Land Surface Microwave Emissivity Dynamics: Observations, Analysis and Modeling

1. Land Surface Microwave Emissivity Dynamics: Observations, Analysis 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. Observations of emissivity dynamics (top-down) 3. Modeling land surface emissivity (bottom-up) 4. Where do we meet? Where to go from there?

3. Land surface emissivity affects precipitation retrievals -- heterogeneous and dynamic

4. 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)

5. Global survey of microwave emission dynamics

6. 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)

9. Microwave emission dynamic regimes shift with season

10. 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)

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

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

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

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

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

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

17. Validating modeled global emissivity and its dynamics -- Seasonal mean Challenging areas: Deserts Mountains Snow, ice and glaciers 17

18. Validating modeled global emissivity and its dynamics -- Standard deviation 18

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

20. Regime diagram also reveals model behavior 20

21. Summary 1. Land surface microwave emissivity is critical 2. Land surface emissivity is heterogeneous and dynamic -- different dynamic regimes for different surfaces 3. Modeling land surface emissivity with LIS+RTM 4. Models quantitatively and qualitatively validated

22. 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

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

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

25. 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

26. Microwave emission dynamics from a global perspective

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

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

32. Microwave emission dynamics from a global perspective

33. Microwave emission dynamic regimes shift with season

34. 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

35. 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)

36. Similar climatic/ecological surfaces may have different dynamics

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

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

39. 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)