Automation of surface energy balance model
{ReSET}
Aymn Elhaddad and Luis Garcia,
Colorado State University, Fort Collins,...
Crop Water Use (ET) Monitoring System

•Ground, air- and space- borne
RS of ET

"Courtesy: National Science Foundation"
2
Traditional ETc Calculation

3
Advantages of Surface Energy Balance
Models Over Other Methods
Classic calculation of crop ET relies on using
reference ET...
Remote Sensing of ET Models
• Most common ones use the energy
balance equation.
• Have large footprint (regional coverage)...
Remote Sensing of Evapotranspiration
Using Surface Energy Balance ET is calculated as a
“residual” of the energy balance
L...
Description of Energy Balance Models
The use of the energy balance equation:
Rn = LE + G + H
Net Radiation (Rn), Soil Heat...
SEB Models Limitations
Imagery availability
If no images are available for a region SEB models
can not be used.
Cloud cove...
Surface Energy Balance Using A Raster
Concept (ReSET-Raster)
• The ReSET-Raster model is a surface energy
balance model th...
ET Modeling
Aerial images with visible
and thermal bands

ET 24 hour grid

ReSET
Model

10
Irrigation Event Captured by the ReSET
Model 5/27/2006

11
ET Variability within Fields Detected by
ReSET Model 7/30/2006

Landsat image

ReSET ET

12
Running a Surface Energy Balance Based
Model Manually
To manually run SEB models users need to have a
background in:
•
•
•...
EB Model
Manual Mode

1dem.mdl
2ls7_1.mdl

2ls5_1.mdl
ArcGis for Albedo
Coef
2ls_2.mdl

2ls7_3_savi.mdl

2ls5_3_savi.mdl

...
Automated ReSET Model Under ArcGIS 10.1

15
Surface Energy Balance Model
Components

Location
characteristics

Surface Albedo

Anchor points
selection

Energy
balance...
Model First Two Components
Image header
file

Digital
Elevation
map

Location Geographic
Characteristics

Multi layer sate...
Surface Albedo calculation

α =

α toa − α path _ radiance
τ sw

2

(Chen and Ohring, 1984; Koepke et al., 1985)

αpath_ra...
Iterative Process for Calculating
Surface Albedo
TOA Albedo

Calculate Surface Albedo
using initial albedo
coefficient

Su...
NDVI image (left) and NDVI mask (right) for Albedo

20
NDVI mask for Surface Albedo

21
Modeling of Fluxes
(Rn = LE + G + H)
• In the energy balance equation various fluxes are modeled as
follows:
• Net Radiati...
Modeling of Fluxes (cont.)
H = ρaCp (T1 – T2)/rah
• Due to the difficulty of computing T1 – T2 the model
uses a dT functio...
dT Function
• To determine dT, two extreme pixels are selected (cold and hot
anchor pixels).
• The cold pixel is selected ...
Selection of Hot and Cold Anchor Points
Surface
temperature

NDVI Filter

Surface Albedo Filter

Clumping

Sieving

Weathe...
Checking the Value of the Cold Anchor
Points

26
Proximity Check for Cold Anchor Points

27
Solving for H
• H = ρaCpa (dT)/rah

T and dT
25

We find H for every pixel
LE = Rn – G – H

20

dT

15
10
5
0
280
-5

290
...
LE and EF Calculation
• Using LE the evaporative fraction (EF) is calculated:
EF = LE/(Rn – G) Evap./Available energy
• It...
Model Inputs
(Acquired data sets)
• Imagery data:
Satellite imagery and header file.

• Weather data:
Daily and hourly ref...
Model Inputs
(User entered parameters)
• User enters several values for the creation of
masks and the filtering process.
•...
Advantages of Automating the Use of
SEB Models
• Help the expansion of surface energy balance
models applications.
• Facil...
Limitations of Automation of the Use of
SEB Models
• The automated model was used in different
areas in the US (Colorado, ...
Overcoming Limitations of Automation
1. Imagery preprocessing can be done to overcome the
previously mentioned limitations...
ReSET Raster Applications

•

South Platte Basin – Estimation of irrigation efficiency
and validation of pumping records, ...
ReSET Model Modes
• ReSET is available in full automatic and semi
automatic mode for all Landsat satellites
imagery (LS 5,...
Questions

37
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2013 ASPRS Track, Developing an ArcGIS Toolbox for Estimating EvapoTranspiration of Vegetation using Remote Sensing Approach (Automation of Surface Energy Balance Model {ReSET}) by Aymn Elhaddad

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Estimating water used by vegetated areas is very important for water resources management and water rights. Traditionally the amount of water delivered to an area is calculated by installing some measuring device (flumes, weirs, flow meters, etc.). The alternative approach presented here estimates the actual water use in a vegetated areas based on ground surface energy balance concept using the ReSET model (Remote Sensing of ET – ReSET developed by IDS group in Colorado state university) that uses satellite and Arial imagery with visible and thermal bands along with weather data to estimate daily actual crop Evapotranspiration (ET) for vegetated areas. Surface energy balance models have been proven to be a robust approach for estimating vegetation evapotranspiration. One of the main limitations of wider application of these models in water resources and irrigation management is the requirement of extensive back ground in surface energy modeling. This presentation shows the development and the application of an ArcGIS toolbox that runs an automated version of the ReSET model. The tool is compatible with NASA/USGS Landsat Legacy Project. The presented ArcGIS tool automates the model in all stages and requires minimum interference from user. The tool presented accommodates both basic and advanced users. The results using the tool were tested and validated using results from manual ReSET model runs.

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2013 ASPRS Track, Developing an ArcGIS Toolbox for Estimating EvapoTranspiration of Vegetation using Remote Sensing Approach (Automation of Surface Energy Balance Model {ReSET}) by Aymn Elhaddad

  1. 1. Automation of surface energy balance model {ReSET} Aymn Elhaddad and Luis Garcia, Colorado State University, Fort Collins, CO. 1
  2. 2. Crop Water Use (ET) Monitoring System •Ground, air- and space- borne RS of ET "Courtesy: National Science Foundation" 2
  3. 3. Traditional ETc Calculation 3
  4. 4. Advantages of Surface Energy Balance Models Over Other Methods Classic calculation of crop ET relies on using reference ET and a crop coefficient. Unfortunately, this methodology does not take into account the local conductions such as: • Water shortage/Waterlogging • Crop Type • Planting dates • Salinity 4
  5. 5. Remote Sensing of ET Models • Most common ones use the energy balance equation. • Have large footprint (regional coverage). • Measure actual, not potential ET. • Several models have been developed: SEBAL, METRIC, ReSET, SAT, ALARM, etc. 5
  6. 6. Remote Sensing of Evapotranspiration Using Surface Energy Balance ET is calculated as a “residual” of the energy balance LE = Rn – G – H H (Heat to air) ET Rn (Radiation from sun) G (Heat to ground) 6
  7. 7. Description of Energy Balance Models The use of the energy balance equation: Rn = LE + G + H Net Radiation (Rn), Soil Heat Flux (G), Sensible Heat Flux (H), and Latent Energy consumed by ET (LE). Model Rn, G and H, then determining LE as a residual. LE = Rn – G – H 7
  8. 8. SEB Models Limitations Imagery availability If no images are available for a region SEB models can not be used. Cloud cover SEB models are sensitive to clouds since clouds impact the thermal band. Calibration In the model without ground calibration if advection occurs it will introduce errors in the results. In the calibrated mode high quality weather data is required. 8
  9. 9. Surface Energy Balance Using A Raster Concept (ReSET-Raster) • The ReSET-Raster model is a surface energy balance model that uses Surface Energy Balance to calculate actual ET for every pixel. • Unlike METRIC or SEBAL, ReSET generates surfaces of every variable (wind, hot, cold, ETr) • Model main inputs 1. Aerial imagery with visible and thermal bands. 2. Ground weather data from available weather stations. 9
  10. 10. ET Modeling Aerial images with visible and thermal bands ET 24 hour grid ReSET Model 10
  11. 11. Irrigation Event Captured by the ReSET Model 5/27/2006 11
  12. 12. ET Variability within Fields Detected by ReSET Model 7/30/2006 Landsat image ReSET ET 12
  13. 13. Running a Surface Energy Balance Based Model Manually To manually run SEB models users need to have a background in: • • • • • • • • Hydrologic science, Behavior of soil, vegetation and water systems, Environmental physics, Radiation, Aerodynamics, Heat transfer, Vegetation systems, Image Processing. The need for these expertise limits the number of model users and eventually hinders the expansion of surface energy balance models usage and applications.
  14. 14. EB Model Manual Mode 1dem.mdl 2ls7_1.mdl 2ls5_1.mdl ArcGis for Albedo Coef 2ls_2.mdl 2ls7_3_savi.mdl 2ls5_3_savi.mdl 3_11_14r_savi.mdl 3_11_14r_savi.mdl ArcGIS to obtain cold and hot points shp 3.mdl pre_it_rain_r1.mdl pre_it_rain.mdl pre_it_r1.mdl H_r.mdl pre_it.mdl H.mdl Rah.mdl 4.mdl SI_r1.mdl 50_51_reset1_ point.mdl SI.mdl H_r.mdl H.mdl 50_evapo_fr _1.mdl Rah.mdl 4.mdl SI.mdl 50_evapo_fr_2.mdl H.mdl H_r.mdl 50_evapo_fr_2r.mdl 50_evapo_fr_2r1.mdl
  15. 15. Automated ReSET Model Under ArcGIS 10.1 15
  16. 16. Surface Energy Balance Model Components Location characteristics Surface Albedo Anchor points selection Energy balance calculations 16
  17. 17. Model First Two Components Image header file Digital Elevation map Location Geographic Characteristics Multi layer satellite imagery •Reflectance of light energy •Vegetation indices (NDVI,TOA-ALBEDO)
  18. 18. Surface Albedo calculation α = α toa − α path _ radiance τ sw 2 (Chen and Ohring, 1984; Koepke et al., 1985) αpath_radiance ~ 0.03 τsw = 0.75 + 2 Bastiaanssen (2000) 10-5 z FAO 56 18
  19. 19. Iterative Process for Calculating Surface Albedo TOA Albedo Calculate Surface Albedo using initial albedo coefficient Surface Albedo Mean value of surface albedo within NDVI mask Surface Albedo NDVI mask Check Recalculate Surface Albedo Update Albedo coefficient Fail Pass Final Surface Albedo
  20. 20. NDVI image (left) and NDVI mask (right) for Albedo 20
  21. 21. NDVI mask for Surface Albedo 21
  22. 22. Modeling of Fluxes (Rn = LE + G + H) • In the energy balance equation various fluxes are modeled as follows: • Net Radiation (Rn) = Gain – Losses Broad band emissivity Rn = (1 – α) Rs + RL – RL – (1-εo) RL • Soil Heat Flux (G) – Empirical equation G = (Ts/α (0.0038α + 0.0074α2) (1 – 0.98NDVI4)) Rn • Sensible Heat Flux (H) H = ρaCp (T1 – T2)/rah Cp = air specific heat ρa = air density Z2 rah dT H Z1 rah = aerodynamic resistance to heat transport 22 (T1 – T2) = dT = near surface temperature diff.
  23. 23. Modeling of Fluxes (cont.) H = ρaCp (T1 – T2)/rah • Due to the difficulty of computing T1 – T2 the model uses a dT function. Z 2 rah dT H Z1 rah = aerodynamic resistance to heat transport (T1 – T2) = dT = near surface temperature diff. H = ρaCp (dT)/rah Cp = air specific heat ρa = air density 23
  24. 24. dT Function • To determine dT, two extreme pixels are selected (cold and hot anchor pixels). • The cold pixel is selected from a well water vegetated area where dTcold= 0 • The hot pixel is selected as a very dry land area where it is expected that LE = 0. dThot = (Rn - G) rah / (ρaCp) H = Rn – G – (LE = 0) • A linear relationship is assumed to exist between the surface temperature and dT, which then enables the calculation of H for each pixel. 24
  25. 25. Selection of Hot and Cold Anchor Points Surface temperature NDVI Filter Surface Albedo Filter Clumping Sieving Weather Stations Proximity Sorting Anchor points 25
  26. 26. Checking the Value of the Cold Anchor Points 26
  27. 27. Proximity Check for Cold Anchor Points 27
  28. 28. Solving for H • H = ρaCpa (dT)/rah T and dT 25 We find H for every pixel LE = Rn – G – H 20 dT 15 10 5 0 280 -5 290 300 310 320 330 340 T (Kelvin) is latent heat of vaporization (j/kg) representing the energy required to evaporate a unit mass of water To extrapolate from instantaneous ET to 24-hour ET, ETrF is multiplied by 24-hour ETr. 28
  29. 29. LE and EF Calculation • Using LE the evaporative fraction (EF) is calculated: EF = LE/(Rn – G) Evap./Available energy • It assumes that this fraction remains constant throughout the day, therefore can be used in determining daily ET as shown below: ET24 = 86,400 * EF * (Rn24 - G24)/ λv • Under calm weather conditions or moderate advection for non-irrigated areas, the assumption of EF being constant can be acceptable. 29
  30. 30. Model Inputs (Acquired data sets) • Imagery data: Satellite imagery and header file. • Weather data: Daily and hourly reference ET as single point values or grids and daily wind run. • Location data: Shapefile of weather station locations and shapefile for the area of interest. 30
  31. 31. Model Inputs (User entered parameters) • User enters several values for the creation of masks and the filtering process. • Those processing parameters are critical but do not require advanced background. • The model initial run uses default parameter values (which are suitable under normal crop growing conditions). • Model parameters are fine-tuned in a learning process through a feed back from the area of interest the model is being applied on. 31
  32. 32. Advantages of Automating the Use of SEB Models • Help the expansion of surface energy balance models applications. • Facilitate the mass production of actual ET maps on several temporal and spatial resolutions (current and historical). • Provide valuable inputs to near real time irrigation scheduling models and ground water models. 32
  33. 33. Limitations of Automation of the Use of SEB Models • The automated model was used in different areas in the US (Colorado, California, Texas) and overseas (Spain, Egypt and Saudi Arabia) with performances that matched or surpassed the manual mode. However, the automation process is unable to accommodate these two limitations: • Pixels affected by clouds. • Bad pixel data ( Landsat 7), image boundaries. 33
  34. 34. Overcoming Limitations of Automation 1. Imagery preprocessing can be done to overcome the previously mentioned limitations. – Clouds: Image pixels affected by clouds should be masked and replaced with no data values. – Bad pixel data: • Satellite images should be clipped at the borders to eliminate pixels with bad data at the edges of the image. • Bad stripes in satellite imagery (Landsat 7) should be filled with same image date adjacent data or masked. 2. Running the model in a semi automated mode where the user can manually select hot and cold anchor points. 34
  35. 35. ReSET Raster Applications • South Platte Basin – Estimation of irrigation efficiency and validation of pumping records, development of Kc. • Arkansas River Basin – Estimation of actual crop water use, Aquifer recharge/depletion, impact of salinity on ET, calculation of canal service area water use. • Palo Verde Irrigation District – Estimation of actual crop water use. • Aragona Irrigation District in Spain – Estimation of actual crop water use. • Nile Delta in Egypt- Evaluate irrigation canal system efficiency. 35
  36. 36. ReSET Model Modes • ReSET is available in full automatic and semi automatic mode for all Landsat satellites imagery (LS 5, LS 7, LS 8) and MODIS. • Colorado State University will be offering a three day training on the ReSET model this fall, for more information please email Aymn.elhaddad@colostate.edu 36
  37. 37. Questions 37

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