Adapting SEBAL for Ogallala region: Limitations and Improvements in ET mapping

1. Adapting SEBAL for Ogallala region: Limitations and Improvements in ET mapping George Paul1, Prasanna H. Gowda3, P.V. Vara Prasad1, Terry A. Howell3, Paul D. Colaizzi3, Stacy L. Hutchinson2, David Steward2 1Agronomy, 2004 Throckmorton Hall, Kansas State University, Manhattan, KS 66506, USA 2 Biological and Agricultural Engineering, 43B Seaton Hall, , Kansas State University, Manhattan, KS 66506, USA 3USDA-ARS Conservation and Production Research Laboratory, P.O. Drawer 10, Bushland, TX 79012, USA

2. Background Penman, H.L. Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences Vol. 193, No. 1032, Apr, 22, 1948, pg. 120-145

3. Motivation Different ET measurement method in field (typical errors) Eddy Covariance (15% ̶ 30%) Bowen Ratio (10% ̶ 20%) Soil Water Balance (10 % ̶ 30%) Lysimeter (5 % ̶ 15%) Remote Sensing EB (10 % ̶ 20%) Why do we need Remote Sensing-ET (ETa) ? Directly gives crop water demand (ETa) Spatial coverage Large utility (apart from agriculture) Based on physical parameterization Accuracy as good as any other measurement Water-Energy-Carbon Nexus Do we need RS-ET for crop water requirement assessment (ETa) ? Kc values unreliable, too much idealized, cannot cope with the newer hybrids.

4. Motivation AGRONOMY JOURNAL, VOL. 66, MAY-JUNE 1974 Landsat Data Continuity Mission/ Landsat 8, 11 Feb 2013

5. Research Gaps REEM SSEB METRIC SEBAL AHAS ReSET SEBTA SEBI TSM SEBS SW Model MOD16 Where are we heading to ?

6. Goals and Objectives The primary goal of our research is to comprehensively evaluate and improve Remote Sensing based ET mapping techniques for developing an operational water resource management and planning tool. In this study we are looking specifically on SEBAL Model

7. Materials and Methods Bushland Evapotranspiration and Agricultural Remote Sensing Experiment (BEAREX) Conducted at the USDA-ARS Conservation Production Research Laboratory, Bushland, TX, during the 2007 and 2008 summer cropping season. Multi-institutional (Four USDA-ARS Labs, Univ. Texas, Utah State Univ., and Kansas State Univ.) research effort initiated in 2007 and continued through 2008. Consisted of 3 large aperture scintillometers, 4 weighing lysimeters, 2 eddy covariance stations and 2 Bowen Ratio stations Included MODIS, LANDSAT, and ASTER satellite data, and airborne multispectral digital data

8. Materials and Methods BEAREX :Uniqueness Simultaneous evaluation of dryland and irrigated conditions Use of high resolution (0.5-3 m) airborne images Multiple images acquired from emergence to vegetative growth period from two years for tall and short crop. Evaluating instantaneous ET (mm h-1) values Evaluating against large precision Lysimeter

9. Materials and Methods Study Area

10. Materials and Methods Image acquisition

11. Materials and Methods Canopy Cover and Water Availability A1–6 June, 07’ Dryland field Grain Sorghum A2–6 June, 07’ Dryland field C. G. Sorghum B1– 30 May, 07’ Irrigated field Forage Sorghum B2– 17 May, 07’ Irrigated field Forage Corn

12. Materials and Methods Canopy Cover and Water Availability A1–26 June, 08’ irrigated field A2–26 June, 08’ dryland field B1– 5 August, 08’ irrigated field B2– 5 August, 08’ dryland field.

13. Materials and Methods Performance statistics

14. Theories and Concepts Energy Balance Equation

15. Theories and Concepts Sensible Heat: The bulk formulation kB-1

16. Theories and Concepts The Principle: HOT AND COLD PIXEL CONCEPT The dT formulation in SEBAL and METRIC for a July 28, 2008 image

17. Results Performance statistics for Ts , Rn , and Go

18. Results SEBAL (kB-1=2.3) Figure:Modeled versus observed ET Table: Performance statistics for instantaneous ET (mm h-1) for the complete data set (Obs. mean 0.52)

19. Results Dryland Field Irrigated Field Table: Performance statistics for instantaneous ET (mm h-1) for the irrigated field (Obs. mean 0.64) and drylandfield (Obs. mean 0.41)

20. Results SEBAL (z1 = 0.1) Figure:Modeled versus observed ET SEBAL (kB-1=2.3) Table: Performance statistics for instantaneous ET (mm h-1) for the complete data set (Obs. mean 0.52)

21. SEBAL (kB-1=2.3) SEBAL (z1=0.1)

22. Results SEBS-SEBAL Hybrid Algorithm • 1. Linear temperature gradient approach from SEBAL . • dT versus Ts linearity assumption could not address the spatial variation of zoh(kB-1) or in other words address the differences between To and Ts • 2. Excess resistance to heat transport from SEBS. • Under both sparse and full vegetation conditions, an appropriate value of kB-1 is required for accurate estimation of H using Ts

23. 185km 54,773,016 Pixels 103,456 Pixels

24. Selection of hot and wet pixel and the variability in 'a' and 'b' coefficient (subset image).

25. Summary Uncertainty exists in the selection of ‘Hot’ and ‘Cold’ pixel. The expected variation in the final ET value due to different selection criteria may ranges from 10% to 30%. SEBAL performance for irrigated fields (greater ET rates, limited soil water deficits, and complete ground cover) and dryland fields (lower ET rates, greater soil water deficits, and sparse ground cover) were markedly different. SEBAL is sensitive to the value of zoh or kB-1. The approach of evading zoh by adopting constant z1 appeared to be a good option under the greater ET rates, limited soil water deficits, and greater ground cover; however, under sparse ground covers this would completely fail. A kB-1model incorporated into SEBAL performed better for both dryland and irrigated fields

26. Acknowledgements Colleagues at Crop Physiology Lab, Agronomy, KSU. Scientists at USDA-ARS-CPRL, Bushland, Texas. We are grateful to Dr. Christopher M.U. Neale, Professor, Civil and Environmental Engineering, Utah State University for conducting the airborne missions for acquiring the remote sensed imagery.

27. Thank You