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Yunyue Yu1, Ming Chen2, Dan Tarpley3 , Jeffrey L. Privette4 ,

Miguel O. Roman5

Present by Ming Chen

1NOAA/NESDIS/STAR, 2IMSG at NOAA/NESDIS, 3Short & Associates, 4NOAA/NESDIS/NCDC, 5NASA/GSFC

IGARSS 2010, Honolulu, Hawaii, USA

Outline

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• Potential Issues of Using Ground Measurement to Validate Satellite LST

• Our Goal and Proposed Approach

• Preliminary Analysis and Results

• Further Plans

IGARSS 2010, Honolulu, Hawaii, USA

Issues of LST Cal/Val

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Different sensor IFOVs, introducing site heterogeneities through

a) landscape b) land cover

c) soil moisture , etc.

Ground data accuracy Possible bias in pixel geo-referencing Cloud contamination

All the above may result in thermal and emissivity uncertainties

Our Goal

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• To evaluate potential scale mismatch uncertainties involved in direct site-to-pixel comparison for ABI/VIIRS LST validation

• To establish scaling model that would provide statistical relationship between ground station LST and the overpass ABI/VIIR pixel LST

Approach

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• Use well-calibrated high-resolution satellite pixels to quantitatively characterize the sub-pixel heterogeneity of a low resolution satellite pixel.

• Establish relationship between the low-resolution pixel and its sub-pixels.

Pixel-Synthesizing Scheme

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Synthetic Pixels: Large circles

Sub-pixels: ASTER 90m TIR Pixels

Each synthetic pixel encloses the target ground site.

The center of synthetic pixel varies around the ground site, for counting variable over-passing satellite footprints, or small geo-referencing bias.

IGARSS 2010, Honolulu, Hawaii, USA

ASTER 90m TIR pixels Ground site

Scaling and Characterizing (1)

drrdrrTTTT occijijijijsat ,,

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Aggregation of Synthetic pixel from high-resolution ASTER pixels

IGARSS 2010, Honolulu, Hawaii, USA

Tij is individual ASTER pixel observation within the synthetic pixel scope, rij is the ASTER pixel position vector, rc is position vector of synthetic pixel center, ro is the position vector of ground site.

Scaling and Characterizing (2)

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}{ gsat TTE }{ gsat TT

CTT gsat

)()( gccsatgsat TTTTTT

Sub-pixel heterogeneity

Quantification of the difference between the Synthetic Pixel and Ground Measurement, that is,

Evaluation: and with

the model:

Note that:

Systematic bias between measurements of different sensors

In-Situ LST and Broadband Emissivity

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umumum cba 125.105.8

• SURFRAD LST need be calculated from SURFRAD radiation measurements.

Where Rup_pir, Rdown_pir are upwelling and downwelling thermal radiations. εs is surface broadband emissivity.

a=0.2122, b=0.3859,c=0.4029 (Wang, 2004)

• Regression of Broadband emissivity

IGARSS 2010, Honolulu, Hawaii, USA

4/1___ )]/()[( spirdownspirdownpirupg RRRT

Regression based on the UW-Madison Baseline Fit Emissivity Database( Seemann et al., 2008).

Enhanced Cloud Screening

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Enhanced cloud filtering is needed for high quality satellite-ground match-up dataset

Cloud

yIGARSS 2010, Honolulu, Hawaii, USA

Computing Algorithms

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Enhanced Cloud Screening Satellite cloud mask data Spatial, temporal ,Spectral variation tests Manual control

Match-up Datasets

LST Algorithms1. Satellite LST calculation or

extraction2 SURFRAD LST calculation

Time-serial Analysis

Synthetic Datasets Spatial analysis models up-scaling models down-scaling models

Geolocation Match-up

Satellite and Ground Time Match-up

Original Datasets

Stations and Match-up Datasets

Stations Latitude-Longitude

Clear CasesBy ASTER Cloud masks

Clear CasesBy Augmented Screening

Desert Rock, Co

36.63oN 116.02oW

63 46

Bondville, IL

40.05oN 105.24oW

115 51

PennState,PA

40.72oN 77.93oW

61 20

Boulder, CO

40.13oN 105.24oW

35 13

Fort Peck, MT

48.31oN 105.10oW

12 8

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In addition to the candidate stations, shown in this map is the occurrence frequency of completely clear sky over US continent, which would affect the data size.

Sample Scatter Plots

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DesertRoc

k

)()( gccsatgsat TTTTTT

Sample Statistics of Desert Rock Site

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Pixel ID Deg E{Tsat-Tc} σ{Tsat-Tc} E{Tsat-Tg} σ{Tsat-Tg}

0 C -0.04 0.69 1.78 2.131 0 0.01 0.60 1.82 2.262 45 -0.08 0.61 1.74 2.203 90 -0.20 0.92 1.61 1.994 135 -0.06 0.96 1.75 2.035 180 0.24 0.98 2.05 2.186 225 0.34 0.80 2.15 2.307 270 0.26 0.65 2.07 2.408 315 0.16 0.60 1.97 2.37

Note that:E{Tc-Tg}: 1.81 K σ{Tc-Tg}: 2.46 K

Topographic Feature and Mean Satellite-In Situ Difference

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Tsat-Tg is vectorized, where the modes are the absolute values of Tsat-Tg (Column 7 in Table) , and the directions are the surface site to the synthetic pixel centers. The contour shapes reflect the spatial structure of Tsat-Tg.

Aggregated Results

04/12/23 IGARSS 2010, Honolulu, Hawaii, USA

Station E{Tsat-Tc} σ{Tsat-Tc} E{Tc-Tg} σ {Tc-Tg} E{Tsat-Tg} σ {Tsat-Tg}

DesertRock, NV 0.07 0.76 1.81 2.46 1.88 2.21

Boulder, CO 0.13 0.72 0.77 2.60 0.90 2.58

Fort Peck, MT 0.06 0.99 -0.20 3.36 -0.15 2.62

Bondville, IL 0.09 1.05 0.59 2.01 0.68 2.08

Penn State, PA 0.08 1.14 -0.25 2.09 -0.17 1.99

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Discussion and Conclusive Remarks

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• The mean difference between the synthetic pixel and the collocated central ASTER pixel is less than 1K, and the standard deviation of such mean difference is around 1K, which indicates that a) SURFRAD stations in this study may be treated as homogenous or slightly heterogeneous sites(within 1Km) for VIIRS/ABI LST cal/val. b) LST of SURFRAD measurements may be used as good references for VIIRS/ABI LST cal/val if the measurements are of high-quality and the in-situ estimation of LST is accurate enough.

• Directional variations are small, which indicates that a) Small geo-referencing (within 1Km) bias may not be an issue affecting VIIRS/ABI LST cal/val at the above SURFRAD sites. b) Directional variation of the potential sub-pixel heterogeneity is found to be consistent with the physical topographic features, even if it is small.

Discussion and Conclusive Remarks

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•The mean difference between the synthetic LST and SURFRAD LST(Tsat-Tg) is generally around 1K and the standard deviation of such difference is around 2K , which is relatively larger than those between the synthetic LST and the collocated ASTER pixel (Tsat-Tc). Systematic bias between measurements of different sensors (Tc-Tg ) is the major source of Tsat-Tg.

•The limited datasets doesn’t allow us to characterize the seasonal variation of heterogeneities, which is more desirable than a simple mean difference. More datasets are expected. And about 1K difference seems unavoidable in practice.

Further Efforts

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• The limited data made it impossible to perform fine analysis over time scales of interest, e.g., seasonal variation. More match-up data will be analyzed.

• Analysis over sites of different surface types

• Up/down-scaling models

• Emissivity uncertainties

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Thank you for your attention !

IGARSS 2010, Honolulu, Hawaii, USA