Snow cover mapping using
multi-temporal Meteosat-8 data
Martijn de Ruyter de Wildt
Jean-Marie Bettems*
Gabriela Seiz**
Armin Grün
Institute of Geodesy and Photogrammetry, ETH Zürich, Switzerland
* MeteoSwiss, Zürich, Switzerland
** now at: ESA-ESRIN, Directorate of Earth Observation, Rome, Italy
A
fellowship, in cooperation with
1
Introduction
Objective: to obtain accurate snow cover maps for the numerical weather
prediction model of MeteoSwiss (aLpine Model, aLMo).
Main problem: discrimination between ice clouds and snow.
• Use high temporal frequency of MSG (15 minutes) in addition to spectral
capabilities (12 channels) to improve separation of clouds and snow
• in real-time, fully automatic
• usable over alpine terrain
2
Data
Areas of interest:
model domains of aLMo (western and central
Europe). Resolution: 7 and 2.2 km.
Training and validation periods:
8 - 10 March, 2004
23 - 24 February, 2005
(only day-time images)
8+1 spectral bands used:
1
VIS
0.635 m
2
VIS
0.81 m
3
NIR
1.64 m
4
IR
3.92 m
7
IR
8.70 m
9
IR
10.80 m
10
IR
12.00 m
11
IR
13.40 m
12
HR-VIS 0.70 m
3
Spectral image classification: “traditional” features (10-3-2004, 12:12 UTC)
ice cloud
ice cloud
snow
r0.81
snow
r1.6
ice cloud
ice cloud
snow
BT10.8
snow
BT3.9 - BT10.8
4
Improved spectral classification II
ice cloud
snow
BT3.9 - BT10.8: snow is as dark as or darker than ice clouds;
BT3.9 - BT13.4: snow is as dark as or brighter than ice clouds;
=> the following feature should enhance the contrast
between snow and ice clouds:
BT3.9  BT10.8
BT3.9  BT13.4
BT3.9 - BT10.8
ice cloud
ice cloud
snow
BT3.9 - BT13.4
snow
(BT3.9 - BT10.8) / (BT3.9 - BT13.4 )
5
Spectral classification
clouds
snow
classification result:
UTC:200403101212
white
dark gray
light gray
black
: snow
: clouds
: snow-free land
: sea
6
Temporal classification
Temporal
test
snow
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Temporal classification
Temporal variability can be quantified for each channel m with:
dm 
1
1
 
i 1 j 1
more ice
w m,i, j
where
more water
m 

1 2
 I m,t  I m
4 t  2
more ice

2
more water
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Temporal classification
The temporal standard deviations of the 9 used channels form a 9-dimensional parameter space,
where some of the parameters are correlated with each-other.
Reduce data redundancy: principal components analysis (PCI); when applied to the difference
between two images, the change information is concentrated into fewer dimensions (Gong, 1993).
Here:
- standardised PCI (applicable to data with variables at different scales)
- applied to the 9 temporal standard deviations
Normalised eigenvalues of the 9 new components, averaged over all training data:
1
2
3
4
5
6
7
8
9
0.587
0.288
0.079
0.024
0.013
0.006
0.002
0.001
0.000
Change information
noise
9
First principal component of the
temporal standard deviation
(10-3-2004, 12:12 UTC):
Second and third components
are also useful for detecting
clouds.
clouds
snow
more ice
more water
10
Spectral and temporal classification
UTC:200403101212
temporal cloudmask is ‘liberal’, only used to check
snowy pixels for misclassifications:
UTC:200403101212
spectral
UTC:200403101212
spectral/temporal
temporal
white
dark gray
light gray
black
: snow
: clouds
: snow-free land
: sea
11
Composite snow map, March 10th, 2004, 07:00 - 12:00 UTC
Composite snow maps
March 10th, 2004, 12:12 UTC
UTC:200403101212
spectral/temporal
Composite snow map, March 8th - March 10th
spectral/temporal
spectral/temporal
white: snow
dark gray: clouds
light gray: snow-free land
black:sea
12
Composite snow maps:
spectral vs. spectral/temporal
March 10th, 2004, 07:00 - 12:00 UTC
spectral
spectral/temporal
white: snow
dark gray: clouds
light gray: snow-free land
black:sea
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High resolution visible (hrv) channel
RGB image, red= rhrv, green= r1.6 (low res.),
red pixels: surface snow OR ice clouds
blue=
BT3.9  BT10.8
BT3.9  BT13.4
(low res.)
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Classification of hrv channel
Use low resolution cloud mask and temporal variability in hrv channel to detect clouds.
Composite snow map, March 10th, 2004, 07:00 - 12:00 UTC
15
Conclusions:
• new spectral feature BT3.9  BT10.8 detects more clouds than
BT3.9  BT13.4
BT3.9 - BT10.8 alone and is less influenced by the solar zenith angle
• spectral classification separates snow and clouds reasonably well,
but: some clouds have the same spectral signature as snow
• using temporal information, most of these clouds can be detected
• temporal classification classifies snow in a conservative way
(somewhat too little snow detected, but with high certainty)
• high frequency strongly reduces cloud obscurance
• snow mapping also possible in hrv channel
• start of implementation at MeteoSwiss this winter
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