Land Surface Microwave Emissivity: Uncertainties, Dynamics and Modeling

1. 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)

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. Microwave emissivity contains rich information of terrestrial states Emissivity×Tsfc Vegetation (e.g., Choudhury et al., 1987; Owe et al., 2001; Joseph et al., 2010; Kurum et al, 2012) Snow (e.g., Pulliainen et al, 1999; Tedesco and Kim, 2006; Foster et al., 2009) Soil moisture (e.g., Njoku and O’Neill, 1982; O’Neill et al., 2011)

4. Land surface emissivity is also a noise (Skofronick-Jackson and Johnson, 2011) <- land surface | rain | light rain, snowfall -> False rain events 3B42V6 CMORPH (Tian and Peters-Lidard, 2007)

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

6. Land surface microwave emissivity can be modeled -- a layered, bottom-up approach -- a semi-physical, semi-empirical business Vegetation: tau-omega model (e.g., Mo et al., 1982; Owe et al., 2001) Snow: HUT model (e.g., Pulliainen et al, 1999; Tedesco and Kim, 2006) Surface roughness: (e.g., Choudhury et al., 1979) Bare, smooth soil: Dielectric constant -> Fresnel equation -> emissivity (e.g., Wang and Schmugge, 1980)

7. Modeling emissivity: coupling LIS with two emissivity models 1. CRTM (Weng et al., 2001) 2. CMEM (Holmes et al., 2008)

8. Emissivity and its dynamics are driven by land surface states

9. Global simulations of microwave emissivity Global emissivity can now be modeled, but how to validate? Sahara desert, V-pol Amazon rainforest, V-pol

10. Emissivity dynamics can be captured by a soil moisture-vegetation phase diagram Leaf Area Index (LAI) Amazon HMT-E soil moisture content (SMC) SGPP

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

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

13. Understanding microwave emissivity dynamics • 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

14. Three indices used to detect land surface dynamics • 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)

15. Tb-based MPDI is close to emissivity-based MPDI at lower frequencies Tb-based MPDI: Emissivity-based: Emissivity-based mpdi

16. MPDI phase diagram reveals model behavior ASMR-E MPDI CRTM mpdi CMEM mpdi

17. Global survey of microwave emission dynamics

20. Microwave emission dynamic regimes shift with season

21. Regime diagram also reveals model behavior 21

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

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

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: Model improvement: Quantitative: parameter tuning Qualitative: desert, snow, mountains 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

26. Microwave emission dynamics from a global perspective

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

28. 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

29. Extra slides

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

31. Similar climatic/ecological surfaces may have different dynamics

32. Microwave emission dynamics from a global perspective • Land surfaces only

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

34. Microwave emission dynamics from a global perspective

35. Microwave emission dynamic regimes shift with season

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

37. Modeling and Predicting Land Surface Emissivity at NASA GSFC • 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 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)

38. 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