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 This document has been reviewed and determined not to contain export control material NASA/JPL Proprietary – Not for Public Release or Redistribution

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

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

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

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

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

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

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

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

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

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

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

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

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

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