High Spectral Resolution Infrared Land Surface Modeling & Retrieval

for MURI

28 April 2004 MURI Workshop Madison, WI

Bob Knuteson

UW-Madison

CIMSS

Land Surface Characterization Needed for Atmospheric Remote Sensing

MURI Topic Areas:

• Spectral emissivity maps from MODIS data (Lucy-UH, Seeman-UW).

• Enhanced Training sets including IR emissivity and Tskin/Tair along with corresponding vertical Temperature/Water Vapor profiles (S. W. Seeman/E. Borbas-UW).

• Radiative Transfer Theory (Jun Li, Youri Plokhenko, R. Knuteson)

• Satellite Validation (H. Revercomb, D. Tobin, R. Knuteson)

Radiative Transfer Theory

The Correlation Problem:

Surface Temperature (K)

Sur

face

Em

issi

vity

Slope at 10 m1% E -0.5 K Ts

For broad-band sensors, such as HIRS, GOES, MODIS, errors in the IR emissivity and surface temperature are highly correlated.

Solution:

High Spectral

Resolution

Infrared

Observations

Infrared Radiative Transfer Equation (lambertian surface)

NTB

NNNe

S

totatmobs

)(

/)(FormalSolution

NeTBedPTBN totS

tot )1()())((

atmNSurfaceEmission

SurfaceReflection

dTsdTsTB

TdB

NTB

TB

e

de

S

S

S

S

)(

)(

)(

)( .

Analytic

Derivative

Varies on/off spectral lines !!!

Simulated Radiance ( Using measured emissivity spectrum)

Ts = 295.4 K

Bare Soil

Vegetation

60%-40%combination

Simulated IR Reflected Radiance Contribution to TOA Radiance

Vegetation

60% Veg.

Bare Soil

Reflectedcontributioncanbe large !

Netot )1(

Rad

ianc

e (m

W/(

m2 s

r cm

-1))

The value of Ts can be determined from the variance of emissivity as a function of surface temperature !!!

Std.Dev.E(Ts)

Emissivityvs.

SurfaceTemperature

Minimum

Intersection

dE

dTs

The change in emissivity with Ts varies on and off atmospheric absorption lines!

E

Satellite Product Validation

Courtesy of A. Trishchenko

DOEARMSouthernGreatPlains(SGP)Site

Land CoverFromMODISData

SIwR jiji

jiOBS ,,

,,

9 miles (15 km)

,,,

, jiji

jiw

)()( ,,,,,

, Sjijiji

jiS TBwTB

Define an Effective Emissivity and Effective Surface Temperature such that

The observed radiance is a linearcombination of uniform scenes.

The Problem of Mixed Scenes

Emissivity Survey• ARM SGP site is dominated by two land cover types “grass vegetation” and “bare soil”.

• In situ UW surface and aircraft measurements can be represented by a linear combination of pure

scene types; bare soil and grass.

Aircraft validation measurements are also consistent with a linear combination of vegetation and bare soil.

Aircraft S-HISLSE

Wavenumber (cm-1)

0.85

1.0

Bare Soil

Pure Vegetation

S-HIS OBS

AIRS Granule for 16 Nov 2002 19:24 UTC

• Brightness temperature across ARM site at 12 m is fairly uniform.

Granule

ARMSGPSite

B.T.Diff9-12 m

AIRS Observations over the DOE ARM SGP site

• Notice the East/West gradient in the B.T. Difference.

AIRS emissivity is consistent with a linear combination of pure scene types. This implies a single vegetation fraction can explain most of the variation in the IR spectra over land.

Wavenumber (cm-1)

Pure Vegetation

Bare Soil

LSEfrom AIRS

RadianceUsingUW

ResearchAlgorithm

Wavenumber (cm-1)

ResearchProduct

AIRS12 µm

B.T.(K)

LST(K)

LST is 2 to 4 degrees warmer than 12 m brightness temperature.

• UW Research Product shows spatial gradient in land temperature.

ResearchProduct

LST(K)

LSE(9 µm)

Emissivity from UW “research” product shows East/West gradient.

High emissivity (grass) is cooler than low emissivity (exposed soil).

ResearchProduct

• Product retrieval is spatially uniform. No East/West gradient!

Tsurf(K)

IR Emiss(9 µm)

AIRS Standard Product Version 3.5.0.0

• B.T. Difference clearly shows East/West spatial gradient !

AIRS Brightness Temperature Observations (9-12 m)

(Lat, Lon)=(36.590, -97.216)

Tobin-ARM SGP Best Estimate for 16 Nov 2002 19:24 UTC

• AIRS standard retrieval is within the AMSU footprint after CC.

TSurfStd 290.5K

TSurfAir 285.7K

• AIRS standard retrieval misses spectral contrast in 9 m emissivity.

AIRS Cloud-Cleared Radiance for 16 Nov 2002 19:24 UTC

*

Tobin-ARM SGP Best Estimate for 16 Nov 2002 19:24 UTC

• ARM “best estimate” interpolates sondes before and after launch.

• AIRS standard retrieval “agrees” with sonde2 in below 500 mb.

• AIRS standard retrieval agrees with sonde1 in above 500 mb.

• AIRS “standard retrieval” agrees well with ARM Best Estimate Profile.

Tobin-ARM SGP Best Estimate for 16 Nov 2002 19:24 UTC

• AIRS “standard level” retrieval looks good near the surface!

Tobin-ARM SGP Best Estimate for 16 Nov 2002 19:24 UTC

• AIRS “100 level” retrieval adds more points near the surface.

AIRS/SGP Overpass19:24 UTC

AIRSTSurfAir 285.7K

TSurfStd 290.5K

• AIRS “surface air” temperature is within 1 degree of truth data!

Tobin-ARM SGP Best Estimate for 16 Nov 2002 19:24 UTC

AERIB.T.“truth”

Questions Raised

• What are the benefits and limitations of the online/offline method for separating surface temperature and emissivity?

• What improvements are needed in radiative transfer models to take advantage of the high spectral surface reflection in operational models?

• Is the AIRS Cloud-Clearing working over land or is it introducing “noise” into the retrievals?

• What are the statistics of the validation of AIRS profile retrievals over land? What about near surface air temperature?

• How can AIRS data best be used to improve the global characterization of infrared spectral emissivity?

Backup Slides

Tskin

Tair

ARM Site Land Use Survey

Wheat57%

Pasture& Range

25%

Bare soil 6%

Rubble 4%

Dense trees 4%Rubble & wheat mixture 4%

Other 4%

November 2002; 63 square mile area.

• Two land cover types dominate: wheat fields and pasture (grassland).

(Osborne, 2003)

ARM SGP LST/LSE “Best Estimate”• Formulated in April 2001 to supply the surface contribution to the ARM/AIRS validation product developed by D. Tobin.

Simulated Radiance (S-HIS resolution = 1 cm-1 apodized)

On-line channels have a greater rate of change, dE/dTs !

B.T. (K)

Simulated Brightness Temperature Spectrum

Ts = 295.4 K

Wavenumber (cm-1)

Emissivityvs.

SurfaceTemperature