Land Surface Microwave Emissivity: Uncertainties, Dynamics and Modeling

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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