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. FUNDAMENTALS OF REMOTE SENSING
Energy source
Atmospheric interactions
Target interactions
Sensor records energy
Transmission to receiving station
Interpretation
Application

4. 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

5. C = l v, where c is speed of light, l is wavelength (m),
And v is frequency (cycles per second, Hz)

6. WAVELENGTHS WE CAN USE MOST EFFECTIVELY

7. Atmospheric absorption and scattering
emission
absorption
scattering

8. RADIATION CHOICES
Absorbed
Reflected
Transmitted

9. Properties of atmosphere and surface
Conservation of energy: radiation at a given wavelength is either:
reflected — property of surface or medium is called reflectance or albedo (0-1)
absorbed — property is absorptance or emissivity (0-1)
transmitted — property is transmittance (0-1)
reflectance + absorptance + transmittance = 1 (for a surface, transmittance = 0)

10. PIXELS: Minimum sampling area
One temperature brightness (Tb) value recorded per pixel

11. 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)

12. 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

13. Visible, Near IR, IR

14. Solar Radiation
Instrument records temperature brightness at certain wavelengths

15. 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)

16. 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)

17. Refractive Index of Light (m)
m = n + ik
The “real” part is n
Spectral variation of n is small
Little variation of n between ice and liquid

18. Attenuation Coefficient
Attenuation coefficient is the imaginary part of the index of refraction
A measure of how likely a photon is to be absorbed
Little difference between ice and liquid
Varies over 7 orders of magnitude from 0.4 to 2.5 uM

19. ADVANCED VERY HIGH RESOLUTION RADIOMETER (AVHRR)
2,400 km swath
Orbits earth 14 times per day, 833 km height
1 kilometer pixel size
Spectral range
Band 1: uM
Band 2: uM
Band 3: uM
Band 4: uM

20. Snow Measurement Satellite Hydrology Program
AVHRR and GOES Imaging Channels

21. Snow Measurement
Remote Sensing of Snow Cover
(NOAA 16)

22. Snow Measurement
NOAA Micron Channel

23. 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

24. 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

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

26. 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

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

28. 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)

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

30. AVIRIS CONCEPT 224 different detectors 380-2500 nm range
10 nm wavelength
20-meter pixel size
Flight line 11-km wide
Flies on ER-2
Forerunner of MODIS

32. AVIRIS spectra

33. Spectra of Mixed Pixels

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

35. GRAIN SIZE FROM SPACE

37. 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

38. 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

39. 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)

40. Snow Measurement
Airborne Snow Survey Program

41. Snow Measurement Airborne SWE Measurement Theory
Airborne SWE measurements are made using the following relationship:
Where:
C and C0 = Uncollided terrestrial gamma count rates over snow and dry, snow-free soil,
M and M0 = Percent soil moisture over snow and dry, snow-free soil,
A = Radiation attenuation coefficient in water, (cm2/g)

42. RMS Agricultural Areas: 0.81 cm
Snow Measurement
Airborne SWE: Accuracy and Bias
Airborne measurements include ice and standing water that ground measurements generally miss.
RMS Agricultural Areas: 0.81 cm
RMS Forested Areas: 2.31 cm

43. Airborne Snow Survey Products

44. Microwave Wavelengths

45. Frequency Variation for Dielectric Function and Extinction Properties
Variation in dielectric 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)

46. Dielectric Properties of Snow
Propagation and absorption of microwaves and radar in snow are a function of their dielectric constant
Instrumentation: Denoth Meter, Finnish Snow Fork, TDR
e = m2 and also has a real and an imaginary component

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

48. Volume Scattering
Volume scattering is the multiple “bounces” radar may take inside the medium
Volume scattering may decrease or increase image brightness
In snow, volume scattering is a function of density

49. SURFACE ROUGHNESS
Refers to the average height variations of the surface (snow) relative to a smooth plane
Generally on the order of cms
Varies with wavelength and incidence angle

50. SURFACE ROUGHNESS
A surface is “smooth” if surface height variations small relative to wavelength
Smooth surface much of energy goes away from sensor, appears dark
Rough surface has a lot of back scatter, appears lighter

51. MICROWAVES WORK 24/7
Penetrate through cloud cover, haze, dust, and all but the heaviest rain
Not scattered by the atmosphere like optical wavelengths
Work at night!

52. ALL OBJECTS EMIT MICROWAVE ENERGY
Emitted by atmosphere
Reflected from surface
Emitted from surface
Transmitted from the subsurface through snow
DRY SNOW: attenuates subsurface energy
WET SNOW: becomes an emission source

53. MICROWAVE MAGNITUDE Temperature Brightness (Tb)
Function of temperature and moisture content
Generally very small amount of energy
Need a large pixel size to have enough energy to measure

54. POLARIZATION POLARIZATION Polarization
HH (horizontal-horizontal)
VV (vertical-vertical)
HV (horizontal-vertical)
VH (vertical-horizontal)
More bands and more polarizations, more info

55. PASSIVE MICROWAVE RADIOMETRY
Passive Microwave (PM): can penetrate clouds & provide information during night
Daily PM data available on a global basis
Satellite Microwave data: To retrieve SWE Chang et al.,1976; Goodison et al.,1986; etc.
Basis of microwave detection of snow:
Redistribution of upwelling radiation (RTM, SM)

56. 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

57. 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

58. Passive Microwave SWE Products

59. ANOTHER PASSIVE MICROWAVE EXAMPLE

60. SYNTHETIC APERTURE RADAR (SAR)

61. SAR WAVELENTHS
Wavebands
L-band (24 cm)
C-band (6 cm)
X-band (3 cm)

62. 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

63. 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:

64. 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

65. 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:

66. 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

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

68. 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