remote sensing of snow cover
DESCRIPTION
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. FUNDAMENTALS OF REMOTE SENSING. Energy source Atmospheric interactions Target interactions - PowerPoint PPT PresentationTRANSCRIPT
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Remote Sensing of Snow Cover
with slides from Jeff Dozier, Tom Painter
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Topics in Remote Sensing of Snow
• Optics of Snow and Ice• Remote Sensing Principles• Applications • Operational Remote Sensing
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FUNDAMENTALS OF REMOTE SENSING
A. Energy sourceB. Atmospheric
interactionsC. Target interactionsD. Sensor records energyE. Transmission to
receiving stationF. InterpretationG. Application
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The EM Spectrum10-1nm 1 nm 10-2m 10-1m 1 m 10 m 100 m 1 mm 1 cm 10 cm 1 m 102m
Gam
ma
Ray
s
X ra
ys
Ultr
a-vi
olet
(UV
)
Vis
ible
(400
- 70
0nm
)
Nea
r Inf
rare
d (N
IR)
Infra
red
(IR)
Mic
row
aves
Wea
ther
rada
r
Tele
visi
on, F
M ra
dio
Sho
rt w
ave
radi
o
Viol
etB
lue
Gre
enYe
llow
Ora
nge
Red
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C = v, where c is speed of light, is wavelength (m),
And v is frequency (cycles per second, Hz)
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WAVELENGTHS WE CAN USE MOST EFFECTIVELY
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PIXELS: Minimum sampling area
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EM Wavelengths for Snow
• Snow on the ground– Visible, near infrared, infrared– Microwave
• Falling snow– Long microwave, i.e., weather radar
• K ( = 1cm)• X ( = 3 cm)• C ( = 5 cm)• S ( = 10 cm)
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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
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Visible, Near IR, IR
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Solar Radiation
Instrument records temperature brightness at certain wavelengths
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Snow Spectral Reflectance
0
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
refle
ctan
ce (%
)
0.05 mm0.2 mm0.5 mm1.0 mm
wavelength (m)
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RADIATION CHOICES• Absorbed• Reflected• Transmitted
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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)
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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
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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
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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
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Snow Measurement• Satellite Hydrology Program
WAVELENGTH (microns)
WAVELENGTH (microns)AVHRR
GOES0.0 1.0 4.02.0 3.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0
0.0 1.0 4.02.0 3.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0
AVHRR and GOES Imaging Channels
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Snow Measurement• Remote Sensing of Snow Cover
0.0 0.5 1.0 1.5 2.0 2.5 3.0WAVELENGTH (microns)
0.0
0.2
0.4
0.6
0.8
1.0
AVHRR Ch. 2AVHRR Ch. 1
GOESCh. 1
r = 0.05 mmr = 0.2 mmr = 0.5 mmr = 1.0 mm
Snow Grain Radius (r)
OpticallyThick
Clouds
1.6 micron(NOAA 16)
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Snow Measurement• NOAA-15 1.6 Micron Channel
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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
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• Assuming linear mixing, the spectrum of a pixel is the area-weighted average of the spectra of the “end-members”
• For all wavelengths ,
• Solve for fn
Analysis of Mixed Pixels
R r fn nn
N
1
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Subpixel Resolution Snow Mapping from AVHRR
May 26, 1995(AVHRR has 1.1 km spatial resolution, 5 spectral bands)
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AVHRR Fractional SCA Algorithm
1
2
3
4
5
AVHRR (HRPT FORMAT)Pre-Processed at UCSB[NOAA-12,14,16]
Snow Map Algorithm Output: Mixed clouds, high reflective bare ground, and Sub-pixel snow cover
AVHRR Bands
Geographic Mask
Thermal Mask
Masked Fractional SCA Map
Composite Cloud Mask
Build Cloud Masks using several
spectral-based tests
Execute Atmospheric Corrections,
Conversion to engineering units
Execute Sub-pixel snow cover algorithm
using reflectance Bands 1,2,3
Application of Cloud, Thermal, and Geographic masks to raw
AVTREE output
Build Thermal Mask
Scene Evaluation: Degree of Cloud Cover
over Study Basins
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Landsat Thematic Mapper (TM)• 30 m spatial
resolution• 185 km FOV• Spectral resolution
1. 0.45-0.52 μm2. 0.52-0.60 μm3. 0.63-0.69 μm4. 0.76-0.90 μm5. 1.55-1.75 μm6. 10.4-12.5 μm7. 2.08-2.35 μm
• 16 day repeat pass
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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)
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Discrimination between Snow and Glacier Ice, Ötztal Alps
Landsat TM, Aug 24, 1989 snow ice rock/veg
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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
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AVIRIS spectra
0
20
40
60
80
100
0.3 0.8 1.3 1.8 2.3wavelength (m)
refle
ctan
ce (%
)
snow
vegetation
rock
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Spectra of Mixed Pixels
0
20
40
60
80
100
0.3 0.8 1.3 1.8 2.3wavelength (m)
refle
ctan
ce (%
)
snowvegetationrockequal snow-veg-rock80% snow, 10% veg, 10% rock20% snow, 50% veg, 30% rock
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Subpixel Resolution Snow Mapping from AVIRIS
(Painter et al., Remote Sens. Environ., 1998)
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GRAIN SIZE FROM SPACE
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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
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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
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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)
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Snow Measurement• Airborne Snow Survey Program
Natural Gamma Sources238U Series, 232Th Series, 40K Series
Soil
Snow
Atmosphere
Radon Daughtersin Atmosphere
Cosmic Rays
Uncollided
Gamma RadiationAbsorbed by Waterin the Snow Pack
Gamma Radiationreaches
Detector in Aircraft
Scattering
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Snow Measurement• Airborne SWE Measurement Theory
– Airborne SWE measurements are made using the following relationship:
SW EA
CC
MM
g cm
1 1 00 1 11
1 00 1 110
0
2ln ln..
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)
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Snow Measurement• Airborne SWE: Accuracy and Bias
Airborne measurements include ice and standing water that ground measurements generally miss.
RMS Agricultural Areas: 0.81 cmRMS Forested Areas: 2.31 cm
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Airborne Snow Survey Products
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Microwave Wavelengths
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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)
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Dielectric Properties of Snow
Material Dielectric Constant
Air 1.0
Ice 3.2
Quartz 4.3
Water 80
• 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
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Modeling electromagnetic scattering and absorption
Soil
(1) (2) (3) (4) (5) (6)
Snow
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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
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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
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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
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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!
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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
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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
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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)
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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 BTVdcSWE GHz 37
19/ GHz 18 GHz 37 BB TVTVbaSWE
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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
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Passive Microwave SWE Products
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ANOTHER PASSIVE MICROWAVE EXAMPLE
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SYNTHETIC APERTURE RADAR (SAR)
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SAR WAVELENTHS• Wavebands
– L-band (24 cm)– C-band (6 cm)– X-band (3 cm)
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POLARIZATIONPOLARIZATION
• Polarization • HH (horizontal-horizontal)• VV (vertical-vertical)• HV (horizontal-vertical)• VH (vertical-horizontal)
• More bands and more polarizations, more info
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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
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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
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SWE and Other Properties derived from SIR-C/X-SAR
Particle radiusSIR-C/X-SAR Snow density Snow depth
Estim
ated
Ground measurements
Snowdensity
Snow depthin cm
Grain radiusin mm
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Active Microwave SWE EstimationRSWE s bmR o
row
Ther
mal
sno
w re
sist
ance
(R
in o C
m3 s
/J)
Backscattering ratio (w
o - ro in dB)
SWE
/ R
Mean snow density (s in km/m3)
Problem: Maximum SWE detectable in order of 400 mmfrom Bernier et al., 1999: Hydrol. Proc. 13: 3041-3051
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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
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Weather Radar vs. Gauge Accumulation
from Fassnacht et al., 2001: J. Hydrol. 254: 148-168
0
50
100
150
200
250
300
0 28 56 84 112 140
time increment (days)
perc
enta
ge a
bsol
ute
diffe
renc
e(r
adar
- ga
uge)
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Particle Characteristics Considerations
from Fassnacht et al., 2001: J. Hydrol. 254: 148-168
0
50
100
150
200
250
0 50 100 150monthly gauge accumulation (mm)
mon
thly
rada
r acc
umul
atio
n (m
m) Wormwood
GreenochEuclid1:1 line
0
50
100
150
200
250
0 50 100 150monthly gauge accumulation (mm)
mon
thly
rada
r acc
umul
atio
n (m
m)
0
50
100
150
200
250
0 50 100 150monthly gauge accumulation (mm)
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Scaling removed
Mixed precipitationRaw
mixed precip + particle shape
![Page 67: Remote Sensing of Snow Cover](https://reader035.vdocuments.net/reader035/viewer/2022062501/56816835550346895dddeaa8/html5/thumbnails/67.jpg)
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