Remote Sensing in Meteorology Applications for Snow Yıldırım METE

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 Spectrum nm 1 nm  m  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 ( nm) Near Infrared (NIR) Infrared (IR) Microwaves Weather radar Television, FM radio Short wave radio Violet Blue Green Yellow Orange Red

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 absorption and scattering absorption scattering emission

RADIATION CHOICES Absorbed Reflected Transmitted

Properties of atmosphere and 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 (T b ) 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

Snow Spectral Reflectance reflectance (%) 0.05 mm 0.2 mm 0.5 mm 1.0 mm wavelength (  m)

General reflectance curves from Klein, Hall and Riggs, 1998: Hydrological Processes, 12, 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: uM –Band 2: uM –Band 3: uM –Band 4: uM

Snow Measurement Satellite Hydrology Program AVHRR and GOES Imaging Channels

Snow Measurement Remote Sensing of Snow Cover (NOAA 16)

Snow Measurement NOAA 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 f n Analysis of Mixed Pixels

Subpixel Resolution Snow Mapping from AVHRR May 26, 1995 (AVHRR has 1.1 km spatial resolution, 5 spectral bands)

AVHRR Fractional SCA Algorithm 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 μm μm μm μm μm μm μm 16 day repeat pass

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 m) (Rosenthal & Dozier, Water Resour. Res., 1996)

Discrimination between Snow and Glacier Ice, Ötztal Alps Landsat TM, Aug 24, 1989 snowicerock/veg

AVIRIS CONCEPT 224 different detectors nm range 10 nm wavelength 20-meter pixel size Flight line 11-km wide Flies on ER-2 Forerunner of MODIS

AVIRIS spectra

Spectra of Mixed Pixels

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 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

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)

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

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 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