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 Spectrum Gamma Rays X rays Ultra-violet(UV) Visible (400 - 700nm) Near Infrared (NIR) Infrared (IR) Microwaves Weather radar Television, FM radio Short wave radio The EM Spectrum 10-1nm 1 nm 10-2mm 10-1mm 1 mm 10 mm 100 mm 1 mm 1 cm 10 cm 1 m 102m Violet Blue Green Yellow Orange Red

EM Wavelengths for Snow Snow on the ground Visible, near infrared, infrared Microwave Falling snow Long microwave, i.e., weather radar K (l = 1cm) X (l = 3 cm) C (l = 5 cm) S (l = 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 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 reflectance (%) 0.05 mm 0.2 mm 0.5 mm 1.0 mm wavelength (mm)

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 Cloud cover “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 0.45-0.52 μm 0.52-0.60 μm 0.63-0.69 μm 0.76-0.90 μm 1.55-1.75 μm 10.4-12.5 μm 2.08-2.35 μm 16 day repeat pass

AVIRIS spectra

Spectra of Mixed Pixels

Analysis of Mixed Pixels Assuming linear mixing, the spectrum of a pixel is the area-weighted average of the spectra of the “end-members” For all wavelengths l, Solve for fn

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

AVHRR Fractional SCA Algorithm Scene Evaluation: Degree of Cloud Cover over Study Basins Execute Sub-pixel snow cover algorithm using reflectance Bands 1,2,3 Snow Map Algorithm Output: Mixed clouds, high reflective bare ground, and Sub-pixel snow cover Execute Atmospheric Corrections, Conversion to engineering units AVHRR (HRPT FORMAT) Pre-Processed at UCSB [NOAA-12,14,16] AVHRR Bands 1 2 3 4 5 Thermal Mask Build Thermal Mask Build Cloud Masks using several spectral-based tests Geographic Mask Application of Cloud, Thermal, and Geographic masks to raw AVTREE output Composite Cloud Mask Masked Fractional SCA Map

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

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)

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

SWE and Other Properties derived from SIR-C/X-SAR Snow density Snow depth Particle radius Snow depth in cm Grain radius in mm Snow density Estimated Ground measurements

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

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 SMMR = scanning multichannel microwave radiometer SSM/I = special sensor microwave imager

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 Estimation Thermal snow resistance (R in oCm3s/J) SWE / R Backscattering ratio (swo - sro in dB) Mean snow density (rs 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

Particle Characteristics Considerations Raw Mixed precipitation Scaling removed mixed precip + particle shape from Fassnacht et al., 2001: J. Hydrol. 254: 148-168

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