1 Department of Agriculture Animal Production and Aquatic Environment 2 Department of Management of Environment and Natural Resources University of Thessaly Volos,GREECE

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

Region of study ( Total area ~ Km2 )

TYRNAVOS AGIA KARDITSA VOLOS AGHIALOS ZAGORA

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

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.

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

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

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

Extreme cold advection (Example)

Normal conditions (Example)

Finally selected images Α/ΑDateTimeΑ/ΑDateTimeΑ/ΑDateTime : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : :00

Correlations Between T s and T min StationRelationsr R2R2R2R2 Fytoko T s = T min Zagora T s =0.9108T min Aghialos T s =0.8455T min Agia T s =0.7365T min Karditsa T s =0.9949T min Tyrnavos T s =0.9715T min

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

Result of the Classification

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) 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 Tmin (x,y) = a΄ Tmin (xi,yi) – a΄ b + ab΄ + b where: a΄: slopes from the regression (i) b΄: intercepts from the regression (i) a: slopes from the regression (ii) b: intercepts from the regression (ii) Tmin (xi,yi): minimum temperature at station’s location.

Validation of the method April model ( ) Έτη Stations 1994 Obser Estim Obser Estim 1997 Obser Estim 2001 Obser Estim Volos4,563,985,755,071,081,82-3,83-3,67 Zagora7,354,831,845,841,221,653,76-2,76 Aghialos3,843,741,691,78-0,180,082,862,85 Agia2,812,57-1,80-0,60-1,10-0,091,311,55 Karditsa3,333,30-2,67-1,500,601,114,345,04 Tirnavos7,307,322,072,380,951,32--

Comparison between observed and estimated values.

Frost risk mapping  Definition of surface temperature thresholds (0 ο C, -1 o C, -2 o C).  Utilization of 18 images of spatial extension (9 per month) and the classification map.  Frost probability (%) division to ten (10) classes for the whole Thessaly region.

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

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

Frost risk mapping results (April) Threshold 0 ο CThreshold -1 ο CThreshold -2 ο C Frost Risk % No of pixels Percentage % No of pixels Percentage % No of pixels Percentage % , , , , , , , ,823512, , , , ,156884,924483, , ,725994, ,738646,186884, ,42240,17410, ,473192,283132, , ,455483,92

Results  High correlation between conventional and satellite data.  Satisfactory pixel by pixel classification of Thessaly region, according to the temperature characteristics of the sub-regions.  Satisfactory spatial and temporal extension of data with average deviation 0.5 ο C.

Conclusions The described procedure: Identifies the areas with common temperature characteristics. 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 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. Could provide accurate information about frost impact in agriculture.

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. R 2 >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 method to whole Greece.