Land Surface Microwave Emissivity: Uncertainties, Dynamics and Modeling Yudong Tian, Christa...
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Transcript of Land Surface Microwave Emissivity: Uncertainties, Dynamics and Modeling Yudong Tian, Christa...
Land Surface Microwave Emissivity: Uncertainties, Dynamics and
Modeling
Yudong Tian, Christa Peters-Lidard, Ken Harrison, Sujay Kumar and Sarah Ringerud
http://lis.gsfc.nasa.gov/PMM/
Sponsored by NASA PMM Program (PI: C. Peters-Lidard)
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Outline
1. Why does land surface microwave emissivity matter?
2. How much do we know of microwave emissivity?
3. Modeling land surface emissivity (bottom-up)
4. Observations of emissivity dynamics (top-down)
5. Where do we meet? Where to go from there?
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Soil moisture(e.g., Njoku and O’Neill, 1982; O’Neill et al., 2011)
Snow(e.g., Pulliainen et al, 1999; Tedesco and
Kim, 2006; Foster et al., 2009)
Vegetation(e.g., Choudhury et al., 1987; Owe et al.,
2001; Joseph et al., 2010; Kurum et al, 2012)
Microwave emissivity contains rich information of
terrestrial states
Emissivity×Tsfc
Land surface emissivity is also a noise
4(Tian and Peters-Lidard, 2007)
(Skofronick-Jackson and Johnson, 2011)
False rain events 3B42V6 CMORPH
<- land surface | rain | light rain, snowfall ->
There are large uncertainties in emissivity retrievals
(Tian et al., 2012) 5
Sahara desert, V-pol
Amazon rainforest, V-pol
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Land surface microwave emissivity can be modeled
-- a layered, bottom-up approach-- a semi-physical, semi-empirical business
Bare, smooth soil:Dielectric constant -> Fresnel equation ->
emissivity(e.g., Wang and Schmugge, 1980)
Surface roughness:(e.g., Choudhury et al., 1979)
Snow: HUT model(e.g., Pulliainen et al, 1999; Tedesco and
Kim, 2006)
Vegetation: tau-omega model
(e.g., Mo et al., 1982; Owe et al., 2001)
Modeling emissivity: coupling LIS with two emissivity models
1. CRTM (Weng et al., 2001)2. CMEM (Holmes et al., 2008)
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Emissivity and its dynamics are driven by land surface states
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Global emissivity can now be modeled, but how to validate?
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Global simulations of microwave emissivity
Sahara desert, V-pol
Amazon rainforest, V-pol
Emissivity dynamics can be captured by a soil moisture-vegetation phase diagram
Amazon
HMT-E
SGPP
soil moisture content (SMC)
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Leaf
Are
a In
dex (
LAI)
Differences in RTMs can be easily seen in phase diagrams
CRTM emissivity CMEM emissivity
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Methodology :
“Understanding emissivity without using emissivity data”
Understanding global microwave emissivity dynamics
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• Data: AMSR-E Tb, 2004-2010 (7 years) at 0.25-deg resolution
• How to “understanding emissivity without using emissivity data”
-- Construct surface-sensitive indices from Tb observations
Understanding microwave emissivity dynamics
AMSR-E 6.9 10.65 18.7 23.8 36.5 89.0Frequencies (GHz)
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Index 1: Microwave Polarization Difference Index (MPDI) at 10.6 GHz
Index 2: Tb36V
Index 3: Tb18V-Tb36V
MPDI: sensitive to surface radiometric properties other
than TsTb36V: sensitive to surface temperature (Ts)Tb18V-Tb36V: sensitive to scattering materials (e.g., dry snow)
Three indices used to detect land surface dynamics
Tb-based MPDI is close to emissivity-based MPDI at lower frequencies
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Tb-based MPDI:
Emissivity-based:
Emissivity-based mpdi
MPDI phase diagram reveals model behavior
ASMR-E MPDI CRTM mpdi CMEM mpdi
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Global survey of microwave emission dynamics
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Microwave emission dynamic regimes shift with season
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Regime diagram also reveals model behavior
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Validating modeled global emissivity and its dynamics
-- Seasonal mean
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Challenging areas: 1. Deserts2. Mountains3. Snow, ice and glaciers
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Validating modeled global emissivity and its dynamics
-- Standard deviation
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Summary
1. Land surface microwave emissivity is critical
2. Large uncertainties in our knowledge of its
dynamics
3. Modeling land surface emissivity with
LIS+RTM
4. Models quantitatively and qualitatively
validated
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Where to go from here:
1. Model improvement:
Quantitative: parameter tuning
Qualitative: desert, snow, mountains
2. Improved model can help:
-- Surface variable retrieval (e.g., soil
moisture)
-- Atmospheric retrieval (e.g.,
precipitation)
-- Radiance-based data assimilation
3. Higher frequencies still a challenge
Microwave emission dynamics from a global perspective
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Tb-based MPDI is close to emissivity-based MPDI at lower frequencies
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Tb-based MPDI:
Emissivity-based:
𝑀𝑃𝐷𝐼=𝑇𝑏𝑉 −𝑇𝑏𝐻𝑇𝑏𝑉 +𝑇𝑏𝐻
Emissivity-based mpdi
Summary
1. Land surface emissivity dynamics is complex
-- Surface types
-- Seasonality
-- Dissimilar dynamics over similar surfaces
2. Regime diagrams and phase diagrams facilitate:
-- model validation
-- model tuning in the absence of “truth”
To do:
-- Model parameter tuning and capability enhancement
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Extra slides
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Modeling microwave emissivity and its dynamics
Start with site with more reliable auxiliary data: precipitation, soil moisture … + field campaigns
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Similar climatic/ecological surfaces may have different dynamics
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Microwave emission dynamics from a global perspective
Land surfaces only
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Similar climatic/ecological surfaces may not have similar MW emission dynamics
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Microwave emission dynamics from a global perspective
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Microwave emission dynamic regimes shift with season
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Snapshots of soil moisture, LAI and emissivity at various episodes
SMC
LAI
19G
wet/sparse
dry/sp
arsewet/dense
med dry/dense
wet/med dense
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Parameters Spatial Resolution Satellite Sensors Reference & ContactLeaf Area Index (LAI) 1km Terra/Aqua MODIS U. Boston(Myneni et al. 2002)Soil moisture 25km Aqua AMSR-E NSIDC(Njoku 2007)Snow cover 500m Terra/Aqua MODIS NASA GSFC(Hall et al. 2002)Snow water equivalent 25km Aqua AMSR-E NSIDC(Kelly et al. 2004)
• Campaign data of critical importance:– Will serve (we hope) as reliable
benchmark to tune the coupled LSM-EM forward model
– Adjudicate satellite-derived inversion- and forward model-based estimates
– Test the latest science related to microwave radiative transfer
– Test accuracy of lower-dimensional approximations to the emissivity dynamics
• In addition, we will be contributing to database to augment with ancillary in situ data
Modeling and Predicting Land Surface Emissivity at NASA GSFC
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How similar are different surfaces?
For a given snow-free land surface, the emissivity variability is largely controlled by two dynamic variables: soil moisture (SMC) and vegetation water content (VWC) -- LAI (leaf area index) can serve as a proxy for VWC -- SMC –LAI phase diagram
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