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

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

Global survey of microwave emission dynamics

5

6

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

7

8

Microwave emission dynamic regimes shift with season

9

10

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)

11

Emissivity and its dynamics are driven by land surface states

12

Global emissivity can now be modeled, but how to validate?

13

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)

14

Leaf

Are

a In

dex (

LAI)

Differences in RTMs can be easily seen in phase diagrams

CRTM emissivity CMEM emissivity

15

Tb-based MPDI is close to emissivity-based MPDI at lower frequencies

16

 

Tb-based MPDI:

Emissivity-based:

Emissivity-based mpdi

Validating modeled global emissivity and its dynamics

-- Seasonal mean

17

Challenging areas: 1. Deserts2. Mountains3. Snow, ice and glaciers

18

Validating modeled global emissivity and its dynamics

-- Standard deviation

MPDI phase diagram reveals model behavior

ASMR-E MPDI CRTM mpdi CMEM mpdi

19

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:

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

Tb-based MPDI is close to emissivity-based MPDI at lower frequencies

23

 

Tb-based MPDI:

Emissivity-based:

𝑀𝑃𝐷𝐼=𝑇𝑏𝑉 −𝑇𝑏𝐻𝑇𝑏𝑉 +𝑇𝑏𝐻

Emissivity-based mpdi

24

Methodology :

“Understanding emissivity without using emissivity data”

Understanding global microwave emissivity dynamics

25

• 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)

Microwave emission dynamics from a global perspective

26

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

27

Extra slides

28

Modeling microwave emissivity and its dynamics

Start with site with more reliable auxiliary data: precipitation, soil moisture … + field campaigns

29

There are large uncertainties in emissivity retrievals

(Tian et al., 2012) 30

Sahara desert, V-pol

Amazon rainforest, V-pol

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

31

Microwave emission dynamics from a global perspective

32

Microwave emission dynamic regimes shift with season

33

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

34

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

35

Similar climatic/ecological surfaces may have different dynamics

36

Microwave emission dynamics from a global perspective

Land surfaces only

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

Land surface emissivity is also a noise

39(Tian and Peters-Lidard, 2007)

(Skofronick-Jackson and Johnson, 2011)

False rain events 3B42V6 CMORPH

<- land surface | rain | light rain, snowfall ->