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

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 r

ays

Ultr

a-vi

olet

(UV

)

Vis

ible

(40

0 -

700n

m)

Nea

r In

frar

ed (

NIR

)

Infr

ared

(IR

)

Mic

row

aves

Wea

ther

rad

ar

Tel

evis

ion,

FM

rad

io

Sho

rt w

ave

radi

o

Vio

let

Blu

eG

ree

nY

ell

ow

Ora

ng

eR

ed

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)

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)

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

refl

ec

tan

ce

(%

)

0.05 mm0.2 mm0.5 mm1.0 mm

wavelength (m)

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

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

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

AVIRIS spectraAVIRIS spectra

0

20

40

60

80

100

0.3 0.8 1.3 1.8 2.3wavelength (m)

refl

ec

tan

ce

(%

)

snow

vegetation

rock

Spectra of Mixed PixelsSpectra of Mixed Pixels

0

20

40

60

80

100

0.3 0.8 1.3 1.8 2.3wavelength (m)

refl

ec

tan

ce

(%

)

snow

vegetation

rock

equal snow-veg-rock

80% snow, 10% veg, 10% rock

20% snow, 50% veg, 30% rock

• 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 PixelsAnalysis of Mixed Pixels

R r fn nn

N

1

Subpixel Resolution Snow Mapping from Landsat Thematic Mapper

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)

Subpixel Resolution Snow Mapping from AVHRR

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

Subpixel Resolution Snow Mapping from AVIRIS

Subpixel Resolution Snow Mapping from AVIRIS

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

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

Discrimination between Snow and Glacier Ice, Ötztal Alps

Discrimination between Snow and Glacier Ice, Ötztal Alps

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

Snow Water EquivalentSnow 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)

Microwave Wavelengths

Frequency Variation for Dialectric Function and Extinction Properties

• Variation in dialectric 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)

Modeling electromagnetic scattering and absorption

Soil

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

Snow

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

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

Est

imat

ed

Ground measurements

Snowdensity

Snow depthin cm

Grain radiusin mm

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

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

Active Microwave SWE EstimationRSWE s bmR o

row

Th

erm

al s

no

w r

esis

tan

ce

(R i

n o

Cm

3s/

J)

Backscattering ratio (w

o - ro in dB)

SW

E /

R

Mean snow density (s in km/m3)

Problem: Maximum SWE detectable in order of 400 mm

from 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

50

100

150

200

250

300

0 28 56 84 112 140

time increment (days)

pe

rce

nta

ge

ab

so

lute

dif

fere

nc

e(r

ad

ar

- g

au

ge

)

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)

mo

nth

ly r

ad

ar

ac

cu

mu

lati

on

(m

m) Wormwood

Greenoch

Euclid1:1 line

0

50

100

150

200

250

0 50 100 150monthly gauge accumulation (mm)

mo

nth

ly r

ad

ar

ac

cu

mu

lati

on

(m

m)

0

50

100

150

200

250

0 50 100 150monthly gauge accumulation (mm)

mo

nth

ly r

ad

ar

ac

cu

mu

lati

on

(m

m)

0

50

100

150

200

250

0 50 100 150monthly gauge accumulation (mm)

mo

nth

ly r

ad

ar

ac

cu

mu

lati

on

(m

m)

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