Remote sensing and Hydrology

Remote sensing:

-Measuring environmental variables without any direct contact with a target

-Measuring strength of electromagnetic radiation

-Extraction of valuable information from the remote sensing data uses mathematically and statistically based algorithms.

• Understand EM radiative transfer

•Understand sensor characteristics resolution, orbit, etc.

Electromagnetic energy:

EM wave travel through vacuum at speed of light (c = 3 x 108 m/s). There are two field – electric field and magnetic field – intersect at right angle. Both vectors are perpendicular to the direction of wave (wave model)

Wavelength and frequency:

Longer wavelength has higher frequency

λc

ν

Where c = speed of light (3.0 x 108 m/s) λ = wavelength

Frequency

Electromagnetic spectrum:The Sun, earth or any objects emit a continuous spectrum of energy from gamma rays to radio waves.

Satellite sensors measure EM radiation from visible through microwave range

Strength of energy emitted depends on physical body temperature (-> blackbody radiation curve).

• Stefan-Boltzmann law -> Determine total energy, f(T) • Wein’s displacement law -> Determine dominant λ

Measure of EM radiation

Radiant flux (Φλ) : energy per unit time, unit = [W]

Radiant flux density (Φλ/A) : unit = [W/m2]

Irradiance: incident radiant flux upon a unit area Exitance: radiant flux leaving from a unit area

Radiance (Lλ) : Irradiance from a certain direction (θ), unit = [W/m2/sr]

dtransmitteabsorbedreflectedi ΦΦΦΦ

the total amount of incident radiant flux in specific wavelengths incident (Φi) must be sum of radiant flux reflected from the surface (Φreflected), the amount of radiant flux absorbed by the surface (Φabsorbed), and the amount of radiant flux transmitted through the surface (Φtransmitted):

transmission

absorption

reflectionincident

Radiation budget equation

Hemispherical Reflectance, Absorptance, and Transmittance

dtransmitteabsorbedreflected ρρρ 1

i

dtransmittedtransmitteρ

ΦΦ

i

reflectedreflectedρ

ΦΦ

i

absorbedabsorbedρ

ΦΦ

Absorptance (emissivity)

Transmittance

Reflectance

Reflectance is often used for remote sensing analysisAll depend on wavelength and materials

Absorptance = emissivity (Kirchhoffs law)

Divide both side of radiation budget equation by incident radiance

Reflectance

Scattering

Mie scattering

Particle size roughly equal to wavelength

Scattering amount proportional to λ-1

Rayleigh scattering

Particle size is smaller than wavelength

Scattering amount proportional to λ-4

Nonselective scattering

Particle size is ~10 times larger than λ

Scattering amount not function of λ

Three types of scattering: Function of particle size (gas molecule, water vapor) relative to wavelength

Redirection of EM radiation by hitting small particles (typically in the atmosphere)

For atmosphere

Active

• EM Energy is emitted by a sensor toward target

• Measure energy reflected by a target

e.g. radar

Passive

• Measure EM energy emitted by earth or sun

e.g. satellite sensors

Active vs. Passive

Remote sensing sensor

Some terminologyInstantaneous field of view (IFOV):

The solid angle over which a measurement is made at any instance. Given the sensor altitude and IFOV, spatial resolutions (linear distance) is determined

Swath width

Width of the strip that can be scanned by the sensor.

Nadir

Point on the earth just underneath the sensor

Source: http://ccrs.nrcan.gc.ca/

nadir

swath

A= IFOV

B= pixel size

C= altitude

Satellite orbit

Polar orbit vs. Equatorial orbit

A polar orbit is 90 degree angle of inclination to the equator (passing north and south poles), whereas an equatorial orbit is zero degree angle of inclination to equator.

Sun-synchronous (polar orbit)

A special case of polar orbit. Platform pass the same location at the (roughly) same local time.

Geostationary orbit (equatorial orbit)

A special case of equatorial orbit. Satellite rotate at the same speed of earth rotation. A satellite appears to be still at the sky all the time. A satellite altitude is very high (35850 km)

More info -> http://www.rap.ucar.edu/~djohnson/satellite/coverage.html

Polar orbit satellite

One rotation

Advantage is daily global coverageThere are ascending path and descending path

Rotations per day

Geostationary

Top view Side view

Need several satellites to cover the entire earth

Geostationary vs. Polar Orbit

G P

Altitude High Low

Speed Slow fast

IFOV Small large

Sensor resolution

Spatial – the size of field of view (pixel size)

Spectral – range of EM spectrum each band of sensor detects

Temporal – frequency of measurements at a certain location

Radiometric – sensitivity of a sensor to difference in EM energy strength

(recording resolution of sensor)

Radiometric: a sensor records EM energy as brightness value (integer)

8-bit8-bit8-bit8-bit

9-bit9-bit9-bit9-bit

0

0

255

511

Conversion from binary to decimal for 2-bit00 = 0x21 +0x20 = 0 01 = 0x21 +1x20 = 110 = 1x21 +0x20 = 211 = 1x21 +1x20 = 3

2-bit2-bit2-bit2-bit 0 3

Sensor resolution

0

127

255

Brightness value range

(typically 8 bit)Associated gray-scale

10 15 17 20

15 16 18 21

17 18

20

22

18

20

22 24

1

2

3

4

1 5432Columns ( j)

Bands (k )

1

2

3

4

X axis Picture element (pixel) at location Line 4, Column 4, in Band 1 has a Brightness Value of 24, i.e., BV4,4,1 = 24 .

black

gray

white21

23

22

25

Lines or rows (i)

0

127

255

Brightness value range

(typically 8 bit)Associated gray-scale

10 15 17 20

15 16 18 21

17 18

20

22

18

20

22 24

1

2

3

4

1 5432Columns ( j)

Bands (k )

1

2

3

4

X axis Picture element (pixel) at location Line 4, Column 4, in Band 1 has a Brightness Value of 24, i.e., BV4,4,1 = 24 .

black

gray

white21

23

22

25

Lines or rows (i)

spatial

spectral

spatial

radiometric

Remote sensing – sensor (visible-thermal)

Band No. Wavelength range (μm) Ground IFOV (m)

1  0.45–0.53 (visible-blue)  30

2  0.52–0.60 (visible-green)  30

3  0.63–0.69 (visible-red)  30

4  0.76–0.90 (Near infrared)  30

5  1.55–1.75 (Near infrared)  30

6 10.40–12.50 (Thermal) 120

7  2.08–2.35 (Mid infrared)  30

Landsat TM (Thematic Mapper )

Platform = Landsat 4, 5 (sun-synchronous orbit)Swath width = 185 km16 day repeat cycle

More info -> http://landsat.usgs.gov/index.php

Remote sensing - sensor (visible-thermal)

Landsat ETM+ (Enhanced Thematic Mapper )

Band No. Wavelength range (μm) Ground IFOV (m)

1  0.45–0.515 (visible-blue) 30

2 0.525–0.605 (visible-green) 30

3  0.63–0.69 (visible-red) 30

4  0.75–0.90 (Near Infrared) 30

5  1.55–1.75 (Near Infrared) 30

6 10.40–12.50 (Thermal) 60

7  2.09–2.35 (Mid Infrared) 30

8  0.52–0.90 (panchromatic) 15

Platform = Landsat 7 (sun-synchronous orbit)Swath width = 185 km16 day repeat cycle

More info -> http://landsat.usgs.gov/index.php

Remote sensing - sensor (visible-thermal)

AVHRR (Advanced Very High Resolution Radiometer)

Band No. Wavelength range (μm) Ground IFOV (km)

1  0.58–0.68 1.09

2 0.725–1.00 1.09

3A  1.58–1.64 1.09

3B  3.55–3.93 1.09

4 10.30–11.30 1.09

5 11.50–12.50 1.09

Platform = NOAA Polar orbiting Environment satelliteSwath width = 2400 kmLong history since 1979Daily global coverage (morning and afternoon acquisition)

More info -> http://edcsns17.cr.usgs.gov/1KM/avhrr_sensor.html

Remote sensing - sensor (visible-thermal) MODIS (Moderate resolution Imaging Spectroradiometer)

Bands used for land surface

Band No. Wavelength range (μm) Ground IFOV (m)

1 0.620–0.670 250

2 0.841–0.876 250

3 0.459–0.479 500

4 0.545–0.565 500

5 1.230–1.250 500

6 1.628–1.652 500

7 2.105–2.155 500

Platform = EOS Terra and Aqua (Sun-synchronous orbit)Terra (morning equator-crossing) and Aqua (morning equator-crossing)Swath width = 2330 km

There are 36 bands (0.4 - 14.385 μm) visible to thermal

More info -> http://modis.gsfc.nasa.gov/about/specifications.php

Remote sensing – sensor (passive microwave)

PolarizationElectric field component (or magnetic field) of EM energy can vibrate in any directions perpendicular to the direction of travel. This vibration direction can also evolve with time

Can measure precipitation, soil moisture, snowpack volume (SWE, depth), Sea Surface temperature (SST)

Not affected by cloud (visible sensor is affected by cloud)

Coarse spatial resolution

vertical horizontal

Fixed vibration plane Rotating Vibration plane

Viewed along the travel direction

Brightness temperature (Tb)

Tb value is usually given for passive mircowave data.

Terrestrial matters are not perfect blackbody (graybody).

Total energy emitted by graybody = blackbody radiation (given by plank law) times emissivity (0<ε<1)

Tb is given using emissivity (Tb = ε*T where T: actual physical temperature [K])

Emissivity is function of polarization, frequency, and materials

Tλkhcx

xλ

hcTEλ

1

125

2

exp

Rayleigh-Jeans approximation -> exp(x) ~ 1+x for longer λ

4

2

λ

TkcTEλ

44

22

λ

Tkc

λ

TkcεTEεTI b

λλ

Rayleigh-Jeans approximation

Plank’s law

Radiation of graybody is given by

Remote sensing – sensor (passive microwave)

SSM/I (Special Sensor Microwave Imager)

Platform = Defense Meteorological Satellite Program (DMSP) sun-synchronous orbitSwath width = 1394 kmDaily global coverage (morning and afternoon acquisition)

More info -> http://nsidc.org/data/docs/daac/nsidc0032_ssmi_ease_tbs.gd.html

Frequency (GHz) Polarization Ground IFOV (km)

19 λ=15.8 mm Horizontal 25

19 Vertical 25

22 λ=13.6 mm Vertical 25

37 λ= 8.8 mm Horizontal 25

37 Vertical 25

85 λ= 3.5 mm Horizontal 12.5

85 Vertical 12.5

Remote sensing – sensor (passive microwave)

AMSR (Advanced Microwave Scanning Radiometer)

Frequency (GHz) Polarization Ground IFOV (km)

6.925 λ=43.3 mm H / V 56

10.65 λ=28.2 mm H / V 38

18.7 λ=16.0 mm H / V 21

23.8 λ=12.6 mm H / V 24

36.5 λ= 8.2 mm H / V 12

89.0 λ= 3.4 mm H / V 5.4

Platform = EOS (Earth Observing System) AquaSwath width = 1445 kmDaily global coverage (morning and afternoon acquisition)

More info -> http://www.ghcc.msfc.nasa.gov/AMSR/index.html

Application for snow measurement

•Snow cover area (SCA)

Pixel level (Snow / no snow per pixel)

Subpixel level (percentage of SCA over pixel)

•Physical properties of snowpack

Albedo

Grain size

Depth (SWE)

Use visible – infrared sensors, passive microwave sensor, depending on what needs to be measured

Only estimate of depth (SWE) requires passive microwave data

SCA algorithm (Normalized difference snow index)

Snow if NDSI > 0.4 & Reflectance (band 2) > 11%

No snow, otherwise

52

52

bandband

bandband

TMTMTMTM

NDSI

64

64

bandband

bandband

MODISMODISMODISMODIS

NDSI

For Landsat TM

Snow if NDSI >0.4

No snow, otherwise

For MODIS

Use reflectance

To discriminate between Snow and cloud

TM band2

MODIS band4

TM band5

MODIS band6

Source: NOAA NOHRSC

SCA algorithm (subpixel level SCA mapping)

Linear spectral mixture analysis

λiλM

iiλ εRFR

,

1

Reflectance measured at each band is a linear combination of reflectance from individual surface (endmembers) such as snow, rock, or vege

Rλ: reflectance measured at band of wavelength λ

Rλ,i: reflectance of endmember, i, for band of wavelength λ

Fi : the fraction of endmember, i, over the pixel

M: the number of endmenber

ελ : residual error at wavelength λ

Find F for each endmember with numerical scheme that minimizes the sum of error

Use multispectral sensors (MODIS, AVHRR, Landsat TM) or hyperspectral sensors (better because of more bands)

Subpixel level SCA mapping

Binary SCA mapping

Source: Dozier, J., and T. H. Painter, Multispectral and hyperspectral remote sensing of

alpine snow properties, Annual Review of Earth and Planetary Sciences, 32, 465-494

SWE (or snow depth) algorithmRequire passive microwave data because EM radiation from shorter wavelength (visible – infrared sensors) cannot penetrate full depth of snowpack, but microwave does.

Tb measured over the snow cover is “cold” compared to bare ground because snow grains scatters microwave radiation (Mie scattering)

Algorithm to extract SWE from Tb data set is under development

Text for remote sensing and useful online

NASA remote sensing tutorial: http://rst.gsfc.nasa.gov/

John R. Jensen, Remote Sensing of the Environment: http://www.cas.sc.edu/geog/rsbook/Lectures/Rse/index.html

Engman, T, E. Recent advances in remote sensing in hydrology, Reviews of Geophysics, VOL. 33, NO. S1, 967-976, 1995.- general overview of remote sensing application to hydrology, no math, a little oldhttp://www.agu.org/revgeophys/engman00/engman00.html

Natural resources Canada, Earth Sciences Sectors:http://ccrs.nrcan.gc.ca/resource/tutor/fundam/index_e.php

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