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Remote sensing and Hydrology

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.

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Remote sensing and Hydrology

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  1. Remote sensing and Hydrology

  2. 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.

  3. 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)

  4. Wavelength and frequency: Frequency Where c = speed of light (3.0 x 108 m/s) λ = wavelength Longer wavelength has higher frequency

  5. 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

  6. 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

  7. 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]

  8. Radiation budget equation 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): incident reflection absorption transmission

  9. Hemispherical Reflectance, Absorptance, and Transmittance Divide both side of radiation budget equation by incident radiance Absorptance (emissivity) Absorptance = emissivity (Kirchhoffs law) Transmittance Reflectance Reflectance is often used for remote sensing analysis All depend on wavelength and materials

  10. Reflectance

  11. Scattering Redirection of EM radiation by hitting small particles (typically in the atmosphere) Three types of scattering: Function of particle size (gas molecule, water vapor) relative to wavelength For atmosphere Rayleigh scattering Particle size is smaller than wavelength Scattering amount proportional to λ-4 Mie scattering Particle size roughly equal to wavelength Scattering amount proportional to λ-1 Nonselective scattering Particle size is ~10 times larger than λ Scattering amount not function of λ

  12. Remote sensing sensor Active vs. Passive • 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

  13. nadir swath 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 A= IFOV B= pixel size C= altitude Source: http://ccrs.nrcan.gc.ca/

  14. 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

  15. Polar orbit satellite One rotation Rotations per day Advantage is daily global coverageThere are ascending path and descending path

  16. Geostationary Top view Side view Need several satellites to cover the entire earth

  17. Geostationary vs. Polar Orbit

  18. 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) Conversion from binary to decimal for 2-bit 00 = 0x21 +0x20 = 0 01 = 0x21 +1x20 = 1 10 = 1x21 +0x20 = 2 11 = 1x21 +1x20 = 3 2-bit 0 3 8-bit 255 0 9-bit 0 511

  19. Sensor resolution radiometric spatial spatial spectral

  20. Remote sensing – sensor (visible-thermal) Landsat TM (Thematic Mapper ) Platform = Landsat 4, 5 (sun-synchronous orbit) Swath width = 185 km 16 day repeat cycle More info -> http://landsat.usgs.gov/index.php

  21. Remote sensing - sensor (visible-thermal) Landsat ETM+ (Enhanced Thematic Mapper ) Platform = Landsat 7 (sun-synchronous orbit) Swath width = 185 km 16 day repeat cycle More info -> http://landsat.usgs.gov/index.php

  22. Remote sensing - sensor (visible-thermal) AVHRR (Advanced Very High Resolution Radiometer) Platform = NOAA Polar orbiting Environment satellite Swath width = 2400 km Long history since 1979 Daily global coverage (morning and afternoon acquisition) More info -> http://edcsns17.cr.usgs.gov/1KM/avhrr_sensor.html

  23. Remote sensing - sensor (visible-thermal) MODIS (Moderate resolution Imaging Spectroradiometer) There are 36 bands (0.4 - 14.385 μm) visible to thermal Platform = EOS Terra and Aqua (Sun-synchronous orbit) Terra (morning equator-crossing) and Aqua (morning equator-crossing) Swath width = 2330 km More info -> http://modis.gsfc.nasa.gov/about/specifications.php

  24. Remote sensing – sensor (passive microwave) • 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 • Polarization • Electric field component (or magnetic field) of EMenergy can vibrate in any directions perpendicular to the direction of travel. This vibration direction can also evolve with time vertical horizontal Fixed vibration plane Rotating Vibration plane Viewed along the travel direction

  25. 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

  26. Rayleigh-Jeans approximation Plank’s law Rayleigh-Jeans approximation -> exp(x) ~ 1+x for longer λ Radiation of graybody is given by

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

  28. Remote sensing – sensor (passive microwave) AMSR (Advanced Microwave Scanning Radiometer) Platform = EOS (Earth Observing System) Aqua Swath width = 1445 km Daily global coverage (morning and afternoon acquisition) More info -> http://www.ghcc.msfc.nasa.gov/AMSR/index.html

  29. Application for snow measurement Use visible – infrared sensors, passive microwave sensor, depending on what needs to be measured • 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) Only estimate of depth (SWE) requires passive microwave data

  30. SCA algorithm (Normalized difference snow index) To discriminate between Snow and cloud Source: NOAA NOHRSC For Landsat TM Use reflectance Snow if NDSI >0.4 No snow, otherwise For MODIS TM band2 MODIS band4 TM band5 MODIS band6 Snow if NDSI > 0.4 & Reflectance (band 2) > 11% No snow, otherwise

  31. 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 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)

  32. 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

  33. 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

  34. Text for remote sensing and useful online • John R. Jensen, Remote Sensing of the Environment: • http://www.cas.sc.edu/geog/rsbook/Lectures/Rse/index.html • NASA remote sensing tutorial: http://rst.gsfc.nasa.gov/ • Natural resources Canada, Earth Sciences Sectors: • http://ccrs.nrcan.gc.ca/resource/tutor/fundam/index_e.php Article for remote sensing for hydrology 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 old http://www.agu.org/revgeophys/engman00/engman00.html

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