results obtained ConclusionsMethodology Comparison of the modeled waveforms with real data
  • Introduction Model Results obtained ConclusionsMethodology Evolution of the different contributions wrt incidence angle ⇒ Interferometric frequency relates to depth of the layer
  • Introduction Model Results obtained ConclusionsMethodology How do we retrieve information? FFT to each of the lag-time series ⇒ elevation domain (cycles/deg) Several bands of main contributions below the surface level appear, corresponding to depths with stronger gradients of snow density/permittivity ⇒ Proper inversion might determine dominant layers of L-band reflections 300 Lag-hologram from previous example Snow Depth (m) 200 100 0 −18 −15 −12 −9 −6 Interf. Freq. (cycles/deg−el)
  • Introduction Model Results obtained ConclusionsOutline 1 Introduction 2 Model 3 Results obtained 4 Conclusions
  • Introduction Model Results obtained ConclusionsLag-holograms from real data Lag-hologram of 256 samples PRN 19 at ∼ 40◦ of elevation from Dec-16. First impressions It show assymetrical behavior: consistent with rotation due to interference from different contributions. Apparently, wider frequency fringes than expected (high frequency bands at short delay lags). Next processing step: averaging To fade away those other features of the data which are not consistently repeated (noise, atmospheric and instrumental induced features). To identify the geometric and experimental parameters which essentially drive the interferometric signal.
  • Introduction Model Results obtained ConclusionsAveraging in elevation 5◦ - 10◦ 10◦ - 15◦ Frequency (cycle/deg−elev.) Frequency (cycle/deg−elev.) Next slides show a series of 20 20 15 15 averaged lag-holograms 10 10 (using all PRN’s) in 5 5 elevation steps of 5◦ from 0 0 −5 −5 December 16th. −10 −10 −15 −15 −20 −20 5◦ ⇒ 65◦ 10 20 30 40 50 60 10 20 30 40 50 60 Delay−lag (15 m.−lag) Delay−lag (15 m.−lag)
  • Introduction Model Results obtained ConclusionsAveraging in elevation 15◦ - 20◦ 20◦ - 25◦ 25◦ - 30◦ Frequency (cycle/deg−elev.) Frequency (cycle/deg−elev.) Frequency (cycle/deg−elev.) 20 20 20 15 15 15 10 10 10 5 5 5 0 0 0 −5 −5 −5 −10 −10 −10 −15 −15 −15 −20 −20 −20 10 20 30 40 50 60 10 20 30 40 50 60 10 20 30 40 50 60 Delay−lag (15 m.−lag) Delay−lag (15 m.−lag) Delay−lag (15 m.−lag)
  • Introduction Model Results obtained ConclusionsAveraging in elevation 30◦ - 35◦ 35◦ - 40◦ 40◦ - 45◦ Frequency (cycle/deg−elev.) Frequency (cycle/deg−elev.) Frequency (cycle/deg−elev.) 20 20 20 15 15 15 10 10 10 5 5 5 0 0 0 −5 −5 −5 −10 −10 −10 −15 −15 −15 −20 −20 −20 10 20 30 40 50 60 10 20 30 40 50 60 10 20 30 40 50 60 Delay−lag (15 m.−lag) Delay−lag (15 m.−lag) Delay−lag (15 m.−lag)
  • Introduction Model Results obtained ConclusionsAveraging in elevation 45◦ - 50◦ 50◦ - 55◦ 55◦ - 60◦ Frequency (cycle/deg−elev.) Frequency (cycle/deg−elev.) Frequency (cycle/deg−elev.) 20 20 20 15 15 15 10 10 10 5 5 5 0 0 0 −5 −5 −5 −10 −10 −10 −15 −15 −15 −20 −20 −20 10 20 30 40 50 60 10 20 30 40 50 60 10 20 30 40 50 60 Delay−lag (15 m.−lag) Delay−lag (15 m.−lag) Delay−lag (15 m.−lag)
  • Introduction Model Results obtained ConclusionsAveraging in elevation 55◦ - 60◦ 60◦ - 65◦ Apparently, the elevation range that shows consistent Frequency (cycle/deg−elev.) Frequency (cycle/deg−elev.) 20 20 15 15 features from deep snow 10 10 5 5 layers goes from 20◦ to 0 0 45◦ . −5 −5 −10 −10 −15 −15 The elevation-rate also −20 −20 10 20 30 40 50 60 10 20 30 40 50 60 plays an important role in Delay−lag (15 m.−lag) Delay−lag (15 m.−lag) this analysis.
  • Introduction Model Results obtained ConclusionsAveraging in elevation-rate 0.0◦ /s - 0.001◦ /s 0.001◦ /s - 0.002◦ /s Next slides show a series of Frequency (cycle/deg−elev.) Frequency (cycle/deg−elev.) 20 20 averaged lag-holograms 15 15 (using all PRN’s) in 10 10 5 5 elevation-rate steps of 0 0 0.001◦ /s from December −5 −5 −10 −10 16th. −15 −15 −20 −20 10 20 30 40 50 60 10 20 30 40 50 60 0.001◦ /s ⇒ 0.008◦ /s Delay−lag (15 m.−lag) Delay−lag (15 m.−lag)
  • Introduction Model Results obtained ConclusionsAveraging in elevation-rate 0.002◦ /s - 0.003◦ /s 0.003◦ /s - 0.004◦ /s 0.004◦ /s - 0.005◦ /s Frequency (cycle/deg−elev.) Frequency (cycle/deg−elev.) Frequency (cycle/deg−elev.) 20 20 20 15 15 15 10 10 10 5 5 5 0 0 0 −5 −5 −5 −10 −10 −10 −15 −15 −15 −20 −20 −20 10 20 30 40 50 60 10 20 30 40 50 60 10 20 30 40 50 60 Delay−lag (15 m.−lag) Delay−lag (15 m.−lag) Delay−lag (15 m.−lag)
  • Introduction Model Results obtained ConclusionsAveraging in elevation-rate 0.005◦ /s - 0.006◦ /s 0.006◦ /s - 0.007◦ /s 0.007◦ /s - 0.008◦ /s Frequency (cycle/deg−elev.) Frequency (cycle/deg−elev.) Frequency (cycle/deg−elev.) 20 20 20 15 15 15 10 10 10 5 5 5 0 0 0 −5 −5 −5 −10 −10 −10 −15 −15 −15 −20 −20 −20 10 20 30 40 50 60 10 20 30 40 50 60 10 20 30 40 50 60 Delay−lag (15 m.−lag) Delay−lag (15 m.−lag) Delay−lag (15 m.−lag)
  • Introduction Model Results obtained ConclusionsAveraging in elevation-rate Consistent features from 0.006◦ /s - 0.007◦ /s 0.007◦ /s - 0.008◦ /s deep snow layers are shown from up to ∼0.005◦ /s. Frequency (cycle/deg−elev.) Frequency (cycle/deg−elev.) 20 20 15 15 10 10 5 5 Expected result: 0 0 interferometric information −5 −5 −10 −10 depends on the evolution of −15 −15 the different paths followed −20 −20 10 20 30 40 50 60 10 20 30 40 50 60 by each layer-contribution Delay−lag (15 m.−lag) Delay−lag (15 m.−lag) during the time-series of data (fixed).
  • Introduction Model Results obtained ConclusionsFirst conclusions from averaging A minimum elevation and elevation-rate are needed for obtaining persistent features in the lag-holograms. The apparently bad results at higher elevations may be due to the impact of low elevation-rate samples in the elevation averaging. Test: Data set divided in cells of elevation and elevation-rate ⇒ Averaging over most populated cell. 0.008 Cell division Elevation rate (deg−elev/s) 30 Counts Elevation rate (deg−elev/s) 0.006 25 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 20 0.008 PRN 0.004 15 0.006 10 0.004 0.002 5 0.002 0.000 10 20 30 40 50 60 70 0.000 10 20 30 40 50 60 70 Elevation angle (deg) Elevation angle (deg)
  • Introduction Model Results obtained ConclusionsAveraging in cell: Repeatability Dec 16th Dec 17th Frequency (cycle/deg−elev.) Frequency (cycle/deg−elev.) 20 20 15 15 10 10 5 5 0 0 −5 −5 −10 −10 −15 −15 −20 −20 10 20 30 40 50 60 10 20 30 40 50 60 Delay−lag (15 m.−lag) Delay−lag (15 m.−lag) Dec 18th Dec 19th Frequency (cycle/deg−elev.) Frequency (cycle/deg−elev.) 20 20 15 15 10 10 5 5 0 0 −5 −5 −10 −10 −15 −15 −20 −20 10 20 30 40 50 60 10 20 30 40 50 60 Delay−lag (15 m.−lag) Delay−lag (15 m.−lag)
  • Introduction Model Results obtained ConclusionsDepth of the contributing layers Most of the reflected signals comes from MODEL first 40 meters: especially 10 first meters –5 to 7 cycle/deg–, and from 30 to 40 meters depth –around 9 cycle/deg–. Remarkable contributions between 75 and 100 meters –10 to 12 cycle/deg–, and around 110 and 140 meters –14 and 16 cycle/deg–. Layers corresponding to depths with stronger gradients of snow density/permittivity from the in-situ measurements. DATA (averaged cell) 300 Snow Depth (m) 200 100 0 −18 −15 −12 −9 −6 Interf. Freq. (cycles/deg−el)
  • Introduction Model Results obtained ConclusionsOutline 1 Introduction 2 Model 3 Results obtained 4 Conclusions
  • Introduction Model Results obtained ConclusionsConclusions Persistent signatures have been found in the collected data-set of GPS reflections over dry snow. The frequency-domain analysis shows interferometric behavior (asymmetrical spectrum) with contributions below the snow surface level. No success when attempting a total inversion of the dry snow layers profile from the data. In spite of the poor results, the link between frequency bands in the lag-holograms and the depth of a reflecting interface is still a source of information. Alternative estimates In [Hawley et al., 2006], the delays of radar measurements are not used to estimate the snow density, but combined with a given density profile to estimate the snow accumulation rates. A similar application can be envisaged for this data set.
  • Introduction Model Results obtained Conclusions Thank you for your attention
  • Introduction Model Results obtained ConclusionsReferences Macelloni et al., 2006: G. Macelloni et al., ”DOMEX 2004: An Experimental Campaign at Dome-C Antarctica for the Calibration of Spaceborne Low-Frequency Microwave Radiometers”, IEEE Trans. Geosci. and Remote Sens., vol. 44, 2006. Wiehl et al., 2003: M. Wiehl, R. Legresy, and R. Dietrich, ”Potential of reflected GNSS signals for ice sheet remote sensing”, Progress in Electromagnetics Research, vol. 40, pp. 177–205, 2003. Hawley et al., 2010: R.L. Hawley, E. M. Morris, R. Cullen, U. Nixdorf, A. P. Shepherd and D. J. Wingham, ”ASIRAS airborne radar resolves internal annual layers in the dry-snow zone of Greenland”, Geophysical Research Letters, vol. 33, 2006.