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Snow formation in the atmosphere: properties of snow and ice crystals

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Remote Sensing of Snow Cover with s from Jeff Dozier, Tom Painter Topics in Remote Sensing of Snow Optics of Snow and Ice Remote Sensing Principles Applications ... – PowerPoint PPT presentation

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Title: Snow formation in the atmosphere: properties of snow and ice crystals


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
  1. Energy source
  2. Atmospheric interactions
  3. Target interactions
  4. Sensor records energy
  5. Transmission to receiving station
  6. Interpretation
  7. Application

4
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
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
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 absorptionand scattering
emission
absorption
scattering
8
RADIATION CHOICES
  • Absorbed
  • Reflected
  • Transmitted

9
Properties of atmosphereand 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 Reflectance
16
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)
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 0.58-0.68 uM
  • Band 2 0.72-1.00 uM
  • Band 3 3.55-3.93 uM
  • Band 4 10.5-11.5 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-15 1.6 Micron Channel

23
Mapping of snow extent
  • Subpixel problem
  • 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
Execute Sub-pixel snow cover algorithm using
reflectance Bands 1,2,3
Scene Evaluation Degree of Cloud Cover over
Study Basins
Snow Map Algorithm Output Mixed clouds, high
reflective bare ground, and Sub-pixel snow cover
Execute Atmospheric Corrections, Conversion to
engineering units
AVHRR Bands
AVHRR (HRPT FORMAT) Pre-Processed at
UCSB NOAA-12,14,16
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
  • 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

28
Subpixel Resolution Snow Mapping from Landsat
Thematic Mapper
(Rosenthal Dozier, Water Resour. Res., 1996)
29
Discrimination between Snow and Glacier Ice,
Ötztal Alps
Landsat TM, Aug 24, 1989
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

31
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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
36
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37
EOS Terra MODIS
  • Image Earths 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
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
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 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

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 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

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
3041-3051
64
SWE and Other Properties derived from SIR-C/X-SAR
Particle radius
SIR-C/X-SAR
Snow density
Snow depth
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
3041-3051
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
Mixed precipitation
Raw
Scaling removed
mixed precip particle shape
from Fassnacht et al., 2001 J. Hydrol. 254
148-168
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
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