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Remote Sensing of Snow Cover

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Remote Sensing of Snow Cover. with slides from Jeff Dozier, Tom Painter. Topics in Remote Sensing of Snow. Optics of Snow and Ice Remote Sensing Principles Applications Operational Remote Sensing. FUNDAMENTALS OF REMOTE SENSING. Energy source Atmospheric interactions Target interactions - PowerPoint PPT Presentation
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Remote Sensing of Snow Cover with slides from Jeff Dozier, Tom Pai
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Page 1: Remote Sensing of Snow Cover

Remote Sensing of Snow Cover

with slides from Jeff Dozier, Tom Painter

Page 2: Remote Sensing of Snow Cover

Topics in Remote Sensing of Snow

• Optics of Snow and Ice• Remote Sensing Principles• Applications • Operational Remote Sensing

Page 3: Remote Sensing of Snow Cover

FUNDAMENTALS OF REMOTE SENSING

A. Energy sourceB. Atmospheric

interactionsC. Target interactionsD. Sensor records energyE. Transmission to

receiving stationF. InterpretationG. Application

Page 4: Remote Sensing of Snow Cover

The EM Spectrum10-1nm 1 nm 10-2m 10-1m 1 m 10 m 100 m 1 mm 1 cm 10 cm 1 m 102m

Gam

ma

Ray

s

X ra

ys

Ultr

a-vi

olet

(UV

)

Vis

ible

(400

- 70

0nm

)

Nea

r Inf

rare

d (N

IR)

Infra

red

(IR)

Mic

row

aves

Wea

ther

rada

r

Tele

visi

on, F

M ra

dio

Sho

rt w

ave

radi

o

Viol

etB

lue

Gre

enYe

llow

Ora

nge

Red

Page 5: Remote Sensing of Snow Cover

C = v, where c is speed of light, is wavelength (m),

And v is frequency (cycles per second, Hz)

Page 6: Remote Sensing of Snow Cover

WAVELENGTHS WE CAN USE MOST EFFECTIVELY

Page 7: Remote Sensing of Snow Cover

PIXELS: Minimum sampling area

Page 8: Remote Sensing of Snow Cover

EM Wavelengths for Snow

• Snow on the ground– Visible, near infrared, infrared– Microwave

• Falling snow– Long microwave, i.e., weather radar

• K ( = 1cm)• X ( = 3 cm)• C ( = 5 cm)• S ( = 10 cm)

Page 9: Remote Sensing of Snow Cover

Different Impacts in Different Regions of the Spectrum

Visible, near-infrared, and infrared

• Independent scattering• Weak polarization

– Scalar radiative transfer• Penetration near surface only

– ~½ m in blue, few mm in NIR and IR

• Small dielectric contrast between ice and water

Microwave and millimeter wavelength

• Extinction per unit volume• Polarized signal

– Vector radiative transfer• Large penetration in dry snow,

many m– Effects of microstructure

and stratigraphy– Small penetration in wet

snow• Large dielectric contrast

between ice and water

Page 10: Remote Sensing of Snow Cover

Visible, Near IR, IR

Page 11: Remote Sensing of Snow Cover

Solar Radiation

Instrument records temperature brightness at certain wavelengths

Page 12: Remote Sensing of Snow Cover

Snow Spectral Reflectance

0

20

40

60

80

100

0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4

refle

ctan

ce (%

)

0.05 mm0.2 mm0.5 mm1.0 mm

wavelength (m)

Page 13: Remote Sensing of Snow Cover

RADIATION CHOICES• Absorbed• Reflected• Transmitted

Page 14: Remote Sensing of Snow Cover

General reflectance curves

from Klein, Hall and Riggs, 1998: Hydrological Processes, 12, 1723 - 1744 with sources from Clark et al. (1993); Salisbury and D'Aria (1992, 1994); Salisbury et al. (1994)

Page 15: Remote Sensing of Snow Cover

Refractive Index of Light (m)• m = n + ik• The “real” part is n• Spectral variation of n is

small• Little variation of n

between ice and liquid

Page 16: Remote Sensing of Snow Cover

Attenuation Coefficient• Attenuation coefficient

is the imaginary part of the index of refraction

• A measure of how likely a photon is to be absorbed

• Little difference between ice and liquid

• Varies over 7 orders of magnitude from 0.4 to 2.5 uM

Page 17: Remote Sensing of Snow Cover

ADVANCED VERY HIGH RESOLUTION RADIOMETER

(AVHRR)• 2,400 km swath• Orbits earth 14 times per day, 833 km height• 1 kilometer pixel size• Spectral range

– Band 1: 0.58-0.68 uM– Band 2: 0.72-1.00 uM– Band 3: 3.55-3.93 uM– Band 4: 10.5-11.5 uM

Page 18: Remote Sensing of Snow Cover

Snow Measurement• Satellite Hydrology Program

WAVELENGTH (microns)

WAVELENGTH (microns)AVHRR

GOES0.0 1.0 4.02.0 3.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0

0.0 1.0 4.02.0 3.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0

AVHRR and GOES Imaging Channels

Page 19: Remote Sensing of Snow Cover

Snow Measurement• Remote Sensing of Snow Cover

0.0 0.5 1.0 1.5 2.0 2.5 3.0WAVELENGTH (microns)

0.0

0.2

0.4

0.6

0.8

1.0

AVHRR Ch. 2AVHRR Ch. 1

GOESCh. 1

r = 0.05 mmr = 0.2 mmr = 0.5 mmr = 1.0 mm

Snow Grain Radius (r)

OpticallyThick

Clouds

1.6 micron(NOAA 16)

Page 20: Remote Sensing of Snow Cover

Snow Measurement• NOAA-15 1.6 Micron Channel

Page 21: Remote Sensing of Snow Cover

Mapping of snow extent

• Subpixel problem– “Snow mapping” should estimate fraction of pixel

covered• Cloud cover

– Visible/near-infrared sensors cannot see through clouds

– Active microwave can, at resolution consistent with topography

Page 22: Remote Sensing of Snow Cover

• Assuming linear mixing, the spectrum of a pixel is the area-weighted average of the spectra of the “end-members”

• For all wavelengths ,

• Solve for fn

Analysis of Mixed Pixels

R r fn nn

N

1

Page 23: Remote Sensing of Snow Cover

Subpixel Resolution Snow Mapping from AVHRR

May 26, 1995(AVHRR has 1.1 km spatial resolution, 5 spectral bands)

Page 24: Remote Sensing of Snow Cover

AVHRR Fractional SCA Algorithm

1

2

3

4

5

AVHRR (HRPT FORMAT)Pre-Processed at UCSB[NOAA-12,14,16]

Snow Map Algorithm Output: Mixed clouds, high reflective bare ground, and Sub-pixel snow cover

AVHRR Bands

Geographic Mask

Thermal Mask

Masked Fractional SCA Map

Composite Cloud Mask

Build Cloud Masks using several

spectral-based tests

Execute Atmospheric Corrections,

Conversion to engineering units

Execute Sub-pixel snow cover algorithm

using reflectance Bands 1,2,3

Application of Cloud, Thermal, and Geographic masks to raw

AVTREE output

Build Thermal Mask

Scene Evaluation: Degree of Cloud Cover

over Study Basins

Page 25: Remote Sensing of Snow Cover

Landsat Thematic Mapper (TM)• 30 m spatial

resolution• 185 km FOV• Spectral resolution

1. 0.45-0.52 μm2. 0.52-0.60 μm3. 0.63-0.69 μm4. 0.76-0.90 μm5. 1.55-1.75 μm6. 10.4-12.5 μm7. 2.08-2.35 μm

• 16 day repeat pass

Page 26: Remote Sensing of Snow Cover

Subpixel Resolution Snow Mapping from Landsat Thematic Mapper

Sept 2, 1993(snow in cirques only)

Feb 9, 1994(after big winter storm)

Apr 14, 1994(snow line 2400-3000 m)

(Rosenthal & Dozier, Water Resour. Res., 1996)

Page 27: Remote Sensing of Snow Cover

Discrimination between Snow and Glacier Ice, Ötztal Alps

Landsat TM, Aug 24, 1989 snow ice rock/veg

Page 28: Remote Sensing of Snow Cover

AVIRIS CONCEPT• 224 different detectors• 380-2500 nm range• 10 nm wavelength• 20-meter pixel size• Flight line 11-km wide• Flies on ER-2• Forerunner of MODIS

Page 29: Remote Sensing of Snow Cover
Page 30: Remote Sensing of Snow Cover

AVIRIS spectra

0

20

40

60

80

100

0.3 0.8 1.3 1.8 2.3wavelength (m)

refle

ctan

ce (%

)

snow

vegetation

rock

Page 31: Remote Sensing of Snow Cover

Spectra of Mixed Pixels

0

20

40

60

80

100

0.3 0.8 1.3 1.8 2.3wavelength (m)

refle

ctan

ce (%

)

snowvegetationrockequal snow-veg-rock80% snow, 10% veg, 10% rock20% snow, 50% veg, 30% rock

Page 32: Remote Sensing of Snow Cover

Subpixel Resolution Snow Mapping from AVIRIS

(Painter et al., Remote Sens. Environ., 1998)

Page 33: Remote Sensing of Snow Cover

GRAIN SIZE FROM SPACE

Page 34: Remote Sensing of Snow Cover
Page 35: Remote Sensing of Snow Cover

EOS Terra MODIS

•Image Earth’s surface every 1 to 2 days•36 spectral bands covering VIS, NIR, thermal

•1 km spatial resolution (29 bands)•500 m spatial resolution (5 bands)•250 m spatial resolution (2 bands)

•2330 km swath

Page 36: Remote Sensing of Snow Cover

Snow Water Equivalent• SWE is usually more relevant than SCA,

especially for alpine terrain• Gamma radiation is successful over flat

terrain• Passive and active microwave are used• Density, wetness, layers, etc. and vegetation

affect radar signal, making problem more difficult

Page 37: Remote Sensing of Snow Cover

SWE from Gamma

• There is a natural emission of Gamma from the soil (water and soil matrix)

• Measurement of Gamma to estimate soil moisture

• Difference in winter Gamma measurement and pre-snow measurement – extinction of Gamma yields SWE

• PROBLEM: currently only Airborne measurements (NOAA-NOHRSC)

Page 38: Remote Sensing of Snow Cover

Snow Measurement• Airborne Snow Survey Program

Natural Gamma Sources238U Series, 232Th Series, 40K Series

Soil

Snow

Atmosphere

Radon Daughtersin Atmosphere

Cosmic Rays

Uncollided

Gamma RadiationAbsorbed by Waterin the Snow Pack

Gamma Radiationreaches

Detector in Aircraft

Scattering

Page 39: Remote Sensing of Snow Cover

Snow Measurement• Airborne SWE Measurement Theory

– Airborne SWE measurements are made using the following relationship:

SW EA

CC

MM

g cm

1 1 00 1 11

1 00 1 110

0

2ln ln..

Where:C and C0 = Uncollided terrestrial gamma count rates over snow and dry, snow-free soil,M and M0 = Percent soil moisture over snow and dry, snow-free soil,A = Radiation attenuation coefficient in water, (cm2/g)

Page 40: Remote Sensing of Snow Cover

Snow Measurement• Airborne SWE: Accuracy and Bias

Airborne measurements include ice and standing water that ground measurements generally miss.

RMS Agricultural Areas: 0.81 cmRMS Forested Areas: 2.31 cm

Page 41: Remote Sensing of Snow Cover

Airborne Snow Survey Products

Page 42: Remote Sensing of Snow Cover

Microwave Wavelengths

Page 43: Remote Sensing of Snow Cover

Frequency Variation for Dielectric Function and Extinction Properties

• Variation in dielectric properties of ice and water at microwave wavelengths

• Different albedo and penetration depth for wet vs. dry snow, varying with microwave wavelength

• NOTE: typically satellite microwave radiation defined by its frequency (and not wavelength)

Page 44: Remote Sensing of Snow Cover

Dielectric Properties of Snow

Material Dielectric Constant

Air 1.0

Ice 3.2

Quartz 4.3

Water 80

• Propagation and absorption of microwaves and radar in snow are a function of their dielectric constant

• Instrumentation: Denoth Meter, Finnish Snow Fork, TDR

• e = m2 and also has a real and an imaginary component

Page 45: Remote Sensing of Snow Cover

Modeling electromagnetic scattering and absorption

Soil

(1) (2) (3) (4) (5) (6)

Snow

Page 46: Remote Sensing of Snow Cover

Volume Scattering• Volume scattering is the

multiple “bounces” radar may take inside the medium

• Volume scattering may decrease or increase image brightness

• In snow, volume scattering is a function of density

Page 47: Remote Sensing of Snow Cover

SURFACE ROUGHNESS• Refers to the average

height variations of the surface (snow) relative to a smooth plane

• Generally on the order of cms

• Varies with wavelength and incidence angle

Page 48: Remote Sensing of Snow Cover

SURFACE ROUGHNESS• A surface is “smooth” if

surface height variations small relative to wavelength

• Smooth surface much of energy goes away from sensor, appears dark

• Rough surface has a lot of back scatter, appears lighter

Page 49: Remote Sensing of Snow Cover

MICROWAVES WORK 24/7• Penetrate through cloud

cover, haze, dust, and all but the heaviest rain

• Not scattered by the atmosphere like optical wavelengths

• Work at night!

Page 50: Remote Sensing of Snow Cover

ALL OBJECTS EMIT MICROWAVE ENERGY

• Emitted by atmosphere• Reflected from surface• Emitted from surface• Transmitted from the

subsurface through snow

• DRY SNOW: attenuates subsurface energy

• WET SNOW: becomes an emission source

Page 51: Remote Sensing of Snow Cover

MICROWAVE MAGNITUDE Temperature Brightness (Tb)

• Function of temperature and moisture content

• Generally very small amount of energy

• Need a large pixel size to have enough energy to measure

Page 52: Remote Sensing of Snow Cover

PASSIVE MICROWAVE RADIOMETRY

• Passive Microwave (PM): can penetrate clouds & provide information during night

• Daily PM data available on a global basis

• Satellite Microwave data: To retrieve SWE Chang et al.,1976; Goodison et al.,1986; etc.

• Basis of microwave detection of snow: Redistribution of upwelling radiation (RTM, SM)

Page 53: Remote Sensing of Snow Cover

Passive Microwave SWE Estimates

• Microwave response affected by:– Liquid water content, crystal size and shape, depth

and SWE, stratification, snow surface roughness, density, temperature, soil state, moisture, roughness, vegetation cover

• Ratio of different wavelengths– Vertically polarized brightness temperature, TB,

gradient

– Single frequency vertical polarized TB BTVdcSWE GHz 37

19/ GHz 18 GHz 37 BB TVTVbaSWE

Page 54: Remote Sensing of Snow Cover

Passive Microwave SWE Estimates

• Advantages:– Daily overpass (SSM/I, Nimbus-7 SMMR)– Large coverage areas– Long time series (eg. Cosmos 243 - Russia 1968)– See through clouds, no dependence on the sun

(unlike visible or near IR)• Disadvantages

– Large pixel size (12.5 – 25 km)– Still problems with vegetation– Maximum SWE & limitations with wet snow

Page 55: Remote Sensing of Snow Cover

Passive Microwave SWE Products

Page 56: Remote Sensing of Snow Cover

ANOTHER PASSIVE MICROWAVE EXAMPLE

Page 57: Remote Sensing of Snow Cover

SYNTHETIC APERTURE RADAR (SAR)

Page 58: Remote Sensing of Snow Cover

SAR WAVELENTHS• Wavebands

– L-band (24 cm)– C-band (6 cm)– X-band (3 cm)

Page 59: Remote Sensing of Snow Cover

POLARIZATIONPOLARIZATION

• Polarization • HH (horizontal-horizontal)• VV (vertical-vertical)• HV (horizontal-vertical)• VH (vertical-horizontal)

• More bands and more polarizations, more info

Page 60: Remote Sensing of Snow Cover

Active Microwave Snow Detection

• Has been used to estimate binary SCA at 15 - 30 m resolution as compared to air photos

• Advantages:– High resolution– Detection characteristics

• Disadvantages:– Repeat of 16 days & narrow Swath width, as per TM– Commercial sensor: ERS-I/II (?), RADARSAT

Page 61: Remote Sensing of Snow Cover

Active Microwave SWE Estimation

• Snow cover characteristics influence underlying soil temperature, this affects the dielectric constant of soil

• Backscatter from soil influenced by dielectric constant and by soil frost penetration depth

• Snow cover insulation properties influence backscatter

from Bernier et al., 1999: Hydrol. Proc. 13: 3041-3051

Page 62: Remote Sensing of Snow Cover

SWE and Other Properties derived from SIR-C/X-SAR

Particle radiusSIR-C/X-SAR Snow density Snow depth

Estim

ated

Ground measurements

Snowdensity

Snow depthin cm

Grain radiusin mm

Page 63: Remote Sensing of Snow Cover

Active Microwave SWE EstimationRSWE s bmR o

row

Ther

mal

sno

w re

sist

ance

(R

in o C

m3 s

/J)

Backscattering ratio (w

o - ro in dB)

SWE

/ R

Mean snow density (s in km/m3)

Problem: Maximum SWE detectable in order of 400 mmfrom Bernier et al., 1999: Hydrol. Proc. 13: 3041-3051

Page 64: Remote Sensing of Snow Cover

Weather Radar for Snowfall• Ground-based NEXRAD system covers most

of the conterminous US, except some alpine areas

• Snowfall estimation improves with time of accumulation, not necessarily required for individual storm events like rainfall

• Variation in attenuation due to particle shape, wet snow, melting snow

• General problems with weather radar

Page 65: Remote Sensing of Snow Cover

Weather Radar vs. Gauge Accumulation

from Fassnacht et al., 2001: J. Hydrol. 254: 148-168

0

50

100

150

200

250

300

0 28 56 84 112 140

time increment (days)

perc

enta

ge a

bsol

ute

diffe

renc

e(r

adar

- ga

uge)

Page 66: Remote Sensing of Snow Cover

Particle Characteristics Considerations

from Fassnacht et al., 2001: J. Hydrol. 254: 148-168

0

50

100

150

200

250

0 50 100 150monthly gauge accumulation (mm)

mon

thly

rada

r acc

umul

atio

n (m

m) Wormwood

GreenochEuclid1:1 line

0

50

100

150

200

250

0 50 100 150monthly gauge accumulation (mm)

mon

thly

rada

r acc

umul

atio

n (m

m)

0

50

100

150

200

250

0 50 100 150monthly gauge accumulation (mm)

mon

thly

rada

r acc

umul

atio

n (m

m)

0

50

100

150

200

250

0 50 100 150monthly gauge accumulation (mm)

mon

thly

rada

r acc

umul

atio

n (m

m)

Scaling removed

Mixed precipitationRaw

mixed precip + particle shape

Page 67: Remote Sensing of Snow Cover

Research / Operational Products

• Snow-covered area– Fractional SCA with Landsat or AVHRR (UAz RESAC)– With AVIRIS, also get albedo– Binary SCA currently from MODIS, VIIRS (NPOESS)

• Snow-water equivalent– L-band dual polarization + C- and X-band– Daily SSM/I over the Midwest and Prairies

• Snow wetness– Near surface with AVIRIS– Within 2% with C-band dual polarization


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