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Remote Sensing of Snow Cover with slides from Jeff Dozier, Tom Painter Slide 2 Topics in Remote Sensing of Snow Optics of Snow and Ice Remote Sensing Principles Applications Operational Remote Sensing Slide 3 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 Slide 4 The EM Spectrum 10 -1 nm 1 nm 10 -2 m 10 -1 m 1 m 10 m 100 m 1 mm 1 cm 10 cm 1 m 10 2 m Gamma Rays X rays Ultra-violet(UV) Visible (400 - 700nm) Near Infrared (NIR) Infrared (IR) Microwaves Weather radar Television, FM radio Short wave radio Violet Blue Green Yellow Orange Red Slide 5 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) Slide 6 WAVELENGTHS WE CAN USE MOST EFFECTIVELY Slide 7 PIXELS: Minimum sampling area Slide 8 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) Slide 9 Different Impacts in Different Regions of the Spectrum Visible, near-infrared, and infrared Independent scattering Weak polarization Scalar radiative transfer Penetration near surface only ~ 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 Slide 10 Visible, Near IR, IR Slide 11 Solar Radiation Instrument records temperature brightness at certain wavelengths Slide 12 Snow Spectral Reflectance 0 20 40 60 80 100 0.40.60.81.01.21.41.61.82.02.22.4 reflectance (%) 0.05 mm 0.2 mm 0.5 mm 1.0 mm wavelength ( m) Slide 13 RADIATION CHOICES Absorbed Reflected Transmitted Slide 14 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) Slide 15 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 Slide 16 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 Slide 17 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 Slide 18 Snow Measurement Satellite Hydrology Program AVHRR and GOES Imaging Channels Slide 19 Snow Measurement Remote Sensing of Snow Cover (NOAA 16) Slide 20 Snow Measurement NOAA-15 1.6 Micron Channel Slide 21 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 Slide 22 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 f n Analysis of Mixed Pixels Slide 23 Subpixel Resolution Snow Mapping from AVHRR May 26, 1995 (AVHRR has 1.1 km spatial resolution, 5 spectral bands) Slide 24 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 Slide 25 Landsat Thematic Mapper (TM) 30 m spatial resolution 185 km FOV Spectral resolution 1.0.45-0.52 m 2.0.52-0.60 m 3.0.63-0.69 m 4.0.76-0.90 m 5.1.55-1.75 m 6.10.4-12.5 m 7.2.08-2.35 m 16 day repeat pass Slide 26 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) Slide 27 Discrimination between Snow and Glacier Ice, tztal Alps Landsat TM, Aug 24, 1989 snowicerock/veg Slide 28 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 Slide 29 Slide 30 AVIRIS spectra Slide 31 Spectra of Mixed Pixels Slide 32 Subpixel Resolution Snow Mapping from AVIRIS (Painter et al., Remote Sens. Environ., 1998) Slide 33 GRAIN SIZE FROM SPACE Slide 34 Slide 35 EOS Terra MODIS Image Earths 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 Slide 36 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 Slide 37 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) Slide 38 Snow Measurement Airborne Snow Survey Program Slide 39 Snow Measurement Airborne SWE Measurement Theory Airborne SWE measurements are made using the following relationship: Where: C and C 0 = Uncollided terrestrial gamma count rates over snow and dry, snow-free soil, M and M 0 = Percent soil moisture over snow and dry, snow-free soil, A= Radiation attenuation coefficient in water, (cm 2 /g) Slide 40 Snow Measurement Airborne SWE: Accuracy and Bias Airborne measurements include ice and standing water that ground measurements generally miss. RMS Agricultural Areas: 0.81 cm RMS Forested Areas: 2.31 cm Slide 41 Airborne Snow Survey Products Slide 42 Microwave Wavelengths Slide 43 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) Slide 44 Dielectric Properties of Snow 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 Slide 45 Modeling electromagnetic scattering and absorption Soil (1)(2)(3)(4)(5)(6) Snow Slide 46 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 Slide 47 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 Slide 48 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 Slide 49 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! Slide 50 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 Slide 51 MICROWAVE MAGNITUDE Temperature Brightness (T b ) Function of temperature and moisture content Generally very small amount of energy Need a large pixel size to have enough energy to measure Slide 52 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) Slide 53 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, T B, gradient Single frequency vertical polarized T B Slide 54 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 Slide 55 Passive Microwave SWE Products Slide 56 ANOTHER PASSIVE MICROWAVE EXAMPLE Slide 57 SYNTHETIC APERTURE RADAR (SAR) Slide 58 SAR WAVELENTHS Wavebands L-band (24 cm) C-band (6 cm) X-band (3 cm) Slide 59 POLARIZATION Polarization HH (horizontal-horizontal) VV (vertical-vertical) HV (horizontal-vertical) VH (vertical-horizontal) More bands and more polarizations, more info Slide 60 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 Slide 61 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 Slide 62 SWE and Other Properties derived from SIR-C/X-SAR Particle radiusSIR-C/X-SARSnow densitySnow depth Estimated Ground measurements Snow density Snow depth in cm Grain radius in mm Slide 63 Active Microwave SWE Estimation Thermal snow resistance (R in o Cm 3 s/J) Backscattering ratio ( w o - r o in dB) SWE / R Mean snow density ( s in km/m 3 ) Problem: Maximum SWE detectable in order of 400 mm from Bernier et al., 1999: Hydrol. Proc. 13: 3041-3051 Slide 64 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 Slide 65 Weather Radar vs. Gauge Accumulation from Fassnacht et al., 2001: J. Hydrol. 254: 148-168 Slide 66 Particle Characteristics Considerations from Fassnacht et al., 2001: J. Hydrol. 254: 148-168 Scaling removed Mixed precipitation Raw mixed precip + particle shape Slide 67 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