Sentinel-1A Ocean Level-2 Products Validation Strategy Sentinel-1 Mission Performance Centre ESL L2 Team and Ocean Data Lab G.Hajduch (1), A.Mouche (2), P.Vincent (1), R.Husson (1), H.Johnsen (3), F.Collard (4) (1) CLS, (2) IFREMER (3) NORUT, (4) Ocean Data Lab CEOS Cal/Val Workshop, October 27-29, 2015 ESA-ESTEC, Noordwijk, The Netherlands 1
S-1 operational products Level 2 OCeaN Products Three main types of measurements: Wind Swell (not for TOPS) Radial Velocity
The OCN validation team Mission Performance Center (MPC) within the PDGS The S-1 MPC ESL L2 Algorithm and Validation team External Validation Team 3
Ocean Wind (OWI) Component (U10 WIND SPEED AND DIRECTION) 4
Wind Inversion Methodology ECMWF wind OWI process A priori wind direction= look angle (1 km2) GRD Bright target detection NRCS (1 km2) + incidence angle GMF -1 Bayesian inversion Wind inversion process SAR Wind speed SAR Wind direction The sea backscattering in C-Band depends on: Incidence angle Wind speed Wind direction vs look angle This is empirically modeled by a wind geophysical model function (GMF) This GMF can be inverted in order to measure wind speed from NRCS, knowing wind direction 5
Example of inverted wind field ECMWF analysis Sentinel-1 inverted wind (IW acquisition) Higher spatial resolution Better capture of local effects 6
Wind Validation strategy Using collocated data from model: ECMWF re-analysed (for WV acquisitions) MF / AROME model (for IW/EW/SM) MF / ARPEGE HR model (for IW/EW/SM) L2 product -SAR Wind direction -SAR wind speed wind model Wind performance Resampling on model grid Comparaison Statistical plots SAR vs model wrt subswaths Bias :SAR model wrt: wind speed look angle elevation angle 7
Wind speed Using collocation data from Ifremer Arpege: 182 files Arome: 100 files SARl SARl model model On IW VV Statistics and performance within specification 8
Wind direction Using collocation data from Ifremer Arpege: 182 files Arome: 100 files SARl SARl model model On IW VV Statistics and performance within specification 9
Calibration constant / relative RCS Done on 1 month of acquisition (September 2015) L2-IW VV For IW and EW bias and trends captured by the ocean calibration are similar to the one captured by the transponders and over rain forest L2-EW VV
NESZ monitoring L2-IW VV Selection of products with Model wind speed < 3m/s Histogram of NRCS wrt elevation angle L2-EW VV 11
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Wave Inversion Methodology (Swell and wind sea) SAR Imagettes are acquired and processed to SLC 3 sub looks are estimated and cross-spectra are estimated: Suppress speckle and estimation of wave propagation direction using phase information Inversion of cross spectra: Suppress the low frequency signature (low wind, atmospheric signals ) Estimate the MTF (Modulation Transfer Function) Spectral Partitioning Estimate the integral parameters (significant wave height, Peak wave length and direction) for each 13
Example of swell measurement Significant Wave Height (SWH) Wave Length Per partition Wave Direction 14
SWH: integration of the full spectrum VV Operational configuration for WV HH Performances seems better in WV1 than in WV2 for VV
Some cases of underestimation of SWH In some cases, low frequency signal is not well removed and lead to underestimation of Hs Under improvement Underestimate Neighbor imagette without underestimate
Ocean Surface Current Measurement WISE - 2014/06/08 - Reading 17
Doppler calibration Wave Mode acquisitions over the ocean DC shift is strongly correlated with radial wind speed Ex: For along track wind, there should be zero DC. Radial wind speed & corresponding doppler shift Results from Envisat/ASAR Doppler shift = DC (sig) DC (geo) Doppler shifts vs. Radial wind speed vs. Doppler shifts vs. Azimuth angle
Doppler calibration Comparison between radial wind and DC shows different behaviors: - Slope changes with latitude S-1 Doppler shift & Latitude Monitoring the DC shift dayby-day, latitude-by-latitude. Envisat/ASAR Doppler shift
Empirical Relationship between Doppler and the radial wind speed for WV1, ascending passes. Top left: no Geometric or mispointing correction strong latitude dependency. Bottom left: For each latitude, bias estimated between expected and observed relationship (btw. Doppler and radial wind speed). Blue and green for 2 different methods. Red is a very simple linear fit on the blue results. change of roughly 40 Hz between -60 and 60 north. About 60 Hz between N and S pole. Top Right: Correction applied to all the data from September and re-plot Doppler with respect to radial wind speed. As expected, no latitude dependency - no matter the wind speed.
Doppler scalloping StripMap Azimuth scalloping in Doppler (ampl. 14Hz) Caused by cal pulses in SL2 Cal pulses removed June 2015: Scalloping disappeared TOPS Scalloping (2Hz) Will be solved by IPF update
Conclusion L2 OCN Wind Within specifications Improvements planned with: Better correction of range radiometric variations and IW bias Denoising during the wind inversion process L2 OCN Swell Promissing results Identified issues to be fixed by the end of the year: Swell: Partitionning, long swell systems, MTF Wind sea: Empirical relation for wind sea estimation L2 OCN Radial Velocity Unexplained dependencies between DC and latitude Changes in DC shift after each star tracker re configuration Identified issued Scalloping on SM solved by removing cal pulses Scalloping on TOPS to be fixed by the end of the year with IPF update 22