Daily evapotranspiration by combining remote sensing with ground observations: Study from Maricopa, Arizona USA

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Remote sensing –Beyond images
Mexico 14-15 December 2013

The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)

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Daily evapotranspiration by combining remote sensing with ground observations: Study from Maricopa, Arizona USA

  1. 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. 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. 3. Penman-Monteith Equation Diabatic Flux Adiabatic Flux
  4. 4. Energy Flux Equations
  5. 5. Surface Flux Modeling Remote Sensing Inputs NDVI Temperature
  6. 6. Surface Energy Balance ASTER El Reno, Oklahoma 4 Sep 2000 Sensible Heat Latent Heat
  7. 7. Daily Evapotranspiration 4 Sep 2000
  8. 8. FAO Irrigation Scheduling Experiment Site Maricopa, Arizona 90 m 91 m 64% 4%
  9. 9. Integrated Monitoring of Crop and Irrigation
  10. 10. Wireless-Based Sensors for Water Management
  11. 11. Maricopa Irrigation Scheduling Experiment 2003 Cotton
  12. 12. Simplifying LST to Assist Daily Forecast of ET
  13. 13. Heat Units and Kalman Filtering to Forecast Spatially Distributed Cover & LST
  14. 14. Need Mean & SD Cover Estimates
  15. 15. Daily ET Modeling & Accuracy Assessment Use parameter uncertainties to model ET uncertainty Compare ET estimates against P-M and soil moisture
  16. 16. Remote Sensing Resolution and Local Vegetation/Soil Scales
  17. 17. Thermal Imaging & ET: Some Variations Irrelevant for Daily Estimates
  18. 18. Newer Remote Sensing Capabilities Multispectral Thermal Infrared Winter Wheat NDVI Emissivity Grazingland NDVI
  19. 19. Emissivity Change at Palo Verde Irrigation District 2007-8
  20. 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

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