Remote Sensing of Snow GEOG 454 / 654

Transcription

1 Remote Sensing of Snow GEOG 454 / 654

2 What crysopheric questions can RS help to answer? 2 o Where is snow lying? (Snow-covered area or extent) o How much is there? o How rapidly is it melting? (Area, depth, density, SWE) (Areal depletion, albedo, water content) o How long is the snow-covered season? (Snow duration) o Which water-bodies are covered by ice?

3 BC Snow Survey Coverage 3 Manual snow-courses Automated snow-pillows

4 Cryospheric RS Pros and Cons Advantages o Independent from surface constraints (eg deep snow, dangerous terrain, dense vegetation, inaccessibility) o Potentially comprehensive and affordable coverage over wide areas o Provides options to obtain observations in sparsely-instrumented areas o Based on objective measurements o Repeatable: able to generate consistent spatio-temporal datasets Disadvantages o Technological constraints / overheads of platforms and sensors o Some variables not directly measurable: difficult to validate o Uncertainties about atmospheric influences on observations o Many wavelengths do not penetrate cloud cover o Requirement for extensive processing of large datasets 4

5 Some obstacles to making sense of RS imagery General 5 o Atmospheric... Absorption Scattering Refraction (occurs within specific wavelengths) (clouds, aerosols, particles) (at boundaries between atmospheric layers) o What does the reflectance of each pixel depict? o Geolocational uncertainties (what is a pixel s footprint?)

6 Some obstacles to making sense of RS imagery Snow-Specific o Obscuration Often cloudy in winter (particularly over mountains) Limited daylight during winter at high latitudes By vegetation (snow on ground, but not on canopy) 6 o May be difficult to discern snow from cloud o Snow is a collection of light-scattering grains, of varying size, shape: radiation of different wavelengths reflects from some grains, passes through others o Snow surface texture is variable, and affects reflection patterns o Snow reflectance depends heavily on relative angles of illumination and viewing o Snowpack metamorphic processes alter reflective properties by (eg) changing grain-size, adding meltwater o Surface reflectance also affected by impurities (dust, soot, pollen, needles, algae)

7 Spectral Irradiance (W/m²/nm) The Electro-Magnetic Spectrum The Solar (Short-Wave) Radiation Spectrum 7 UV Visible Near IR (NIR) Short-Wave IR (SWIR) Variation with wavelength of intensity of radiation energy arriving on Earth surface SW HF HE Gamma X-Rays U V Infra- Red Microwave Radio wavelength λ (nm) LW LF LE Gamma Vis - IR Micro Most useful wavelength ranges for cryospheric RS purposes

8 Spectral Reflectance Profiles Different types of surface-cover have contrasting reflectance profiles 8

9 Spectral Reflectance Profiles Different types of snow / ice have contrasting reflectance profiles 9

10 Spectral Reflectance Profiles Snow reflectance sensitivity varies with wavelength Visible Near Infra-Red Short-Wave Infra-Red 10 Blue to Green o Insensitive to Grain-Size o Sensitive to impurities wavelength λ (μm) Red to SWIR o Sensitive to Grain-Size o (Largely) Insensitive to Impurities Dozier J. (2013): Remote sensing of snow in visible and near-infrared wavelengths NASA Snow Remote Sensing Workshop Boulder, Aug. 2013

11 Angular Variation of Snow Reflectance Bi-directional Reflectance Distribution Function (BRDF) 11 o Reflectance varies with relative angles of illumination and viewing o Both Direct-Beam and Diffuse radiation play important roles o Need to know and account for relative positions of Sun + sensor Diffuse Reflected Directional Reflected Diffuse Incident Directional Incident Diffuse Reflected Snow Surface

12 Satellite Remote Sensing of Snow Operational Land Imager (OLI) on LandSat 8 Moderate Resolution Imaging Spectro-Radiometer (MODIS) on Terra, Aqua 12 Polar, sun-synchronous orbits: altitude 705 km: time per orbit ~99 minutes High spatial resolution (1x15m, 8x30m, 2x100m) Narrow swath (185km) Low temporal resolution (16-day re-visit interval) Lower spatial resolution (2x250m, 5x500m, 29x1000m) Wide swath (2330km) Daily view interval for any location (but at varying look-angles) hlc5frq11y_02_04_2013/large/landsat8.jpg

13 Satellite Remote Sensing of Snow 13 MODIS spectral resolution (high-spatial-resolution bands) Visible Near Infra-Red Short-Wave Infra-Red MODIS Bands 500m / 250m Dozier J. (2013): Remote sensing of snow in visible and near-infrared wavelengths NASA Snow Remote Sensing Workshop Boulder, Aug. 2013

14 Satellite Remote Sensing MODIS Sinuisoidal Projection 14 Tile h10v03

15 Satellite Remote Sensing MODIS Scene / Tile 15 Tile h10v03 21 April 2013 Red = Band 6 (SWIR) Green = Band 2 (NIR) Blue = Band 4 (Red)

16 Satellite Remote Sensing MODIS Scene / Tile April 2013 Red = Band 6 (SWIR) Green = Band 2 (NIR) Blue = Band 4 (Red) u r here

17 Satellite Remote Sensing of Snow MODIS 21 April Red = Band 6 (SWIR) Green = Band 2 (NIR) Blue = Band 4 (Red) 500m spatial resolution L Daily view J Daily views have different look-angles LJ Pixel footprint uncertainty L

18 Satellite Remote Sensing of Snow LandSat8 OLI 21 April Red = Band 6 (SWIR) Green = Band 5 (NIR) Blue = Band 4 (Red) 30m spatial resolution J 16-day re-visit interval L

19 Satellite Remote Sensing of Snow LandSat8 OLI 21 April 2013 Red = Band 6 (SWIR) Green = Band 5 (NIR) Blue = Band 4 (Red) 19

20 Satellite Remote Sensing of Snow / Ice LandSat8 OLI 10 January 2017 Red = Band 6 (SWIR) Green = Band 5 (NIR) Blue = Band 4 (Red) 20

21 Satellite Remote Sensing of Snow / Ice LandSat8 OLI 10 January 2017 Thermal Infra-Red 21

22 Satellite Remote Sensing of Ice Identifying date of lake ice-on/off 22 Coles Lake area, NE BC 15 May 2014 LandSat8 OLI Red = Band 4 (Red) Green = Band 5 (NIR) Blue = Band 6 (SWIR1)

23 Satellite Remote Sensing of... clouds 23 Obscuration is a problem! (Particularly with long revisit interval)

24 Snow Extent Normalised Difference Snow Index (NDSI) 24 NDSI = (ρ vis ρ SWIR ) (ρ vis + ρ SWIR ) ρ vis ρ SWIR visible (usually green) reflectance Short-Wave Infra-Red reflectance o Helps to distinguish snow from other landcover (makes use of different contrasts in reflectances at these wavelengths) o Because reflectances from snow in these bands should have similar relative magnitudes, even under differing conditions, this ratio approach reduces influences of Atmospheric effects Variations in illumination vs viewing geometry

25 Snow Extent NDSI LandSat8 OLI 21 April Need to filter-out... o open water (not snow!) o clouds (not snow-free!)

26 Snow Extent MODIS to Collection 5 (Discontinued Dec 2016) 26 1) Binary snow-cover: pixel deemed to be > 50% snow, based on NDSI, NDVI Shortcomings: Misses low-fraction snow cover (early + late in season) Misses forest snow when canopy is snow-free Over-estimates snow-cover in higher elevations 2) Fractional snow-cover Based on linear relation between NDSI and pixel % snow-cover in LandSat ETM+ imagery Shortcomings: Over-estimates through winter Over-estimates in forested terrain Under-estimates in early winter, spring

27 Snow Extent MODIS Snow-Cover and Grain-Size (MODSCAG) o Estimates fractional cover in each MODIS pixel of end-members : Snow (and - importantly - its grain-size) Vegetation Rock / soil Shade 27 o Matches reflectance profile across the 7 250m / 500m MODIS bands with the best-fitting analogue from a library of lab.-derived profiles (built by combining different fractions of end-members) o Smaller commission / omission errors than MODIS o Less sensitive to... Vegetation type / fractional cover Snow grain size Land surface temperature Heterogeneity of snow or vegetation cover Where there is substantial snow heterogeneity, Finds too much snow in shrublands Misses snow in barren lands

28 Mean %age pixel snowcover Snow Extent MODIS Snow-Cover and Grain-Size (MODSCAG) Does not work as expected at higher latitudes! Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec modscg mod10 modscg - mod10-80

29 Snow Extent MODIS Collection 6 snow-cover 29 o Pixel fractional snow represented only as NDSI o Up to user to decide what this means in terms of actual % snow (!!) o Additional quality screens provided (e.g. temperature, reversals)

30 Snow Depth Problem: How to infer 3 rd dimension of snowpack? 30 o Variation of radar back-scatter with snow depth (limited use so far) o Lidar: Light Radar - uses stream of Laser pulses to build DEMs o Snow depth from Structure-From-Motion using digital photography

31 Snow Depth Lidar Survey o Travel-time from emitter target detector measured for every pulse o Variety of platforms: Usually airborne Some experimentation from satellites Increasing use of terrestrial systems 31 Lidar Scanner On-Board GPS + IRS Provide Location Details GPS: Global Positioning System IRS: Inertial Reference System GPS Ground Stations Improve Accuracy esa-projects/142-alpsar

32 Snow Depth Lidar point cloud 32

33 Snow Depth Lidar: Multiple returns see through canopy 33 Processing enables extraction of ground-surface Digital Elevation Model

34 national/photos-and-images/jemez-catalina/ecohydrology/ehp_fig3_899_445_80auto.jpg Snow Depth Multiple passes enable inference of snow depth (by subtraction of snow-covered DEM from snow-free DEM) 34 Highly dependent on precision of location / attitude instrumentation

35 University of Saskatchewan Centre for Hydrology, Coldwater Lab. Snow Depth Inferring Snow Depth using Structure-From-Motion (SFM) 35

36 Snow Depth Inferring Snow Depth using Structure-From-Motion (SFM) 36 University of Saskatchewan Centre for Hydrology, Coldwater Lab.

37 37 Snow Depth Inferring Snow Depth using Structure-From-Motion (SFM) University of Saskatchewan Centre for Hydrology, Coldwater Lab.

38 Snow Depth Inferring Snow Depth using Structure-From-Motion (SFM) 38 Total Loss of Snow Depth, 19 May to 1 June (m) University of Saskatchewan Centre for Hydrology, Coldwater Lab.

39 Snow Depth Inferring Snow Depth using Structure-From-Motion (SFM) 39 Rate of Loss of Snow Depth, 19 May to 1 June (cm / day) University of Saskatchewan Centre for Hydrology, Coldwater Lab.

40 Snow-Water Equivalent Options for inferring SWE from RS data 40 o Multiple efforts to improve capabilities: progress being made o Microwave radiation (wavelengths ~1mm to 1m) is sensitive to water content, and is used to estimate SWE (and sometimes snow depth) o Three principal techniques: Passive Microwave Active Microwave (Radar) Passive gamma surveys also useful and accurate (SWE within ~1cm), but airborne - so expensive, + limited availability

41 Snow-Water Equivalent Passive Microwave o Basic principles: Microwave radiation is emitted naturally from Earth surface This radiation is scattered by water in snowpack o Longer wavelengths, so much lower energy than visible / IR: therefore relatively coarse spatial resolution (~25 km) o Water in snowpack scatters microwaves: SWE inferred from variations in ratio of brightness temps at two wavelengths (1.5cm, 0.8 cm) using empirically-derived equation (currently linear) o BUT also affected by grain-size, depth, snowpack stratigraphy, meltwater fraction, ponds / lakes within field of view o Principal benefit: these wavelengths not obscured by cloud o Of greatest use over large areas with dry, shallow snowpacks: used operationally over prairies and tundra since 1978 o Much more challenging to apply in areas with wetter and/or deeper snow, or in those with significant amounts of above-snow vegetation (veg. attenuates emissions from surface, but adds its own) o But - sensitivity to water makes this useful for identifying melt onset 41

42 Snow-Water Equivalent Passive Microwave SWE estimate, 5 Feb

43 Snow-Water Equivalent Active Microwave (Radar) o Basic principles: Microwave radiation emitted by satellite / airborne instrument In dry snow, microwave radiation penetrates easily Penetration decreases as water content increases Reflected back-scatter or brightness measured 43 o Scattering occurs at Air / snow surface At interfaces between snowpack layers At snowbase / ground interface From ground surface o Two microwave bands used to make sense of this Ku (1.7 cm): sensitive to surface scattering X (3.1 cm): sensitive to volume scattering o Higher energy of active system improves spatial resolution o But again, problems when water and/or vegetation are present o Estimates improved with direct sampling of snow structure, grain size, vegetation (but this diminishes benefits of remote sensing!)

44 Snow-Water Equivalent Passive Gamma SWE estimates, 3-6 Mar Passive system, but short wavelength / high energy of gamma radiation provides high spatial resolution

45 Summary 45 o Wide range of sensors and platforms used in RS of snow o Need to have a firm understanding of What different data products represent The post-processing which has been applied to them, and The information they are able (and not able) to provide o Important to select consistent datasets appropriate for o spatial and temporal scales of interest o likely internal frequencies of variation o surface context