SARP2009_Nandita

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INTRODUCTION

1. Site Description: Location: Lost Hills(35 30N 119 39W), Central Valley,

California Climate: Semi Arid

Crop type: Almond trees

2. Extent of study: Use of Simplified Surface Energy Balance Index(S-SEBI) to

generate evapotranspiration estimates. Comparison of S-SEBI estimates with METRIC and CIMIS


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Relevance of study

1. Importance of Evapotranspiration in agriculture ET is the major consumptive use of water by plants Accurate ET estimates important for optimum yield

2. ET measurements in field differ from CIMIS estimates Actual ET measurements in the field show that CIMIS

underestimates ET for the almond fields CIMIS uses a modified Penman-Monteith equation. Determine whether another model could give better ET

estimates


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Why S-SEBI?

1. Simplified Surface Energy Balance Index, S-SEBI Measures reflectance dependant maximum temperature for dry

conditions and reflectance dependant minimum temperature forwet conditions and then partitions latent and sensible heatfluxes as per actual land surface temperature

Inputs in the S-SEBI algorithm (all but 1 available from remotesensing imagery) include: Temperature Albedo Normalized difference vegetation index, NDVI Net Radiation

Works well under cloud free conditions Requires presence of extreme temperature pixels i.e. very hot

(bare soil) and very cold (water) pixels


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S-SEBI

1. Energy Balance equation: Energy Balance equation: Rn = G + H +LE

Rn = Net radiation

H = Sensible heat fluxLE = Latent heat fluxG = Soil heat flux

2. Overview of Calculations Rn : Measured in field or available from weather station G : Calculated as a function of NDVI H and LE : Lumped together and calculated as Evaporative

Fraction


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Evaporative Fraction

1. Evaporative Fraction (): = LE/(Rn G)

= (TH - T0)/(TH TE )

TH = Rn G |E = 0

TE = Rn G |H = 0

2. Albedo and Land Surface Temperature Low reflectance (water bodies): Surface temperature almost

constant with albedo Evaporation controlled change: Heating

Radiation Controlled: Cooling

Source: Roerink et al,


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TH and TE

1. TH= aH + bH*(r)

2. TE = aE + bE*(r)

r = albedo

aE and bE are regression

coefficients for points onthis polyline. Pointsobtained by eyeballing

and visual estimation

aH and bH are

regression coefficientsfor points on thispolyline. Pointsobtained by eyeballing


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Albedo, NDVI and

1. Albedo, r r = 0.512R + 0.418NIR, Brest and Goward (1987)

2. Normalized difference vegetation index, NDVI

(NIR Red)/(NIR + Red)

3. Evaporative Fraction = LE/(Rn G)

= (TH - T0)/(TH TE )

= (aH + bH*(r) - T0)/(aH - aE + (bH + bE)*r)


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Soil Heat Flux and ET

1. G = .325 0.208*NDVI, Daughtry et al, 1990

3. LE = *(Rn G)

5. ET (mm/hr) = LE*3600/(*L)

= Density of water = 1000kg/cu.m

L = 2.5E6 2.386E3*(T-273.15) (Hendersen-Sellers, 1984) (Thanks to James)

T = Land surface temperature obtained from the MASTER imagery(Thanks to Shawn, Nick and Cassie)


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Values and Estimates


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Results

PET estimates from CIMIS = 0.88 mm/hr

PET estimates from field = 1.62 mm/hrET estimates from S-SEBI = 0.576 mm/hr

S-SEBI


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Results

PET estimates from CIMIS = 0.88 mm/hr

PET estimates from field = 1.62 mm/hrET estimates from METRIC = 1.07 mm/hr

METRIC

Calculations: Courtesy Jessie


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METRIC v/s S-SEBI

METRIC S-SEBI


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Conclusions

1. METRIC estimates ET by about 49.5% more than S-SEBI

2. METRIC estimates are closer to the ET estimates found by Blake,the extension agent, in his experimental field. METRIC

overestimates ET when compared to CIMIS.3. S-SEBI estimates ET closer to the results obtained from CIMIS.

4. S-SEBI shows better contrast between ET estimates from fields towhich different irrigation schemes are being applied. METRICdoesnt highlight the difference.


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Future Work

1. I would like to see if the same results hold at a coarser scale2. Looking at relationship between different crop treatments and ET3. Looking in depth into the relation between ET and different irrigation

techniques


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References

1. Roerink G.J., Su Z., Menenti M. 2000. S-SEBI: A simple remote sensingalgorithm to estimate the surface energy balance. Phys. Chem .Earth (B),Vol. 25, No. 2, pp. 147-157

2. Prasanna H. Gowda, Jose L. Chavez, Paul D. Colaizzi, Steve R. Evett, TerryA. Howell, Judy A. Talk, 2008. ET mapping for agricultural watermanagement: present status and challenges 26:223237

3. Brest CL, Goward SN (1987) Driving surface albedo measurements fromnarrow band satellite data. Int J Remote Sensing 8:351367

4. Clothier, B. E., Clawson, K. L., Pinter, P. J., Jr., Moran, M. S., Reginato, R. J.,and Jackson, R. D. (1986), Estimation of soil heat flux from net radiationduring growth of alfalfa, Agric. For. Meteorol. 37:319-329.

5. Daughtry C.S.T., Kustas W.P. Moran M.S., Pinter P.J., Jr., Jackson R.D.,Brown P.W., Nichols W.D., Gay L.W.,1990: Spectral estimates of netradiation and soil heat flux. Remote Sensing of the environment 32:111-124


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Acknowledgments

I would like to thank NASA, NSERC, Rose and the rest of theDryden hangar team, Mike and his graduate students and Blake withwhose help we got to collect the rich dataset. A big thanks to Alex fororganizing the whole program!

I would like to thank Dr. Susan Ustin, all the other professors and thepeople from NASA who gave us enriching lectures and set theplatform for us.I would like to thank Shawn for continuous guidance and support.

Last only in order of thanking and in no way else I would like toextend a thanks to all the SARP students and especially the

agricultural group who added multiple dimensions to the learningthat Ill take back with me.

SARP2009_Nandita

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