On the estimation of land surface temperature from AMSR-E

measurements

On the estimation of land surface temperature from AMSR-E

measurements

Jean-Luc Moncet

P. Liang, J. Galantowicz, G. Uymin, A. Lipton, C. Prigent*, B. Hornbuckle**

Atmospheric and Environmental Research, Inc.*LERMA

**Iowa State University

First version of global monthly average surface emissivities (includes std dev and QC flags) available from: http://www.aer.com/scienceResearch/mwrs/emis.html

89-11GHz, ~40km resolution

AMSR-E land surface emissivity atlas

AMSR-E land surface emissivity atlas

A: Instantaneous emissivity estimate at AMSR-E frequencies using MODIS LST (Tsfc_MW ~ Tsfc_IR)

B: Arid regions (subsurface penetration) – use 1D thermal model. Surface forcing described by 2 term cosine series expansion with mean temperature T0. Parameters estimated from Aqua/Terra MODIS LST (amplitude and T0) and AMSR-E/SSM/I 89V Tbs (phase). Depth parameter adjusted to match thermal cycle amplitude at each MW frequency. Emissivity simultaneously adjusted to match T0.

C: Vegetated/frequently cloudy: substitute with A estimates from clearer areas with same surface type

- 2 -

March 2003

ApproachApproach

Use MODIS LST as reference in derivation of surface emissivity – removes biases between AMSR-E and MODIS in the clear-sky

Key advantage: good spatial temporal co-location between the 2 instruments Use clear-sky derived MW surface emissivity to perform MW analysis in cloudy conditions

- 3 -

11GHz polarization ratio used to monitor changes in physical surface characteristics

Daily outliers removed and flagged (studied separately)

Amazon

Bamba, Mali

11V emissivity standard deviations(July 2003)

11V emissivity standard deviations(July 2003)

- 4 -

Good consistency between MODIS and AMSR measurements results in stable emissivities

AMSR-E/MODIS derived product

SSMI/ISCCP LST derived product (from Prigent)

Seasonal stabilitySeasonal stability

- 5 -

AMSR-EDatabase

(emissivities more stable in arid and semi

arid areas)

SSM/IDatabase

Known issuesKnown issues

19 GHz calibration (~2K bias; Meissner and Wentz, 2010)89 GHz appears too warm (89-37 GHz emissivity larger than with SSM/I)Unexplained latitudinal dependent bias in 22GHz emissivities (?)New calibration work on going at RSS

- 6 -

Examples of retrieved emissivity spectra over Amazonian forest

Current workCurrent work

Goal: assess usefulness of microwave data (in combination with dynamic surface emissivity atlas) for surface/atmosphere characterization (non-precipitating environment) over land

Good knowledge of surface emissivity is necessary but not necessarily sufficient for “useful” atmosphere/surface temperature estimation in retrieval applications (model constraints available in assimilation environment)Focus on 2 parameters: surface temperature and PWApplications: climate (spatial/temporal averaging), IR cloud property (e.g. IWP) retrieval (instantaneous estimates)

- 7 -

IR LST:No estimate provided under overcast conditionsLST estimate only representative of clear portion of the grid boxImpacted by undetected (thin) clouds/dust

Example of MODIS daily LST product

MODIS vs. AMSR-E monthly averagediurnal surface temperature differences

MODIS vs. AMSR-E monthly averagediurnal surface temperature differences

- 8 -

200307 Monthly mean of LST day/night difference

Loose QCStrict QC

MODIS vs. AMSR-E monthly averagediurnal surface temperature differences

MODIS vs. AMSR-E monthly averagediurnal surface temperature differences

- 9 -

200307 Monthly mean of LST day/night difference

Loose QC

Soil temperature over desertSoil temperature over desert

Penetration effects give rise to significant emission temperature gradients (even over rocky areas)Retrieval strategy over deserts consists of assuming known surface emissivity (from 1b algorithm) and retrieve subsurface temperature profiles

Makes physical sense over horizontally homogeneous surfacesEnough degrees of freedom to account for thermal and emissivity inhomogeneities ?Surface temporal changes monitored by 11 GHz polarization ratio

- 10 -

89 -19GHz temperature difference maps89 -19GHz temperature difference maps

- 11 -

Positive 89-19 GHz temperature differences may be due to impact of

calibration errors

0.67 0.55 0.47 um 0.67 0.55 0.47 um

Liquid cloud over desertsLiquid cloud over deserts

Retrieval of liquid water over penetrating surfaces may be difficultCould at least detect presence of liquid clouds from microwave signalImpact of clouds on retrieved Teff(89GHz) – Teff(19GHz) due to:

Neglecting CLW in retrieval (and NCEP water vapor errors)Impact of clouds on net surface radiation

- 12 -

Classified as ice clouds by IR algorithm

Other issuesOther issues

Observed polarization differences in retrieved Teff (89GHz) may be indicative of errors in specification of atmospheric termPlans to look at water vapor correctionOther?

11-12 um

11-12 um

Positive day/night emissivity anomaly in the Midwest

Positive day/night emissivity anomaly in the Midwest

Systematic positive day/night differences in our AMSR-E/MODIS emissivity product are observed during the summer months in the Midwest

Spatial pattern appears to coincide with corn/soybean cropAre these differences real or artifacts of our process/data?

- 14 -

JulJun

Monitoring corn growing season at 11 GHz

Comparison of DN>0 & DN0 regionsJuly-August, 2003

Comparison of DN>0 & DN0 regionsJuly-August, 2003

10 GHz DN>0 (Iowa) 10 GHz DN0 (Missouri)

e(day): 0.94 – 0.96 & e(night)<e(day) usually e(day) e(night): 0.94 – 0.96

DN: 0 – 0.04 & v-pol. h-pol. DN: -0.02 – 0.01 & v-pol. h-pol.

Polarization ratio (TBH/TBV): no large differences between regions- 15 -

Evidence for emissivity reduction by dew on large-leaf crops (corn/soybean)Evidence for emissivity reduction by

dew on large-leaf crops (corn/soybean)

1. DN>0 occurs most days in July-August2. Nighttime dew at AMSR-E overpass time (~0130) is also

persistent3. DN>0 region daytime emissivities are consistent with nearby

DN0 regions e(night) occasionally rises to level of e(day)

4. DN is independent of polarization & there is little day–night polarization ratio difference i.e., effect is quasi-polarization-neutral (not due to soil

moisture)5. Effect is strongly associated with mature, large-leaf crops (corn

& soybeans) Ground surface is obscured at 10 GHz Large, dew-covered leaves may induce scatter-darkening

(also seen at 1.4 GHz, Hornbuckle et al., 2007)

- 16 -

Preliminary analysis with 2009 (SMEX09) Iowa dew field

measurements*

Preliminary analysis with 2009 (SMEX09) Iowa dew field

measurements*

Nighttime AMSR-E overpass times without detected dew

3 automatic dew sensors (mV output)

Sensor disagreement suggests light dew amountAd hoc “no-dew” algorithm:

Any of 3 sensors reporting <280 mV

*Experiment conducted by Brian Hornbuckle from U. of

Iowa

Reasonably good agreement between MODIS LST and in

situ air temperatures in the

clear-sky (night time)

Bias = -0.3KStd dev = 0.77K

- 17 -

ResultsResults

Preliminary analysis indicate correlation between DDN anomalies and occurrence of dew deposition on corn leaves

- 18 -

AMSR-E emissivities derived using in situ air temperatures at night

(b) Dew

(a) No dew (see previous slide)

Night time emissivities

X: No dew X: Dew

Future plansFuture plans

Continue assessing value of AMSR-E derived surface temperatures (NASA/NEWS)

Implement water vapor correction over desertsIR surface temperature prediction over desertsCompare with MODIS/validation

Refine QCSnow/RFI flagsIncrease yield (QC too strict in certain areas)Improve surface classification approachAdd dew index

Planned improvementsOpen water correctionProcess Terra/MODIS over penetrating surfaces

Plan to regenerate emissivity database only when new AMSR-E L2A product is available

- 19 -