gnss reflectometry for earth remote sensingweb.stanford.edu/group/scpnt/pnt/pnt19/... · r a i d i...

R a

d i

o N A V I G A T I O N

l a b

GNSS Reflectometry for Earth Remote Sensing

James L. GarrisonPurdue University, West Lafayette, USA

Stanford PNT SymposiumInvited Presentation

Stanford, CAOctober 30, 2019

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

Outline

• Basic Principles of Reflectometry• Early oceanographic applications

• Spaceborne reflectometry• Research directions: Data assimilation

• Other variables• Future work – beyond GNSS

• Conclusions

2

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019 3

GNSS Basic Principles

cτ1 = (X − X1)2 + (Y −Y1)

2 + (Z − Z1)2 +ctb

cτ 2 = (X − X2 )2 + (Y −Y2 )

2 + (Z − Z2 )2 +ctb

cτ 3 = (X − X3)2 + (Y −Y3)

2 + (Z − Z3)2 +ctb

cτ 4 = (X − X4 )2 + (Y −Y4 )

2 + (Z − Z4 )2 +ctb4+ Observations

4 Unknowns

τ1

τ 2

τ 3τ 4

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

• Ideal navigation signal – infinitely-long sequence of random pulses (in reality – Pseudorandom noise (PRN)

• Received and local signals not aligned

• Received and local signals partially aligned

4

GNSS Basic Principles

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

• Cross-correlation of ( -long) sequence of random pulses – large values for narrow range of delay

-Tc Tc

1

5

GNSS Basic Principles

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

GNSS-R Basic Principles

6

Receiver

glistening zone

glistening zone

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

Rough Surface Scattering

7

(From Chapron and Ruffini, 2003 GNSS-R workshop, Barcelona. Photo taken at Le Conquet, Brittany)

Phenomenon observed in visible light at sunset: (water is calm inside the red ellipse)

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

Outline

• Basic Principles of Reflectometry• Early oceanographic applications• Spaceborne reflectometry• Research directions: Data assimilation• Other variables• Future work – beyond GNSS• Conclusions

8

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

Early Airborne Experiments

Measured Waveform

Surface Wind Speed (Assumed wave spectrum model)

[Garrison, et al. GRSL 2011]

Best Fit of Model Waveform

9

[Garrison, et al TGARS 2002]

Brightness Temperature

Ca. 1997

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

Outline

• Basic Principles of Reflectometry• Early oceanographic applications• Spaceborne reflectometry• Research directions: Data assimilation• Other variables• Future work – beyond GNSS• Conclusions

10

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

Why GNSS-R from Space ?

11

C. S. Ruf, et al., Bull. Am. Met. Soc., V. 97, N. 3, pp. 385–395, 2016. DOI: 10.1175/BAMS-D-14-00218.1

Simulated Coverage of Hurricane Frances: ASCAT vs. CYGNSS

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

Going to Space …Maximum Delay for 25 km resolution

12

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

• Spaceborne observation – 2D “delay-Doppler Map” (DDM)• 25 km resolution requirement: 3 X 5 pixels used in L2 retrieval • Downloaded to the ground: 17 x 11 pixels around specular point

13

Going to Space …

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

CYGNSS

14

• NASA Earth Ventures Mission: Launched Dec. 2016• 8 satellite GNSS-R constellation

• Improved forecast of tropical cyclone intensification:

1. Better penetration of rain (L-band vs K-band)

2. Higher revisit rate (2.8 H med. vs 11-35 H)

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

CYGNSS

15

https://www.youtube.com/watch?time_continue=1&v=rRBqn6JPtv8

Cyclone GNSS (CYGNSS) Launch 12-Dec-2016[Ruf, et al. JSTARS, 2018, DOI:10.1109/JSTARS.2018.2833075]

[University of Michigan]

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

Outline

• Basic Principles of Reflectometry• Early oceanographic applications• Spaceborne reflectometry• Research directions: Data assimilation• Other variables• Future work – beyond GNSS• Conclusions

16

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

Let’s return to this picture …

17

Data Assimilation

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

Data Assimilation

18

Integralover surface

Cross-section: Proportional to

Probability of slope scattering

in correct direction

Path lossTrans-surface

Path lossSurface-receiverMasking of

Surface by delay-Doppler mapping

Masking ofSurface by

Receiver antenna

Delay-Doppler Map (DDM)

As an equation …

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019 19

Wind speed

Waves

AntennaPattern, S/C AttitudeGPS EIRP …

Observable

Wind Speed Retrieval (point)

True Wind field Observed DDM

Wind direction

(Data is just for illustration – not actual observations)

Data Assimilation

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019 20

Wind speed

Waves

AntennaPattern, S/C AttitudeGPS EIRP …

Diff

True Wind field

(Data is just for illustration – not actual observations)

Wind direction

Observed DDM

Model Wind field Model DDM

ForwardModel

Adjustment

Data Assimilation

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

• Experience from GNSS Radio-occultation (GNSS-RO):

Water vapor -> Refractivity -> bending angle

21

• Direct inversion (bending angle -> refractivity) assumes uniform propertiesover area covered by the integral

• Alternative: assimilate bending angle directly into weather models.

Data Assimilation

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

DDM forward model

• Sensitivity of DDM sample to surface wind speed

DDM bins

2 4 6 8 10Doppler

2

4

6

8

10

12

14

16

Delay

304.5 305 305.5Longitude

16.5

17

17.5

18

Latitude

Sensitivity to surface grid points

DDM bins

2 4 6 8 10Doppler

2

4

6

8

10

12

14

16

Delay

304.5 305 305.5Longitude

16.5

17

17.5

18

Latitude

Sensitivity to surface grid points

Delay-Doppler ambiguity

22

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

Synthetic Aperture Radar (SAR) (Monostatic) [history.nasa.gov]

Bistatic Reflectometry[Zavorotny & Voronovich, 2000]

DDM forward model

“Delay-Doppler Ambiguity” – Have you heard this before ?

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

Outline

• Basic Principles of Reflectometry• Early oceanographic applications

• Spaceborne reflectometry• Research directions: Data assimilation

• Other variables• Future work – beyond GNSS

• Conclusions

24

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

Other Variables

25

[1] Remote Sens. of Envi. DOI: 10.1016/j.rse.2017.06.020[2] Scientific Reports DOI: 10.1038/s41598-018-27127-4

Mapping inland waterways under dense biomass [1]

Post-Hurricane Flood Inundation [1]Soil Moisture [1]

Freeze-Thaw State [2]

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

Outline

• Basic Principles of Reflectometry• Early oceanographic applications• Spaceborne reflectometry• Research directions: Data assimilation• Other variables• Future work – beyond GNSS• Conclusions

26

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

Why GNSS ?

• Continuous global coverage• L-band good penetration of atmosphere, vegetation,

rain, etc … (but not good penetrating soil)• Pseudorandom noise (PRN) code – designed for ranging• My career began with GNSS! (not radiometry, radar or communications)

27

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

What about other signals ?

• Approximately 400 communication satellites in GEO

• High-powered (~30 dB above GNSS) signals

• Allocations in most bands used for remote sensing:

L,S, C, Ku/Ka

• Designed for data transmission – Not ranging!• Assumption: Compression & Encryption are very efficient

at filling available spectrum– Data is nearly random

– Direct signal can be used as reference

28

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

Signals of Opportunity (SoOp)

29

Self-Ambiguity Function(Defined in: [Baker, et al., 2005, DOI: 10.1049/ip-rsn:20045083])

R a

d i

o N A V I G A T I O N

l a b

ScienceNav/CommShared

Why would SoOp be useful ?

NTIA Spectrum Allocations: Very Valuable “Real Estate”

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

Two New Applications

31

P-band: Long wavelength, Better Penetration (RZSM & SWE)

Ku/K-band: Wide bandwidth “For free” (coastal altimetry)

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

Two New Applications

31

P-band: Long wavelength, Better Penetration (RZSM & SWE)

Ku/K-band: Wide bandwidth “For free” (coastal altimetry)

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

Root Zone Soil Moisture (RZSM)• Water in top ~1m of soil • Essential for understanding water cycle & agricultural forecast.• Global RZSM from model assimilation (e.g. SMAP L4)

32

P-bandI-band L-band

5 cm

100 cm

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

Wideband SoOp Altimetry

33

• Improved spatial-temporal sampling & coverage from SoOp Altimetry

(Example: 9 days, Monterey Bay)

SSH Signature Altimeter (e.g. Jason-2/3) 10-day repeat

8-satellite W-SoOpconstellation

[Data from R. Shah, JPL]

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

The “Virtual Swath”

34

Future Mission ConceptR

a d

i o

N A V I G A T I O N

l a b

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

35

SoOp (Bistatic)

Soil Tg

Vegetation canopy

PT PRa GPTTB=eTg

Passive

Conservation of Energy: G=1-e

Active (Monostatic)

PTPra s0PT

SoOp as the “Third Way”

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

Conclusions

• Spaceborne GNSS-R wind retrievals successfully demonstrated under some conditions.• Perhaps wind speed is not the best variable ?

• Wind/Wave coupling• Direct DDM Assimilation

• Other geophysical variables can be retrieved• GNSS-R just the beginning - many other signals are out there!

• Diversity of frequencies in a crowded spectrum• High EIRP (+30 dB Vs. GNSS)• Broadly – “Signals of Opportunity” may introduce a

“third way” of microwave remote sensing.

36

R a

d i

o N A V I G A T I O N

l a bStanford PNT Symposium, Oct. 11, 2019

Data Sources• CYGNSS (at the JPL PO.DAAC): https://podaac.jpl.nasa.gov/CYGNSS• UK TDS-1:http://merrbys.co.uk/

37