Remote Sensing in Meteorology Applications for

Snow

Yıldırım METE 110010231

Topics in Remote Sensing of Snow

• Optics of Snow and Ice• Remote Sensing Principles• Applications • Operational Remote Sensing

FUNDAMENTALS OF REMOTE SENSING

A. Energy source

B. Atmospheric interactions

C. Target interactions

D. Sensor records energy

E. Transmission to receiving station

F. Interpretation

G. Application

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 r

ays

Ultr

a-vi

olet

(UV

)

Vis

ible

(40

0 -

700n

m)

Nea

r In

frar

ed (

NIR

)

Infr

ared

(IR

)

Mic

row

aves

Wea

ther

rad

ar

Tel

evis

ion,

FM

rad

io

Sho

rt w

ave

radi

o

Vio

let

Blu

eG

ree

nY

ell

ow

Ora

ng

eR

ed

C = v, where c is speed of light, is wavelength (m),

And v is frequency (cycles per second, Hz)

C = v, where c is speed of light, is wavelength (m),

And v is frequency (cycles per second, Hz)

WAVELENGTHS WE CAN USE MOST EFFECTIVELY

Atmospheric absorptionand scattering

absorption

scattering

emission

RADIATION CHOICES

• Absorbed• Reflected• Transmitted

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)

PIXELS: Minimum sampling area

One temperature brightness (Tb) value recorded per pixel

One temperature brightness (Tb) value recorded per pixel

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)

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

Solar Radiation

Instrument records temperature brightness at certain wavelengths

Instrument records temperature brightness at certain wavelengths

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

refl

ec

tan

ce

(%

)

0.05 mm0.2 mm0.5 mm1.0 mm

wavelength (m)

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)

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

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

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

Snow Measurement

• Satellite Hydrology Program

WAVELENGTH (microns)

WAVELENGTH (microns)AVHRR

GOES

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

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

Snow Measurement• Remote Sensing of Snow Cover

0.0 0.5 1.0 1.5 2.0 2.5 3.0

WAVELENGTH (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

0.0 0.5 1.0 1.5 2.0 2.5 3.0

WAVELENGTH (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)

Snow Measurement• NOAA-15 1.6 Micron Channel

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

• 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 PixelsAnalysis of Mixed Pixels

R r fn nn

N

1

Subpixel Resolution Snow Mapping from AVHRR

Subpixel Resolution Snow Mapping from AVHRR

May 26, 1995

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

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

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

Subpixel Resolution Snow Mapping from Landsat Thematic Mapper

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)

Discrimination between Snow and Glacier Ice, Ötztal Alps

Discrimination between Snow and Glacier Ice, Ötztal Alps

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

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

AVIRIS spectraAVIRIS spectra

0

20

40

60

80

100

0.3 0.8 1.3 1.8 2.3wavelength (m)

refl

ec

tan

ce

(%

)

snow

vegetation

rock

Spectra of Mixed PixelsSpectra of Mixed Pixels

0

20

40

60

80

100

0.3 0.8 1.3 1.8 2.3wavelength (m)

refl

ec

tan

ce

(%

)

snow

vegetation

rock

equal snow-veg-rock

80% snow, 10% veg, 10% rock

20% snow, 50% veg, 30% rock

Subpixel Resolution Snow Mapping from AVIRIS

Subpixel Resolution Snow Mapping from AVIRIS

(Painter et al., Remote Sens. Environ., 1998)

GRAIN SIZE FROM SPACE

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

Snow Water EquivalentSnow 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)

Snow Measurement• Airborne Snow Survey Program

Natural Gamma Sources

238U Series, 232Th Series, 40K SeriesSoil

Snow

Atmosphere

Radon Daughtersin Atmosphere

Cosmic Rays

Uncollided

Gamma RadiationAbsorbed by Waterin the Snow Pack

Gamma Radiationreaches

Detector in Aircraft

Scattering

Natural Gamma Sources

238U Series, 232Th Series, 40K SeriesSoil

Snow

Atmosphere

Radon Daughtersin Atmosphere

Cosmic Rays

Uncollided

Gamma RadiationAbsorbed by Waterin the Snow Pack

Gamma Radiationreaches

Detector in Aircraft

Scattering

Snow Measurement

• Airborne SWE Measurement Theory– Airborne SWE measurements are made using

the following relationship:

SW EA

C

C

M

Mg cm

1 1 0 0 1 11

1 0 0 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)

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

Airborne Snow Survey Products

Microwave Wavelengths

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)

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

Modeling electromagnetic scattering and absorption

Soil

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

Snow

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

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

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