Presentation SARP Sagarika

8/14/2019 Presentation SARP Sagarika

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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

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