SAR Altimetry Applications over Water Surfaces

Size: px

Start display at page:

Download "SAR Altimetry Applications over Water Surfaces"

Angelica Ray
5 years ago
Views:

1 SAR Altimetry Applications over Water Surfaces Cristina Martin-Puig(1), Jose Marquez(1), Giulio Ruffini(1), R. Keith Raney (2), Jérôme Benveniste(3) (1) Starlab Barcelona S.L. (2) Johns Hopkins University APL (3) ESA - ESRIN

2 Presentation Contents i. SAMOSA Project SAMOSA Context & Background SAR Altimetry in Brief SAMOSA in Brief SAMOSA Project Description Current Status: TASK 2 ii. Reduced SAR mode Cryosat Acquisitions Modes Cryosat Data Types L1 data What is reduced SAR mode? Why reduced SAR? How can we achieve Reduced SAR mode data? Cramer Raô-Bound to assess on reduced SAR mode performance Conclusions and SAMOSA Future work 2

SAMOSA context & background 2009, Cryosat-2 SIRAL altimeter apply Synthetic Aperture Radar (SAR) techniques to classical radar altimetry In addition to Cryospheric communities,

3 SAMOSA context & background 2009, Cryosat-2 SIRAL altimeter apply Synthetic Aperture Radar (SAR) techniques to classical radar altimetry In addition to Cryospheric communities, Hydrosphere and Oceanographic communities SAMOSA is the short name of Development of SAR Altimetry Mode Studies and Applications over Ocean, Coastal Zones and Inland Waters 3 Cryosat - ESA

4 SAR Altimetry in Brief SAR ALTIMETRY key innovation along track processing for: increased resolution multi-look processing better speckle reduction 4 [Ref] Raney, R.K., The Delay / Doppler Radar Altimeter, IEEE Trans. Geosci. Rem ote Sensing, 36, ,

5 SAMOSA in Brief Main objective Quantification of the improvement of SAR altimetry to conventional altimetry over water surfaces COASTAL, OCEAN, and IN LAND Consortium: Leader: Satellite Observing Systems (SOS,UK) The Danish National Space Centre (NDSC, Denmark) De Montfort University (DMU,UK) The National Oceanography Centre (NOCS, UK) Starlab Barcelona S.L (STARLAB,Spain). ESA Project Officer: Jérôme Benveniste (ESRIN) External collaboration of: Dr. Keith Raney (Johns Hopkins University) 5

6 SAMOSA Project Description Task 1 State of the art assessment Task 2 Range Error As a f on of ocean surface Task 3 Potential capabilities of SAR m ode data ov er ocean. How the higher resolution of SAR may enhance the capability to prov ide info on shortwav elength greop. Signals in areas of high wav es Task 6 Improv ement of capabilities for coastal zones estuaries, riv ers and lakes Task 5 New re-tracking m ethod ov er water Task 4 SAR Altimeter echo ov er water -Non circular ground resolution cell - Multiple looks Task 7 Assessm ent of RA-2 indiv idual echoes ov er water Task 8 Validation using ASIRAS data 6

7 Task 2 Three subtasks Theoretical modelling to characterize expected gain between high PRF reduced SAR mode data and Low PRF classical Low Resolution Mode (LRM) data Development of a software to reduce SAR FBR data to reduced SAR mode data Processing of simulated data sets over slowly varying open ocean Numerical Simulations Using CRYMPS 7

8 Cryosat Acquisition Modes PRF = 17.8KHz [Ref] Cy osat Mission and Data Description. Page 28th. ESA Doc No. CS-RP-ESA-SV

9 Cryosat Data Types L1 data - FBR A /D IFFT f s 2 Controllabl e delay Σ LRM data Level 1 FBR. Conventional Altimeter Multilooked echoes at a rate of ~20 Hz Chirp Generator Height Estimation Height Tracker SAR data Level 1 FBR Individual complex I,Q echoes SAR Altimeter Doppler Positon Maping A/D 2D Store AT FFT ifft 2 Σ f s Doppler Shift RCMC Phase Chirp Generator Height Tracker 9

10 What is reduced SAR mode data? Why reduced SAR? What? Reduced SAR mode data intends to emulate LRM starting with FBR SAR. Why? This data would let us achieve two different product types from the same acquisition mode (SAR) The resulting data would support quantitative comparison of the measurement precision delivered by the two styles of altimeter over identical sea states 10

11 How can we achieve Reduced SAR mode data? Echo wav eforms LRM PRF LRM = 1970 Hz 92 echoes at a rate of ~ 20 Hz t How many waveforms can we achieve for a reduced SAR mode? 64 Echo wav eforms. SAR PRF SAR = 17.8KHz 8-pulse pre-sum... n = 8 t How do we will analyze the performance? Pre-summing of n pseudo-lrm waveforms after pre-sum PRFSAR n = = 9 PRF 11 LRM CRAMÉR-RAO BOUND

12 Cramér-Rao Bound (CRB) I N point dataset X = {X[0], X[1],, X[N-1]} and X = f on (θ) we are interested in determine θ from X, or define a good estimator of θ! ˆ = g( X ) Provided that the data are inherently random p( X ;! ) CRB help us find the minimum variance of an unbiased estimator if it exists. CRB theorem If p( X ;! ) satisfies: " d ln( p( X ;! )) # E % = 0 for $! ' d! & ( var(! ˆ) " 1 2 #% ln p( X ;! ) $ & E ' 2 ( ) %! * 12

13 CRB II decorrelated signal simplification Focusing on complex, vectorial Gaussian-distributed signals * 1 # ( X E[ X ]) ( X E[ X ]) $ ( % % &'& % ) p( X,! ) = e card ( X ) " ' Being г the covariance matrix # ( [ ])( [ ]) * ij = E! X i $ E X i X j $ E X " % j ' &( CRB ugly expression ; de-correlated signal as is the case var(! ˆ) # CRB CRB " $% i 2 & $ E[ X i ] ' =, + % $! % ( $! ) * + i i i 2 13

14 Conventional Altimetry Complex Waveform s ( ) R t n( t ) s ( t) d A/D IFFT X =! S # S + " N # S = i R d d =! u + " N # S d Sea surface scat t ering Speckle st at ist ics Complex, Vect orial, zero mean, Gaussian PDF Thermal noise Complex, Vect orial, wit h Im and Re components both Gaussian, zero mean and st dev 1 Tricky part The reflect ed waveform will also be Gaussian wit h: E[ X ] = 0 i! $ = + % * ij E[ uiu j ] " #; 2 4! SNR1 1 H ( ) 1/3! = S " S d 14

15 Reduced SAR mode vs LRM The main difference of both modes is the term SNR 1 With reduced SAR for approximately 20Hz rate we will achieve 32 pseudo-lrm de-correlated echoes, while for LRM we will have 92 de-correlated echoes. This means that the reduce SAR mode SNR 1 will be approximately 10 log (sqrt(32/92)) = db worse than a conventional altimeter. 15

16 How much will be the previous degradation in precision? Considering that E[ X i ] = 0 CRB simplifies to: var(! ˆ) CRB " 2 # CRB = i 2 1 $% i % $! & # =! (" ; ) i ( 1/3 ) i fon H! ("; H 1/3 ) is related to θ and SWH, and θ related to r 16

17 Conclusions & future work The main difference in both acquisition modes is a SNR 1 degradation Future work needs to be done in numerically simulate the performance of range vs SWH. The theoretical work is completed A software to transform FBR data into pseudo-lrm data is almost completed This software will permit numerical simulations with CRYMPS data to assess on range precision (NOCS) Jensen & Raney DDA: Better Measurement Precision

18 Acknowledgements My collaborators: Jose Marquez, Giulio Ruffini and Dr. Keith Raney The SAMOSA team Jérôme Benveniste (ESRIN) Maria Milagro (ESRIN) 18

19 Thanks! Contact: Cristina Martin-Puig Starlab Barcelona S.L. Camí de l Observatori s/n Barcelona Spain cristina.martin@starlab.es SAMOSA Project Contact: David Cotton; d.cotton@satobsys.co.uk 19

20 De-correlated Signal Signal BW = 350 MHz Sampling rate approx 638.3MHz Nyquist = 2*BW = 700MHz Under-sampling, de-correlated signal 20

21 Coherence time r 1 "( c! + 2 H ) R # 1/3 0 = $ % 1 + R0 / Re & ' 1/2 f 1 f 2 Acros s track d Along track 21

Analysis of Doppler signals from nadir altimeters over ocean. F. Boy (CNES)

Analysis of Doppler signals from nadir altimeters over ocean F. Boy (CNES) Delay-Doppler Phase Altimetry of Radar Altimeter Pulses Launched in 2016, Sentinel-3A has been measuring oceans, land, ice to