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Land Surface Hydrology Research GroupCivil 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
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
Introduction❶
Outline
ET estimation algorithm❷
MODIS data and near real-time operational system❸
Retrospective ET estimation❹
➢
Conclusions and Future Plan❺
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
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.
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
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/
Land Surface Hydrology Research GroupCivil 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
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
Land Cover Type
Surface Reflectance
Land Surface Temperature
Emissivity Vegetation
Indices Albedo
ET
GCIP SRB (Surface Radiation Budget)
Land Surface Hydrology Research GroupCivil 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 7 ET estimation algorithm❷
Linear two-source model
1-fveg fveg
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
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
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
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
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
Introduction❶
Outline
ET estimation algorithm❷
MODIS data and near real-time operational system❸
Retrospective ET estimation❹➢
Conclusions and Future Plan❺
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
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
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
MODIS data and operational system❸ 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
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
MODIS data and operational system❸ 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
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
MODIS data and operational system❸ Tang, Qiuhong 24 Oct 2007 Slide 14
Data processing – NDVI and Temperatures
8 days composite
RS imagery
Window
Image resolution = 0.0025 degreeWindow size = 0.125 degree
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
MODIS data and operational system❸ 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
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
(GCIP SRB) (albedo) (temperature, emissivity) (temp, emissivity, albedo)
MODIS data and operational system❸ 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
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
MODIS data and operational system❸ 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
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
MODIS data and operational system❸ Tang, Qiuhong 24 Oct 2007 Slide 18
Results – EF, instantaneous ET
EF ET_ins (W s-2) ET_ins (mm/day)
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
MODIS data and operational system❸ 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
Tday
Tnight
ETallday = Qallday * EF ETallday (W s-2) ETallday (mm/day)
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
Introduction❶
Outline
ET estimation algorithm❷
MODIS data and near real-time operational system❸
Retrospective ET estimation❹➢Conclusions and Future Plan❺
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
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
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
Retrospective ET estimation❹ Tang, Qiuhong 24 Oct 2007 Slide 22
Monthly
Klamath River Basin
Daily
ETallday: 1.45 mm/ day
ET_VIC: 1.27 mm/ day
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
Retrospective ET estimation❹ Tang, Qiuhong 24 Oct 2007 Slide 23
VIC ET RS ET DIFF (VIC - RS)
Klamath River Basin
1.45 mm/ day1.27 mm/ day
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
Retrospective ET estimation❹ 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
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
Retrospective ET estimation❹ Tang, Qiuhong 24 Oct 2007 Slide 25
VIC ET RS ET DIFF (VIC - RS)
Klamath River Basin
– Irrigation Area
0.80 mm/ day 1.36 mm/ day
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
Introduction❶
Outline
ET estimation algorithm❷
MODIS data and near real-time operational system❸
Conclusions and Future Plan❺➢Retrospective ET estimation❹
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
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.
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
Conclusions and Future Plan❺ Tang, Qiuhong 24 Oct 2007 Slide 28
Land Surface Hydrology Research GroupCivil and Environmental Engineering
University of Washington
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.