Richard Kelly Department of Geography University of Waterloo Ontario, Canada

AMSRE Science Team 2008Telluride 14-16 July 2008 Richard E.J. Kelly (

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The impact of physical temperature on brightness

temperature observations over snow for NASA’s AMSR-E

Richard KellyRichard Kelly

Department of Geography Department of Geography University of WaterlooUniversity of Waterloo

Ontario, CanadaOntario, Canada

Marco TedescoMarco TedescoCity College of New York - CUNYCity College of New York - CUNY

New York, USANew York, USA

Thorsten Markus & James FosterThorsten Markus & James FosterNASA/GSFC, USANASA/GSFC, USA

AMSR-E St. Louis Creek, CO

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18V

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xv36

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57.07N, 86.22E

ObservationThere are high temporal frequency variations in the brightness temperatures (and therefore retrievals) at 36, 18 and 10 GHz.

QuestionWhat controls/causes high frequency (day to a few days) changes?


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Outline

• Simple theory

• Met station measurements

• AMSR-E observations

• Summary & further work


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What controls the brightness temperature (Tb) variation from a snow-covered scene as observed by a spaceborne microwave radiometer?

(1)

• Tbs is snow Brightness Temperature

• Tbv is vegetation (tree canopy) Brightness Temperature

is a atmospheric transmissivity

• Tbatm atmospheric brightness temp (up & down) (assume negligible in this case)

• NB Tb responses are frequency dependent.

€

Tbscene = (Tbs + Tbv )Γτ + Tbatm

Simple theoretical standpoint

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Tbv

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Tbs

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Tbv

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Tbground = ffTbv + (1− ff )Tbs


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What controls the brightness temperature (Tb) variation from a snow-covered scene as observed by a spaceborne microwave radiometer?

Deconstructing previous expression:

(2)

• Ts is snow physical temperature:– Air temperature is the driver here and changes through time: the

snowpack thermal gradient is constantly adjusting.– Sub-nivean temperature probably stable

• es is snow emissivity and related to bulk snow properties: • grain size, snow crystal packing, number of scatters in the path

length [SWE], water content [free or bounding] • probably (?) buried vegetation effects too

• Tv is vegetation physical temperature

• ev is vegetation emissivity

€

Tbscene = (esTs + evTv )

€

Tbscene = (Tbs + Tbv )


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What is the role of Tv or Ts ?

• In the models, Tv and Ts are often equated or combined as the effective temperature, T0, where:

(3)

• T0 is also computed through (e.g.)

(4)

where Tair is the air temperature and Ts is the snow temperature.

But, are there overlooked implications to these assumptions ?

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Tbscene = T0(es + ev )

€

T0 =Tair + Ts

2


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What do physical temperature measurements suggest?

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Tsskin

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Tv

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Tspack

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Tsoil


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CLPX Experiment Data

Colorado: 19-24 Feb. 2003• 3 MSAs (25x25km)• Each MSA had 3 ISAs

(1x1km):– Fraser ISA: moderate

snow accumulations & denser forest fraction

– Rabbit Ears: deep snow accumulations & less dense forest fraction


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Fraser Experimental Catchment MSA

• St. Louis Creek ISAs (forest and moderate snow) and LSOS site.

SWEmean 189mm

SWE55 mm

Depthmean 80 cm

Depth 20 cm


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Rabbit Ears MSA

• Walton Creek ISA (moderate forest and deep snow)

SWEmean 580 mm

SWE115 mm

Depthmean 189 cm

Depth 55 cm


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In situ measurements: dense pine at CLPX LSOS


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In situ measurements: Rabbit Earsless dense forest


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Summary of in situ measurements• Scene Tb’s are sensitive to (constituent surface) physical

temperature.

• (Tv) Vegetation canopy temperature is likely affected by air temperature– overall large fluctuations

• (Ts) Snow temperature at the near air-snow interface varies more than at near basal snow temperature.– overall small fluctuations

€

Tbscene = (esTs + evTv )


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How might Tphys affect PM SWE retrievals?

AMSR-E Observations


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Retrieval approaches based on R-T theory (Chang et al., 1987 & 1996):

where a is a calibration coefficient and ff the forest fraction. If this is deconstructed further:

where es18 and es36 are snow emissivities at 18 and 36 GHz respectively.

Is SWE a function of To / Tv / Ts ?

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SWE = a(Tbscene18 −Tbscene36) /(1− ff ) [mm]

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SWE = a[(es18Ts + ev18Tv ) − (es36Ts + ev37Tv )]/(1− ff )


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AMSR-E Tbs


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xv36xv18xv18-xv36


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AMSR-E Tbs for Fool Creek, CO

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xv10 xv18 xv36 xv89

Tbs at adjacent CLPX ISA sites (separated by ~8km)


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Variations of surface temperature (Tair) and Tbs at 18V & 36V

Fraser: St. Louis data

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Date

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xv18

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Tphys


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SWE = a(Tbscene18 −Tbscene36) [mm]

Variations of surface temperature (Tair) & Tb18V-Tb36V [K]

But which of these channels contributes most to the variations?


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Variations of Tair match Tb variations (somewhat) at low frequencies but less at 36 GHz ……

Fraser (St. Louis Creek), Colorado - dense tree cover.

10V GHz 18V GHz 36V GHz

R2 = 0.4775

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Surface Tphys (Deg C)

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Again, variations of Tair match well variations at low frequencies and to some extent the 36 GHz ……

Rabbit Ears, (Walton Creek), Colorado - dense tree cover.

R2 = 0.3115

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R2 = 0.5215

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R2 = 0.6433

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10V GHz 18V GHz 36V GHz


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SummaryWhat causes apparent fluctuations in the SWE estimates

or Tb18-Tb36?•Contribution of Tair to Tbs at lower frequencies is greater than higher frequencies;•‘Surface’ temperature-related effects (driven by air temps) are a likely cause of Tb fluctuations;•Vegetation temperatures are likely to change with air temperature;•Vegetation emissivity changes are small (excepting snow in the canopy);

•Snowpack temperature variations Ts are not a likely cause;

•Ground temperature/emissivity variations are not a likely cause;•Snow emissivity changes in response to punctuated snowfall events and seasonal snowpack evolution but not at the time scale under consideration.

€

SWE = a[(es18Ts + ev18Tv ) − (es36Ts + ev37Tv )]/(1− ff )


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Conclusions & Further Work•We are looking at correcting for Ts & Tv in the retrievals.

•Can we estimate Tair from AMSR-E? (synergy w/ John Kimball). If achievable, Tair could be used to help drive a snowpack stratigraphy model (information needed in retrieval parameterization).•Other sites under test (Canada: tundra and Boreal forest; Russia).•A simple fix could be to ratio Tb18/Tb36 rather than subtract Tb18-Tb36•Validation of current version is in progress for Sept 2008 - refinement activity will follow.


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Rationale• Retrieval approach is often snapshot in scope

• Algorithms generate coarse-resolution SWE estimates at ~25 x 25 km

• Uncertainties in the estimates are related to algorithms and spatial resolution

Monthly average


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In situ measurements: open pine at CLPX LSOS

Documents

Richard Kelly Department of Geography University of Waterloo Ontario, Canada