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8th Circumpolar Symposium on Remote Sensing of Polar Environments, 8-12 June 2004, Chamonix

Evaluation of Algorithms for the Retrieval of Snow Surface Temperature from Medium Resolution Satellite Data. 8th Circumpolar Symposium on Remote Sensing of Polar Environments, 8-12 June 2004, Chamonix Jostein Amlien, Hans Koren, Rune Solberg, Norwegian Computing Center (NR). Outline.

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8th Circumpolar Symposium on Remote Sensing of Polar Environments, 8-12 June 2004, Chamonix

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  1. Evaluation of Algorithms for the Retrieval of Snow Surface Temperature from Medium Resolution Satellite Data 8th Circumpolar Symposium on Remote Sensing of Polar Environments, 8-12 June 2004, Chamonix Jostein Amlien, Hans Koren, Rune Solberg, Norwegian Computing Center (NR)

  2. Outline • Introduction Background • Physical principles • Algorithm • Examples • Conclusion

  3. SnowLab Projects • Snow variables • Coverage (SCA) • Albedo (SSA)/BRDF • Wetness (SLW / SW) • Temperature (STS) • Grain size (SGS) • SnowMan (NRC) • Monitoring of snow variables for use in improved hydrological models • SCA, BRDF, SLW/SW, (STS) • EuroClim (EU) • Monitoring of the cryosphere for improved climate forecasts • Snow, glaciers, sea ice • Snow variables: SCA, BRDF, SSA, STS, SLW/SW, SGS • EnviSnow (EU) • Monitoring of snow variables and soil moisture for use in improved hydrological models • SCA, BRDF, SLW/SW, (STS) EuroClim EnviSnow SnowLab SnowMan

  4. Information retrieval Cryospheric product time series Updated climate model scenarios’ climate-change indicators Change indicator variables User The EuroClim concept Satellite remote sensing EuroClim Cryospheric variable retrieval In situ measurements Climate modelling Database

  5. Surface Temperature of Snow (STS) • Snow temperature • Snow melting • Climate modelling • Energy balance • Snow metamorphosis • Melting of snow • flood warning • hydro-power • Remote sensing : • Predict melting: STS from optical thermal data • Monitoring melting : • wet snow from radar data • SSA, SGS and SCA from optical data

  6. STS in EuroClim / EnviSnow • Cryospheric variable retrieval • Select and implement state-of-the-art algorithms for the production line • Approach • Litterature review • Implement relevant algorithms in SnowLab • Pilot sudy : • Compare results with field reference data • Select algorithm • Implement in the EuroClim and EnviSnow production lines

  7. Brightness temperature (BT) • Derives from measured thermal radiance • Planck’s law • BT depends on • Surface temperature • Surface emissivity • surface type • wavelength • Atmospheric attenuation • atmosphere type • path length • wavelength • STS can be retrieved from Brightness temperatures • wavelength (11 µm &12µm) • view angle / path length

  8. Main techniques for STS retrieval • Split-window techniques • combines two wavelengths Ts=a+bT11+c(T11-T12) • simple split-window • Coll’s global algorithm • Key’s algorithm Key et al (1997) • correct for atmospheric attenuation utilizing path length Ts=a+bT11+c(T11-T12) + d(T11-T12)/cos(Ø) • Dual view techniques • utilizes the dependence of atmospheric attenuation of view angles • single channel DV1CTs=a+bTn +c(Tn-Tf) vf / (vn- vf) • dual channel DV2C Ts=a+bT11n +cT11f + d T12n + e T12f

  9. Calibrationdata • The algorithms are both physical and empirical • need calibration data • Calibration data sets from litt. review • Coll: fixed / calulated • Stroeve: simulated four atmospheres • split-window, DV1C, DV2C • Key: Arctic and Antarctic, 3 temperature ranges • separate for each sensor • Key, DV2C

  10. Reference and field data • Field data • Field observations of snow variables incl. STS • Jotunheimen,southern Norway 2001, 2003, 2004 • locations at Heimdalshøe and Valdresflya • Satellite data • Terra MODIS large datasets downloaded • NOAA AVHRR numeruous frames • ERS-2 ATSR a few frames

  11. Implementation of operational algorithm • Image input • Modis (mod02), HDF-files downloaded • Selected dataset (11 mm, 12 mm, viewangle) • Cloud detection [Mod35-prod / Mod02 classified ] • Snow detection [Mod35-prod / SCA = 100%] • Geometrical correction • e.g. UTM-33 • Radiometrical calibration • brightness temperature • Retrieval of surface temperature • Key’s algorithm • Export

  12. STS Febr. 2003

  13. STS March 2003

  14. STS April 2003

  15. STS May 2003

  16. STS June 2003

  17. Snow surface temperature, grain size index, and snow cover area STS SGS SCA 2003.04.22 2003.05.11 2003.05.31

  18. | Snow surface temperature and snow grain size index HH-Heimdalshø (1840 m), VF-Valdresflya (1380 m) Precipitation Beito (blue), Skåbu (red)

  19. Conclusion • Requires 100 % SCA within the 1km2 pixel • Results closely related to other snow parameters • Will integrate STS in retrieval of other snow parameters • Snow wetness and liquid water content • multi-sensor approaches (wet snow from radar) • Fast delivery is crucial for snow wetness

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