1. The potential of AltiKa to address SWOT phenomenology: Water/Land so Contrast
Mark Haynes, Brent Williams, Eva Peral, Daniel Esteban-Fernandez, Ernesto Rodriguez
NASA-JPL California Institute of Technology
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2. Jet Propulsion Lab, CalTech
Motivation
Normalized radar cross section: so
Characterizes the radar backscatter power of diffuse scattering scenes (ocean, trees, etc)
Depends on frequency, incidence angle, scattering statistics of scene
The ability of SWOT to distinguish land vs. water for hydrology depends heavily on the so contrast between land and water
Current sources for Ka-Band so values (ocean/land)
Difficult to model and measure
Few published measurements
JPL bridge measurements
AirSWOT will be a critical source
AltiKa benefits
Global Ka-band radar altimeter
Provides reflected power measurements
Same frequency, similar incidence angles as SWOT
AltiKa drawbacks
Nadir incidence produces bright returns from smooth water surfaces
Large footprint covers multiple land types
so angle dependence difficult to extract so so
AltiKa
SWOT
Frequency
35.75 GHz
Bandwidth
480 MHz
200 MHz
Incidence angle
0o +/- 0.5o
0.5o – 4.5o
30 meter range window
8 km swath
SWOT SDT Jan 14-16, 2014
Jet Propulsion Lab, CalTech

3. AltiKa Global Ocean Sigma0 Statistics
Histograms of AltiKa 1 Hz so data product
Orbit cycles {4, 5, 6} (1 cycle  1 month)
Upper bound ocean so for SWOT
AltiKa measures nadir incidence, so decreases with increasing observation angle
Cycle 4: mean 11.8 dB, upper 68% cutoff = 10.1 dB
Cycle 5: mean 11.8 dB, upper 68% cutoff = 10.1 dB
Cycle 6: mean 11.6 dB, upper 68% cutoff = 10.1 dB
*Atmospheric correction included
SWOT SDT Jan 14-16, 2014
Jet Propulsion Lab, CalTech

4. Relative Water/Land Power Contrast
Initial approach: use AltiKa echoes as a basic reflected power measurement
Compare water/land integrated waveforms to obtain a relative comparison of reflected power
Hand picked water/land transitions with visually homogeneous vegetation without major bodies of water or irrigation
Caveats: nadir incidence, 30m range window over 8 km footprint
SWOT SDT Jan 14-16, 2014
Jet Propulsion Lab, CalTech

5. Case #1: Northwestern Australia Cycle 6, Pass 34
Clean leading edges
Signal above noise level
Specular echo
(tides, beach slope,
estuary)
Integrated waveform value plotted as antenna footprint
30 meter total range window
at 0.3 meter resolution
4 dB ocean/land relative reflected power contrast
SWOT SDT Jan 14-16, 2014
Jet Propulsion Lab, CalTech

6. Jet Propulsion Lab, CalTech
Case #1: Cycles 4 and 5, Pass 34
Shows variability of water return echoes between cycles of the same scene
Dominated by first returns, not representative of higher incidence angles
Cycle 4
Cycle 5
4 dB ocean/land contrast
10 dB ocean/land contrast
Calm wind conditions
SWOT SDT Jan 14-16, 2014
Jet Propulsion Lab, CalTech

7. Case #2: West Papua Cycle 6, Pass 62
AltiKa’s nadir incidence angle and large footprint renders smooth water echoes unusable
River crossing
AltiKa
0o +/- 0.5o
incidence
SWOT
0.5o – 4.5o incidence
Inland forest
Coastal forest
Coastal water
River
basin
Ocean
Mountain
6 dB contrast
10 dB contrast
4 dB contrast
SWOT SDT Jan 14-16, 2014
Jet Propulsion Lab, CalTech

8. Next steps: Quantitative inversion for land so
Use CNES and JPL simulation tools to invert for land so
Step 1) Altimeter waveform simulator
Goal: predict and confirm observed contrast levels and scattering phenomena, understand the sensitivity of the simulator to topography
CNES AltiKa Simulator
JPL SWOT Ocean Simulator
Step 2) Inversion algorithm
Inversion needed to separate topographic and incidence angle contributions to so
Approaches: a) Map echo power onto a DEM, b) in addition, iterate so map with the waveform simulation
SWOT SDT Jan 14-16, 2014
Jet Propulsion Lab, CalTech

9. AltiKa Waveform Simulation (CNES, JPL)
Simulated
Case study #1 : Northwest Australia (Cycle 6, Pass 34)
Confirmation that land/sea contrast is ~5 dB
Input for CNES and JPL simulations
Ocean: s0 = 11 dB, Land: s0 = 6 dB, Beach: s0 = 45 dB
CNES: Lidar DEM
JPL: SRTM DEM
Run with simplified assumptions
Simulated returns highly sensitive to accuracy of DEM
Restricts areas of investigation to areas with good DEM
Variations of power with range can be simulated, can extrapolate to get angle variation in so
Measured
Simulated
SWOT SDT Jan 14-16, 2014
Jet Propulsion Lab, CalTech

10. Simulation of Beach (CNES)
Case study #1 : Northwest Australia (Cycle 6, Pass 34)
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
s0 = 45 dB, backscatter angular variation model = a gaussian with FWHM = 0.25 deg
SWOT SDT Jan 14-16, 2014
Jet Propulsion Lab, CalTech

11. so Inversion from Measured Waveforms
Version 1 (non-iterative)
Project the power of each waveform and range line onto DEM, normalize by intersected area
Requires accurate DEM
Version 2 (iterative)
Use waveform simulator to model returns
Parameterize angle dependence of so
Also requires land type classification and accurate DEM
Power projection onto DEM
Same pixels are seen from multiple sensor positions
Inversion separates the contribution of each pixel to different waveforms
SWOT SDT Jan 14-16, 2014
Jet Propulsion Lab, CalTech

12. Viable Global Land Echoes for so Statistics
dB (power)
Ultimate goal is to obtain global statistics of land so at Ka band and 0.5o-4.5o incidence angles
Want to use AltiKa to provide SWOT with an upper bound
AltiKa not designed to work over land (30 m range window)
Anywhere the return is above the noise can potentially be used as a power measurement
Look for integrated land echo powers above the noise
Low SNR: extreme topography (mountains), diffuse scattering
Good SNR: Altimeter was tracking
Noise level = 51 dB
Min SNR (dB)
Percent > min SNR 5
73% 10
53% 15
35%
5, 10, 15 dB minimum SNR
50% global land surface potentially available for land so statistics
Noise level = 51 dB
SWOT SDT Jan 14-16, 2014
Jet Propulsion Lab, CalTech

13. Viable Global Ice Echoes for so Statistics
AltiKa has numerous ice so products (have not yet looked at these in detail)
Same integrated waveform analysis as land (previous slide)
Segmented with ‘ice_flag’
80% of integrated ice returns 10 dB above noise
Useful for hydrology in frozen conditions
Noise level = 51 dB
5, 10, 15 dB minimum SNR
Min SNR (dB)
Percent > min SNR 5
93% 10
80% 15
27%
Noise level = 51 dB
80% of ice surface potentially available for ice so statistics
SWOT SDT Jan 14-16, 2014
Jet Propulsion Lab, CalTech

14. Jet Propulsion Lab, CalTech
Conclusions
Initial evaluation of AltiKa to address SWOT phenomenology
AltiKa is a valuable data set for SWOT (global, similar system parameters)
Excellent ocean so statistics
Reasonable relative ocean/land contrast
Early evaluation is promising, more information to extract
Issues using AltiKa for SWOT evaluation
Nadir incidence and limited range window
Scope of next steps:
Refine waveform simulators (CNES, JPL), update DEMs (SRTM 30m)
Formal inversion for so with complete models
Investigate ground truth (US National Land Classification Data)
Automate site selection for global sampling
Schedule of next steps
In the next 2-3 months, determine if we can extract hard values for land so as a function of incidence angle with global sampling
Design AirSWOT campaigns to cross validate these efforts (already have AirSWOT flights under AltiKa lines)
SWOT SDT Jan 14-16, 2014
Jet Propulsion Lab, CalTech

15. Jet Propulsion Lab, CalTech
AltiKa Geometry
Area projection for 65 range bins
Incidence angle taken at leading edge of bin
Assume, flat surface
h = 787 km (for ocean waveforms on previous slide)
Areas are nearly the same qi
h + nDr
0.4 deg incidence h a Dr
Jet Propulsion Lab, CalTech