Friend or Foe? - UNAVCO...Friend or Foe? Outline • Multipath as Foe • Some background on GPS multipath • Multipath as Friend: –Soil Moisture – Snow Depth – Vegetation Water

GPS Multipath

Kristine M. Larson Department of Aerospace Engineering Sciences

University of Colorado

Friend or Foe?

Outline

• Multipath as Foe• Some background on GPS

multipath• Multipath as Friend:

– Soil Moisture– Snow Depth – Vegetation Water Content

Larson, Bilich, and Axelrad, Improving high-rate GPS precision, JGR. 2007

Multipath as foe:

correct multipath today by using “yesterday”

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Larson, unpublished 1-Hz GPS records from the Parkfield earthquake

can also be critical for 1-hz positions

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Agnew and Larson, Finding the Repeat Times of the GPS Constellation, GPS Solutions, 2007

1-hz time series with multipath removed

152004 2008200720062005 20102009

source: PBO website

But if precision is better than a mm without modeling multipath...

Multipath Depends On

• reflector geometry (elevation angle and antenna height) and GPS transmit frequency

• the reflector characteristics (dielectric constant, smoothness)

• the gain pattern of the antenna

Multipath I

cartoon credit: Bilichtheta is the elevation angle

antenna height

reflected

direct

Multipath II

antenna gain pattern.

Figures courtesy of Felipe Nievinski

Right-Hand Circularly Polarized Left-Hand Circularly Polarized

Multipath III

How Multipath Appears in GPS Observables

The oscillations in the Direct and Reflected Signals behave like an interferometer.

The oscillations in the Direct and Reflected Signals behave like an interferometer.

The frequency of multipath oscillations are determined by the antenna height and the GPS frequency; surface & antenna gain pattern determine amplitude

The oscillations in the Direct and Reflected Signals behave like an interferometer.

Map view of GPS footprint

60 40 20 0 20 40 60

60

40

20

0

20

40

60

meters

met

ers

First Fresnel Zones For GPS Site in North America

5o

10o

20o

30o

0 50 100 1500

100

200

300

400

500

600

700including antenna gain pattern

minutes

SNR

[V]

0 50 100 15040

30

20

10

0

10

20

30

Simulated SNR for specular reflector with antenna ht 1.9 meters

Direct Signal

Reflected Signal

0.1 0.2 0.3 0.4 0.5 0.6 0.70

100

200

300

400

500

600

700Simulated SNR data with gain pattern

sin(elevation angle)

SNR

[V]

0.1 0.2 0.3 0.4 0.5 0.6 0.740

30

20

10

0

10

20

30

SNR as function of sine (elevation angle)

0.1 0.2 0.3 0.4 0.5 0.6 0.70

100

200

300

400

500

600

700Composite SNR Signal

sine(elevation angle)

SNR

[V]

11 12 13 14 15 1630

35

40

45

50

55Observed Composite Signal

dBH

z

hours (UTC)

What is observed?

11 12 13 14 15 160

50

100

150

200

250

300

350

400Observable S2 Linear Scale

SNR

(V)

hours (UTC)

SNR - Linear Scale

0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.550

40

30

20

10

0

10

20

30

40

50Observed Multipath Signal

sine(elevation angle)

SNR

(V)

the reflected signal

Changes in these oscillations (frequency, amplitude) are related to changes in the reflectors.

three scientific targets (so far)

vegetationsnowsoil moisture

How else do they measure soil moisture content?

high-tech low-tech

water content reflectometers

relation between volumetric water content and multipath phase

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4230

235

240

245

250

255

260

265

VWC (cm3/cm3)

(deg

rees

)PRN 29

initial results

Larson, Small, Gutmann, Braun, Zavorotny, and Bilich, Use of GPS receivers as a soil moisture network for water cycle studies, Geophys. Res. Lett., 2008

Water Content Reflectometers GPS

Larson, Small, Gutmann, Braun, Zavorotny, and Bilich, GPS Multipath and Its Relation to Near-Surface Soil Moisture Content, IEEE J-STARS, 2010

GPS Snow Sensing

How else do you measure snow depth?

high-tech low-tech

ultrasonic snow depth sensors

0.1 0.2 0.3 0.460

30

0

30

60

sin(elevation angle)

B.

volts

/vol

ts

Modeled GPS SNR Data

0.1 0.2 0.3 0.460

30

0

30

60Observed GPS SNR Data

volts

/vol

ts

A. no snow

35 cm ofnew snow

Larson, Gutmann, Braun, Zavorotny, Williams, and Nievinski, Can we measure snow depth with GPS receivers?, Geophys. Res. Lett., 2009

295 297 299 301 303 305 307 3095

0

5

10

15

20

25

30

35

40

45

50hand measurements

GPS satellitesultrasonic snow sensors

day of year (2009)

snow

dep

th (c

m)

Plate Boundary Observatory Site P041

Plate Boundary Observatory Site P360

2009.85 2009.95 2010.05 2010.150

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

year

snow

dep

th (m

)

Preliminary Results for p360

GPS Vegetation Sensing

PBO site in Parkfield, CA

4 months later

photo courtesy of John Langbein

How else do they measure soil moisture?

low-tech low-tech

vegetation water content

5 10 15 2060

40

20

0

20

40

60

volts

grass cut

70 cm grass

elevation angle

Munson Hay Site

Munson Grass Site

Vegetation Effects in SNR Data

problem: L2 SNR data quality is poor for most PBO sites

(one) solution: multipath can also be observed in MP1

Example of MP1 time series - single satellite, single station

MP1 usually reported as the mean of the RMS for each satellite.

10 20 30 40 50 60 70 802

1.5

1

0.5

0

0.5

1

1.5

2P422 2008 July 1

elevation angle

MP1

(m)

2007 2007.5 2008 2008.5 20090.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

0.65

years

cmMP1 P070

how do people typically use mean RMS MP1?

10 20 30 40 50 60 70 802

1.5

1

0.5

0

0.5

1

1.5

2P422 2008 July 1

elevation angle

MP1

(m)

10 20 30 40 50 60 70 802

1.5

1

0.5

0

0.5

1

1.5

2P422 2008 April 1

elevation angle

MP1

(m)

Any evidence that MP1 varies during the year - as vegetation would?

10 15 20 25 30 35 40 45 50 55 60 65 70 750

10

20

30

40

50

60

70

80

elevation angle

stan

dard

dev

iatio

n (c

m)

P0422 MP1 PRN17

SpringSummer

What can we compare MP1 variations to?

ratio of spectral reflectance in the near-infrared and red regions, i.e. how green it is.

NDVI: Normalized Difference Vegetation Index

NDVI MODIS: every 16 days, 250 m by 250 m pixel

Foothill, Idaho P422

2007 2008 2009 2010

44

40

36

32

28

MP1

(cm

)p422

2007 2008 2009 20100.1

0.3

0.5

0.7

0.9

ND

VI

Small, Larson, and Braun, Sensing Vegetation Growth With Reflected GPS Signals, Geophys. Res. Lett., in review.

Battle Mountain, Nevada P085

58

54

50

46

42

38

MP1

(cm

)

p085

0.1

0.3

0.5

ND

VI

2007 2008 2009 20100

50

100

perc

ent

average

annual accumulated precipitation

year

low-tech low-tech

Does MP1 correlate with standard measures of vegetation water content?

Munson Farms

50

45

40cm

MP1 (low elevation only)

140 160 180 200 220 2400

1

2

3

4

kg/m

/m

Vegetation Water Content

day of year

comparison with in situ data

Small, Larson, and Braun, Sensing Vegetation Growth With Reflected GPS Signals, Geophys. Res. Lett., in review.

Value to NSF

• expand the use of an existing GPS network to new communities (hydrology, ecology, atmospheric sciences, cryosphere, water management)

• provide data products to improve weather prediction and climate studies (outreach and broader impacts).

Value to NASA• potential validation network and supplementary sensor (i.e.

vegetation) for new environmental satellite missions, especially SMAP, Desdyni.

Conclusions!

• Locating your GPS antenna in a corn field is an effective multipath suppressant.

• GPS multipath is sensitive to soil moisture, snow, and vegetation water content.

• More studies are needed to calibrate multipath data so that they can be assimilated into land surface/atmospheric/hydrological models.

Acknowledgements

• NSF AGS and EAR (0740515 and 0935725)• Eric Small and John Braun • Ethan Gutmann, Mark Williams, Valery Zavorotny,

Felipe Nievinski, Andria Bilich, Penina Axelrad, and Bob Munson

• Plate Boundary Observatory, esp. Fred Blume and Mike Jackson.

• UNAVCO, esp. Chuck Meertens, Jim Normandeau, Dave Maggert, Lou Estey, and Sarah Doelger.

• CU Interdisciplinary Seed Grants