Spectral reflectance of clean snowSpectral reflectance of clean snow

Snow is a Snow is a collection collection of of scattering scattering grainsgrains

Snow spectral reflectance and Snow spectral reflectance and absorption coefficient of iceabsorption coefficient of ice

6

Snow/cloud discrimination with Snow/cloud discrimination with LandsatLandsat

Bands 3 2 1 (red, green, blue) Bands 5 4 2

Spectral solar irradianceSpectral solar irradiance

Net solar radiationNet solar radiation

Analyses of snow properties from Analyses of snow properties from imaging spectrometryimaging spectrometry

• Spectral albedo and conversion to broadband albedo– [Nolin & Dozier, Remote Sens. Environ.,

2000]• Fractional (subpixel) snow-covered area,

along with albedo– [Painter et al., Remote Sens. Environ., 2003]

• Liquid water in surface layer– [Green et al., Water Resour. Res., 2006]

• Absorbing impurities– [Painter et al., Appl. Environ. Microbiol.,

2001; Geophys. Res. Lett., 2007]

Conventional approach to estimating Conventional approach to estimating albedoalbedo

Satellite radiance (~5% error)

Surface reflectance (>5%)

Narrowband albedo (5-10%)

Broadband albedo (5-10%)

Estimate grain size from the1.03Estimate grain size from the1.03m absorption m absorption featurefeature

Measured vs remotely sensed grain Measured vs remotely sensed grain sizesize

500 550 600

Spectral mixture analysisSpectral mixture analysis

Spectral mixture equation, per pixel

Spectral residuals, per pixel

RMS error, per pixel

Snow-covered area in the Tokopah Snow-covered area in the Tokopah Basin (Kaweah River drainage), Sierra Basin (Kaweah River drainage), Sierra NevadaNevadaAVIRIS

05 May 1997

21 May 1997

18 June 1997

20 km

Grain size in the Tokopah Basin Grain size in the Tokopah Basin (Kaweah River drainage), Sierra (Kaweah River drainage), Sierra NevadaNevada

05 May 1997

21 May 1997

18 June 1997

20 km

Absorption by three phases of waterAbsorption by three phases of water

Wet snowWet snow

(Green et al, WRR, forthcoming)

Surface wetness with AVIRIS, Mt. Surface wetness with AVIRIS, Mt. Rainier,Rainier,14 June 199614 June 1996

AVIRIS image, 409, 1324, 2269 nm

precipitable water, 1-8

mm

liquid water, 0-5 mm

path absorption

vapor, liquid, ice

(BGR)

Progression of snow wetness Progression of snow wetness throughout morningthroughout morning

AVIRIS20 May 1996

Surfacewetness11:32 am

Surfacewetness09:54 am

70 km

N

Dust Dust and and algaealgae

Spectral reflectance of dirty snow and Spectral reflectance of dirty snow and snow with red algae (snow with red algae (Chlamydomonas Chlamydomonas nivalisnivalis))

RadiativRadiative e forcing forcing conceptconcept

24

Radiative forcing by dust in snowRadiative forcing by dust in snow

Applications: snowmelt modeling,Applications: snowmelt modeling,Marble Fork of the Kaweah RiverMarble Fork of the Kaweah River

Snow Covered Area

net radiation > 0 degree days > 0

where:

mq = Energy to water depth conversion, 0.026 cm W-1 m2 day-1

ar = Convection parameter, based on wind speed, temperature, humidity, and roughness

convection parameter, based on wind speed, humidity, and roughnessra

Melt Flux net q d rR m T a SCA

Magnitude of snowmelt: Modeled – Observed snow Magnitude of snowmelt: Modeled – Observed snow water equivalentwater equivalent

assumedalbedo

assumed w/ update

AVIRISalbedo

SWE difference, cmTokopah basin, Sierra Nevada

27

AcknowledgmentsAcknowledgments Steve Warren &

Warren Wiscombe– Model for the spectral

albedo of snow (1980)

NASA– Funding on remote

sensing of snow since 1977

• UC Santa Barbara– Great place to think

about snow and ice

Former and current students on this topic– Bert Davis, Rob

Green, Danny Marks, Anne Nolin, Tom Painter, Karl Rittger, Walter Rosenthal, Jiancheng Shi