Land Surface Microwave Emissivity: Uncertainties, Dynamics and Modeling Yudong Tian, Christa Peters-Lidard, Ken Harrison, Sujay Kumar and Sarah Ringerud Sponsored by NASA PMM Program (PI: C. Peters-Lidard)

2 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?

3 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

There are large uncertainties in emissivity retrievals (Tian et al., 2012) 5 Sahara desert, V-pol Amazon rainforest, V-pol

6 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) 7

Emissivity and its dynamics are driven by land surface states 8

Global emissivity can now be modeled, but how to validate? 9 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 SGP P soil moisture content (SMC) 10 Leaf Area Index (LAI)

Differences in RTMs can be easily seen in phase diagrams CRTM emissivity CMEM emissivity 11

12 Methodology : “Understanding emissivity without using emissivity data” Understanding global microwave emissivity dynamics

13 Data: AMSR-E Tb, (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

14 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 Ts Tb36V: 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 15 Tb-based MPDI: Emissivity-based: Emissivity-based mpdi

MPDI phase diagram reveals model behavior ASMR-E MPDICRTM mpdiCMEM mpdi 16

Global survey of microwave emission dynamics 17

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Microwave emission dynamic regimes shift with season 20

Regime diagram also reveals model behavior 21

Validating modeled global emissivity and its dynamics -- Seasonal mean 22 Challenging areas: 1.Deserts 2.Mountains 3.Snow, ice and glaciers

23 Validating modeled global emissivity and its dynamics -- Standard deviation

24 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

25 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 26

Tb-based MPDI is close to emissivity-based MPDI at lower frequencies 27 Tb-based MPDI: Emissivity-based: 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 28

Extra slides 29

Modeling microwave emissivity and its dynamics Start with site with more reliable auxiliary data: precipitation, soil moisture … + field campaigns 30

Similar climatic/ecological surfaces may have different dynamics 31

Microwave emission dynamics from a global perspective Land surfaces only 32

Similar climatic/ecological surfaces may not have similar MW emission dynamics 33

Microwave emission dynamics from a global perspective 34

Microwave emission dynamic regimes shift with season 35

Snapshots of soil moisture, LAI and emissivity at various episodes SMC LAI 19G wet/sparse dry/sparse wet/dense med dry/dense wet/med dense 36

Parameters Spatial Resolution Satellite Sensors Reference & Contact Leaf 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 37

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 38