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 10 8 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 Where c = speed of light (3.0 x 10 8 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 (Φ λ ) : energy per unit time, unit = [W] Radiant flux density (Φ λ /A) : unit = [W/m 2 ] Irradiance: incident radiant flux upon a unit area Exitance: radiant flux leaving from a unit area Radiance (L λ ) : Irradiance from a certain direction (θ), unit = [W/m 2 /sr]

the total amount of incident radiant flux in specific wavelengths incident (Φ i ) must be sum of radiant flux reflected from the surface (Φ reflected ), the amount of radiant flux absorbed by the surface (Φ absorbed ), and the amount of radiant flux transmitted through the surface (Φ transmitted ): transmission absorption reflection incident Radiation budget equation

Hemispherical Reflectance, Absorptance, and Transmittance Absorptance (emissivity) Transmittance Reflectance Reflectance is often used for remote sensing analysis All 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 terminology Instantaneous 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: / 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 ->

Polar orbit satellite One rotation Advantage is daily global coverage There 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 GP AltitudeHighLow SpeedSlowfast IFOVSmalllarge

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-bit 9-bit9-bit Conversion from binary to decimal for 2-bit 00 = 0x2 1 +0x2 0 = 0 01 = 0x2 1 +1x2 0 = 1 10 = 1x2 1 +0x2 0 = 2 11 = 1x2 1 +1x2 0 = 3 2-bit2-bit 0 3

Sensor resolution spatial spectral spatial radiometric

Remote sensing – sensor (visible-thermal) Band No.Wavelength range (μm)Ground IFOV (m) –0.53 (visible-blue) –0.60 (visible-green) –0.69 (visible-red) –0.90 (Near infrared) –1.75 (Near infrared) –12.50 (Thermal) –2.35 (Mid infrared) 30 Landsat TM (Thematic Mapper ) Platform = Landsat 4, 5 (sun-synchronous orbit) Swath width = 185 km 16 day repeat cycle More info ->

Remote sensing - sensor (visible-thermal) Landsat ETM+ (Enhanced Thematic Mapper ) Band No.Wavelength range (μm)Ground IFOV (m) –0.515 (visible-blue) –0.605 (visible-green) –0.69 (visible-red) –0.90 (Near Infrared) –1.75 (Near Infrared) –12.50 (Thermal) –2.35 (Mid Infrared) –0.90 (panchromatic)15 Platform = Landsat 7 (sun-synchronous orbit) Swath width = 185 km 16 day repeat cycle More info ->

Remote sensing - sensor (visible-thermal) AVHRR (Advanced Very High Resolution Radiometer) Band No.Wavelength range (μm)Ground IFOV (km) – – A 1.58– B 3.55– – – Platform = NOAA Polar orbiting Environment satellite Swath width = 2400 km Long history since 1979 Daily global coverage (morning and afternoon acquisition) More info ->

Remote sensing - sensor (visible-thermal) MODIS (Moderate resolution Imaging Spectroradiometer) Bands used for land surface Band No.Wavelength range (μm)Ground IFOV (m) – – – – – – – 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 ( μm) visible to thermal More info ->

Remote sensing – sensor (passive microwave)  Polarization Electric 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 verticalhorizontal 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

Rayleigh-Jeans approximation -> exp(x) ~ 1+x for longer λ 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 orbit Swath width = 1394 km Daily global coverage (morning and afternoon acquisition) More info -> Frequency (GHz)PolarizationGround IFOV (km) 19 λ=15.8 mmHorizontal25 19Vertical25 22 λ=13.6 mmVertical25 37 λ= 8.8 mmHorizontal25 37Vertical25 85 λ= 3.5 mmHorizontal Vertical12.5

Remote sensing – sensor (passive microwave) AMSR (Advanced Microwave Scanning Radiometer) Frequency (GHz)PolarizationGround IFOV (km) λ=43.3 mmH / V λ=28.2 mmH / V λ=16.0 mmH / V λ=12.6 mmH / V λ= 8.2 mmH / V λ= 3.4 mmH / V5.4 Platform = EOS (Earth Observing System) Aqua Swath width = 1445 km Daily global coverage (morning and afternoon acquisition) More info ->

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 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 Reflectance measured at each band is a linear combination of reflectance from individual surface (endmembers) such as snow, rock, or vege R λ : reflectance measured at band of wavelength λ R λ,i : reflectance of endmember, i, for band of wavelength λ F i : the fraction of endmember, i, over the pixel M: the number of endmenber ε λ : 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,

SWE (or snow depth) algorithm  Require 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:  John R. Jensen, Remote Sensing of the Environment: Engman, T, E. Recent advances in remote sensing in hydrology, Reviews of Geophysics, VOL. 33, NO. S1, , general overview of remote sensing application to hydrology, no math, a little old  Natural resources Canada, Earth Sciences Sectors: Article for remote sensing for hydrology