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Near Real-time Evapotranspiration Estimation Using Remote Sensing Data. by Qiuhong Tang 24 Oct 2007 Land surface hydrology group of UW. Outline. ❶. ➢. Introduction. ET estimation algorithm. ❷. MODIS data and near real-time operational system. ❸. Retrospective ET estimation. ❹.

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near real time evapotranspiration estimation using remote sensing data

Near Real-time Evapotranspiration Estimation Using Remote Sensing Data

by Qiuhong Tang

24 Oct 2007

Land surface hydrology group of UW

outline
Outline

Introduction

ET estimation algorithm

MODIS data and near real-time operational system

Retrospective ET estimation

Conclusions and Future Plan

slide3

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-timeET spatial distribution over large areas using primarily remote sensing data.

Introduction

Tang, Qiuhong 24 Oct 2007 Slide 3

slide4

Introduction

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/

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.

Introduction

Tang, Qiuhong 24 Oct 2007 Slide 4

outline1
Outline

Introduction

ET estimation algorithm

MODIS data and near real-time operational system

Retrospective ET estimation

Conclusions and Future Plan

slide6

GCIP SRB

(Surface

Radiation

Budget)

ET

slide7

1-fveg

fveg

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;

Linear two-source model

ET estimation algorithm

Tang, Qiuhong 24 Oct 2007 Slide 7

slide8

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 algorithm

Tang, Qiuhong 24 Oct 2007 Slide 8

slide9

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 factor

f(PAR): photosynthetic active radiation factor

f(VPD): VPD = e* -e = saturated vapor pressure – vapor pressure

f(u): leaf-water potential factor

f(CO2): CO2 concentration control stomatal conductance

ET estimation algorithm

Tang, Qiuhong 24 Oct 2007 Slide 9

slide10

Outline

Introduction

ET estimation algorithm

MODIS data and near real-time operational system

Retrospective ET estimation

Conclusions and Future Plan

slide11

Data processing flowchart

GCIP SRB

*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.

MODIS data and operational system

Tang, Qiuhong 24 Oct 2007 Slide 11

slide12

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

MODIS data and operational system

Tang, Qiuhong 24 Oct 2007 Slide 12

slide13

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

GCIP SRB

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

MODIS data and operational system

Tang, Qiuhong 24 Oct 2007 Slide 13

slide14

Data processing – NDVI and Temperatures

RS imagery

Window

Image resolution = 0.0025 degree

Window size = 0.125 degree

8 days composite

MODIS data and operational system

Tang, Qiuhong 24 Oct 2007 Slide 14

slide15

Data processing – Temperatures (Tsoilmax, Tsoil, Tsoilmin)

NDVI / LST

Window

T (LST)

Tsoilmax

Tsoil

Tsoilmin

VI (NDVI)

Tsoilmax Tsoil Tsoilmin (Ta, Tveg)

MODIS data and operational system

Tang, Qiuhong 24 Oct 2007 Slide 15

slide16

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

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

MODIS data and operational system

Tang, Qiuhong 24 Oct 2007 Slide 16

slide17

Land surface energy partition

Qsoil Qveg Qall PAR

GCIP SRB

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

MODIS data and operational system

Tang, Qiuhong 24 Oct 2007 Slide 17

slide18

Results – EF, instantaneous ET

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

MODIS data and operational system

Tang, Qiuhong 24 Oct 2007 Slide 18

slide19

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:

ETallday = Qallday * EF

ETallday (W s-2) ETallday (mm/day)

Temperature

Tday

Tnight

6:00 14:00 24:00 6:00

Local Time

MODIS data and operational system

Tang, Qiuhong 24 Oct 2007 Slide 19

slide20

Outline

Introduction

ET estimation algorithm

MODIS data and near real-time operational system

Retrospective ET estimation

Conclusions and Future Plan

slide21

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

Retrospective ET estimation

Tang, Qiuhong 24 Oct 2007 Slide 21

slide22

ETallday: 1.45 mm/ day

ET_VIC: 1.27 mm/ day

Klamath River Basin

Daily

Monthly

Retrospective ET estimation

Tang, Qiuhong 24 Oct 2007 Slide 22

slide23

Klamath River Basin

VIC ET

RS ET

DIFF (VIC - RS)

1.27 mm/ day

1.45 mm/ day

Retrospective ET estimation

Tang, Qiuhong 24 Oct 2007 Slide 23

slide24

ETallday: 1.36 mm/ day

ET_VIC: 0.80 mm/ day

Klamath River Basin – Irrigation Area

Daily

Monthly

Retrospective ET estimation

Tang, Qiuhong 24 Oct 2007 Slide 24

slide25

Klamath River Basin

– Irrigation Area

VIC ET

RS ET

DIFF (VIC - RS)

0.80 mm/ day

1.36 mm/ day

Retrospective ET estimation

Tang, Qiuhong 24 Oct 2007 Slide 25

slide26

Outline

Introduction

ET estimation algorithm

MODIS data and near real-time operational system

Retrospective ET estimation

Conclusions and Future Plan

slide27

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 Plan

Tang, Qiuhong 24 Oct 2007 Slide 27

slide28

Conclusions and Future Plan

Tang, Qiuhong 24 Oct 2007 Slide 28

slide29

Land surface hydrology group of UW

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

slide33

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.