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Snow cover mapping usingmulti-temporal Meteosat-8 data

Martijn de Ruyter de WildtJean-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

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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

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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

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r1.6

BT3.9 - BT10.8BT10.8

Spectral image classification: “traditional” features (10-3-2004, 12:12 UTC)

r0.81

snow

ice cloud

snow

snowsnow

ice cloud

ice cloud

ice cloud

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BT3.9 - BT10.8

BT3.9 - BT13.4

Improved spectral classification II

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:

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BTBT

BTBT

−−

(BT3.9 - BT10.8) / (BT3.9 - BT13.4 )

snow

ice cloud

snow

ice cloud

snow

ice cloud

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Spectral classification

classification result:

white : snowdark gray : cloudslight gray : snow-free landblack : sea

UTC:200403101212

clouds

snow

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Temporal classification

Temporaltest

snow

8

∑ ∑−= −=

σ=1

1i

1

1jj,i,mm wd

Temporal classification

Temporal variability can be quantified for each channel m with:

where ( )∑−=

−=2

2

2

,4

1

tmtmm IIσ

more ice more water more ice 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 0.5872 0.2883 0.0794 0.0245 0.0136 0.0067 0.0028 0.0019 0.000

Change information

noise

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First principal component of thetemporal standard deviation(10-3-2004, 12:12 UTC):

Second and third componentsare also useful for detectingclouds.

more ice more water

clouds

snow

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white : snowdark gray : cloudslight gray : snow-free landblack : sea

UTC:200403101212

UTC:200403101212

temporal

spectral

temporal cloudmask is ‘liberal’, only used to check snowy pixels for misclassifications:

spectral/temporal

Spectral and temporal classification

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UTC:200403101212

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Composite snow map, March 10th, 2004, 07:00 - 12:00 UTC

March 10th, 2004, 12:12 UTC

white: snow dark gray: clouds light gray: snow-free land black:sea

spectral/temporal

UTC:200403101212

Composite snow map, March 8th - March 10th

spectral/temporal

spectral/temporal

Composite snow maps

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Composite snow maps: spectral vs. spectral/temporal

March 10th, 2004, 07:00 - 12:00 UTC

white: snow dark gray: clouds light gray: snow-free land black:sea

spectral spectral/temporal

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High resolution visible (hrv) channel

RGB image, red= rhrv, green= r1.6 (low res.), blue= (low res.)

red pixels: surface snow OR ice clouds 13.43.9

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BTBT

BTBT

−−

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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

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Conclusions:

• new spectral feature detects more clouds than

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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BTBT

BTBT

−−