remote sensing of snow - university of northern british...

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


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


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


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


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


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


Terra Orbit Track 4 Feb 2015


MODIS 21 April 2013

R = 6 (SWIR)

G = 2 (NIR)

B = 4 (Red)


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)


16-day re-visit interval

L

30m spatial resolution

J


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


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

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!

remote sensing of snow - university of northern british...

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