Frost Risk Mapping Frost Risk Mapping Using Satellite DataUsing Satellite Data

C. DomenikiotisC. Domenikiotis11, M. Spiliotopoulos, M. Spiliotopoulos22, E. Kanelou, E. Kanelou22 and and

N. R. DaleziosN. R. Dalezios11

1Department of Agriculture Animal Production and Aquatic Environment2Department of Management of Environment and Natural Resources

University of ThessalyUniversity of ThessalyVolos, Volos,

GREECEGREECE

ObjectivesObjectivesExamination of cases with radiation frost. Examination of cases with radiation frost. Comparison of satellite derived LST and air Comparison of satellite derived LST and air

temperature as recorded at the meteorological temperature as recorded at the meteorological stations of this area.stations of this area.

Classification of Thessaly region according to Classification of Thessaly region according to the temperature pattern of meteorological the temperature pattern of meteorological stations.stations.

AimAimFrostFrost risk mapping risk mapping

Region of study (Region of study (Total area ~Total area ~14.000 14.000

Km2Km2))

TYRNAVOSAGIA

KARDITSA VOLOS

AGHIALOS

ZAGORA

DatasetDataset

Air Temperature Data, from six Air Temperature Data, from six meteorological stations in Thessaly region, for meteorological stations in Thessaly region, for the years 1999, 2000, 2001.the years 1999, 2000, 2001.

Satellite Data from NOAA/AVHRR for the Satellite Data from NOAA/AVHRR for the years 1999,2000,2001.years 1999,2000,2001.

Meteorological maps (850hPa and 500hPa).Meteorological maps (850hPa and 500hPa).

MethodologyMethodology

StepsSteps Processing of temperature dataProcessing of temperature data.. Preprocessing of satellite dataPreprocessing of satellite data.. Correlation between satellite and meteorological Correlation between satellite and meteorological

datadata.. Classification of the study areaClassification of the study area.. Spatiotemporal expansion of dataSpatiotemporal expansion of data.. Validation.Validation. Frost risk mappingFrost risk mapping..

Processing of temperature data Processing of temperature data

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

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

Selection of “clear” Selection of “clear” ((non cloudnon cloud) ) imagesimages, ,

(50 (50 imagesimages).).

Extreme cold advectionExtreme cold advection(Example)(Example)

Normal conditionsNormal conditions(Example)(Example)

Finally selected imagesFinally selected images

Α/Α Date Time Α/Α Date Time Α/Α Date Time

1 31-01-2000 6:00 18 24-03-2000 6:00 35 07-02-2001 6:00

2 01-02-2000 6:00 19 25-03-2000 6:00 36 08-02-2001 6:00

3 02-02-2000 6:00 20 07-04-2000 7:00 37 09-02-2001 6:00

4 03-02-2000 6:00 21 09-04-2000 7:00 38 13-02-2001 6:00

5 05-02-2000 6:00 22 05-05-2000 7:00 39 14-02-2001 6:00

6 07-02-2000 6:00 23 06-01-2001 6:00 40 16-02-2001 6:00

7 08-02-2000 6:00 24 07-01-2001 6:00 41 17-02-2001 6:00

8 25-02-2000 6:00 25 10-01-2001 6:00 42 18-02-2001 6:00

9 29-02-2000 6:00 26 11-01-2001 6:00 43 21-02-2001 6:00

10 01-03-2000 6:00 27 12-01-2001 6:00 44 22-02-2001 6:00

11 02-03-2000 6:00 28 13-01-2001 6:00 45 23-02-2001 6:00

12 07-03-2000 6:00 29 15-01-2001 6:00 46 16-03-2001 6:00

13 08-03-2000 6:00 30 16-01-2001 6:00 47 03-04-2001 7:00

14 09-03-2000 6:00 31 19-01-2001 6:00 48 04-04-2001 7:00

15 12-03-2000 6:00 32 03-02-2001 6:00 49 07-04-2001 7:00

16 15-03-2000 6:00 33 05-02-2001 6:00 50 25-04-2001 7:00

17 23-03-2000 6:00 34 06-02-2001 6:00

Correlations Between TCorrelations Between Tss and T and Tminmin

StationStation RelationsRelations rr RR22

FytokoFytoko TTss= 0.6297T= 0.6297Tminmin-5.5553-5.5553 0.850.85 0.720.72

ZagoraZagora TTss =0.9108T=0.9108Tminmin+3.5965+3.5965

0.960.96 0.930.93

AghialosAghialos TTss =0.8455T=0.8455Tminmin+0.6077+0.6077

0.870.87 0.750.75

AgiaAgia TTss =0.7365T=0.7365Tminmin+2.5588+2.5588

0.850.85 0.730.73

KarditsaKarditsa TTss =0.9949T=0.9949Tminmin+1.6338+1.6338

0.950.95 0.880.88

TyrnavosTyrnavos TTss =0.9715T=0.9715Tminmin+2.3536+2.3536

0.980.98 0.960.96

Classification of the study areaClassification of the study area

Correlation between the LST corresponding to every Correlation between the LST corresponding to every station and any pixel of the whole Thessaly region.station and any pixel of the whole Thessaly region.

Selection of the highest correlation for each pixelSelection of the highest correlation for each pixel Assignment of each pixel to one of the stationsAssignment of each pixel to one of the stationsClassification of the whole area, based on Classification of the whole area, based on

meteorological stations.meteorological stations.Mapping of Thessaly in six sub- regions.Mapping of Thessaly in six sub- regions.

Result of the ClassificationResult of the Classification

Spatiotemporal extension of the Spatiotemporal extension of the air temperature dataair temperature data

Combination of two regression equations:Combination of two regression equations:

(i. between air temperature and surface temperature and (i. between air temperature and surface temperature and

ii. between pixel corresponding to the meteorological station and other pixels ii. between pixel corresponding to the meteorological station and other pixels of the region)of the region)

Tmin (Tmin (xx,y) = a,y) = a΄́ Tmin ( Tmin (xxi,yi) – ai,yi) – a΄ ΄ b + abb + ab΄́ + b + b

wherewhere: :

aa΄: slope΄: slopess from the regression (i) from the regression (i)

b΄: interceptb΄: interceptss from the regression (i)from the regression (i)

a: slopea: slopess from the regression (ii)from the regression (ii)

b: interceptb: interceptss from the regression (ii)from the regression (ii)

Tmin (Tmin (xxi,yi): i,yi): minimum temperature at station’s location.minimum temperature at station’s location.

Validation of the methodValidation of the methodApril modelApril model

(1994-1995-1997-2001)(1994-1995-1997-2001)Έτη

Stations1994

Obser.1994

Estim.1995

Obser.1995Estim

1997Obser.

1997Estim

2001Obser.

2001Estim

Volos 4,56 3,98 5,75 5,07 1,08 1,82 -3,83 -3,67

Zagora 7,35 4,83 1,84 5,84 1,22 1,65 3,76 -2,76

Aghialos 3,84 3,74 1,69 1,78 -0,18 0,08 2,86 2,85

Agia 2,81 2,57 -1,80 -0,60 -1,10 -0,09 1,31 1,55

Karditsa 3,33 3,30 -2,67 -1,50 0,60 1,11 4,34 5,04

Tirnavos 7,30 7,32 2,07 2,38 0,95 1,32 - -

Comparison between observed and Comparison between observed and estimated values.estimated values.

Correlation between observed and estimated values for April

y = 0,8973x + 0,4004

R2 = 0,9753

-8

-4

0

4

8

-5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8

Observed values

Estim

ated

valu

es

Correlation between observed and estimated values for March

y = 0,863x + 0,4959

R2 = 0,9591

-12

-8

-4

0

4

8

-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8

Observed values

Estim

ated

value

s

Frost risk mappingFrost risk mapping

Definition of surface temperature thresholdsDefinition of surface temperature thresholds(0(0οο C, -1C, -1oo C, -2 C, -2oo C). C).

Utilization ofUtilization of 18 18 imagesimages of spatial extensionof spatial extension ( (9 9 per monthper month) ) and the classification mapand the classification map..

Frost probability (%) division to Frost probability (%) division to ten (10) ten (10) classesclasses for the whole Thessaly region.for the whole Thessaly region.

Frost risk mapFrost risk map((MarchMarch - temperature threshold- temperature threshold -1-1oo C C))

Frost risk mapFrost risk map((AprilApril - temperature threshold- temperature threshold -1-1oo C C))

Frost risk mapping resultsFrost risk mapping results (April)(April)

Threshold 0ο C Threshold -1ο C Threshold -2ο C

Frost Risk

%

No of pixels

Percentage%

No of pixels

Percentage%

No of pixels

Percentage%

0-10 1943 13,89 3659 26,17 6070 43,41

11-20

1923 13,75 3213 22,98 3283 23,48

21-30

1212 8,67 1094 7,82 351 2,51

31-40

1525 10,91 1582 11,31 1643 11,75

41-50

1140 8,15 688 4,92 448 3,20

51-60

3024 21,62 1499 10,72 599 4,28

61-70

1221 8,73 864 6,18 688 4,92

71-80

59 0,42 24 0,17 41 0,29

81-90

345 2,47 319 2,28 313 2,24

91-100

1592 11,38 1042 7,45 548 3,92

ResultsResults

High correlationHigh correlation between conventional and between conventional and satellitesatellite datadata. .

SatisfactorySatisfactory pixel by pixel classificationpixel by pixel classification of of Thessaly regionThessaly region, , according to the temperatureaccording to the temperature characteristicscharacteristics of the of the sub-regionssub-regions..

SatisfactorySatisfactory spatial and temporalspatial and temporal extension of dataextension of data with average deviation with average deviation 0 0..55οο CC. .

ConclusionsConclusions

The described procedure:The described procedure:Identifies the areas with common temperature Identifies the areas with common temperature

characteristics.characteristics.Could be a useful tool for the estimation of Could be a useful tool for the estimation of

minimum air or surface temperature for each minimum air or surface temperature for each 1x1 km pixel.1x1 km pixel.

Could provide accurate information about Could provide accurate information about frost impact in agriculture.frost impact in agriculture.

RecommendationsRecommendations

Dense network of meteorological stations as well as more Dense network of meteorological stations as well as more representative stations is requiredrepresentative stations is required..

Utilization of minimum correlation threshold for the pixel by Utilization of minimum correlation threshold for the pixel by pixel classification pixel classification ((e.g. Re.g. R22>>70%)70%)..

Application of the method to a more satisfactory data seriesApplication of the method to a more satisfactory data series.. Application of the method to agriculture,Application of the method to agriculture, crop yielding, as crop yielding, as

well traffic protection.well traffic protection.

Extension of the methodExtension of the method to whole Greeceto whole Greece..