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Snow Recognition Product over Turkey as derived from METOP AVHRR

Contents METOP AVHRR Channels Snow Recognition with AVHRR Imagery Image Interpretation & Retrieval Algorithm METOP Snow Product Chain Conclusion. Snow Recognition Product over Turkey as derived from METOP AVHRR. Aydın Gürol ERTÜRK Turkish State M e teorological Service. Contributions:

JasminFlorian
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Snow Recognition Product over Turkey as derived from METOP AVHRR

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  1. Contents • METOP AVHRR Channels • Snow Recognition with AVHRR Imagery • Image Interpretation & Retrieval Algorithm • METOP Snow Product Chain • Conclusion Snow Recognition Product over Turkey as derived from METOP AVHRR Aydın Gürol ERTÜRK Turkish State Meteorological Service Contributions: Peter Romanov, NOAA Zuhal Akyürek, METU Serdar Sürer, METU

  2. METOP AVHRR Channels

  3. EUMETSAT Satellite Programmes • Geostationary Orbit – METEOSAT • METEOSAT 7 at 63° E • METEOSAT 8-9 (MSG 1-2) at 0° and 3.4° E; • MSG 3-4 Under Construction • MTG (METEOSAT Third Generation) • Polar Orbit – METOP • METOP A: Flying. • METOP 2-3: Under Construction

  4. METOP AVHRR Channels

  5. Snow RecognitionImage Interpretation

  6. Different Absorption of Ice- and Watercloud in 1.6 mHigher absorption in the ice phase Absorption of Ice- and Water cloud After Source: D. Rosenfeld

  7. A B Snow is white (bright) in (A) VIS0.6 image due to high reflectance but dark in (B) NIR1.6 image due to very low reflectance. In the same way, ice clouds are white (bright) in (C) VIS0.6 image as water clouds but dark in (D) NIR1.6 image and possible to discriminate. VIS Channel Application- Ice Clouds & Snow C D IR Renk Tablosu

  8. VIS Channel Application : Snow VIS06 & NIR16 VIS06 NIR16 Snow is white in VIS0.6 image due to high reflectance but dark in NIR1.6 image due to very low reflectance. Low level water cloud is same reflectance in both channels. ( MSG Images, 13/01/2008 12:00 UTC.)

  9. RGB Application : Snow VIS06,NIR16 & RGB A B C D A) VIS 0.6 , B) NIR1.6, C) “Natural Colors” RGB (RGB 3,2,1) and D) “Snow RGB” (RGB 1,3,4-9) ( MSG Images, 13/01/2008 12:00 UTC.)

  10. Comparison of VIS06 and NIR 1.6 Images • Watercloudsarewhite in bothimages. • Snow is dark in NIR1.6 image but white in VIS0.6 image. (MSG Images 21/12/2007 11:00 Z) VIS06 Snow over Alps NIR16 Snow in dark over Alps

  11. RGB Application : Snow VIS06,NIR16 & RGB

  12. Loop: Snow VIS06 & RGB

  13. Snow RecognitionRetreival Algorithm

  14. Snow Recognition Methodology • Snow identification technique is based on satellite observations in the visible, near-infrared, shortwave-infrared and infrared bands. • The basic principle behind the snow recognition algorithm is on the different spectral characteristics of snow, cloud and land.

  15. METOP AVHRR Channels used

  16. Study Area

  17. Algorithm Flow Chart for METOP Snow Product Cloud Snow

  18. Snow Recognition Product 25/02/2012

  19. Snow Recognition Product LoopNov 2011 – April 2012

  20. Validation & Hydrological Impact b) a) a) DailySnow Recognitionproduct derivedfrom NOAA AVHRR and b) MODIS Daily Snow Cover map (MOD10A1.5) date 22 February 2008. b) a) a) MSG RGB321 image and b) NOAA AVHRR RGB134 image date 22 February 2008.

  21. Validation & Hydrological Impact Validation results for the time period of 2007-2008

  22. Validation & Hydrological Impact a) Snow cover variationand runoff measurement relationship over Upper Euphrates Basinfor water year 2007-2008 b) snow cover variation of 2008-2009, c) snow cover variation of 2009-2010.

  23. Results and Discussions • Snow Recognition method based on optical data is introduced. • Overall performance of the snow product algorithm and the accuracy of the corresponding snow cover product appear reasonable. The overall accuracies for 2007-2008 is calculated as 87 %. • Adaptation of the algorithm to the AVHRR onboard METOP satellite is successful. • Hydrological impact is also promising. • Cloud contamination is one of the important limitation of Snow Recognition Algorithms retrieved from VIS imagery.

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