USING REMOTELY SENSED DATA FOR LEAF WETNESS DURATION MEASUREMENT

Transcription

1 USING REMOTELY SENSED DATA FOR LEAF S. Dietrich (1), R. Alilla (1), A. Cicogna (2), R. Fabbo (3), M. Gani (2), R. Giovanardi (4), S. Orlandini (5), M. Sandra (2,4), M. Severini (1), G. Maracchi (5) (1) Istituto di Scienze dell Atmosfera e del Clima (ISAC)/CNR, Roma, Italy, (2) Centro Servizi Agrometeorologici (CSA) per il Friuli Venezia Giulia, (3) ARPA FVG Osservatorio Meteo Regionale, (4) Università di Udine, (5) Universitàdi Firenze

2 TOPIC The idea is to develop and to validate a physical model for leaf wetness duration (LWD) measurement working in topographical way, taking advantage from precipitation fields supplied by polarimetric radars (and/or microwave satellite in a subsequent phase), and from network measurements of ground based weather stations (and/or satellite measurements in a subsequent phase).

3 In order to achieve such multidisciplinary objective, the combined job of three different research units is required: 1) Remote sensing unit, having the task to develop algorithms for the production of maps of duration of the precipitation, dealing with particular care the the rain/no rain threshold. 2) Modeling unit, having the task to develop the physical model taking into account parameters of the energetic and radiative budget in order to simulate dynamics of water condensation and evaporation from vegetable textures. 3) Calibration/validation unit, having the tasks to acquire measurements of other variables, and to validate LWD model outputs by means of LW sensor measurements.

4 WHY A MODEL FOR LWD MEASUREMENT Leaf wetness produced by dew, fog or precipitation is one of the most important factors controlling fungal plant disease because it is a precondition for spores infecting leaves. A great number of measuring principles and construction techniques are available for the monitoring of LW duration, but no standard exists for operational measurement. Therefore, LW modelling seems to be a useful alternative.

5 WHY A MODEL FOR LWD MEASUREMENT Most sensors measure LWD indirectly and have different physical properties from leaves, sensors require calibration to represent a particular crop. Model can produce LW data having high spatial and temporal resolution if fed with spatialized meteorological data. LW model can be applied to local climatological data for optimal site selection procedure Once tested on RS data, model can be applied on forecasted meteorological data. That allows to make use of circulation model outputs for leaf wetness forecasting. LW model measurements can also help weather forecasting models in characterize the interaction soil- atmosphere.

6 Input from agrometeo stations (punctual) Input from remote sensors (gridded) Gridded Agrometeorological Dataset Data Interpolation Data Calibration/Validation Leaf Wetness Models Bf class Period Measured Sweb Dropben Elman NN Dry (83%) 2571 (81%) (87%) 3055 (87%) Wet (81%) 1821 (93%) (63%) 1193 (86%) Dry April (92.6%) Wet April (77.2%) Model for grapevine downy mildew Gridded LWD Data OUTPUT Areal Disease Forecasting Model

7 Input from agrometeo stations (punctual) Input from remote sensors (gridded) Gridded Agrometeorological Dataset Data Interpolation Data Calibration/Validation Leaf Wetness Models Bf class Period Measured Sweb Dropben Elman NN Dry (83%) 2571 (81%) (87%) 3055 (87%) Wet (81%) 1821 (93%) (63%) 1193 (86%) Dry April (92.6%) Wet April (77.2%) Model for grapevine downy mildew Gridded LWD Data OUTPUT Areal Disease Forecasting Model

8 A dense net of meteorological stations installed in 1991 by the ERSA of Friuli-Venezia Giulia and now managed from the ARPA registers at 1 hour intervals the following data: total rain (mm) temperature at 180 cm height( C) humidity at 180 cm height(%) leaf-wetness (min) mean wind direction at 10 m (last 10 minutes/hour) (gr. north) mean wind speed at 10 m (last 10 minutes/hour) (m/s) per hour mean wind speed at 10 m height(m/s) per hour mean wind speed at 2 m height (m/s) per hour mean pressure at the station level (hpa)

9 total per hour global radiation (kj/mq) total per hour sunshine duration (min) per hour mean temperature at 50 cm height ( C) per hour mean temperature at 20 cm height ( C) per hour mean temperature at 0 cm height ( C) per hour soil mean temperature at 10 cm depth ( C) per hour soil mean temperature at 20 cm depth ( C) The resulting network covered the plain and the alpine area. The stations, equipped with automatic instruments, allow high frequency measurements. At the moment the mesonetwork is composed of 23 hourly stations; a subset of them takes measurements every 5 min. BACK

10 Characteristics of the GPM-500C radar Polarization type linear Frequency (MHz) Peak Power (kw) 500 The standard products: Maps PPI (Plan Position Indicator) Maps CAPPI (Constant Altitude PPI) Maps RHI (Range Height Indicator) Maps VMI (Vertical Maximum Intensity) Antenna type Antenna diameter (m) Beam width Maximum sidelobe level (db) Polarization transmitted Power tube Receiver type Local oscillator MDS Noise figure (3 MHz band) Measured parameters Pulse lenght (ms) PRF (s-1) Number of integrated pulses Resolution volume size (m) Cassegrain dual-offset linear V or H - ferrite circulator, switching time <3 ms, maximum switching rate >1500 s-1 Klystron Varian VKC 7762 C Double conversion with LNA STALO (bank of 8 quartz) -110 dbm 3.65 db Acquisition parameters ZH, ZDR, V, sv Precipitation maps SRI, (Surface Rainfall Intensity) and SRT (Surface Rainfall Total). BACK

11 Bechini R., E. Gorgucci, G. Scarchilli, and S. Dietrich: The operational weather radar of Fossalon di Grado (Gorizia, Italy): accuracy of reflectivity and differential reflectivity measurements, Meteorol Atmos Phys 79 (2002) 3-4, b) BACK 6

12 16 September 2000 : Comparison raingauge - radar Station lon lat name daily rainfall amount hours with rain radar rain gauges radar rain gauges (5' sampling) 1 12,77 46,08 Vivaro 32,9 23,0 3,0 2,8 2 12,84 45,92 S.Vito 101,3 49,4 3,3 2,3 3 12,55 45,92 Brugnera 60,0 50,6 2,3 2,3 4 13,35 46,14 Faedis 78,6 53,2 6,0 4,4 5 13,08 46,10 Fagagna 104,2 51,6 4,5 3,8 6 13,23 46,03 Udine 113,4 55,8 5,2 3,4 7 13,15 45,88 Talmassons 101,8 85,6 3,2 2,6 8 13,05 45,80 Palazzolo 41,3 33,6 2,2 1,8 9 13,54 45,96 Capriva 79,9 41,8 5,5 3, ,48 45,89 Gradisca 89,6 77,8 2,8 4, ,46 45,71 Fossalon 54,8 44,0 1,5 1, ,34 45,85 Cervignano 104,3 76,9 2,7 2, ,80 45,65 Trieste 23,9 15,4 1,8 1, ,74 45,74 Sgonico 28,8 30,0 2,0 1, ,12 46,26 Gemona 72,0 35,4 3,8 4, ,97 45,95 Codroipo 76,7 60,6 4,7 3, ,68 45,95 Pordenone 130,9 114,0 3, ,14 45,70 Lignano 43,9 27,6 1,3 1, ,42 46,08 Cividale 103,1 43,8 6,5 3, ,93 46,50 Zoncolan 35,5 13,4 2,3 1, ,52 46,48 Lussari 15,9 16,0 5,5 3, ,60 46,51 Tarvisio 180,6 6, ,87 46,41 Enemonzo 19,9 11,2 4,2 2, ,41 45,68 Grado 57,1 56,4 1,5 1,1

13 16 September 2000 Rain gauges interpolated daily rainfall Radar daily rainfall Differences

14 16 September 2000 Hours with rain as resulting from interpolated raingauges data. Hours with rain as resulting from radar data Differences BACK

15 The possibility to estimate the leaf-wetness duration through the utilization of the radar precipitation data seems therefore very interesting in order to obtain a spatial integration of the data measured on the ground and the optimisation of the net density. So we compared the values of leaf-wetness measured at the ground and the precipitation estimated by the radar on the vertical of each station site. Precipitation > 0.1 mm/h Leaf-wetness > 1 min Precipitation > 0.1 mm/h Leaf-wetness = 0 min Precipitation = 0 mm/h Leaf-wetness > 1 min Precipitation = 0 mm/h Leaf-wetness = 0 min N. cases % Yes/Yes % Yes/No % No/Yes % No/No % The radar GPM-500 polarimetric radar (Fossalon di Grado) is a good instrument to estimate leafwetness in condition of rain BACK

16 Comparative study of different leaf wetness estimation methods The aim of this work is to compare different approaches to simulate LW duration. LW duration Models: Artificial Neural Networks (NNs), Dropben, Sweb. spiking checking Artificial Neural Networks ANN is a new type of model for numerical elaboration, able to map the input/output relationships between the phenomenon and influencing quantities. It consits of computation units (neurons) connected through communication channels (synapses). Usually neurons are organised in three layers: input, hidden and output layer. Dropben The model consists of 2 submodules. The first one simulates dew formation and dew evaporation and it is active during rainless periods. The physical principle is based on the energy balance equation. The second module simulates the evaporation time of a rain droplet settled on a leaf. The LW begins when precipitation sets in and it ends when the drop is totally evaporated. OutputLayer Hidden Layer Input Layer DAE SDD SV SP Context neuron for output layer Context text for Hidden Layer SWEB The SWEB model is composed of 4 modules: a surface water distribution module, a canopy water budget, an energy balance module, a transfer coefficient calibrated to surface wetness.

17 Comparative study of different leaf wetness estimation methods The comparison between Artificial Neural Networks, Dropben, Sweb (LW duration Models) produces the following results: Bf class Period Measured Sweb Dropben Elman NN Dry (83%) 2571 (81%) (87%) 3055 (87%) Wet (81%) 1821 (93%) (63%) 1193 (86%) Dry April (92.6%) Wet April (77.2%) CONSIDERATIONS Every model which has been used lead to an over-estimation of LW duration. The most important limit seems to be a too long period of water drops evaporation from leaves. The use of different sensors will also influence the measurement of LW; up to now there is a lack of objective sensors. Finally, a lot of models give the output as 0 or 1 (dry, wet) so there is a need to establish an unambiguous threshold between dryness and wetness to have standard results. BACK

18 Plant disease simulation model The grape vine downy mildew disease caused by Plasmopora viticola is the most important fungal disease in vine-growing areas due to its high sensitivity to humid weather conditions. PERO is a physical model that states the duration of the incubation period, the occurrence of secondary infections and the severity of the disease expressed by the number of oilspots per hectare. INPUTS: - day of present primary infection, - latitude and longitude of station, - hourly relative humidity, - hourly precipitation, - hourly temperatures, - hourly leaf wetness: LW DATA by Friuli s measurement; LW estimations by Dropben and Sweb; RESULTS: - PERO model overestimates downy mildew infections when applied with leaf wetness data measured in FVG. 1.00E E E E E E E E E E E Total oilspots - Due to the large overestimation of LW duration, this error becomes bigger when we use as input the LW simulated by the models. BACK measured LW dropben LW sweb LW control LW

19 Dew estimation Energy balance Energy balance Rn + G + H + V = 0 Rn= net radiative flux density G= soil heat flux density H= air sensible heat flux density V= air latent heat flux density Water vapor flux density (E) Energy flux density [W/m^-2] Time [h] Rn G H V E E=V/L L= water latent heat of evaporation E<0 is a measure of evapotranspiration E>0 measures dew deposition Water vapor flux density [g m^-2 s^-1] 0,04 0,02 0-0,02-0,04-0,06-0,08-0,1-0,12-0,14-0,16 Figure Result: The model is a good estimator in clear sky conditions Time [h]

20 Dew estimation Energy balance Rn + G + H + V = 0 Rn= net radiative flux density G= soil heat flux density H= air sensible heat flux density V= air latent heat flux density Energy flux density [Wm^-2] Energy balance Time [h] Rn G H V Water vapor flux density (E) E=V/L L= water latent heat of evaporation E<0 is a measure of evapotranspiration E>0 measures dew deposition Water vapor flux density [g m^-2 s^-1] 0,04 0,02 0-0,02-0,04-0,06-0,08-0,1-0,12-0,14 E -0, Time [h] Result: the lack of net radiation measurements decreases the reliability of model output in cloudiness conditions

21 Conclusions at this early stage GPM-500 polarimetric radar (Fossalon di Grado) data are a very good source of spazialized rain-derived leaf-wetness measurements. Radar is not enough to describe LWD fields because large part of wetness is due to evapotranspiration processes. In particular, dew is not enough solved, by the available models based on the energy balance applied over the Friuli region. LW models cannot be used as black box. They need to be carefully calibrated over the region. Net radiation measurements could improve the application for cloudiness conditions.

22 Stefano Dietrich Istituto di Scienze dell'atmosfera e del Clima (ISAC) - Sezione di Roma Tel: Uff Mob Fax: S.Dietrich@isac.cnr.it METEOSAT IR: ? m