land surface hydrology research group civil and environmental engineering university of washington...

Land Surface Hydrology Research GroupCivil and Environmental Engineering

University of Washington



Near Real-time Evapotranspiration Estimation Using Remote Sensing Data

by Qiuhong Tang24 Oct 2007

Land surface hydrology group of UW



Introduction❶

Outline

ET estimation algorithm❷

MODIS data and near real-time operational system❸

Retrospective ET estimation❹

➢

Conclusions and Future Plan❺



Introduction❶ Tang, Qiuhong 24 Oct 2007 Slide 3

Introduction

Many water resources and agricultural management applications require the knowledge of surface evapotranspiration (ET) over a range of spatial and temporal scales.

However, it is impractical to obtain ET using ground-based observations over large area.

Satellite remote sensing is a promising tool to estimate the spatial distribution of ET with minimal use of in situ observational data.

The objective of this study is to map near real-time ET spatial distribution over large areas using primarily remote sensing data.



Introduction❶ Tang, Qiuhong 24 Oct 2007 Slide 4

Introduction

An operational ET estimation algorithm is adopted in this study.

Critical model input and parameters are routinely available at daily time.

The algorithm is robust. ET estimations are constrained by energy and mass conservation and have relatively lower sensitivity to input data.

The algorithm is insensitive to constraints imposed by the daily overpass of the satellite and cloud screening.

Remote sensing cannot readily provide atmospheric variables like wind speed, air temperature, and vapor pressure that are needed to estimate evaporation over large heterogeneous areas.

Figure from NASA. http://asd-www.larc.nasa.gov/erbe/



❶

Outline

❷

❸

❹

➢Introduction

ET estimation algorithm

MODIS data and near real-time operational system

Retrospective ET estimation

❺ Conclusions and Future Plan



Land Cover Type

Surface Reflectance

Land Surface Temperature

Emissivity Vegetation

Indices Albedo

ET

GCIP SRB (Surface Radiation Budget)



Evaporation Fraction (EF)

Q: available energy which an be transferred directly into atmosphere as either sensible heat flux (H) or latent flux. Q = H + ET = Rn – G;

EF is a linear parameter for ET; EF is a suitable index for surface moisture condition;EF is nearly constant during most daytime in many cases and is useful for temporal scaling;

Tang, Qiuhong 24 Oct 2007 Slide 7 ET estimation algorithm❷

Linear two-source model

1-fveg fveg



ET estimation algorithm❷ Tang, Qiuhong 24 Oct 2007 Slide 8

EF of soil (EFsoil)

EF of soil is related to temperatures and available energy of soil. [Nishda et al, 2003]

Qsoil0 is the available energy when Tsoil is equal to Ta.

EF of vegetation (EFveg)

Assuming the complementary relationship and the advection aridity:ET + PET = 2 ET0 i.e. ET + PETPM = 2 ETPT

EFveg is [Nishda et al, 2003]:

(It is a controversial equation.)

= 1.26 is Priestley-Taylor's parameter. is derivative of the saturated vapor pressure in term of temperature. is psychrometric constrant



ET estimation algorithm❷ Tang, Qiuhong 24 Oct 2007 Slide 9

ra (aerodynamic resistance)

U : wind speed. Wind speed is estimated from 1/rsoil = 0.0015 U1m.

rc (surface resistance of the vegetation canopy)

f(Ta): temperature factorf(PAR): photosynthetic active radiation factorf(VPD): VPD = e* -e = saturated vapor pressure – vapor pressuref(u): leaf-water potential factorf(CO2): CO2 concentration control stomatal conductance



Introduction❶

Outline



Retrospective ET estimation❹➢

Conclusions and Future Plan❺



MODIS data and operational system❸ Tang, Qiuhong 24 Oct 2007 Slide 11

Data processing flowchart

*The resolutions of remote sensing data vary from 250m to 500m. The data are reprojected to 0.0025 degree resolution.**When the temperature data becomes available, the ET is estimated.***Composite technique is used for time insensitive data. The most recent available data are used when the data are not available because of cloud.

GCIP SRB




Remote sensing data- MOD11A1 (Land Surface Temperature/Emissivity Daily L3 Global 1km)

LST at Day Time LST at Night Time Day view time Night view time

Sample data: Aug 01 2007




Remote sensing data- MOD09GQ (Surface Reflectance Daily L2G Global 250m)

Surface Reflectance (620-670 nm) Surface Reflectance (841-876 nm) Cloud state Albedo

GCIP SRB




Data processing – NDVI and Temperatures

8 days composite

RS imagery

Window

Image resolution = 0.0025 degreeWindow size = 0.125 degree




Data processing – Temperatures (Tsoilmax, Tsoil, Tsoilmin)

Tsoilmax Tsoil Tsoilmin (Ta, Tveg)

NDVI / LST

Window

T (LST)

VI (NDVI)

Tsoilmax

Tsoilmin

Tsoil



(GCIP SRB) (albedo) (temperature, emissivity) (temp, emissivity, albedo)


Land surface energy partition

Rd Ru Ld Lu(incoming short-wave radiation) (reflected short-wave radiation) (incoming long-wave radiation) (outgoing long-wave radiation)

GCIP SRB




Land surface energy partition

Qsoil Qveg Qall PAR

Available energy: Q = Rn – G = (1-Cg) Rn

GCIP SRB




Results – EF, instantaneous ET

EF ET_ins (W s-2) ET_ins (mm/day)




Results – daily ET

Assume: 1) EF does not change within one day, which is truth in many cases in daytime.2) Temperatures for longwave radiation estimation:

Temperature

Local Time 6:00 14:00 24:00 6:00

Tday

Tnight

ETallday = Qallday * EF ETallday (W s-2) ETallday (mm/day)



Introduction❶

Outline



Retrospective ET estimation❹➢Conclusions and Future Plan❺



Retrospective ET estimation❹ Tang, Qiuhong 24 Oct 2007 Slide 21

The Remote Sensing evapotranspiration estimation approach was performed at the domain of (124.5W,119.5W,37.5N,44N) in 2004. The Remote Sensing estimated evapotranspiration was compared with the evaporation estimated by 1/16 degree VIC model.

RS ET LAND

COVER




Monthly

Klamath River Basin

Daily

ETallday: 1.45 mm/ day

ET_VIC: 1.27 mm/ day




VIC ET RS ET DIFF (VIC - RS)

Klamath River Basin

1.45 mm/ day1.27 mm/ day




Monthly

Klamath River Basin – Irrigation Area

Daily

ETallday: 1.36 mm/ day

ET_VIC: 0.80 mm/ day




VIC ET RS ET DIFF (VIC - RS)

Klamath River Basin

– Irrigation Area

0.80 mm/ day 1.36 mm/ day



Introduction❶

Outline



Conclusions and Future Plan❺➢Retrospective ET estimation❹



Conclusions and Future Plan❺ Tang, Qiuhong 24 Oct 2007 Slide 27

Conclusion and Future Plan

1) An operational ET estimation system using remote sensing data is developed.

2) The system is daily updating. The algorithm is robust and flexible.

3) The result will be calibrated and validated with ground observations.

4) High resolution remote sensing data such as ASTER, TM data may be used in the future.

5) Estimated ET in irrigation area may be used for agriculture management.



Conclusions and Future Plan❺ Tang, Qiuhong 24 Oct 2007 Slide 28



Land surface hydrology group of UW

http://www.hydro.washington.edu/forecast/rset_ca/

http://www.hydro.washington.edu/forecast/rset_ca/

References

Nishida, K., R. R. Nemani, S. W. Running, and J. M. Glassy (2003), An operational remote sensing algorithm of land surface evaporation, J. Geophys. Res., 108(D9), 4270, doi:10.1029/2002JD002062.

Cleugh, Helen A., Leuning, R., Mu, Q., Running, S.W. (2007). Regional evaporation estimates from flux tower and MODIS satellite data. Remote Sensing of the Environment, 106(3), 285-304.

Jiang, L., and S. Islam (2001), Estimation of surface evaporation map over southern Great Plains using remote sensing data, Water Resour. Res., 37(2), 329-340.

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