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Remote Sensing of Snow
Remote Sensing Basics
o Potentially comprehensive and affordable coverage over wide areas
• Advantages
A definition: The inference of an area’s or object’s physical characteristics
by distant detection of the range of
electromagnetic radiation it reflects and/or emits
o Requirement for extensive processing of large datasets
o Independent from surface constraints
(eg deep snow, dangerous terrain, inaccessibility)
o Based on objective measurements
o Repeatable: able to generate spatio-temporal datasets
o Technological constraints / overheads of platforms and sensors
o Many variables not directly measurable: difficult to validate
• Disadvantages
o Provides options to obtain observations in sparsely-instrumented areas
Usually assumed to be from satellite-borne sensors
But also... http://earthobservatory.nasa.gov/NaturalHazards/
view.php?id=80291
http://pubs.usgs.gov/pp/p1386a/images/gallery-3/
full-res/pp1386a3-fig06.jpg
Remote Sensing Platforms (Stages)
Aircraft
Remote Sensing Platforms
http://fairbanksfodar.com/wp-content/uploads/2014/09/matt_pano2.png
http://www.nasa.gov/centers/armstrong/news/
FactSheets/FS-046-DFRC.html
NASA ER-2
Fairbanks FODAR
Dirigibles
http://publicradioeast.org/post/look-tethered-aerostats
Remote Sensing Platforms
UAVs / Drones
http://www.sciencebuzz.org/kiosks/future-earth/eyes-ice
http://www.dailygalaxy.com/photos/uncategorized/
2008/01/24/a_antarctica_composite_rov_image_2.jpg
Remote Sensing Platforms
http://nsidc.org/greenland-today/files/
2014/08/GT_15Aug2014_Fig4.png
Terrestrial Instrumentation
http://www.rocksense.ca/Research/RemoteSensingGeneral.html
http://www.usask.ca/ip3/download/
ws4/presentations/4e_hayashi.pdf
Remote Sensing Platforms
http://webhelp.esri.com/arcgisserver/9.3/java/geodatabases/raster_storage.gif
Image Data
Images stored as ‘raster’ (gridded) datasets
Satellite Remote Sensing
o Orbital altitude: controls speed, and thus orbital period
(also affects swath + spatial resolution,
for given sensor capability)
Principal factors affecting RS capabilities
• Sensor...
• Platform...
o Swath:
lateral spatial coverage from single pass
o Spatial resolution: size of smallest detectable object: depends on sensor and altitude
o Temporal resolution:
re-visit interval: depends on swath and platform orbital velocity
o Spectral resolution:
number, width, position, sensitivity of wavelength bands
o Radiometric resolution:
number of sensor ‘sensitivity levels’ for given band:
eg, 8-bit 256 levels
Orbital Speed = G . M
r
G Gravitational constant 6.673 x 10-11
M Mass of Earth 5.976 x 1024 kg r Orbit radius ie, altitude + 6378 km
http://upload.wikimedia.org/wikipedia/commons/8/82/Orbitalaltitudes.jpg
Satellite Remote Sensing
Low Earth Orbit < 2000 km
eg ISS 340 km
Hubble 595 km
Most EO platforms
Medium Earth Orbit 2000 km – 35786 km
eg GPS constellation 20350 km
High Earth Orbit > 35786 km
Mostly communications,
but some wide-area
observation platforms
Geosynchronous Orbit 35786 km
Orbital period ~ 1 sidereal day
Satellite Orbits
Satellite Remote Sensing
http://gallery.usgs.gov/images/02_04_2013/
hlc5FRq11Y_02_04_2013/large/Landsat8.jpg
http://gmao.gsfc.nasa.gov/operations/candp/images/Terra.jpg
Operational Land Imager (OLI)
on LandSat 8
Moderate Resolution Imaging
Spectro-Radiometer (MODIS)
on Terra, Aqua
• High spatial resolution
(1x15m, 8x30m, 2x100m)
• Narrow swath (185km)
• Low temporal resolution
(16-day re-visit interval)
• Daily re-visit interval
(at different look-angles)
• Wide swath (2330km)
• Lower spatial resolution
(2x250m, 5x500m, 29x1000m)
Polar, sun-synchronous orbits: altitude 705 km: time per orbit ~99 minutes
http://www.ssec.wisc.edu/datacenter/terra/GLOBAL2015_02_04_035.gif
Satellite Remote Sensing
Terra Orbit Track 4 Feb 2015
Satellite Remote Sensing
MODIS 21 April 2013
R = 6 (SWIR)
G = 2 (NIR)
B = 4 (Red)
Satellite Remote Sensing
MODIS 21 April 2013
R = 6 (SWIR)
G = 2 (NIR)
B = 4 (Red)
Daily pass
J
500m spatial resolution
L
Daily passes
have different look-angles
LJ
LandSat8 OLI 21 April 2013
R = 6 (SWIR)
G = 5 (NIR)
B = 4 (Red)
Satellite Remote Sensing
16-day re-visit interval
L
30m spatial resolution
J
LandSat8 OLI 21 April 2013
Satellite Remote Sensing
Satellite Remote Sensing
Identifying lake ice-off date
Coles Lake area,
NE BC
15 May 2014
LandSat8 OLI
R = 4 (Red)
G = 2 (NIR)
B = 6 (SWIR)
Some obstacles to making sense of RS imagery
o Atmospheric...
• General
o scattering (clouds, aerosols, particles)
o refraction (at boundaries between atmospheric layers)
o absorption (occurs within specific wavelengths)
o What does the reflectance of each pixel depict?
o Geolocational uncertainties (what is the ‘footprint’ of a pixel?)
Some obstacles to making sense of RS imagery
o Obscuration
o Often cloudy (particularly over mountains) in winter
o By vegetation (snow on ground, but not on canopy)
• Snow-Specific
o Snow reflectance depends heavily on relative angles of
illumination and viewing
o Snow is a collection of scattering grains: radiation of different
wavelengths reflects from some grains, passes through others
o Snow surface texture is variable, and affects reflection patterns
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)
o May be difficult to discern snow from cloud
Angular Variation of Snow Reflectance
Directional
Incident
Diffuse
Incident
Diffuse
Reflected
Directional Reflected
• Both Direct-Beam and Diffuse radiation play important roles
• Reflectance varies with relative angles of illumination and viewing
• Need to know and account for relative positions of Sun + sensor
Bi-directional Reflectance Distribution Function (BRDF)
Snow Surface
U
V X-
Rays Gamma
Infra-
Red Micro
wave Radio
λ (nm)
Sp
ec
tra
l Ir
rad
ian
ce
(W
/m²/
nm
)
Visible Near IR Short-Wave IR UV
The Solar
(Short-Wave) Radiation Spectrum
The Electro-Magnetic Spectrum
Most useful wavelength ranges for cryospheric RS purposes:
From (nm) To (nm) (Gamma) 0.01
Visible 380 750
Near IR (NIR) 750 1400
Microwave 1000 1000000
Snow & Ice Spectral Reflectance Profiles
Reflectance profile of snow contrasts with that
of other surface-covers
http://www.esa.int/images/image051.jpg
Snow & Ice Spectral Reflectance Profiles
Also possible to distinguish different types of snow / ice
by their contrasting reflectance profiles
http://www.esa.int/SPECIALS/Eduspace_Global_EN/SEMPJ7TWLUG_0.html
Dozier J. (2013): Remote sensing of snow in visible and near-infrared wavelengths NASA Snow Remote Sensing Workshop Boulder, Aug. 2013
Near Infra-Red Short-Wave Infra-Red Visible
Snow Reflectance Profiles
Reflectance sensitivities vary with wavelength
Blue to Green: o Insensitive to Grain-Size o Sensitive to impurities
Red to SWIR o Sensitive to Grain-Size o (Largely) Insensitive to Impurities
3 4 1 2 5 6 7
Near Infra-Red Short-Wave Infra-Red Visible
Dozier J. (2013): Remote sensing of snow in visible and near-infrared wavelengths NASA Snow Remote Sensing Workshop Boulder, Aug. 2013
MODIS Bands
500m / 250m
MODIS Spectral Resolution
MODIS high-res. bands provide useful range of information
• Snow-covered area (extent)
• Snow-water equivalent
• Melt onset
Applications of RS to Snow Studies
What snow-related information is RS able to provide?
o Binary (pixel is ‘snow’ or ‘no-snow’)
o Fractional (pixel %age snow-cover)
o Sub-pixel (MODIS Snow Cover and Grain-size, MODSCAG)
• Albedo
• Snow depth
Snow Extent
Normalised Difference Snow Index (NDSI)
• Helps to distinguish between clouds and snow
• Ratio approach diminishes influence of
o atmospheric effects
o variations in illumination vs viewing geometry
(Because these bands should have similar
relative magnitudes under differing conditions)
(ρvis – ρSWIR)
(ρvis + ρSWIR) NDSI =
ρvis visible (usually green) reflectance
ρSWIR SWIR reflectance
Snow Extent
NDSI
LandSat8 OLI 21 April 2013
Snow Extent
MODIS Binary Snow-Cover
• Identifies pixels as ‘snow-covered’ (ie, > 50% snow) when
o NDSI > 0.4
o and ρNIR1 > 0.11 o and ρGREEN > 0.10 o and sfc. temperature <= 280K (+7°C)
• OR
o NDSI < 0.4 and NDVI* > 0.1 * NDVI: Normalised Diff. Vegetation Index
• Tends to...
o Miss low-fraction snow cover (early + late in season) o Miss forest snow when canopy is snow-free
o Over-estimate snow-cover in higher elevations
• Available from Terra- and Aqua-borne sensors as
o 500 m: daily and 8-day o 0.05° Climate Modelling Grid: daily, 8-day and monthly
Snow Extent
• Based on empirically-established linear relation
between...
o MODIS NDSI
o pixel fractional snow-cover derived from LandSat ETM+ imagery
MODIS Fractional Snow-Cover
Salomonson V.V. and Appel I. (2004)
Estimating fractional snow cover from MODIS using the normalized difference snow index
Remote Sensing of Environment 89: pp. 351-360
• Tends to...
o over-estimate through winter
o over-estimate in forested terrain
o under-estimate in early winter, spring
• Made available with binary snow-cover dataset
Snow Extent
• Estimates fractional cover in each MODIS pixel of ‘end-members’: o snow (and - importantly - its grain-size)
o vegetation
o rock / soil
o shade
MODIS Snow-Cover and Grain-Size (MODSCAG)
• Improves on errors of commission / omission found in MODIS datasets
• 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)
• Less sensitive to
o vegetation type / fractional cover
o snow grain size
o land surface temperature
o heterogeneity of snow or vegetation cover:
o where there is substantial snow heterogeneity,
o finds too much snow in shrublands o misses snow in barren lands
Snow Extent
MODIS Snow-Cover and Grain-Size (MODSCAG)
Rittger K., Painter T. H. and Dozier J. (2013)
Assessment of methods for mapping snow cover from MODIS
Advances in Water Resources 51: pp. 367-380
Positive values
imply over-
estimates
of fractional
snow-cover
Snow Depth
Problem: How to infer 3rd dimension?
• Lidar: ‘Light Radar’ - uses stream of Laser pulses to build DEMs
• Variation of radar back-scatter with snow depth (limited use so far)
• Snow depth from ‘Structure-From-Motion’ using digital photography
Snow Depth
Lidar Survey
http://www.dielmo.com/images-general/201101100306370.adquisicionLidarAereo.jpg
Lidar
Scanner
On-Board
GPS + IRS
Provide Location
Details
GPS Ground Stations
Improve Accuracy
o Travel-time from emittertargetdetector measured for every pulse
o Variety of platforms (‘stages’):
o usually airborne
o some experimentation from satellites
o increasing use of terrestrial systems
http://www.enveo.at/joomlaEnveo/index.php/
esa-projects/142-alpsar
Snow Depth
Lidar point cloud
http://www.orefind.com/images/blog-figures/topo2_fig2.png?sfvrsn=0
Snow Depth
Lidar: Multiple returns ‘see through’ canopy
http://www.franepal.org/fra-nepal-project/component-2-forest-mapping/component-2-lidar-assisted-multisource-programme-in-tal/
Processing enables extraction of ground-surface Digital Elevation Model
Snow Depth
Multiple passes enable inference of snow depth
(by subtraction from snow-free DEM)
http://criticalzone.org/images/made/images/remote/https_criticalzone.org/images/
national/photos-and-images/Jemez-Catalina/ecohydrology/ehp_fig3_899_445_80auto.jpg
Highly dependent on precision of location / attitude instrumentation
Snow Depth
http://www.drmattnolan.org/photography/2014/uav/
Structure-From-Motion (SFM)
Series of digital
photographs
taken from
known (x,y,z)
locations
Software infers
digital point
cloud, builds 3D
model
Snow Depth
http://www.drmattnolan.org/photography/2014/uav/
Structure-From-Motion (SFM)
Snow Depth
http://www.drmattnolan.org/photography/2014/uav/
Structure-From-Motion (SFM)
Snow Depth
Summer (13 June 2014)
Winter (20 April 2014)
Nolan M., Larsen C.F. and Sturm M. (2015) Mapping snow-depth from manned-aircraft on landscape scales at centimeter resolution using
Structure-from-Motion photogrammetry The Cryosphere Discussions 9: pp. 333-381
http://www.the-cryosphere-discuss.net/9/333/2015/tcd-9-333-2015.html
Inferring Snow Depth using Structure-From-Motion (SFM)
Inferred (additional) winter snow depth
SWE
Options for inferring SWE from RS data
• Multiple efforts to improve capabilities: progress being made
• Two principal techniques:
o Passive Microwave
o Active Microwave
• Microwave radiation (wavelengths ~1mm to 1m) is sensitive to water
content, and is used to estimate SWE (and sometimes snow depth)
SWE
Passive Microwave
• Basic principles:
o microwave radiation is emitted naturally from Earth surface
o this radiation is scattered by water in snowpack
• Of greatest use over dry, shallow snowpacks: used operationally over prairies and tundra since 1978
• Principal benefit: these wavelengths not obscured by cloud
• 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)
• 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)
• BUT also affected by grain-size, depth, snowpack stratigraphy,
meltwater fraction, ponds / lakes within field of view
• But - sensitivity to water makes this useful for identifying melt onset
• Longer wavelengths equate to much lower energy than visible, IR:
therefore wide-area, relatively coarse spatial resolution (~25 km)
SWE
Passive Microwave SWE estimate, 5 Feb 2002
http://pubs.usgs.gov/pp/p1386a/images/gallery-3/full-res/pp1386a3-fig09.jpg
SWE
Active Microwave (Radar)
• Basic principles:
o microwave radiation emitted by satellite / airborne instrument
o in dry snow, microwave radiation penetrates easily
o less penetration as water content increases
• Scattering occurs at
o air / snow surface
o within snowpack
o at snowbase / ground interface
o from ground surface
• Two microwave bands used to make sense of this o Ku (1.7 cm): sensitive to surface scattering
o X (3.1 cm): sensitive to volume scattering
• Higher energy of active system improves spatial resolution c/f passive
• But again, problems when water and/or vegetation are present
Albedo
Which Albedo do we want?
• Albedo: the ratio of reflected to incident radiation
• But...
o What combination of incident / reflected directional and/or diffuse?
o What relative angle between illumination and viewing?
o What wavelength(s)? o Narrowband (‘spectral albedo’)?
o Broadband?
• Important indicator of energy dynamics
Albedo
Which Albedo do we want?
Schaepman-Strub G., Schaepman M.E., Painter T.H., Dangel S. and Martonchik J.V. (2006) Reflectance quantities in optical remote sensing -
definitions and case studies Remote Sensing of Environment 103: pp. 27-42: DOI:10.1016/j.rse.2006.03.002
Note:
• Cases 5, 6, 8, 9 are measurable
• Cases 1-4 and 7 are conceptual (‘directional’ => infinitessimally small)
‘Black-Sky’
Albedo
‘White-Sky’
Albedo
Albedo
Albedo sources
• MODIS albedo: based on 16-day BRDF o 16-day path repeat interval
o provides 16 different illumination / viewing angles
o used to approximate BRDF, and thus provide BSA, WSA for
o 7 MODIS 250m / 500m bands
o visible light o NIR / SWIR
o full solar spectrum
• Multi-angle Imaging Spectro-Radiometer (MISR) o 9 cameras: 4 forward-looking, 4 rearward-looking (max. 70.5°)
o obtains multiple near-instantaneous (7 mins. from first to last) views
o the variety of reflectances observed provides ‘slice’ through BRDF
• Computed from snow grain-size provided by MODSCAG
o uses exponential function using coefficients dependent on
illumination angle to transform grain-size to broadband albedo
• LandSat sensor (TM / ETM+ / OLI) algorithm (Liang 2000, Smith 2010)
o weighted sum / scaling of reflectances in 5 bands
http://wiki.landscapetoolbox.org/doku.php/remote_sensor_types:misr
MISR: Multi-angle Imaging Spectro-Radiometer
Summary
• Wide range of sensors and platforms used in RS of snow
• Need to have a firm understanding of what different data products
represent, and the information they are able (and not able) to provide
• Important to select consistent datasets appropriate to a study’s
spatial and temporal scales, and likely internal frequencies of variation
• If you see this in your future - build your GIS, RS and coding skillsets!
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