the potential of altika to address swot phenomenology (part 1) · the potential of altika to...
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The potential of AltiKa to address SWOT phenomenology (part 1)
SWOT SDT mee)ng, Washington, January 2014, 14 -‐ 16
Denis Blumstein, Nathalie Steunou, Nicolas Picot Stéphane Calmant, Fernando Niño
Introduction
AltiKa can bring large scale view of the geophysical situation � Topography � Backscatter (not a lot of measurements available in Ka band) But not only. It could also be used to investigate � Interaction of Ka radar waves with the terrain (surface/volume, absolute calibration) � Backscatter variability at a few hundred meters scale � Backscatter angular variation in the range 0, 0.5 degrees of incidence � … � With some work (waveforms inversion)
� Some examples follows
SWOT SDT meeting, Washington, January 2014, 14 - 16 2
Computation of sigma0 in AltiKa products (GDR, SGDR) 1/2
Results of 4 retrackers available (ocean, ice1, ice2, seaice) Main hypothesis � The surface is flat and horizontal at large scale � The backscatter is uniform This is consistent with what has been done for Envisat products Other important points � No correction for atmospheric attenuation applied to σ0 40 Hz � σ0 ocean must be used with great caution over land � σ0 ice1 and ice2 do not take into account antenna gain pattern
SWOT SDT meeting, Washington, January 2014, 14 - 16 3
Computation of sigma0 in AltiKa products (GDR, SGDR) 2/2
As is, this provides useful information. But users must be conscious of these hypothesis if they do not work for them. Some examples will be shown � When the backscattered power comes mainly from a small part of the footprint
(narrow river, coast) � When there are sharp backscatter contrasts across the scenery � When there is a terrain slope or « random » variations of altitude
Traditionally, users/scientists for which the preceeding hypothesis do not work (e.g. for glaciology applications) provide their own geophysical corrections
è Brenner [1983], Rapley [1986], Remy [1989], … è in these cases, additional information must be used to be able to retrieve the « real »
backscatter of the terrain
SWOT SDT meeting, Washington, January 2014, 14 - 16 4
Waveforms inversion
AltiKa background � Footprint computed on a flat surface is around 8 km (diameter, 3dB beam limited
footprint, the pulse limited footprint is slightly larger) � Along track sampling distance is around 160 meters This creates some redundancy in the measurements that can be used to get information at a resolution largely better than the footprint size à waveforms inversion
Inspiration came mainly from 2 papers � V.Enjolras and E.Rodriguez, Using Altimetry Waveform Data and Ancillary Information
From SRTM, Landsat, and MODIS to Retrieve River Characteristics, 2009 � J.Tournadre, B.Chapron, N.Reul , High-Resolution Imaging of the Ocean Surface
Backscatter by Inversion of Altimeter Waveforms, 2011 The techniques we use today for the inversion are different And from many fruitful discussions with collegues at LEGOS, CNES and CLS.
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Case study #1 : Narrow river (cy1, tr149)
� Very narrow river (around 30 meters wide according to google earth) � Need to know the width of the river in order to compute the real backscatter
è Optical image or maybe « Global River bankfull width and depth database » (Andreadis et al) � Main return is from the water (very specular return)
è Backscatter angular variation model can be computed from analysis of along track peak power evolution and waveforms shape
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Landsat image June, 17 2013
Case study #2 : Contrast inversion (cy1, tr149)
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� Backscatter greater on the island than on the river � Peak of return power comes from a flat surface (water under the trees ?) � σ0 = 44 dB (TBC), very specular return
è backscatter angular variation model = a gaussian with FWHM = 0.05 deg)
Landsat image June, 17 2013
Case study # 3 ─ Hydrology (Japura River) – comparison Ka/Ku
Tr 078 Tr 121
From a series of 3 crossing points provided by Stéphane Calmant River width 1.8 km
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Case study #3 ─ Hydrology (Japura River)
Measurements (sigma0 ice1) � contrast between river and land : inversion between cycle 4 and cycle 5 � caused by the island ? (closer from satellite track in cycle 5)
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Landsat image December, 29 2013
Case study #3 – Altitude difference AltiKa - SRTM
Differences versus SRTM � Small on the river � 15 à 20 meters on the forest
Track 78 Cycles 1 to 8 (March to
November)
10 km
10
Case study #3 – Comparison AltiKa – Envisat (Ka/Ku)
Cycles 4 – 8 : June – November 2013 Cycles 86-‐90 : January – June 2010 è Retracker ice1 results (from the GDR) è Similar behaviour Ku/Ka but better spatial sampling for AltiKa
è Water seems to be present under the trees (and seen by AltiKa)
è Some behaviour of the height rerievals not yet completely understood
è Note: error in height retrieval under the canopy certainly not negligible
Conclusions and Perspectives
� This is work in progress � Waveforms inversion could not be performed on a global scale but for AirSWOT data
analysis or case studies this is possible and can be useful � Analysis of AltiKa data could also bring our attention on some particular behaviour of
Ka Band phenomenology that must be addressed (for example to develop the SWOT Algorithms)
� Analyse AltiKa data to get statistically significative distribution of specular returns in Ka over inland water
� Identify simpler cases for studies of the impact of the incidence angle (0 to 0.5 deg) in case of water under the canopy
è research : success not guaranted but interest makes worth the effort è more uniform forests, other areas / kind of forests / wetlands / boreal regions
� AirSWOT campaigns � Some activities are planned by CNES project in 2014 regarding backscatter analysis
in Ka band
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Perspectives (2/2)
In the frame of AltiKa data exploitation � Comparison of the different retracking methods and the resultant sigma0
estimations � Comparison of the estimations over some dedicated areas between Ka and Ku � Studies of high rate AltiKa data : some acquisitions over oceans in high data rate
mode will provide waveforms at the PRF rhythm in amplitude and phase. Analysis of the correlation will be done
Experimental and theoretical studies � Measurements of backscatter in wind tunnel, with simultaneous measurement in
Ka and Ku band in different wind conditions � In parallel : improvement of the backscattering models
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Reminder: impact of the topography
AltiKa First Verification Meeting, Toulouse, 28 Août 2013 15
For AltiKa & Envisat Altitude (~ 810 km) over Antarctica
� Generally, the altimeter manage to track the closest point not the nadir � Geophysical corrections are needed � This is not a new subject (Brenner [1983], Rapley [1986], Remy [1989], …)
α y dhdeg km m0,0 0,0 00,2 2,8 50,4 5,6 190,6 8,4 440,8 11,2 781,0 14,0 1221,2 16,8 175
y
Impact of terrain slope on the apparent sigma0
SARAL/AltiKa 1st Verification Meeting, Toulouse,, August 2013, 27th-29th 16
0° 0.1° 0.2° 0.3° 0.4° 0.5° 0° 0.1° 0.2° 0.3° 0.4° 0.5°
� Computed with a synthetic antenna diagram (gaussian)
Case study #4 : NorthWest Australia (cy6, tr034)
� Confirmation that contrast land/sea is around 5 dB Input for the simulation
� Ocean : σ0 = 11 dB, Land : σ0 = 6 dB (see slide 8 for simulation of the beach) è But lot of variability of the land surface
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Case study #4 : NorthWest Australia (cy6, tr034)
Input for the simulation � Ocean : sigma0 = 11 dB
� Land : σ0 = 6 dB
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Case study #4 : NorthWest Australia (cy6, tr034)
Focus on the bright return near the beach � Not permanent, not specific to Ka (also seen in Envisat data) � Seems to be explained by a quasi specular return on a slightly inclined beach
è σ0 = 45 dB (TBC), backscatter angular variation model = a gaussian with FWHM = 0.25 deg
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Rough, hummocky (1 cm thick), snow
Smooth, mostly clear ice
Smooth; congelated pancake ice
Snow-covered ice
Double-layered ice
Snow, small patches of clear ice
Snow, small patches of clear ice
Courtesy Alexei Kouraev
466, cycle 1, 30 March 13
Open ice in coastal areas High backscatter when no snow Courtesy Alexei Kouraev