Near Real-time Evapotranspiration Estimation Using Remote Sensing Data

Size: px

Start display at page:

Download "Near Real-time Evapotranspiration Estimation Using Remote Sensing Data"

Patrick Hawkins
5 years ago
Views:

1 Near Real-time Evapotranspiration Estimation Using Remote Sensing Data by Qiuhong Tang 08 Aug 2007 Land surface hydrology group of UW Land Surface Hydrology Research Group

2 ❶ ❷ ❸ ❹ Outline Introduction ET estimation algorithm MODIS data and near real-time operational system Conclusions and Future Plan

Introduction Many water resources and agricultural management applications require the knowledge of surface evaporation (ET) over a range of

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

3 Introduction Many water resources and agricultural management applications require the knowledge of surface evaporation (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 realtime ET spatial distribution over large areas using primarily remote sensing data. ❶ Introduction Tang, Qiuhong 08 Aug 2007 Slide 3

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.

4 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. A operational ET estimation algorithm is adopted in this study. Critical model input and parameters is 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. ❶ Introduction Tang, Qiuhong 08 Aug 2007 Slide 4

5 Outline ❶ ❷ ❸ ❹ Introduction ET estimation algorithm MODIS data and near real-time operational system Conclusions and Future Plan

6 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 1-fveg fveg ❷ ET estimation algorithm Tang, Qiuhong 08 Aug 2007 Slide 6

7 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 (It is a controversial equation.) EFveg is [Nishda et al, 2003]: = 1.26 is Priestley-Taylor's parameter. is derivative of the saturated vapor pressure in term of temperature. is psychrometric constrant ❷ ET estimation algorithm Tang, Qiuhong 08 Aug 2007 Slide 7

8 ra (aerodynamic resistance) U : wind speed. Wind speed is estimated from 1/rsoil = 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 ❷ ET estimation algorithm Tang, Qiuhong 08 Aug 2007 Slide 8

9 Outline ❶ ❷ ❸ ❹ Introduction ET estimation algorithm MODIS data and near real-time operational system Conclusions and Future Plan

10 Data processing flowchart *The resolutions of remote sensing data vary from 250m to 500m. The data are reprojected to 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. ❸ MODIS data and operational system Tang, Qiuhong 08 Aug 2007 Slide 10

11 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 ❸ MODIS data and operational system Tang, Qiuhong 08 Aug 2007 Slide 11 Sample data: Aug

Surface Reflectance (841-876 nm) Cloud state Albedo ❸

12 Remote sensing data- MOD09GQ (Surface Reflectance Daily L2G Global 250m) Surface Reflectance ( nm) Surface Reflectance ( nm) Cloud state Albedo ❸ MODIS data and operational system Tang, Qiuhong 08 Aug 2007 Slide 12

13 Data processing NDVI & Cloud RS imagery (Cloud state) Window 8 days composite Cloud Image resolution = degree Window size = degree ❸ MODIS data and operational system Tang, Qiuhong 08 Aug 2007 Slide 13

Tsoilmin VI (NDVI) Tsoilmax Tsoil Tsoilmin (Ta, Tveg) ❸

14 Data processing Temperatures (Tsoilmax, Tsoil, Tsoilmin) NDVI / LST Window T (LST) Tsoilmax Tsoil Tsoilmin VI (NDVI) Tsoilmax Tsoil Tsoilmin (Ta, Tveg) ❸ MODIS data and operational system Tang, Qiuhong 08 Aug 2007 Slide 14

15 Land surface energy partition Ra Rd Ru Ld Lu (extraterrestrial radiation) (incoming short-wave radiation) (reflected short-wave radiation) (incoming long-wave radiation) (outgoing long-wave radiation) (view time) (cloud) (albedo) (temperature, emissivity) (temp, emissivity, albedo) ❸ MODIS data and operational system Tang, Qiuhong 08 Aug 2007 Slide 15

16 Land surface energy partition Qsoil Qveg Qall PAR Wind speed Available energy: Q = Rn G = (1-Cg) Rn PAR = Rd *2.05 ❸ MODIS data and operational system Tang, Qiuhong 08 Aug 2007 Slide 16

17 Results EF, instantaneous ET EF ET_ins (W s-2) ET_ins (mm/day) ❸ MODIS data and operational system Tang, Qiuhong 08 Aug 2007 Slide 17

$Results daily ET Assume: 1) EF does not change within one day, which is truth in many cases in daytime. 2) Cloud fraction is the same as the instantaneous cloud for shortwave radiation estimation.$

18 Results daily ET Assume: 1) EF does not change within one day, which is truth in many cases in daytime. 2) Cloud fraction is the same as the instantaneous cloud for shortwave radiation estimation. 3) 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 ❸ MODIS data and operational system Tang, Qiuhong 08 Aug 2007 Slide 18

19 Outline ❶ ❷ ❸ ❹ Introduction ET estimation algorithm MODIS data and near real-time operational system Conclusions and Future Plan

20 Conclusions 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 cropland will be summarized and may be used for agriculture management. ❹ Conclusions and Future Plan Tang, Qiuhong 08 Aug 2007 Slide 20

21 ❹ Conclusions and Future Plan Tang, Qiuhong 08 Aug 2007 Slide 21

22 Land surface hydrology group of UW

26 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: /2002jd 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), Jiang, L., and S. Islam (2001), Estimation of surface evaporation map over southern Great Plains using remote sensing data, Water Resour. Res., 37(2),

Evapotranspiration. Andy Black. CCRN Processes Workshop, Hamilton, ON, Sept Importance of evapotranspiration (E)

Evapotranspiration. Andy Black. CCRN Processes Workshop, Hamilton, ON, Sept Importance of evapotranspiration (E) Evapotranspiration Andy Black CCRN Processes Workshop, Hamilton, ON, 12-13 Sept 213 Importance of evapotranspiration (E) This process is important in CCRN goals because 1. Major component of both terrestrial