Remote Sensing of Snow
Presented to: ENSC 454/654
Presented by: Jinjun Tong
Date: January 22, 2009
Outline
 Fundamentals of remote sensing
 Satellites and sensors
 Application of remote sensing
 Remotely sensed snow distribution
in the Quesnel River Basin (QRB)
Definition of Remote Sensing
Several of the human
senses gather their
awareness of the
external world almost
entirely by perceiving a
variety of signals, either
emitted or reflected,
actively or passively,
from objects that
transmit this information
in waves or pulses.
 Remote Sensing is a technology for
sampling electromagnetic radiation to
acquire and interpret non-immediate
geospatial data from which to extract
information about features, objects, and
classes on the Earth's land surface, oceans,
and atmosphere (and, where applicable, on
the exteriors of other bodies in the solar
system, or, in the broadest framework,
celestial bodies such as stars and galaxies).
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Energy Source or Illumination
(A)
Radiation and the Atmosphere
(B)
Interaction with the Target (C)
Recording of Energy by the
Sensor (D)
Transmission, Reception, and
Processing (E)
Interpretation and Analysis (F)
Application (G)
Electromagnetic Radiation
ultraviolet
Visible
Infrared
Microwaves
Interactions with the Atmosphere
Scattering
Absorbing
Those areas of the spectrum which are not
severely influenced by atmospheric absorption
and thus, are useful to remote sensors, are
called atmospheric windows
Target Interactions
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Absorption (A) occurs when radiation (energy) is absorbed into the
target while transmission (T) occurs when radiation passes through a
target. Reflection (R) occurs when radiation "bounces" off the target
and is redirected.
 water and vegetation may reflect somewhat
similarly in the visible wavelengths but are
almost always separable in the infrared.
Passive vs. Active Remote Sensing
Passive Sensing
Active Sensing
Satellites and Sensors
 In order for a sensor to collect and record energy
reflected or emitted from a target or surface, it must
reside on a stable platform removed from the target or
surface being observed. Platforms for remote sensors
may be situated on the ground, on an aircraft or
balloon (or some other platform within the Earth's
atmosphere), or on a spacecraft or satellite outside of
the Earth's atmosphere. Although ground-based and
aircraft platforms may be used, satellites provide a
great deal of the remote sensing imagery commonly
used today.
Satellite Orbits
Geostationary orbits
Sun-synchronous orbits
Near-polar orbits
Weather Satellites/Sensors
 TIROS-1(launched in 1960 by the United States)
 GOES (Geostationary Operational Environmental Satellite)
-GOES-1 (launched 1975), GOES-8 (launched 1994)
 Advanced Very High Resolution Radiometer(NOAA AVHRR)(sun-
synchronous, near-polar orbits)
 FengYun-1, FengYun-2, FengYun-3, FengYun-4 (China)
 GMS (Japan)
 Meteosat (European)
Land Observation Satellites/Sensors
 Landsat (Landsat-1 was launched by NASA in 1972, near-polar,
sun-synchronous orbits).
-Return Beam Vidicon (RBV), MultiSpectral Scanner (MSS), Thematic Mapper (TM)
 SPOT(SPOT-1 was launched by France in 1986, sun-synchronous,
near-polar orbits)
-Twin high resolution visible (HRV)
 Multispectral Electro-optical Imaging Scanner(MEIS II)
Compact Airborne Spectrographic Imager(CASI)(airborne
sensors)(Canada)
 CBERS-1 (China & Brazil)
Marine Observation Satellites/Sensors
 Nimbus-7 satellite (launched by NOAA in 1978)
-Coastal Zone Colour Scanner (CZCS)
 Marine Observation Satellite (MOS-1)( launched by Japan in
February, 1987)
-a four-channel Multispectral Electronic Self-Scanning Radiometer (MESSR),
-a four-channel Visible and Thermal Infrared Radiometer (VTIR),
-a two-channel Microwave Scanning Radiometer (MSR)
 SeaWiFS (Sea-viewing Wide-Field-of View Sensor), SeaStar
spacecraft, (NASA)
 HY-1 (launched by China in 2001)
Data Reception, Transmission, and Processing
In Canada, CCRS operates two ground receiving stations one at Cantley, Québec (GSS), just outside of Ottawa, and
another one at Prince Albert, Saskatchewan (PASS)
Applications of Remote Sensing
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Agriculture
Forestry
Geology
Oceans & Coastal Monitoring
Mapping
Hydrology
Land Cover & Land Use
Snow Ice
Agriculture
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crop type classification
crop condition assessment
crop yield estimation
mapping of soil characteristics
mapping of soil management practices
compliance monitoring (farming practices)
Forestry
 reconnaissance mapping:
-forest cover type discrimination
-agroforestry mapping
 Commercial forestry:
-clear cut mapping / regeneration assessment
-burn delineation
-infrastructure mapping / operations support
-forest inventory
-biomass estimation
-species inventory
 Environmental monitoring:
-deforestation (rainforest, mangrove colonies)
-species inventory
-watershed protection (riparian strips)
-coastal protection (mangrove forests)
-forest health and vigour
Geology
• surficial deposit / bedrock mapping
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lithological mapping
structural mapping
sand and gravel (aggregate) exploration/ xploitation
mineral exploration
hydrocarbon exploration
environmental geology
geobotany
baseline infrastructure
sedimentation mapping and monitoring
event mapping and monitoring
geo-hazard mapping
planetary mapping
Oceans & Coastal Monitoring
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Ocean pattern identification
Storm forecasting
Fish stock and marine mammal assessment
Oil spill
Shipping
Intertidal zone
Mapping
 planimetry
 digital elevation models (DEM's)
 baseline thematic mapping/topographic
mapping
Hydrology
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wetlands mapping and monitoring,
soil moisture estimation,
snow pack monitoring / delineation of extent,
measuring snow depth,
determining snow-water equivalent,
river and lake ice monitoring,
flood mapping and monitoring,
glacier dynamics monitoring (surges, ablation)
river /delta change detection
drainage basin mapping and watershed modelling
irrigation canal leakage detection
irrigation scheduling
Land Cover & Land Use
• natural resource management
• wildlife habitat protection
• baseline mapping for GIS input
• urban expansion / encroachment
• routing and logistics planning for seismic /
exploration / resource extraction activities
• damage delineation (tornadoes, flooding,
volcanic, seismic, fire)
• legal boundaries for tax and property evaluation
• target detection - identification of landing strips,
roads, clearings, bridges, land/water interface
Sea Ice
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ice concentration
ice type / age /motion
iceberg detection and tracking
surface topography
tactical identification of leads: navigation: safe
shipping routes/rescue
ice condition (state of decay)
historical ice and iceberg conditions and
dynamics for planning purposes
wildlife habitat
pollution monitoring
meteorological / global change research
Remotely sensed snow distribution and its
relationships with the hydrometeorology in
the QRB, Canada
Outline
 Research background and area
 Data processing methods
 Evaluation of Moderate Resolution Imaging
Spectroradiometer (MODIS) data
 Snow distribution in the QRB
 Relationships between snow cover extent
(SCE), snow cover fraction (SCF), snow cover
duration (SCD), topography, streamflow, and
climate change.
 Conclusions
DEM in the QRB
 Snow plays a vital role
in the energy and
water budgets of
drainage basins.
 the SCE and snow
water equivalent (SWE)
are important
parameters for
various hydrologic
models.
 The QRB is one of 13
main sub-watersheds
in Fraser River Basin,
which is one of the
world's most
productive salmon
river systems with five
salmon species and
65 other species of
fish.
Elevation percentage every 100 m
20
Percent (%)
15
10
5
0
0
500
1000
1500 2000
Elevation (m)
2500
3000
Data
 MODIS daily and 8-day SCE
 Global land one-kilometer base
elevation (GLOBE) DEM
 Daily streamflow of Quesnel River
 Daily snow depth, temperature and
precipitation of nine ground stations
EOS-MODIS
Let’s watch the video about the
EOS-MODIS instruments first
Snowmap
 The snow-mapping algorithm (Snowmap)
employs a Normalized Difference Snow
Index (NDSI) to identify and classify
snow on a pixel-by-pixel basis.
Reflectance of
snow and ice
NDSI
A Normalized Difference Snow Index
(NDSI) is computed from Band 4 (green)
and Band 6 (SWIR):
 Snow is determined if: NDSI ≥ 0.4, and the reflectance in
Band 2 (near-IR) ≥ 0.11, and Band 4 (green) ≥ 0.10, to
eliminate water and other dark surfaces from being
classified as snow. A Normalized Difference Vegetation
Index (NDVI) is computed from MODIS Band 1 (Red) and
Band 2, and the NDSI and NDVI are used together to map
snow in dense forests.
 The NDSI is also used for MODIS sea ice
products. In regions illuminated by the sun,
the NDSI is used to differentiate sea ice from
open water. A second method, one based on
Ice Surface Temperature (IST), is also used
for detection of sea ice. This is especially
useful in areas lacking solar illumination.
MODIS Bands 31 and 32, near 11.6 μm, are
used in a split-window technique to derive IST,
utilizing coefficients specific to sea ice.
 Let’s watch the animation shows the global
advance and retreat of daily snow cover along with
daily sea ice surface temperature over the Northern
Hemisphere from September 2002 through May
2003.
Data Processing
Reclassify images as snow, no snow, and cloud
Cloud
Central
point P
No
P=Original value
Yes
Cloud for all
other 8
points
Yes
P=Cloud
No
Snow>=No
snow
Spatial filter method points
No
P=No snow
Yes
P=Snow
Flow chart of spatial filter method
Comparison of snow maps of MOD10A1, MOD10A2, and SF in the QRB within the same period and
8-day annual average cloud coverage of MOD10A1, MOD10A2, and SF from 2000-2007 in the QRB.
Evaluation of MODIS data
MODIS
Ground
Snow
No snow
Snow
No snow
a
b
c
d
Accuracy 
ad
abcd
Accuracy of different MODIS snow data
Stations
Elevation m
MOD10A1, %
MOD10A2, %
SF, %
Horsefly Lake/Gruhs Lake
777
88.31
88.92
91.49
Boss Mountain Mine
1460
71.14
81.25
82.72
Yanks Peak East
1670
62.17
73.85
74.15
Relationships between
topography, SCF and
SCD
<1000 m
1000-1500 m
1500-2000 m
2000-2500 m
>2500 m
Total
Snow cover fraction (%)
100
80
60
40
20
(MOD10A2)
0
1/1
2/2
3/6
4/7
5/9
6/10
7/12
8/13
9/14
10/16
11/17
12/19
2/2
3/6
4/7
5/9
6/10
7/12
Date
8/13
9/14
10/16
11/17
12/19
Snow cover fraction (%)
100
80
60
40
20
(SF)
0
1/1
Annual cycle of the SCF
distribution in different
elevation bands, 2000-2007.
The SCD for different periods
across the QRB based on the
MOD10A2 (left) and SF (right)
products, 2001-2007. The SCD
days for the entire year equal 3
times the values in the legend.
140
120
100
80
60
40
20
0
400
140
120
100
80
60
40
20
0
400
360
300
240
180
120
60
0
400
MOD10A2
(Snow melt season)
800
1200 1600 2000 2400 2800
(Snow accumulation season)
800
1200 1600 2000 2400 2800
Standard deviation (days)
Snow cover duration (days)
SF
3
2.5
2
1.5
1
0.5
0
400
(Snow melt season)
800
1200 1600 2000 2400 2800
2.5
2
1.5
1
0.5
0
400
(Snow accumulation season)
800
1200 1600 2000 2400 2800
4
3
2
1
(Entire year)
800
1200 1600 2000 2400 2800
Elevation (m)
0
400
The mean (left)
and standard
deviations
(right) of SCDs
for 10-m
elevation bands
for 3 seasons
based on the
MOD10A2 and
SF products,
2001-2007.
(Entire year)
800
1200 1600 2000 2400 2800
Elevation (m)
The correlation coefficients between SCDs and elevations within different periods
(p<0.001) and the corresponding d(SCD)/dz (days (100 m)-1) in parentheses.
Snow melt season
Snow accumulation season
Entire year
SF
0.986 (4.31)
0.961 (3.76)
0.965 (11.51)
MOD10A2
0.976 (3.94)
0.933 (3.42)
0.938 (11.26)
(a) The mean elevational
dependence of snow cover
fraction (SCF) for the months
of February to July, 2000-2007.
(b) The mean (points) and
standard deviation (bars) of
the rate of change in SCF at
different elevations, 26
February to 26 June, 20002007.
The average annual
cycle of snow cover
fraction distribution
in different slope
and aspect bands,
2000-2007.
Relationships between runoff, SCF and SCE
SCF (100%)
2001
0
1
2002
0
1
2003
0
1
0
0.8
0.2 0.8
0.2 0.8
0.2 0.8
0.2
0.6
0.4 0.6
0.4 0.6
0.4 0.6
0.4
0.4
0.6 0.4
0.6 0.4
0.6 0.4
0.6
0.2
0.8 0.2
0.8 0.2
0.8 0.2
0.8
1
1
1
1
0
2/26 3/30 5/1 6/2 7/4
0
2/26 3/30 5/1 6/2 7/4
SCF of SF
2004
1
0
2/26 3/30 5/1 6/2 7/4
SCF of MOD10A2
Normalized accumulated runoff
2005
0
1
0
2/26 3/30 5/1 6/2 7/4
2006
0
1
2007
0
1
0
0.8
0.2 0.8
0.2 0.8
0.2 0.8
0.2
0.6
0.4 0.6
0.4 0.6
0.4 0.6
0.4
0.4
0.6 0.4
0.6 0.4
0.6 0.4
0.6
0.2
0.8 0.2
0.8 0.2
0.8 0.2
0.8
Normalized accumulated runoff
2000
1
Lagged correlation
coefficients between 8-day
maximum SCE in the QRB
and streamflow of QR during
snow melt seasons from
2000-2007.
-0.7
0
2/26 3/30 5/1 6/2 7/4
1
0
2/26 3/30 5/1 6/2 7/4
1
0
2/26 3/30 5/1 6/2 7/4
1
0
2/26 3/30 5/1 6/2 7/4
SF
MOD10A2
1
-0.72
Date
The 8-day MODIS maximum snow cover fraction of
the QRB and the corresponding 8-day runoff at
Quesnel gauge station from February 26,2000 to
December 31,2007.
Correlation coefficient
-0.74
-0.76
-0.78
-0.8
-0.82
-0.84
0
8
16
24
32
40
48
Lagged time (days)
56
64
72
Scatter plots between (normalized) SCE and (normalized) streamflow
during snow ablation seasons from 2000-2007 in the QRB
MOD10A2
Relationships between SCF, runoff and climate change
Correlation coefficients (a) and scatter plot (b) between average temperature within
different periods and SCF50% and scatter plot (c) between SCF50% and R50% during snow
melt seasons from 2000-2007 in the QRB.
Conclusions
 Spatial filter method can decrease the cloud
cover fraction from average 15% to 10% with
increasing the accuracy of the MODIS snow
products.
 Spatial filter method can improve the
analyses between the MODIS snow products
and other characteristics such as streamflow,
SCE, SCF and SCD.
 There are significant correlations between
the SCE and streamflow of QRB during the
snow melt seasons with a correlation
coefficient -0.8 (p<0.001).
 The snow melt process is highly correlated
with the mean temperature in the QRB with a
correlation coefficient -0.85 (p<0.01). The
runoff has significant linear relationship with
SCF with a correlation coefficient 0.82
(p<0.01).
 The SCD is correlated with the elevations
significantly in QRB with correlation
coefficient over -0.95 (p<0.001). There is
perennial snow over 2500 m in the QRB.