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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
Outline
• Introduction• MicroWEXs• Forward models
– Passive– Active
• Assimilation algorithms• Results• Summary
UF/IFAS
• 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
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
MicroWEXs contd.
X X X X
C L
XX
Early MicroWEXs
• 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.
MicroWEX-10
UFLMR MOSS
TMRS
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
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
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.
• 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
Passive – Vegetation
Passive – Vegetation
• Combined Wigneron et al. (2007) and Casanova & Judge (2007) : tau is dependent upon angle and polarization
Liu & Judge, 2011UF/IFAS
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
Assimilation Algorithms
• Used EnKF-based assimilation for simultaneous estimation of States and Parameters using TB
• Bare soil:
Monsivais-Huertero, Nagarajan, Judge, 2011UF/IFAS
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
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.