This document discusses combining remote sensing data with ground observations to estimate daily evapotranspiration (ET) for agricultural water management. It summarizes using remote sensing to model spatial land surface temperature and vegetation cover hourly, integrating them to compute daily ET. It also describes using wireless sensors at an experimental cotton field in Maricopa, Arizona to monitor crops and irrigation as part of an integrated monitoring system for irrigation scheduling. The goal is to provide reasonably accurate and cost-effective daily ET estimates at resolutions useful to growers.

1. Daily evapotranspiration by combining
remote sensing with ground
observations: Study from Maricopa,
Arizona USA
Andy French and Doug Hunsaker
andrew.french@ars.usda.gov
U.S. Arid Land Agricultural Research Center, USDA/ARS, Maricopa, AZ USA

2. Obtaining Daily ET Estimates Useful for Growers
•Remote sensing provides synoptic views and ways to model instantaneous ET
•Obstacles;
•Image data too infrequent
•Or too coarse
•Costly
•Combine remote sensing data with ground observations
•Obtain image data as available
•Continuous monitoring of meteorology and land surface temperatures
•Model spatial LST and vegetation cover
•Compute surface energy balance hourly, integrate to daily

3. Penman-Monteith Equation
Diabatic
Flux
Adiabatic
Flux

4. Energy Flux Equations

5. Surface Flux Modeling Remote Sensing
Inputs
NDVI
Temperature

6. Surface Energy Balance ASTER
El Reno, Oklahoma
4 Sep 2000
Sensible
Heat
Latent
Heat

7. Daily Evapotranspiration
4 Sep 2000

8. FAO Irrigation Scheduling Experiment Site
Maricopa, Arizona
90 m
91 m
64%
4%

9. Integrated Monitoring of Crop and Irrigation

10. Wireless-Based Sensors for Water
Management

11. Maricopa Irrigation Scheduling Experiment 2003
Cotton

12. Simplifying LST to Assist Daily Forecast of ET

13. Heat Units and Kalman Filtering to Forecast Spatially
Distributed Cover & LST

14. Need Mean & SD Cover Estimates

15. Daily ET Modeling & Accuracy Assessment
Use parameter uncertainties to model ET uncertainty
Compare ET estimates against P-M and soil moisture

16. Remote Sensing Resolution and Local Vegetation/Soil Scales

17. Thermal Imaging & ET: Some Variations Irrelevant for Daily Estimates

18. Newer Remote Sensing Capabilities
Multispectral Thermal Infrared
Winter Wheat
NDVI
Emissivity
Grazingland
NDVI

19. Emissivity Change at Palo Verde Irrigation District 2007-8

20. Conclusions
•Combined Remote Sensing & Ground-Based Sensors
•Daily ET for Water Management
•Reasonable resolution & forecast potential
•Reasonable Cost
•New satellite sensors will improve ET estimation accuracies
•Uncertainty estimation important part of advisory system