1 / 16

Snow cover mapping using multi-temporal Meteosat-8 data Martijn de Ruyter de Wildt

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

keaton-mcintyre
Télécharger la présentation

Snow cover mapping using multi-temporal Meteosat-8 data Martijn de Ruyter de Wildt

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. 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

  3. 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

  4. Spectral image classification: “traditional” features (10-3-2004,12:12 UTC) ice cloud ice cloud snow snow r0.81 r1.6 ice cloud ice cloud snow snow BT10.8 BT3.9 - BT10.8

  5. 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: ice cloud snow BT3.9 - BT10.8 ice cloud ice cloud snow snow BT3.9 - BT13.4 (BT3.9 - BT10.8) / (BT3.9 - BT13.4 )

  6. clouds snow Spectral classification classification result: UTC:200403101212 white : snow dark gray : clouds light gray : snow-free land black : sea

  7. Temporal test snow Temporal classification

  8. more ice more water more ice more water Temporal classification Temporal variability can be quantified for each channel m with: where

  9. 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 0.587 2 0.288 3 0.079 4 0.024 5 0.013 6 0.006 7 0.002 8 0.001 9 0.000 Change information noise

  10. clouds snow more ice more water First principal component of the temporal standard deviation (10-3-2004, 12:12 UTC): Second and third components are also useful for detecting clouds.

  11. Spectral and temporal classification UTC:200403101212 temporal cloudmask is ‘liberal’, only used to check snowy pixels for misclassifications: UTC:200403101212 UTC:200403101212 spectral UTC:200403101212 spectral/temporal white : snow dark gray : clouds light gray : snow-free land black : sea temporal

  12. 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

  13. spectral/temporal Composite snow maps: spectral vs. spectral/temporal March 10th, 2004, 07:00 - 12:00 UTC spectral white: snow dark gray: clouds light gray: snow-free land black:sea

  14. High resolution visible (hrv) channel RGB image, red= rhrv, green= r1.6 (low res.), blue= (low res.) red pixels: surface snow OR ice clouds

  15. 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

  16. 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

More Related