1. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington Near Real-time Evapotranspiration Estimation Using Remote Sensing Data by Qiuhong Tang 24 Oct 2007 Land surface hydrology group of UW

2. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington Introduction ❶ Outline ET estimation algorithm ❷ MODIS data and near real-time operational system ❸ Retrospective ET estimation ❹ ➢ Conclusions and Future Plan ❺

3. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington Introduction ❶ 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.

4. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington Introduction ❶ 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/

5. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington 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

6. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington ET GCIP SRB (Surface Radiation Budget)

7. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington 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 7ET estimation algorithm ❷ Linear two-source model 1-f veg f veg

8. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington ET estimation algorithm ❷ Tang, Qiuhong 24 Oct 2007 Slide 8 EF of soil (EF soil ) EF of soil is related to temperatures and available energy of soil. [Nishda et al, 2003] Q soil0 is the available energy when T soil is equal to T a. EF of vegetation (EF veg ) Assuming the complementary relationship and the advection aridity: ET + PET = 2 ET 0 i.e. ET + PET PM = 2 ET PT EF veg 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

9. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington ET estimation algorithm ❷ Tang, Qiuhong 24 Oct 2007 Slide 9 r a (aerodynamic resistance) U : wind speed. Wind speed is estimated from 1/r soil = 0.0015 U 1m. r c (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(CO 2 ): CO 2 concentration control stomatal conductance

10. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington Introduction ❶ Outline ET estimation algorithm ❷ MODIS data and near real-time operational system ❸ Retrospective ET estimation ❹ ➢ Conclusions and Future Plan ❺

11. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington MODIS data and operational system ❸ 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

12. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington MODIS data and operational system ❸ Tang, Qiuhong 24 Oct 2007 Slide 12 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

13. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington MODIS data and operational system ❸ Tang, Qiuhong 24 Oct 2007 Slide 13 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

14. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington MODIS data and operational system ❸ Tang, Qiuhong 24 Oct 2007 Slide 14 Data processing – NDVI and Temperatures 8 days composite RS imagery Window Image resolution = 0.0025 degree Window size = 0.125 degree

15. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington MODIS data and operational system ❸ Tang, Qiuhong 24 Oct 2007 Slide 15 Data processing – Temperatures (Tsoilmax, Tsoil, Tsoilmin) Tsoilmax Tsoil Tsoilmin (Ta, Tveg) NDVI / LST Window T (LST) VI (NDVI) Tsoilmax Tsoilmin Tsoil

16. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington (GCIP SRB) (albedo) (temperature, emissivity) (temp, emissivity, albedo) MODIS data and operational system ❸ Tang, Qiuhong 24 Oct 2007 Slide 16 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

17. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington MODIS data and operational system ❸ Tang, Qiuhong 24 Oct 2007 Slide 17 Land surface energy partition Qsoil Qveg Qall PAR Available energy: Q = Rn – G = (1-Cg) Rn GCIP SRB

18. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington MODIS data and operational system ❸ Tang, Qiuhong 24 Oct 2007 Slide 18 Results – EF, instantaneous ET EF ET_ins (W s-2) ET_ins (mm/day)

19. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington MODIS data and operational system ❸ Tang, Qiuhong 24 Oct 2007 Slide 19 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 T day T night ETallday = Qallday * EF ETallday (W s-2) ETallday (mm/day)

20. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington Introduction ❶ Outline ET estimation algorithm ❷ MODIS data and near real-time operational system ❸ Retrospective ET estimation ❹ ➢ Conclusions and Future Plan ❺

21. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington Retrospective ET estimation ❹ 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 ETLAND COVER

22. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington Retrospective ET estimation ❹ Tang, Qiuhong 24 Oct 2007 Slide 22 Monthly Klamath River Basin Daily ETallday: 1.45 mm/ day ET_VIC: 1.27 mm/ day

23. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington Retrospective ET estimation ❹ Tang, Qiuhong 24 Oct 2007 Slide 23 VIC ETRS ETDIFF (VIC - RS) Klamath River Basin 1.45 mm/ day1.27 mm/ day

24. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington Retrospective ET estimation ❹ Tang, Qiuhong 24 Oct 2007 Slide 24 Monthly Klamath River Basin – Irrigation Area Daily ETallday: 1.36 mm/ day ET_VIC: 0.80 mm/ day

25. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington Retrospective ET estimation ❹ Tang, Qiuhong 24 Oct 2007 Slide 25 VIC ETRS ETDIFF (VIC - RS) Klamath River Basin – Irrigation Area 0.80 mm/ day1.36 mm/ day

26. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington Introduction ❶ Outline ET estimation algorithm ❷ MODIS data and near real-time operational system ❸ Conclusions and Future Plan ❺ ➢ Retrospective ET estimation ❹

27. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington Conclusions and Future Plan ❺ 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.

28. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington Conclusions and Future Plan ❺ Tang, Qiuhong 24 Oct 2007 Slide 28

29. Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington Land surface hydrology group of UW http://www.hydro.washington.edu/forecast/rset_ca/

33. 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.