Mapping Evapotranspiration in the Sacramento San Joaquin Delta using simulated ECOSTRESS Thermal Data: Validation and Inter-comparisonAndy Wong1, Yufang Jin1, Eric Kent1, Kyaw Tha Paw U1, Jay Lund1; Ruyan He2; Joshua B. Fisher3, Glynn Gulley3, Gerardo Rivera3, Christine Lee3, Simon Hook3; JosuéMedellín-Azuara4; Feng Gao5

1 University of California, Davis, CA, USA2 China University of Mining & Technology (Beijing), Beijing, China3 Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA, USA4 University of California, Merced5 U.S. Dept. of Agriculture - Agriculture Resource Service, Beltsville, MD, USA

April 3, 2017.

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Surface energy balance remote sensing ET models:

𝜆𝜆𝜆𝜆 = 𝑅𝑅𝑛𝑛 − 𝐺𝐺 − 𝐻𝐻𝜆𝜆𝜆𝜆 = 𝛼𝛼 ∆ 𝑇𝑇𝑎𝑎

∆ 𝑇𝑇𝑎𝑎 +𝛾𝛾 𝑇𝑇𝑎𝑎(𝑅𝑅𝑛𝑛 − 𝐺𝐺)

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Land Surface Temperature (LST)

Brightness Temperature fromLandsat 8 Thermal Infrared Sensor:> Spatial Resolution: 100 m/pixel

[resampled or sharpened to 30m/pixel]> Temporal Resolution: 16 Days

Single Channel Method

Daily ETInterpolation or Data Fusion

“Evapotranspiration (ET) is perhaps the most difficult hydrological flux to measure or model, especially at regional scales and greater.” – Hydrology 2020: An Integrating Science to Meet World Water Challenges

(IAHS Press, 2006)

Motivation

Motivation“Evapotranspiration (ET) is perhaps the most difficult hydrological flux to measure or model, especially at regional scales and greater.” – Hydrology 2020: An Integrating Science to Meet World Water Challenges

(IAHS Press, 2006)

Surface energy balance remote sensing ET models:

𝜆𝜆𝜆𝜆 = 𝑅𝑅𝑛𝑛 − 𝐺𝐺 − 𝐻𝐻𝜆𝜆𝜆𝜆 = 𝛼𝛼 ∆ 𝑇𝑇𝑎𝑎

∆ 𝑇𝑇𝑎𝑎 +𝛾𝛾 𝑇𝑇𝑎𝑎(𝑅𝑅𝑛𝑛 − 𝐺𝐺)

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Land Surface Temperature (LST)

Brightness Temperature fromLandsat 8 Thermal Infrared Sensor:> Spatial Resolution: 100 m/pixel

[resampled or sharpened to 30m/pixel]> Temporal Resolution: 16 Days

Single Channel Method

Daily ETInterpolation or Data Fusion

ECOSTRESS

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> Ground Sample Distance (m): ~68.5 x 38.5> 5 TIR bands> Temporal Resolution: ~4 Days> Spatial Coverage: Continental US &

ECOSTRESS Projects sites> Launch Date: Jun 6, 2018> Nominal mission lifetime: 1 year > Irregular overpassing time due to ISS orbit.

Help reduce uncertainty inRemote Sensing ET estimates

Landsat

VIIRS

Sensor Overpassing time at the SSJ Delta

New uncertainty

Research Objectives

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How ECOSTRESS will reduce uncertainty in remote sensing ET estimates?

1) What is the uncertainty of Landsat data driven remote sensing ET estimates? → Compare Landsat remote sensing ET against ground measurement and model inter-comparison.

2) How does uncertainty in LST propagate in ET model?3) How does increased temporal frequency of LST data reduce uncertainty in ET estimate?

→ Generate simulated ECOSTRESS LST data.→ Compare remote sensing Rn, ET driven by simulated ECOSTRESS LST data against ground measurement and Landsat driven ET estimates.

Remote Sensing ET models

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> METRIC + ETrF Interpolation 𝜆𝜆𝜆𝜆 = 𝑅𝑅𝑛𝑛 − 𝐺𝐺 − 𝐻𝐻

Use Landsat LST to compute instantaneous Rn & HEstimate 30m instantaneous ET during Landsat overpassing date.Derive fraction of reference evapotranspiration (ETrF) by dividing instantaneous ET by reference ET.Assume ETrF is constant throughout the day and Interpolate overpassing ETrF to estimate 30m daily ET.

> PT-UCD + ETrF Interpolation 𝜆𝜆𝜆𝜆 = 𝛼𝛼 ∆ 𝑇𝑇𝑎𝑎∆ 𝑇𝑇𝑎𝑎 +𝛾𝛾 𝑇𝑇𝑎𝑎

(𝑅𝑅𝑛𝑛 − 𝐺𝐺)

Use Landsat and MODIS LST to compute daily Rn.PT Coefficient, 𝛼𝛼, partition available energy (𝑅𝑅𝑛𝑛 − 𝐺𝐺) to 𝜆𝜆𝜆𝜆Use Landsat derived LAI and NDMI to estimate 𝛼𝛼. The relationship is crop specific and calibrated with ground measurement.Interpolate ETrF and estimate 30m daily ET.

Ground MeasurementsCrop Type Site Counts

Alfalfa 7

Corn 6

Pasture 4

Rice 1

Crop Type Site Counts

Beardless Wheat 1

Tomato/Watermelon 1

ID Project/Network System Research Group

D1-14 Crop Consumptive Water Use Estimation in the Sacramento-San Joaquin Delta

Surface Renewal Dr. Kyaw Tha Paw U

US-Twt,Tw3US-Bi1-2 Ameriflux network Eddy

CovarianceDr. Dennis Baldocchi

RRT1-2 ECOSTRESS calibration and validation sites Eddy Covariance JPL

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Model Comparison: Daily ET

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1) What is the uncertainty of Landsat data driven remote sensing ET estimates? [Preliminary Results]→ Compare Landsat remote sensing ET against ground measurement and model inter-comparison.

> EToF = ET/ETo (on Landsat overpassing date)> ET (on Landsat overpassing date) = interpolated EToF x ETo

Model Comparison: Daily ET

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1) What is the uncertainty of Landsat data driven remote sensing ET estimates? [Preliminary Results]→ Compare Landsat remote sensing ET against ground measurement and model inter-comparison.

> EToF = ET/ETo (on Landsat overpassing date)> ET (on Landsat overpassing date) = interpolated EToF x ETo

Model Comparison: Daily ET

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Crops (mm/day) PTUCD + Landsat

METRIC + Landsat

AllRMSE 0.88 1.88

R2 0.82 0.72MAE 0.68 1.46

AlfalfaRMSE 0.64 1.12

R2 0.86 0.75MAE 0.50 0.87

PastureRMSE 0.82 1.63

R2 0.91 0.72MAE 0.69 1.22

CornRMSE 1.23 2.89

R2 0.87 0.65MAE 0.98 2.66

RiceRMSE 1.11 2.23

R2 0.75 0.89MAE 0.76 1.95

WheatRMSE 0.61 0.94

R2 0.00 0.44MAE 0.54 0.76

Water-melon

RMSE 1.15 2.54R2 0.98 0.92

MAE 1.12 2.43

PTUCD + Landsat METRIC + Landsat

Model Comparison: Inst. ET

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Crops (mm/hr) METRIC + Landsat

AllRMSE 0.18

R2 0.58MAE 0.15

AlfalfaRMSE 0.12

R2 0.59MAE 0.10

PastureRMSE 0.12

R2 0.19MAE 0.11

CornRMSE 0.29

R2 0.07MAE 0.24

RiceRMSE 0.28

R2 0.47MAE 0.25

WheatRMSE 0.12

R2 0.40MAE 0.10

Water-melon

RMSE 0.16R2 0.94

MAE 0.13

METRIC + Landsat> Estimate and measuring Instantaneous ET is challenging.

> Uncertainty in METRIC’s instantaneous ET estimates contributes to the scatering of METRIC’s daily ET estimates.

> METRIC assume EToF is constant throughout the day, which may not be applicable in some region.

Estimating Daily ET

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Crops (mm/day) Station inst. EToF x ETo

PTUCD + Landsat

METRIC + Landsat

AllRMSE 0.87 0.88 1.88

R2 0.76 0.82 0.72MAE 0.65 0.68 1.46

AlfalfaRMSE 0.67 0.64 1.12

R2 0.80 0.86 0.75MAE 0.48 0.50 0.87

PastureRMSE 0.66 0.82 1.63

R2 0.69 0.91 0.72MAE 0.63 0.69 1.22

CornRMSE 1.29 1.23 2.89

R2 0.74 0.87 0.65MAE 1.15 0.98 2.66

RiceRMSE 1.36 1.11 2.23

R2 0.73 0.75 0.89MAE 1.05 0.76 1.95

WheatRMSE 0.29 0.61 0.94

R2 0.02 0.00 0.44MAE 0.23 0.54 0.76

Water-melon

RMSE 0.42 1.15 2.54R2 0.95 0.98 0.92

MAE 0.22 1.12 2.43

Station Inst. EToF x ETo

Estimating Daily ET

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Crops (mm/day) 9:45 am 11:15 am 12:45 pm 2:15 pm 3:45 pm

AlfalfaRMSE 0.97 0.63 0.46 0.38 0.44

R2 0.94 0.98 0.99 0.99 0.99MAE 0.74 0.50 0.35 0.28 0.33

CottonRMSE 0.57 0.39 0.33 0.28 0.38

R2 0.94 0.97 0.97 0.98 0.96MAE 0.44 0.29 0.26 0.21 0.28

Dryland grain

sorghum

RMSE 0.79 0.44 0.34 0.42 0.54R2 0.90 0.95 0.98 0.97 0.94

MAE 0.63 0.34 0.26 0.32 0.45

Bare soilRMSE 1.54 1.28 0.57 0.72 1.08

R2 0.51 0.36 0.56 0.37 0.30MAE 1.22 0.87 0.43 0.51 0.60

Source: P. D. Colaizzi, P.D., S. R. Evett, S.R., T. A. Howell, T.A., J. A. Tolk, J.A., 2006. Comparison of Five Models to Scale Daily Evapotranspiration from One-Time-of-Day Measurements. Trans. ASABE 49, 1409–1417. doi:10.13031/2013.22056

> The constant EToFassumption has less uncertainty in the afternoon.

> ECOSTRESS LST in the afternoon might improve daily ET estimates for METRIC.

Landsat

VIIRS

Sensor Overpassing time at the SSJ Delta

Simulated ECOSTRESS data

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2) How does uncertainty in LST propagate in ET models?3) How does increased temporal frequency of LST data reduce uncertainty in ET estimate?

Image Date: 07/13/2016

LST (K)

ASTER GED

MERRA2

VIIRS VIAE L1

VIIRS VNP09

Compute Atmospheric Correction Parameters

with RTTOV

Emissivity (100m)

NDVI (100m)

NDVI (375m)

LST (375m)TIR Radiance (375m)

NDVI (70m)

LST (70m)

SharpeningAtmospheric & Emissivity Correction

PTUCD w/ Simulated data: Rn

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PTUCD + Landsat PTUCD + ECOSTRESSRMSE (W/m2) 19.08 41.45

R2 0.91 0.41Absolute Bias (W/m2) 15.23 27.74

PTUCD + Landsat PTUCD + ECOSTRESS

PTUCD w/ Simulated data: ET

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PTUCD + Landsat PTUCD + ECOSTRESSRMSE (mm/day) 0.88 1.47

R2 0.82 0.51Absolute Bias (mm/day) 0.68 1.10

PTUCD + Landsat PTUCD + ECOSTRESS

Upcoming Works

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1) Quantify the uncertainty in Landsat and simulated ECOSTRESS LST data.2) Improve the simulation process to get a more realistic LST.3) Investigate how uncertainty in simulated LST propagate in METRIC and PTUCD.4) Increase temporal frequency of simulated data and run the models to quantify the improvement of

daily ET estimation as result of increasing temporal frequency in LST data.5) Replicate the study for DisALEXI and PTJPL.

AcknowledgementFunding and Research Support from:

Dataset Contributors

North Delta Water Agency, Central Delta Water Agency,South Delta Water Agency.

Dr. K.T. Paw UDr. D. Baldocchi

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Thank you!

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