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

A. Energy sourceB. Atmospheric

interactionsC. Target interactionsD. Sensor records energyE. Transmission to

receiving stationF. InterpretationG. Application

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

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

And v is frequency (cycles per second, Hz)

WAVELENGTHS WE CAN USE MOST EFFECTIVELY

PIXELS: Minimum sampling area

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)

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

Visible, Near IR, IR

Solar Radiation

Instrument records temperature brightness at certain wavelengths

Snow Spectral Reflectance

0

20

40

60

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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)

RADIATION CHOICES• Absorbed• Reflected• Transmitted

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)

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

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

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

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

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)

Snow Measurement• NOAA-15 1.6 Micron Channel

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

• 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

Subpixel Resolution Snow Mapping from AVHRR

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

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

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

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)

Discrimination between Snow and Glacier Ice, Ötztal Alps

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

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

AVIRIS spectra

0

20

40

60

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100

0.3 0.8 1.3 1.8 2.3wavelength (m)

refle

ctan

ce (%

)

snow

vegetation

rock

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

Subpixel Resolution Snow Mapping from AVIRIS

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

GRAIN SIZE FROM SPACE

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

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

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)

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

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)

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

Airborne Snow Survey Products

Microwave Wavelengths

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)

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

Modeling electromagnetic scattering and absorption

Soil

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

Snow

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

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

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

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!

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

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

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)

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

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

Passive Microwave SWE Products

ANOTHER PASSIVE MICROWAVE EXAMPLE

SYNTHETIC APERTURE RADAR (SAR)

SAR WAVELENTHS• Wavebands

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

POLARIZATIONPOLARIZATION

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

• More bands and more polarizations, more info

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

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

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

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

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

Weather Radar vs. Gauge Accumulation

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

0

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0 28 56 84 112 140

time increment (days)

perc

enta

ge a

bsol

ute

diffe

renc

e(r

adar

- ga

uge)

Particle Characteristics Considerations

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

0

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0 50 100 150monthly gauge accumulation (mm)

mon

thly

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m) Wormwood

GreenochEuclid1:1 line

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mon

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m)

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0 50 100 150monthly gauge accumulation (mm)

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Scaling removed

Mixed precipitationRaw

mixed precip + particle shape

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