1. UF UNIVERSITYof FLORIDA Activeand Passive Microwave Observationsto Improve soil moisture predictions under dynamic vegetation conditions J. Judge*, A. Monsivais-Huertero**, K. Nagarajan*, P. W. Liu* *Center for Remote Sensing, U. of Florida **ESIME Ticoman, Instituto Politecnico Nacional, Mexico Financial Support from NASA-NIP, NSF-EAR, NASA-THP

2. Outline Introduction MicroWEXs Forward models Passive Active Assimilation algorithms Results Summary UF/IFAS

4. Approach:

5. Couple SVAT + Vegetation growth  Microwave models

6. Develop / validate assimilation algorithms for these integrated models

7. Problem: Very few detailed (diurnal, season long, high temporal frequency) datasets exist that allow development and testing of coupled models and assimilation algorithms using microwave observations during dynamic vegetationUF/IFAS

9. Corn (78 days) – 5 seasons, cotton (130 days) – 2 seasons, Elephant grass (10 months !) – 2 seasons

10. Soils - fine sand; heavily irrigated crop

11. Observations

12. microwave passive (C, L-band), active (L-band)

13. soil moisture & temperatures at 2, 4, 8, 16, 32, 64, 120, 170cm

14. soil heat fluxes & physical properties

15. vegetation properties: growth, development, geometric

16. micro-met, latent heat, sensible heat fluxes, up/down solar & longwaveUF/IFAS

17. MicroWEXs contd. Early MicroWEXs C L X X X X X X

19. Mechanical Controls and Data Aquistion: UF

22. Co-I Roger DeRoo, Mahta Moghaddam, Tony England

23. Observations in Sweet Corn and Elephant grass

24. Active & Passive observations under same micromet conditionsUF/IFAS

26. VSM0-5 from MicroWEX-5

27. Soil porosity = 0.37

28. Rms height = 0.62 cm

29. Correlation length = 8.4 cmLiu, DeRoo, England, Judge, 2011 UF/IFAS

31. Example: rms height =0.41cm, corr. length=8.4cm, porosity 0.55UF/IFAS

33. Passive – Vegetation

37. Bare soil:Monsivais-Huertero, Nagarajan, Judge, 2011 UF/IFAS

39. Coupled SVAT-Crop models  MB and Backscattering

40. MicroWEX-10 being conducted during summer 2011 will offer unprecedented diurnal A/P observations, with high temporal frequency

41. Current microwave algorithms provide unrealistic brightness during and immediately following the ppt/irrigation events; need a better, more physically-based canopy opacity model during dynamic vegetation

42. Looking forward to the diurnal Active & Passive observations during MicroWEX-10 for future improvements in models and assimilation algorithmsUF/IFAS

43. Thank You!!

45. Co-I Roger DeRoo, Mahta Moghaddam, Tony England - U. Michigan

46. Conducted during the corn season; June –August, 2009

47. Intensive Observation Period (IOP) in August, 2009

48. Fully mature corn canopy; cleared the footprint  bare soil

49. Concurrent active and passive, along w/ Lidar observations

50. Active – U. of Michigan (Moghaddam)

51. Passive – U. of Florida (Judge)

52. Lidar – NCALM; U. of Florida  U. of Houston (Shrestha)UF/IFAS