Development and Improvement of Land Surface Emissivity Model (LandEM)

Ming Chen1,2 and Fuzhong Weng1

1.NOAA Center for Satellite Applications and Research2. Joint Center for Satellite Data Assimilation

CRTM Baseline Surface Emissivity Modules

Ocean Water Sea Ice Snow Canopy Desert

Empirical & Semi-Empirical sea ice MW emissivity model (Yan and Weng, 2003; 2008)

IR emissivity model (Wu and Smith, 1991)

FASTEM 4/5 MW emissivity model (English et al., 1998; Liu, Weng, English, 2010)

IR emissivity model (Wu and Smith, 1991; van Delst et al., 2001)

Physical canopy and soil MW emissivity model (Weng et al., 2001)

Desert microwave emissivity library (Yan and Weng, 2010)

NPOESS Infrared emissivity data base

Physical snow MW emissivity model (Weng et al., 2001)

Empirical & Semi-Empirical snow MW emissivity (Yan and Weng, 2003; 2008)

NPOESS Infrared emissivity data base

Outline

1. Overview of the physical MW LandEM in the latest CRTM release (REL-2.1)

2. Ongoing improvements of the physical MW LandEM3. Development of the physical Vis-IR LandEM4. Development of semi-empirical and semi-physical MW

snow and sea-ice emissivity model5. Summary and Future plans

Updates of Microwave LandEM in CRTM REL 2.1

• Instead of using default constants, the LandEM now employs type-based values for the functional parameters of vegetation and soil, which allows the model to account for the variation of the underlying surface characteristics to some extent and to reduce the uncertainty of model inputs.

• 13 vegetation types and 9 soil types are used. The mapping of these types at global scale is identical to that in NCEP GDAS.

• A modification of the two-stream RT solution is made to account for the impact of temperature difference between surface skin temperature and subsoil temperature.

Index IGBP Name Percentage1 E BroadForest 9.12 D BroadForest 3.93 MixedForest 4.44 E NeedleForest 13.65 D NeedleForest 6.96 WoodySavannas 9.67 Grasslands 4.28 ClosedShrubs 6.99 OpenShrubs 8.1

10 MixedShrubs 12.311 Bare Soil 8.612 Croplands 12.3

Index USGS Name Percentage1 Sand 25.62 Silty Clay Loam 45.23 Clay 13.54 Sandy Loam 7.15 Sandy Clay 0.26 Clay Loam 6.87 Sandy Clay Loam 0.18 Silty Loam 1.5

Vegetation and Soil Types Used in MW LandEM

ATMS O-B vs. Vegetation Types

Index IGBP Name1 E BroadForest2 D BroadForest3 MixedForest4 E NeedleForest5 D NeedleForest6 WoodySavannas7 Grasslands8 ClosedShrubs9 OpenShrubs

10 MixedShrubs11 Bare Soil12 Croplands

In comparison with the LandEM in REL 2.0, the LandEM in REL 2.1 has greatly reduced data points with large negative bias, meanwhile bring about more points with large positive bias.

Comparison of AMSUA 23.8GHz TB Obs - CRTM SimAM vs. PM Orbit and NCEP vs. ECMWF NWP

Extensive analyses also indicated that there exists fairly different model performance for ascending and descending orbits, and different NWP inputs.The data points of |O-S|<2.5K are reduced about 30% in ascending orbit in comparison with ascending orbit.The model tends to have positive bias in ascending orbit, which is not so normal.To account more physical details, meanwhile keeping the model integrity and consistency on every aspects, the microwave LandEM is under systematical refinement recently.

Ascending Descending

NCEP NCEP

ECMWF ECMWF

NOAA-18

Ongoing Improvements of Physical Microwave Land Emissivity Model

Physical Land Emissivity Model with Non-isothermal Layers

I0Layer 1 ԑ1

Layer 2 ԑ2

Layer 3

I0R12

I0(1-R12 )

Is (Ts ,i , qs,i ,i)

I(τ0,μ) (1-R21 )

I(τ1,-μ) I(τ1,-μ) R23

I(τ0,μ) R21 τ0

τ1

Bc (Tc)

ccss BRIRII )1()1(),( 3212000

BAII

dd

),(),(

II

I

12

21

A

c

c

BB

)1()1(

B

Where,

Solution:

Canopy Physical RT Model (Weng et al , 2001)

This equation bears very clear physical interpretation of the total canopy upper-welling radiance reaching the air-canopy interface from blow: the sky incidence that is first transmitted into the canopy then scattered by the canopy, the soil emission that is first transmitted into the canopy then scattered by the canopy, and the contribution from the canopy emission.

dzd

BIddI

i

iii

)](1([)](1)[1]()(1[),( 11

111

1

j

i

jjiiii

N

ii zRBzRzRzI

Soil Physical RT Model (Burke et al, 1979)

For soil with fine particle size

Canopy Model with Improved Volume Scattering Scheme

ρp

τp

ipUlaby et al., 1981

Leaf slab d leaf thickness

Leaf-Level Model (Weng & Yan, 2001)

Rp

Tp

Ip

Leaf slab 1

Leaf slab 2

Leaf slab N

…

Canopy-Level Model (Weng & Yan , 2001)

air

air

Leaf slab d leaf thickness

ρp τp

τp ρp

Leaf-Level Model (Chen & Weng, 2013)

n~

i~ l~

l

i l

)( I

scaterring ackward ),()(scattering orward ),()(

122211

122211

bffffffRfffffffT

iiiiiiscat

iiiiiiscat

Canopy-Level Model(Chen & Weng, 2013)

Improvement of Leaf Single Scattering

This figure shows the single scattering albedo spectra at nadir and 50Deg view angle. The leaf thickness is set to 0.18mm. It is found the old single scattering albedo doesn’t vary with respect to mean LIA, but provides proper mean values. At 50Deg view angle, the single scattering of the old algorithm is much lower than the new one when the mean LIA is 0 (i.e., leaf is horizontal).

Comparison of AMSUA 23.8GHz TB Obs - CRTM SimAM vs. PM Orbit and NCEP vs. ECMWF NWP

In comparison with LandEM in REL 2.1, consistent model performance is achieved. Data points that could be possibly assimilated have increased (about 30% more). Error structure is also improved.

Ascending Descending

NCEP NCEP

ECMWF ECMWF

NOAA-18

Comparison of AMSUA TB Obs - CRTM SimLandEM in REL-2.1 vs. Ongoing

In comparison with the LandEM in REL-2.1, the ongoing improvements have significantly increased the data points that are possibly assimilated, especially over desert / bare soil regions.Both window-channel and surface sensible sounding channels are improved.

REL 2.1 Ongoing

REL 2.1 Ongoing

23.8GHz 50.3GHz

Development of Physical VIS-IR Emissivity Model

Physical Canopy VIS-IR Emissivity Model

LEAF MODEL (PROSPECT) (ρl , l )=L(N, Cab, Cbp , Cw , Cdm , n(λ) , ki(λ))

N Leaf structural layers Cab Chlorophyll a+b contentCbp Brown pigment contentCw Water contentCdm Dry matter content

n(λ) Refractive index (KK-Analysis mixture model)ki(λ) Absorption coefficient of leaf constituants

CANOPY MODEL (SAIL) (Is , Io , I+ , I-)=RT(LAI, θl, Θ , φ , ρl , l )

LAI Leaf Area Index θl Mean leaf angle Θ Solar zenith angle φ View zenith angleρl Leaf reflectance l Leaf transmittance

ρs Soil reflectance

Chen & Weng, JGR, 2012

Jacquemoud et al, 1990, 2009

Kubelka-Munk ,1931; Allen, 1970 ; Suits,1972; Verhoef, 1984

The specific absorption coefficients of leaf componentsfrom the PROSPECT and Gerber et al. [2011] and (left) the leaf refractive index by Variational Kramers_kronig Analysis (Chen and Weng, 2012)

Pure Water

Dry Leaf

Fresh Leaf

Water

Dry Mass

Chlorophyll a+bWater

Dry Mass

0

22

1)(2)( dxwx

xnpwwk

022

)(21)( dxwxxxkpwn

Kramers-Kronig Dispersion Relations

Kramers-Kronig Analysis of Leaf Refarctive Index

Model Simulation vs. Emissivity LUTs

(A) The current CRTM IR LUT data and (B) the University of Wisconsin Global Infrared Land Surface Emissivity Database (UWLSED, July 2008), where 10 hinge-point were used to form the curve shapes. The model simulation is similar to UWLSED, but without the prior assumption of the curve shapes.

KK-Based Model

This spectrum library is generated using the IGBP surface type classifications, where land covers are simulated by the model, soil data corresponding to the land covers are from JPL measurements.

Development of Semi-empirical and Semi-Physical MW Emissivity Model

Type-based snow emissivity spectrum library is constructed and validated from well-controlled lab measurements and in-situ observations, which may provide more reliable spectral structure than other empirical method.For non-polarized (or mixed-polarization) channels, snow Library:

But it is hard to establish a global type map in practice, especially when types vary within small-scale time, e.g, snow. Snow Typing:1) Use the observations of few window

channels to generate “real-time” typing and meanwhile avoid the looping of data usage for data assimilation system

2) Utilize the library spectral structure to keep the emissivity quality of the sounding channels.

V-pol H-pol

)(sin)()(cos)()( 22scanzqscanzpz

Snow MW Emissivity Modeling Based on Spectrum Library

Polarized Snow Emissivity Library by Yan and Weng

Comparison of AMSUA TB O-S Over SnowLibrary-Based Regression Model vs. TELSEM Atlas

Two different CRTM runs were performed for analyzing the CRTM forward performances over snow covers: 1) run with MW Atlas datasets(TELSEM) 2) run with snow MW library (Yan)

The results indicated that the snow library-based runs are generally much better than those with the Atlas.

TELSEM

MODEL

NOAA-18 23.8 GHz

Limitation of Regression-Based Snow Typing Algorithm

23.8GHz

AMSUA 89.0

GHz

AMSUA

23.8GHz

ATMS88.2GHz

ATMS

The AMSU snow-typing is based on empirical regression, and not transferable for ATMS channels without dedicated regression parameters. Although it has very good performance for AMSU , but it doesn’t work for ATMSQuestion:Can we have a typing method which is based on some physical analysis? If we can, the type-based library may be easily applied for different sensors, e.g, GPM imagers and sounders?

Development of Physics-Based Snow Typing Algorithm

Simulation With Regression-basedSnow-Typing

Simulation With Diagnosis-based Snow-Typing

89.0GHz

89.0GHz

A semi-empirical and semi-physical snow-typing algorithm has been developed. Instead of using pre-established empirical “discriminators”, the new snow-typing algorithm uses a simplified TB equation to diagnose the candidate Library spectrum from window-channel observations, where the atmosphere contribution is assumed to be much smaller than that of surface.

AMSUA

AMSUAsTdTb )(

)min(,1,

1

LibraryiLibraryi

iiTbTb

d

Comparison of ATMS TB Obs - CRTM Sim Over SnowDiagnosis-Based Model vs. TELSEM Atlas

50.3GHz 88GHz 183GHz TELSEM

Model

Development of Diagnosis-Based Sea Ice MW Emissivity Model

• A library-based sea-ice MW emissivity model has been developed for ATMS. This model is similar to the library-based snow MW emissivity model .

• The library consists of 13 sea-ice emissivity spectra.

• The physics-based typing algorithm also proved very efficient in practice.

Comparison of ATMS TB Obs - CRTM Sim Over Sea IceDiagnosis-Based Model vs. TELSEM Atlas

TELSEM

MODEL

50.3GHz 50.3GHz 23.8GHz 23.8GHz

Summary

• Physically, it is essential to account the vertical soil temperature variation and the temperature difference between canopy and land surface in the surface emissivity modeling. Yet, the modification in the CRTM Release 2.1 has some inconsistent features. In fact, the original two-stream RT model developed by Weng et al (2001) already provided proper mechanism framework to account for the possible temperature differences among canopy, skin surface and deep soil except the implementation of some mechanism details. A refined model solution which account for the non-isothermal layers has been performed.

• The volume scattering by surface vegetation canopy depends on the radiative properties of individual leaves (e.g., the reflectivity and transmissivity of leaf as single scatter) and the morphological configuration of the canopy. Canopy architecture is generally described by two essential parameters − LAI and leaf angle distribution (LAD), the latter of which was not implemented in the previous version of the physical microwave emissivity model. A distribution model (Campbell ,1986) has been implemented. The volume scattering parameters, e.g, single scattering albedo and asymmetric property, are now the function of to both view angle and LIA which varies with respect to land cover types.

• A semi physical type-based snow/sea ice emissivity model has been developed. The type-based emissivity model consists of a type-based library and a typing algorithm. The performance of the type-based snow emissivity model is generally much better than those of the retrieved emissivity atlas, which benefits from two aspects: the quality library data as the first guess and the “real-time” adjustment based on the observations.

• A physical diagnosis-based snow-typing has been developed. The new snow-typing is applicable for both AMSU and ATMS channels even without modifications, which indicates that the physical diagnosis-based algorithm is more general, and may be applicable for other sensors.

Plans and Further Work

• Develop and implement a unified land-cover and soil-type mapping system for use in both MW and VIS-IR physical emissivity models.

• Continue to refine the model physical details and optimize the model parameters of MW LandEM.

• Continue to develop the physical VIS-IR land surface emissivity model.• Continue to refine the multi-layered soil MW RT model by including

scattering mechanism .• Evaluation of the emissivity atlas.

Physical Land Surface Emissivity Model Structure

CANOPY Radiative Transfer Canopy structure Volume Scattering Effective Emissivity based

on radiance

LEAF Radiative Transferρl Leaf reflectance

l Leaf transmittance

SOIL RT MODELρs - Soil reflectance

Soil Emittance

SURFACE Classification System USGS, IGBP, UMD, MODIS

Soil Classification System STATSGO

Functional TY

PED

Parameter Translator/A

dapter

SOIL ParameterSoil textures, Wilting point, Maximum Soil Water content

LEAF ParameterLeaf thickness , Leaf gravimetric moisture, Mass density of cell materials

Model IN

PUT Param

eter Transformer/A

dapter

MODEL INPUT PARAMETERS MODEL CORE