1. Remote Sensing of Snow Cover
with slides from Jeff Dozier, Tom Painter

2. Topics in Remote Sensing of Snow
Optics of Snow and Ice
Remote Sensing Principles
Applications
Operational Remote Sensing

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

4. 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)

5. General reflectance curves
from Klein, Hall and Riggs, 1998: Hydrological Processes, 12, with sources from Clark et al. (1993); Salisbury and D'Aria (1992, 1994); Salisbury et al. (1994)

6. Snow Spectral Reflectance20 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)

7. 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

8. Visible, Near IR, IR

9. 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

10. Landsat Thematic Mapper (TM)
30 m spatial resolution
185 km FOV
Spectral resolution μm μm μm μm μm μm μm
16 day repeat pass

11. AVIRIS spectra

12. Spectra of Mixed Pixels

13. 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

14. 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 m)
(Rosenthal & Dozier, Water Resour. Res., 1996)

15. Subpixel Resolution Snow Mapping from AVHRR
May 26, 1995
(AVHRR has 1.1 km spatial resolution, 5 spectral bands)

16. 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

17. Subpixel Resolution Snow Mapping from AVIRIS
(Painter et al., Remote Sens. Environ., 1998)

18. 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

19. Discrimination between Snow and Glacier Ice, Ötztal Alps
Landsat TM, Aug 24, 1989
snow
ice
rock/veg

20. 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

21. 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)

22. Microwave Wavelengths

23. 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)

24. Modeling electromagnetic scattering and absorption
(1)
(2)
(3)
(4)
(5)
(6)
Snow
Soil

25. 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

26. 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

27. Passive Microwave SWE Estimates
Advantages:
Daily overpass (SSM/I, Nimbus-7 SMMR)
Large coverage areas
Long time series (eg. Cosmos 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

28. Passive Microwave SWE Products

29. Active Microwave Snow Detection
Has been used to estimate binary SCA at 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

30. 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:

31. 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:

32. 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

33. Weather Radar vs. Gauge Accumulation
from Fassnacht et al., 2001: J. Hydrol. 254:

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

35. 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