The Color Colour of Snow and its Interpretation from Imaging Spectrometry

Spectral reflectance of clean snow

Topics Spectral reflectance of snow and its variability Reasons: optics of ice –Snow is a collection of ice grains –Along with dust, algae, and (eventually) liquid water Why do we care? –Implications for energy balance of snowpack Implications for remote sensing –What can we measure? –How do we integrate imaging spectrometry with multispectral measurements? –How can we use the information in hydrology?

Snow is a collection of scattering grains

Snow spectral reflectance and absorption coefficient of ice

6 Snow/cloud discrimination with Landsat Bands (red, green, blue)Bands 5 4 2

Spectral solar irradiance

Net solar radiation

Analyses of snow properties from imaging 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]

10 Airborne Visible Infrared Imaging Spectrometer (AVIRIS) 400 – 2500nm at 10nm res. 224 spectral bands 20m spatial resolution 4 spectrometers: VIS, NIR, SWIR1, SWIR2

Conventional approach to estimating albedo Satellite radiance (~5% error) Surface reflectance (>5%) Narrowband albedo (5-10%) Broadband albedo (5-10%)

Estimate grain size from the1.03  m absorption feature

Measured vs remotely sensed grain size

Spectral mixture analysis Spectral mixture equation, per pixel Spectral residuals, per pixel RMS error, per pixel

Snow-covered area in the Tokopah Basin (Kaweah River drainage), Sierra Nevada AVIRIS 05 May May June km

Grain size in the Tokopah Basin (Kaweah River drainage), Sierra Nevada 05 May May June km

Absorption by three phases of water

Wet snow (Green et al, WRR, forthcoming)

Surface wetness with AVIRIS, Mt. Rainier, 14 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 throughout morning AVIRIS 20 May 1996 Surface wetness 11:32 am Surface wetness 09:54 am 70 km N

Dust and algae

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

Radiative forcing concept

24 Radiative forcing by dust in snow

Applications: snowmelt modeling, Marble Fork of the Kaweah River Snow Covered Area net radiation > 0 degree days > 0 where: m q = Energy to water depth conversion, cm W -1 m 2 day -1 a r = Convection parameter, based on wind speed, temperature, humidity, and roughness

Magnitude of snowmelt: Modeled – Observed snow water equivalent assumed albedo assumed w/ update AVIRIS albedo SWE difference, cm Tokopah basin, Sierra Nevada

27 Acknowledgments  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