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Active and Passive Microwave Observations to 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
UFUNIVERSITY of
FLORIDA
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Outline
• Introduction• MicroWEXs• Forward models
– Passive– Active
• Assimilation algorithms• Results• Summary
UF/IFAS
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• Application: Assimilate remotely sensed microwave observations to improve root-zone soil moisture estimates in Soil-Vegetation-Atmosphere Transfer (SVAT) models for dynamic vegetation
UF/IFAS
Introduction
• Approach: Couple SVAT + Vegetation growth Microwave models Develop / validate assimilation algorithms for these integrated models
• 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 vegetation
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Microwave water and energy balance experiments (MicroWEXs)
UF/IFAS
• Series of season-long field experiments conducted on a 9-acre field in North Central Florida
• Corn (78 days) – 5 seasons, cotton (130 days) – 2 seasons, Elephant grass (10 months !) – 2 seasons
• Soils - fine sand; heavily irrigated crop
• Observations microwave passive (C, L-band), active (L-
band) soil moisture & temperatures at 2, 4, 8, 16, 32,
64, 120, 170cm soil heat fluxes & physical properties vegetation properties: growth, development,
geometric micro-met, latent heat, sensible heat fluxes,
up/down solar & longwave
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MicroWEXs contd.
X X X X
C L
XX
Early MicroWEXs
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• RF electronics and antenna: Roger DeRoo & Ruzbeh Akbar @ U. Michigan
• Mechanical Controls and Data Aquistion: UF
• Provide diurnal observations w/ high temporal frequency
UF – CRS L band Automated Scatterometer System MicroWEX contd.
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MicroWEX-10
UFLMR MOSS
TMRS
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UF/IFAS
42 m X 21m
C L UF- Active UM -Active
75 m X 75 m
UM -Passive
9 m X 9 m
MicroWEX – 10: June – Sept, 2011
10 m X 10 m
MicroWEX contd.
• NASA-THP project Co-I Roger DeRoo, Mahta Moghaddam, Tony England
• Observations in Sweet Corn and Elephant grass• Active & Passive observations under same micromet
conditions
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UF/IFAS
Forward Models - Passive• Bare soil: Current brightness models jprovide unrealistic TB during
and immediately following precipitation/irrigation events – challenge for applicability in irrigated agricultural regions…….
VSM0-5 from MicroWEX-5 Soil porosity = 0.37 Rms height = 0.62 cm Correlation length = 8.4 cm
Liu, DeRoo, England, Judge, 2011
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UF/IFAS
• Obtain surface roughness, porosity, and VSM in top 1 mm from C-band
Example: rms height =0.41cm, corr. length=8.4cm, porosity 0.55
Passive – Bare Soil contd.
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• Dynamic vegetation: opacity formulation dependent upon the growth of the corn crop; compared with the Jackson bW model (Casanova & Judge ( 2008)
Passive – Vegetation
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Liu and Judge, 2011
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Passive – Vegetation
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Passive – Vegetation
• Combined Wigneron et al. (2007) and Casanova & Judge (2007) : tau is dependent upon angle and polarization
Liu & Judge, 2011UF/IFAS
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UF/IFAS
Forward models - Active
• Growing corn: Compared the incoherent and coherent formulations; impact of row structure and location of leaves and ears
Monsivais-Huertero and Judge 2011
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Assimilation Algorithms
• Used EnKF-based assimilation for simultaneous estimation of States and Parameters using TB
• Bare soil:
Monsivais-Huertero, Nagarajan, Judge, 2011UF/IFAS
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Summary
• MicroWEXs offer season-long high temporal frequency datasets to develop/validate models and assimilation algorithms …. Data available for community use.• Coupled SVAT-Crop models MB and Backscattering
• MicroWEX-10 being conducted during summer 2011 will offer unprecedented diurnal A/P observations, with high temporal frequency
• 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
• Looking forward to the diurnal Active & Passive observations during MicroWEX-10 for future improvements in models and assimilation algorithms
UF/IFAS
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MicroWEX–8 in 2009
UF/IFAS
• A risk-reduction experiment for the NASA-THP project Co-I Roger DeRoo, Mahta Moghaddam, Tony England - U. Michigan
• Conducted during the corn season; June –August, 2009• Intensive Observation Period (IOP) in August, 2009
Fully mature corn canopy; cleared the footprint bare soil Concurrent active and passive, along w/ Lidar observations Active – U. of Michigan (Moghaddam) Passive – U. of Florida (Judge) Lidar – NCALM; U. of Florida U. of Houston (Shrestha)
MicroWEXs contd.