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Estimation of Evapotranspiration from Remote Sensing baseSEBAL model in Central Valley, California
Sagarika RoyGraduate StudentMontclair State
University
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Outline Introduction
Objective
Method
v Image Classification, Post Classification & Maskingv SEBAL Theory and Method-Actual Evapotranspiration estimation
Results & Discussion
Validation of remote sensing based SEBAL model to ground based Penmann-Montheith Eqfrom CIMIS and Field for ET estimation.
Conclusion
References
Acknowledgments
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Introduction
The Central Valley is a large, flat valley that dominates thecentral portion of the California
Extent: 400 miles from north to south
Sacramento drains the northern of the Central Valley. In the
southern, the San Joaquin flows 330 miles (530 km) northfrom valleys.
Annual rainfall: 20 inches (arid to semi arid climate).
Agriculture: Tomatoes, almonds,grapes, cotton, apricots,and asparagus
Economy: 17 billion USD from agriculture.
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Geometric andradiometric Corrected Pixel
SEBAL
EnergyBalance
Land Use/Land
Raw Satellite
Individual actual ET of Almond
Pre-Corrected ima e
Classification
Evapotranspirat
Masking
Objective
To estimate actualevapotranspiration from fromRemote Sensing tool based onSurface Energy Balance Algorithm
(SEBAL) Model of Almond class usingImage classification and Mask
Final Actual ET map
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SoPistachi
Almon Wate NPUrba Road
Metho
Land use/Land Cover
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Image ClassificationIdentification of individual pixels or groups ofpixels with
similar spectral responses (spectral signatures) to incomingradiation.
UnsupervisedNo training data. Model inference andapplicationboth rely on test data exclusively
SupervisedUse training data to infer model,compared with model to test data
K-means MaximumLikelihood
Spectral AngleMapper
Assumes that thereflectance values foreach class in each bandare normally distributedand calculates theprobability that a given
Classifications use statisticaltechniques to group n-dimensional data into their
natural spectral classes.
An automated algorithm inENVI that compares imagespectra to reference spectra
(endmembers) from ASCIIfiles, ROIs, or spectrallibraries.It calculates the angulardistance between eachspectrum in the image andendmember in n-dimensions,
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K-MaximumSpectral Angular
Unsupervised Supervis Supervis
Comparison of Unsupervised with
Almond
Non Photosynthesis
Urba
Wate
Pistachi
Other Green
So
Almond
Non Photosynthesis
Urba
Wate
Pistachi
Other Green
So
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Post Classification-Confusion Matrix ofMaximum Likelihood Using Ground TruthROI
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Masking
Almond
Non Almond
Ground Reference data
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Evapotranspiration using SEBAL Model
Evapotranspiration (ET) is the loss of water to the atmosphere by the
combined processes of evaporation (from soil and plant surfaces) andtranspiration (from plant tissues)
SEBAL (Surface Energy Balance Algorithm for Land) is a one-layer energybalance model that estimates latent heat flux and other energy balancecomponents without information on soil, crop, and management practices
A specific feature of SEBAL is that DT ((vertical air temperature differencebetween the z1 and zm) is determined from the hot (dry) and cold (wet) pixelswith assumed values of sensible heat flux (H).
H is estimated at extreme dry (H=RnG) and wet locations (H=0),
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Rn : net radiation flux at the surf(W/m2)Go : soil heat flux (W/m2),H : sensible heat flux to the air(W/m2
E : latent heat flux density (W/m : Latent heat of vaporization J
ET24
=
R24
/
(mm/day)
SEBAL
ET is related to surface energy
Go = 0.3811 exp
H = Cp DT /Rah
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Input data to SEBAL IDL Code
Meteorological parameters (CIMIS):Wind speed (miles/h)Humidity (F)Solar radiation (Ly/m)Air Temperature (F)Albedo
MASTER data:leaf area index ((from Emily)vegetation index (NDVI) (Callie)surface temperature (Cassie)
Referred Literature parametersEmmisivity (e)
albedoSpecific heat at constant pressure Cp(J/kg/K)
Data Requirements for IDL
Output data from IDL code for SEBALmodel
Soil heat flux (G) (W/m2)
Sensible heat flux (H ) (W/m2)
Crop Coefficient (Kc) (W/m2)
Latent heat flux density (E ) (W/m2 )
Evaporative fraction ( )Net Radiation (Rn) (W/m2)
Actual Evapotranspiration (mm/h)
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Hourly actual evapotranspiration (ETa) of Almond on August 24,
Actual ET Map(Using IDL code applied to ENVI to estimate pixel by pixel crop
ETa
Eta 0.669 to 0.681
Eta 0.699 to 0.703
Eta 0.682 to 691
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Result and Discussion
Fig
Fig
F
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Validation of SEBAL estimated Actual ET with Penmann-Montheith fromCIMIS
The correlation coefficient of Eta estimates from remote sensing with EToare 0.8571
The regression coefficient 0.7347
The mean difference between actual ETa from SEBAL in almond and Penman-Monteith for over allobservations associated with ETa is 0.77 mm
Avarage ETa (SEBAL) : 0.6745mm/h
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Comparison of actual ET from SEBAL and Penmenn-Monteith
Mean Percent difference forETa
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Conclusion The types/land use classes were identified from the MASTER image using multi-layered maximum likelihood
classification shows 97% accuracy to mask only almond class from the Image.
The results of the regression between land surface temperature (Ts), NDVI and, evapotranspiration (Eta) show negative(-) correlation. On the other hand Ts possessed a slightly stronger negative correlation with the ETa than with NDVI forAlmond class.
The actual evapotranspiration (ET a) estimated from SEBAL is 0.639 to 0.703 mm/h. Avarage is 0.6745 mm/h
The average actual ET estimated from SIMIS using crop coefficient (Kc) 1.02 is 0.774 mm/h
The correlation coefficient of actual ET (ETa) estimates from remote sensing with Reference (Eto)from Penmann Monteithare 0.8571
The mean difference between actual ETa from SEBAL in almond and Penman-Monteith for over all observationsassociated is 0.77
Hence the avarage ETa from CIMIS is marginally higher than ETa estimated from SEBAL model. Mean percentagedifference is 0.109%
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Reference
Allen, R.G., A. Morse, M. Tasumi, R. Trezza, W. G.M. Bastiaanssen, J.L.Wright,and W. Kramber, 2002. Evapotranspiration from a Satellite-BASED Surfaceenergybalance for the Snake Plain Aquifer in Idaho, Proc. USCID conference San Luis
Obispo,uly 2002Bastiaanssen, W.G.M., M. Menenti, R.A. Feddes and A.A.M. Holtslag, 1998.Aremote sensing Surface Energy Balance Algorithm for Land (SEBAL), part 1:formulation,J. of Hydr., 212-213: 198-212Bastiaanssen, W.G.M., M. Ud-din-Ahmed and Y. Chemin, 2002. Satellitesurveillance of water use across the Indus Basin, Water Resources Research, vol.
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Acknowledgement
I I would like to thank Prof Susan Ustin & Shawn for supporting the presentation of this research output, and forpossible incorporation into the project and the CIMIS for provision of climatic data for stations in the study area. Iwould also like to thank the
I am also thankful to Student Airborne Research Program co-ordinators / team/ mentors for smoothly conductingthe research program.
Thanks to all SARP student team for the brilliant team work and data sharing.
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Thank You
Questions?