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Frost Risk Mapping Using Satellite Data C. Domenikiotis 1 , M. Spiliotopoulos 2 , E. Kanelou 2 and N. R. Dalezios 1

Frost Risk Mapping Using Satellite Data C. Domenikiotis 1 , M. Spiliotopoulos 2 , E. Kanelou 2 and N. R. Dalezios 1. 1 Department of Agriculture Animal Production and Aquatic Environment 2 Department of Management of Environment and Natural Resources University of Thessaly Volos, GREECE.

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Frost Risk Mapping Using Satellite Data C. Domenikiotis 1 , M. Spiliotopoulos 2 , E. Kanelou 2 and N. R. Dalezios 1

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  1. Frost Risk Mapping Using Satellite DataC. Domenikiotis1, M. Spiliotopoulos2, E. Kanelou2 and N. R. Dalezios1 1Department of Agriculture Animal Production and Aquatic Environment2Department of Management of Environment and Natural Resources University of Thessaly Volos, GREECE

  2. Aim Objectives • Frost risk mapping • Examination of cases with radiation frost. • Comparison of satellite derived LST and air temperature as recorded at the meteorological stations of this area. • Classification of Thessaly region according to the temperature pattern of meteorological stations.

  3. Region of study (Total area ~14.000 Km2)

  4. AGIA TYRNAVOS KARDITSA VOLOS ZAGORA AGHIALOS

  5. Dataset • Air Temperature Data, from six meteorological stations in Thessaly region, for the years 1999, 2000, 2001. • Satellite Data from NOAA/AVHRR for the years 1999,2000,2001. • Meteorological maps (850hPa and 500hPa).

  6. Methodology Steps Processing of temperature data. Preprocessing of satellite data. Correlation between satellite and meteorological data. Classification of the study area. Spatiotemporal expansion of data. Validation. Frost risk mapping.

  7. Processing of temperature data Selection of minimum air temperature (06:00 for summer time or 07:00 for winter time). Satellite images are georeferenced, and values of brightness temperature are retrieved. Comparison of satellite and in situ data.

  8. Image Processing Utilization of sixty-six (66) non-cloud night images from NOAA/AVHRR, where radiation frost is appearing. Examination of the synoptic conditions of the 66 selected days. 16 night images were rejected, where cold or warm advections are observed. Finally fifty (50) images with normal conditions are utilized.

  9. Selection of “clear” (non cloud) images, (50 images).

  10. Extreme cold advection(Example)

  11. Normal conditions(Example)

  12. Finally selected images

  13. Correlations Between Ts and Tmin

  14. Classification of the study area • Correlation between the LST corresponding to every station and any pixel of the whole Thessaly region. • Selection of the highest correlation for each pixel • Assignment of each pixel to one of the stations • Classification of the whole area, based on meteorological stations. • Mapping of Thessaly in six sub- regions.

  15. Result of the Classification

  16. Spatiotemporal extension of the air temperature data Combination of two regression equations: (i. between air temperature and surface temperature and ii. between pixel corresponding to the meteorological station and other pixels of the region) Tmin (x,y) = a΄ Tmin (xi,yi) – a΄ b + ab΄ + b where: a΄: slopesfrom the regression (i) b΄: interceptsfrom the regression (i) a: slopesfrom the regression (ii) b: interceptsfrom the regression (ii) Tmin (xi,yi): minimum temperature at station’s location.

  17. Validation of the methodApril model(1994-1995-1997-2001)

  18. Comparison between observed and estimated values.

  19. Frost risk mapping • Definition of surface temperature thresholds (0οC, -1o C, -2o C). • Utilization of 18 imagesof spatial extension (9 per month) and the classification map. • Frost probability (%) division to ten (10) classesfor the whole Thessaly region.

  20. Frost risk map(March- temperature threshold-1o C)

  21. Frost risk map(April- temperature threshold-1o C)

  22. Frost risk mapping results(April)

  23. Results • High correlationbetween conventional and satellitedata. • Satisfactorypixel by pixel classificationof Thessaly region, according to the temperaturecharacteristicsof the sub-regions. • Satisfactoryspatial and temporalextension of datawith average deviation 0.5οC.

  24. Conclusions The described procedure: • Identifies the areas with common temperature characteristics. • Could be a useful tool for the estimation of minimum air or surface temperature for each 1x1 km pixel. • Could provide accurate information about frost impact in agriculture.

  25. Recommendations • Dense network of meteorological stations as well as more representative stations is required. • Utilization of minimum correlation threshold for the pixel by pixel classification (e.g. R2>70%). • Application of the method to a more satisfactory data series. • Application of the method to agriculture,crop yielding, as well traffic protection. • Extension of the methodto whole Greece.

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