G.singh.IGARSS-11.pdf
Upcoming SlideShare
Loading in...5
×
 

G.singh.IGARSS-11.pdf

on

  • 703 views

 

Statistics

Views

Total Views
703
Views on SlideShare
695
Embed Views
8

Actions

Likes
1
Downloads
18
Comments
0

1 Embed 8

http://www.grss-ieee.org 8

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

G.singh.IGARSS-11.pdf G.singh.IGARSS-11.pdf Presentation Transcript

  • Potential Assessment of SAR in Compact and Full Polarimetry Mode for Snow DetectionGulab Singh, Yoshio Yamaguchi, Sang-Eun Park Gopalan Venkataraman Niigata University, Japan IIT Bombay, India
  • Outline• Introduction• SAR Measurements• Snow monitoring methods• Study Area: Part of Himalayan Snow and Glacier Covered Region• Summary
  • Introduction: previous studies[1] J. C. Souyris, et. al., “Compact polarimetry based on symmetry properties of geophysical media: The π/4 mode,” IEEE TGRS, vol. 43, no. 3, pp. 634–646, Mar. 2005.[2] R. K. Raney, “Dual polarized SAR and Stokes parameters,” IEEE GRSL., vol. 3, no. 3, pp. 317–319, Jul. 2006[3] R. K. Raney, “Hybrid-polarity SAR architecture”, IEEE TGRS, vol 45, no. 11, pp. 3397-3404, 2007.[4] P. Dubois-Fernandez, et. al., “Compact polarimetry at low frequency”, IEEE TGRS vol. 46, no. 10, pp. 3208–3221, 2008Applications in land parameters estimation over flat terrain /region[5] M. Lavalle, “Full and Compact Polarimetric Radar Interferometry for Vegetation Remote Sensing”, Ph.D. Thesis, Université de Rennes 1, France, 2009.[6] T. L. Ainsworth, J. P. Kelly and J.-S. Lee, “Classification comparisons between dual-pol, compact polarimetric and quad-pol SAR imagery”, ISPRS Journal of Photogrammetry and Remote Sensing, 64, pp. 464-471, 2009.[7] F.J. Charbonneau, B. Brisco, R.K. Raney, H. McNairn, et. al., “ Compact Polarimetry overview and applications assessment”, Can. J. Remote Sensing, vol. 36, no. S2, pp. S298-S315, 2010.****************************************************************************************************************************************[8] S. R. Cloude, Polarisation: Applications in Remote Sensing. London, U.K.: Oxford Univ. Press, 2009 The compact assumptions in [1],[4]-[6] do not apply to scattering from sloped terrain  [2],[3],[7] , hybrid system 3-dB loss in the radar signal , mismatching the transmitter and receiver polarization basis the system and theoretical justification issues*****************************************************************************************************************************************out of several land parameters ……… snow……………
  • SASE Observatory at Solang, HimachalSnowfall SASE HQ 19-01-2006 Snow parameters in mountain areas are particularly sensitive to changes in environmental conditions.Timely information about snow parameters andtheir temporal and spatial variability representsa significant contribution in climatology, localweather, avalanche forecasting and for thehydropower production in high mountainousareas.
  • Ground-based method represents only exact location measurements of field observations which may not be representative of a large area or basin. 20-01-2009 Snow covered : gentle slopeDue to the strong spatial and time 23-01-2009dependent dynamics of snow cover,frequent observation cycles are necessary. Snow free : Steep Slope Snow covered: River (Solang Nala) Bank, Himachal
  • SAR interaction with snowpackSAR Air/snow interface snowsnow/ground interface Ground
  • m3 Dry snowm3m3m3 εs>> εs
  • 1.27 GHz 95 90 5.6 GHz 85 at snow density at 300 kg/m3Penetration Depth (δp in cm) 80 9.6 GHz 75 70 65 60 55 50 45 40 35 30 25 εs>> εs 20 15 10 5 0 .5 .5 .5 .5 .5 5 5 5 5 5 5 5 5 5 5 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10 11 12 13 14 Snow Wetness (Ws in %)
  • SAR measurementsSingle Polarization (ERS-1/2,JERS/PALSAR, Radarsat-1/2, ASAR, TSX)Dual -Polarization (ASAR, PALSAR, TSX, Radarsat-2)Quad Polarization (PALSAR, TSX, Radarsat-2)Compact Polarization (MiniSAR/Chandrayaan-1)(Hybrid C-L)a few satellites are planned by leading space agencies for earth observations Snow/ice monitoring ??
  • • With the quad polarization capabilities, newer generation spaceborne SAR sensors are expected to lead significant improvements in easily snow identification based on microwave scattering mechanisms 24-05-2010 AVNIR-2 06-06-2010 PALSAR
  • • Is SAR acquisition in quad polarization advantageous as compared to SAR acquisition in single, dual and hybrid polarization for monitoring snow cover in mountainous area (Himalayas)?
  • ENVISAT-ASAR APS and ALOS-PALSAR SLC data Date Sensor Polarization Off-nadir Orbit pass GMT Himalayan angle (0) (hh:mm:ss) Regions19/05/2007 ENVISAT-ASAR HH+VV 39.1– 42.8 Descending 04:35:58 Badrinath10/11/2007 ENVISAT-ASAR HH+VV 39.1– 42.8 Descending 04:37:42 Badrinath12/05/2007 ALOS-PALSAR Quad-Pol 21.5 Ascending 17:04:40 Badrinath12/11/2007 ALOS-PALSAR Quad-Pol 21.5 Ascending 17:04:31 Badrinath22/05/2009 ALOS-PALSAR Quad-Pol 23.1 Ascending 17:13:13 Siachen N Siachen Badrinath 126cm – 886cm
  • Snow Monitoring Methods Based on-Single Polarization (temporal changes)-Dual –Polarization (Pol. ratio)-Quad Polarization-Compact (Hybrid CL)Polarization SAR measurements
  • PALSAR Backscatter Response ~10 times lower σ0
  • Problem with single/Dual Pol. SAR data for snow mappingAVNIR-2 (06-05-07) Snow Map (ASAR) Snow Map (PALSAR) Snow map (PALSAR)
  • ALOS PALSAR Quad Polarization SLC Data Snow Extract Scattering Matrix(S) DetectionMulti-Looked (6×1) in (Azimuth × Range) Algorithm and make Coherency Matrix (T3) (SDA) (HV≈VH) Polarimetric Speckle Filtering Generate Eigenvalues Image (λ1, λ2, λ3) Generate Polarization Fraction value image 33 0  PF  1  1 1  2  3 PF >=0.55 && Normalized λ3<0.015 NO YES Non-snow feasible Area Snow Area
  • Problem with Single/Dual Pol. SAR Data for Snow Mapping - Resolved by Quad Pol. SDA based Snow Map Snow Cover Area Non-snow feasible Area
  • snow cover (magenta) derived from PALSAR (26-05-11), Discrimination of snow from overlaid to AVNIR-2 (24-05-11) other Bragg scattering dominant Agassizhorn region, surface may be problematic. Bernese Alps, SwitzerlandL-band fully polarimetric SAR isnot able to detect shallow-depth snow
  • Study Area (snow and glacier covered terrain) Part of Indian Himalaya (place of ice ) Siachen Glacier area Standing snow Length ~73 km 1.2-8.8 m (low-high altitude) SWE Product of AMSR-E Aug., 2007Feb., 2007
  • FP vs CPFP [C16]  [C9] (monosatic) (refl. sym.)[C5]  SDA  CP[J4] refl. & rot. sym.  *C’5] m-δ Tx=LHC, Rx=H,V [1]-[8]
  • FP vs CPFP [C16]  [C9] (monosatic) (refl. sym.)[C5]  SDA  CP[J4] refl. & rot. sym.  *C’5] m-δ Tx=LHC, Rx=H,V PolSARPro ver. 4.1.5 (ESA) ver. 2.0 [1]-[8]
  • [C9] (SHV=SVH) [C5] (SHHS*HV ≈ S VVS*VH ≈0) SDA CP [J4] = [Cꞌ5] (SHHS*HV ≈ S VVS*VH ≈0) Degree of polarization[1]-[8] Relative Phase
  • PF-λ3 approach (FP) PF-λ3 approach(FP-RS) PF-λ3 approach (CP) ζ0HV/ζ0HH 22-05-2009 SD126-886 cm (low-high altitude) Non- snow m-δ approach  feasible area FP : Full Polarimetry CP : Compact PolarimetryFP-RS : Full Polarimetry with Reflection Symmetry condition Data  Dual-Pol CP CP FP-RS FP Approach σ0HV/σ0HH m-δ PF-λ3 PF-λ3 PF-λ3 Non-snow feasible area 71.38 67.21 56.56 45.52 40.98 (%) Snow area (%) 28.62 32.79 43.44 54.48 59.02
  • Summary• Importance of snow studies• PALSAR backscattering coefficient response for various features• Comparisons between single, dual, compact and quad polarization data for snow detection• Identification of suitable polarimetric descriptors for discriminating the snowpack – PF and normalized λ3
  • Summary• Results with single polarization SAR (C-&L-band) for snow discrimination not good.• Results with dual polarization SAR measurements better than single pol. But it does not care of unwanted topographic distorted area.• Full polarimetry SAR technique SDA has produced promising results.• SDA ……takes care of unwanted topographic distorted area ...... suitable for CP too. ****CP shows capability ̴ 15% less than FP****
  • Snow PracticabilityQuad Pol.>Compact Pol.>Dual Pol.>Single Pol. PF-λ3 > m-δ > σ0 0 HV/σ HH
  • 3-CSPD (True) 3-CSPD (Pseudo) m-δ DecompositionMay 22, 2009
  • Odd-bounce Scatterers80000 Peak700006000050000 Volume scatterers Peak40000 Even-bounce Scatterers30000 Peak2000010000 0 -3.14 -1.57 0 1.57 3.14
  • PF-λ3 approach ζ0HV/ζ0HH m-δ approach
  • Transmission V H-jV +j Left Circular Transmission (LC)Reception 2 Rotation: Anti - Clockwise Wave Vector: H+j•V H Reception HH + j • HV 1 LH Scattering Vector: = j • VV + HV 2 LV
  • Badrinath Region ASAR SNOW FREE SNOW COVERWet Snow Cover in month of May 2006
  • Badrinath Region ASAR SNOW FREE SNOW COVERWet Snow Cover in month of September 2006
  • PF Images
  • RSI based snow cover mappingFP ̴3% SCA more than CP
  • Symmetry Test (Noise test) -20 Himalayan RegionBackscattering Coefficient (in dB) PALSAR image on 12-05-2007 -25 -30 HV VH SNOW COVERED AREA -35 1 31 61 91 121 151 Distance in Pixels Backscattering Coefficient (in dB) 0 -5 HV VH -10 -15 -20 DEBRIS COVERED GLACIER -25 1 51 101 151 201 Distance in Pixels
  • FRASlopeMapover partof Himalaya
  • Penetration Depth at Snow Cover Terrain Penetration depth can be written with some approximations ε’ >> ε” asDry Snow = ice particles + air + no liquid water Wet Snow = ice particles + air + contents of liquid water Ice εds = 1 + 1.7 ρs + 0.7 ρs2 Air Grain Boundary (Tiuri et al. 1984) Liquid water εds = 1 + 1.5995 ρs + 1.861 ρs3 f0= 10 GHz for ρs <0.45 gm/cm3 (Matzler 1996) εds" = εice" ( 0.52ρs + 0.62 ρs2 ) (Matzler 1987) where εice" = 0.008 (Matzler 1988)