remote sensing of evapotranspiration using sebal and ... · many techniques have been developed for...
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Evaluation of SEBAL and METRIC Energy Balance Models for Evapotranspiration Estimation in Precision Agriculture
a) Plant & Soil Sciences Dept. Texas Tech University, USA.
b) Faculty of Agriculture, Damanhour University, Egypt.
c) Centrale Valutativa srl, Roma, Italy.
d) Southeast Watershed Research Laboratory, USDA, USA.
e) DAFNAE, University of Padova, Italy.
f) Crop and Soil Sciences Department, University of Georgia, USA.
Ahmed H. Rabia a,b, Stefano Lo Presti c, Jerome Maleski d, Stefano Gobbo e, David Bosch d , Francesco Morari e , and George Vellidis f
GU2020--Online session display -HS6.8 Irrigation estimates and management from remote sensing and hydrological modelling
© Rabia et al., All rights reserved
Many techniques have been developed for irrigation scheduling.
• Water Balance Methods
• Checkbooks
• Soil Moisture Sensors
• Computer Models
• Mobile Apps
GU2020--Online session display -HS6.8 Irrigation estimates and management from remote sensing and hydrological modelling
© Rabia et al., All rights reserved
Smartphone Apps for Irrigation Scheduling
www.smartirrigationapps.org
CORN
Available for iOS and Android
Beta testing by farmersAvailable 2019
Under developmentAvailable 2020
© Rabia et al., All rights reserved
0% Deficit
100% of Available Soil Moisture(Full Profile)
50% Deficit
50% of AvailableSoil Moisture
(Irrigation Threshold)
75% Deficit
25% of AvailableSoil Moisture(Dry Profile)
Crop Evapotranspiration
(ETc)
Rain + Irrigation
Soil Profile Water Balance
where
ETc = estimated crop ET
Kc = crop coefficient
ETo = Penman-Monteith reference ET (FAO 56)
KcEToETc
© Rabia et al., All rights reserved
1stSquare
1stFlower
1stOpen Boll
Crop Coefficient, Kc KcEToETc
© Rabia et al., All rights reserved
Objectives
• Find a Suitable model to directly estimate crop Evapotranspiration (ETc) through remotely sensed data.
• Test the model against ground-truth data to evaluate the applicability of integrating the model into the SmartIrrigation Apps.
© Rabia et al., All rights reserved
Data processing
CropEvapotranspiration
Bezerra et. al., 2015
Flowchart of SEBAL model
Numata et. al., 2017
Flowchart of METRIC model
© Rabia et al., All rights reserved
Study area
Soil: Loamy sand
Plant: Corn
Area: 60 AcresTift County, Georgia, USA
Season: 2016Ground-truth
Crop Evapotranspiration (ETc) measurements
Eddy Covariance Flux Tower
© Rabia et al., All rights reserved
GU2020--Online session display -HS6.8 Irrigation estimates and management from remote sensing and hydrological modelling
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Crop Evapotranspiration (ETc - mm/day) Eddy Covariance
Day of the year (DOY)
ETc
mm
/day
© Rabia et al., All rights reserved
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Crop Evapotranspiration (ETc - mm/day) Eddy Covariance Rainfall
Day of the year (DOY)
ETc
mm
/day
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Rai
nfa
ll (m
m)
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Crop Evapotranspiration (ETc - mm/day) Eddy Covariance SmartIrrigation App Rainfall
Day of the year (DOY)
ETc
mm
/day
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Rai
nfa
ll (m
m)
© Rabia et al., All rights reserved
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Crop Evapotranspiration (ETc - mm/day) Eddy Covariance SmartIrrigation App METRIC SEBAL Rainfall
Day of the year (DOY)
ETc
mm
/day
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Rai
nfa
ll (m
m)
Correlation Coefficient (r)
SmartIrrigation App 0.40
SEBAL Model 0.50
METRIC Model 0.67
© Rabia et al., All rights reserved
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SmartIrrigation App - Over & Under Estimation of ETc (mm/day)
Time of season (Days after planting)
mm
/day
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Over & Under-Estimation of ETc during the growing season
METRIC model
Time of growing season
SmartIrrigation App
mm
/day
SEBAL model
© Rabia et al., All rights reserved
Crop Evapotranspiration Maps
SEBAL Model9 maps during the season
METRIC Model13 maps during the season
ETc (mm/day) ETc (mm/day)
Experimental Location
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Conclusions• Using the crop coefficient curve in the SmartIrrigation mobile App to
estimate crop evapotranspiration led to underestimation of crop waterrequirements during both the first and the last growing stages.
• Rainfall amount input from a rain gage in the field or close weather stationenhances significantly the ETc estimations
• METRIC model showed the highest correlation to the ground-truth datawith overestimated values of ETc by the end of the growing season.
• Integrating the METRIC model into the SmartIrrigation App may enhancethe App efficiency in irrigation scheduling.
• SEBAL model needs to be adjusted before applying in humid environments.
GU2020--Online session display -HS6.8 Irrigation estimates and management from remote sensing and hydrological modelling
© Rabia et al., All rights reserved
Future work
• Repeat the same methodology over a number of years to form a clearerpicture about the efficiency of the SmartIrrigation Apps and both SEBALand METRIC models in estimating the crop evapotranspiration
• Integrate the METRIC model into the SmartIrrigation app to improve theirrigation scheduling during the growing season
• Comparing the performance of the SmartIrrigation app before and afterthe integration of the METRIC model with ground truth values ofevapotranspiration
GU2020--Online session display -HS6.8 Irrigation estimates and management from remote sensing and hydrological modelling
© Rabia et al., All rights reserved
Acknowledgements
Cultural Affairs and Missions SectorMinistry of Higher Education of Egypt
University of
Damanhour
Vell idisResearch Group
