1. Remote Sensing of Alpine Snow Jeff Dozier http://www.bren.ucsb.edu/ [links to people->faculty] http://www.bren.ucsb.edu/ Acknowledgements to Rob Green, Anne Nolin, Tom Painter, Walter Rosenthal

2. 2

3. 3 Different concepts in different parts of 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 wave 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

4. Applications Energy balance calculations Remote sensing (inversion of properties from signal)

5. 5 N=n+ik, Index of refraction (complex) irir I0I0 I dx

6. 6 Optical properties of ice — visible and near-infrared wavelengths wavelength,  m (Warren, Applied Optics, 1982)

7. 7 Basic scattering properties of a single grain Mie theory, based on N and x=2  r/ –  — single- scattering albedo –g — asymmetry parameter –Q ext — extinction efficiency

8. 8 Multiple scattering in snow

9. 9 Snow spectral reflectance and absorption coefficient of ice

10. 10 Snow/cloud discrimination

11. 11 Landsat Thematic Mapper (TM) 30 m spatial resolution 185 km FOV Spectral resolution 1.0.45-0.52μm 2.0.52-0.60μm 3.0.63-0.69μm 4.0.76-0.90μm 5.1.55-1.75μm 6.10.4-12.5μm 7.2.08-2.35μm 16 day repeat pass

12. Snow properties from remote sensing in VIS, NIR, SWIR

13. MODIS (EOS Terra and Aqua) Image Earth’s surface every 1 to 2 days 36 spectral bands covering VIS, NIR, thermal 1km spatial resolution (29 bands) 500m spatial resolution (5 bands) 250m spatial resolution (2 bands) 2330 km swath

14. 14 Landsat snow mapping and cloud discrimination, Kern River basin 1985 Jan 24 bands 542Jan 24 snow mask Feb 25 bands 542 snow mask

15. 15 Simple (snow/no-snow) algorithm for TM bands on MODIS Normalized difference snow index Thresholds for MODIS algorithm (Hall) –If TM band 4 >11% – and NDSI>0.4 –Pixel is >50% snow covered MODIS, Sierra Nevada, 4/29/2000

16. Spectra of mixed pixels

17. 17 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

18. 18 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)

19. 19 Subpixel resolution snow mapping from MODIS North Park, Rocky Mountains, 2/15/2002

20. 20 Concept of an imaging spectrometer

21. 21 Airborne Visible Infrared Imaging Spectrometer (AVIRIS) 400 – 2500nm at 10nm res. 224 spectral bands 20m spatial resolution 4 spectrometers: VIS, NIR, SWIR1, SWIR2

22. 22 Snow spectral reflectance and absorption coefficient of ice

23. 23 Grain size=f(1.03  m absorption)

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25. 25 Snow area and grain size

26. 26

27. 27 Absorption by water vapor, water, and ice

28. 28 Surface wetness with AVIRIS, Mt. Rainier, 6/14/1996 AVIRIS image, 409, 1324, 2269 nm precipitable water, 1-8 mm liquid water, 0-5 mm path absorption vapor, liquid, ice (BGR)

29. Bidirectional Reflectance Distribution Function (BRDF) FIGOS - Zürich PARABOLA - GSFC ASG - UCSB

30. 30 Grain scattering and rough surface both affect BRDF

31. Measurements of Snow BRDF

32. 32 Summary – Visible & Near-Infrared Radiative transfer model seems accurate for albedo –Still need good characterization of BRDF to enable inversion from measurements –Model of nonspherical grain by collection of spheres is promising (Grenfell & Warren) –Separation of shallow from dirty snow Remote sensing of snow extent and albedo possible with spectrometer –Need extension to operational sensors (MODIS, VIIRS on NPOESS) –Need to fully explore effect of topography, shallow snow, impurities, vegetation Use of snow surface temperature not explored much in snow models

33. 33 II: Microwave Spectrum, 1-15 GHz

34. 34 SIR-C/X-SAR results Landsat TM, Apr 14, 1994 SIR-C perspective (images Apr 10-17, 1994)

35. 35 Dielectric function for ice and water, microwave region

36. 36 Frequency dependence of snow’s extinction properties Frequency, GHz Albedo 2 % 5 % Frequency, GHz Penetration Depth, m Dry SnowWet Snow

37. 37 Modeling electromagnetic scattering and absorption Model Characteristics: 1) Volume scattering properties - Dense or Random Media Model 2) Surface scattering properties - IEM model 3) Boundary conditions - Bistatic IEM model (1)(2)(3)(4)(5)(6) Snow Discrete Random

38. 38 Different concepts of snow Dense medium Discrete scatterers and spherical grains Modified extinction properties –coherent scattering between grains (near- field effect) Advantages –detailed description of snow microstructure »grain size, size variation, cluster, and stratification Random medium Randomly fluctuating permittivity described by a autocorrelation function and correlation length Wave theory used for solution, more rigorous (in principle) than radiative transfer Advantage –applicable to any shape of scatterers

39. 39 Snow water equivalence and other properties derived by SIR-C/X-SAR Particle radiusSIR-C/X-SARSnow densitySnow depth Estimated Ground measurements Snow density Snow depth in cm Grain radius in mm

40. 40 Snow and ice mapping in Khumbu Himalaya from SIR-C/X-SAR http://www.icess.ucsb.edu/~albright/khumbu.html ( Albright et al., JGR–Planets, 1998)

41. 41 Snow wetness from SIR-C C-band data (5.6 GHz), April 11, 1994 0% 8% N Radar VV HH VH rms  1.3% (Shi & Dozier, IEEE Trans. Geosci. Remote Sens., 1996)

42. 42 Model input for wet snow What can we measure? All dry snow properties are required Liquid water content Surface roughness properties – surface rms. height, correlation function and correlation length (air-snow interface) Micro-structure of liquid water – no technique Model assumptions: Coated sphereIndividual sphereDraining finger and channels

43. 43 Passive Microwave - Products

44. 44 Summary — microwave Theory of scattering and extinction exceeds technology to characterize real snow microstructure Multifrequency, multipolarization observations have resulted in significant advances for estimation of distributed snow properties –e.g. density, depth, wetness, and particle size –But field characterization at pixel scale is problematic Research is needed for quantitative description of response of interferometric measurements to snow properties