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Validating Satellite Land Surface Temperature Products for GOES-R and

JPSS Missions

Yunyue Yu, Mitchell Goldberg, Ivan Csiszar

NOAA/NESDISCenter for Satellite Applications and Research

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Motivation-- LST validation needs

Motivation-- LST validation needs

LST Products Derived from Different Sensors for Decades– POES

• NOAA AVHRR (TIROS-N to NOAA-19) since 1978 • ESA ATSR/AATSR since 1991• EOS MODIS since 1999 • MetOp AVHRR since 2006

– GOES• US GOES/Imagers since 1975• Meteosat (MVIRI since 1995 and then) SEVIRI since 2004

Future LST products JPSS/NPP GOES-R

LST Climate Data Record

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Motivation-- LST validation issues

Motivation-- LST validation issues

LST Validation Difficulties– In situ data limitation

• Measurement difficulty

– Cloud contamination effect • particularly the partial or thin

cloudy pixels

– Spatial and temporal variations

• Spot vs pixel difference• Sub-pixel heterogeneity• Accurate match-up process

(different sampling rates and sampling timing)

– Others • i.e. angle effect Surface heterogeneity is shown in a 4km x 4km Google map

(1km x 1km, in the center box) around the Bondville station area

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Approach-- Strategy

Approach-- Strategy

Using Existing Ground Observation Data Cost consideration Site representativeness and selection: characterization

analysis

Match-up Dataset Generation Stringent cloud filtering: additional measures Data pair quality control

Site-to-pixel Model Development Synthetic pixel analysis using high resolution sensor data Proxy data testing Real satellite data evaluation

Validation Methodology Direct comparisons Indirection comparisons

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ApproachApproach

T(x,y,t)

T(x0,y0,t0)

ASTER pixel The site pixelMODIS pixel

Quantitatively characterize the sub-pixel heterogeneity and decide whether a ground site is adequately representative for the satellite pixel. The sub-pixels may be generated from pixels of a higher-resolution satellite.

For pixel that is relatively homogeneous, analyze statistical relationship of the ground-site sub-pixel with the surrounding sub-pixels: {T(x,y) } ~ T(x0,y0)

Establish relationship between the objective pixel and its sub-pixels (i.e., up-scaling model), e.g., Tpixel = T(x,y) + DT (time dependent?)

Synthetic pixel analysis using ASTER data— an integrated approach for site representativeness analysis and site-to-pixel model development

The Synthetic pixel/sub-pixel model

ASTER scene (90m) pixel

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ApproachApproach

Each synthetic pixel has the target ground site enclosed, but the distance between the ground site and the center of synthetic pixel varies, which mimics the possible over-passing VIIRS swaths.

Distance of every synthetic pixel center from the ground site is within the pixel size (~1Km).

Different colors are used for the 9 synthetic pixels, and the center of each pixel is marked with a small numbered square of the same corresponding color.

The numbers on the squares are the pixel IDs used in the relevant analysis.

Colored squares: Ground site synthetic VIIRS pixels

A Site Characterization Simulation Model – synthesizing VIIRS pixel using higher-resolution ASTER TIR pixels.

ApproachApproach

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

Evaluation: and with

the model:

Note that:

}{ gsat TTE }{ gsat TT

CTT gsat

)()( gccsatgsat TTTTTT

Sub-pixel heterogeneity

Systematic bias between ASTER and ground measurements

Tsat – satellite pixel LSTTg -- ground site LSTTc -- central sub-pixel LST

Data SetsData Sets

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Data Acquisition Inventory preparation of clear-sky ASTER scenes passing over 23 ground

sites (SURFRAD and CRN) during 2001-2007. Collection of the clear-sky ASTER data sets associated with six SURFRAD

sites and two CRN sites. AST_04, AST_05, AST_08 and L1B (in total, about 2000 ASTER swath scenes).

Collection of the six SURFRAD and the two CRN ground data. Collection of the two CRN ground data. Collection of MODIS LST product data (MOD11_L2).

All the swaths passing over the SURFRAD sites in 2001 All the swaths corresponding to the ASTER scenes during 2001-2007

Collection of the narrow-band emissivity data sets UW-Madison Baseline Fit Emissivity Database North American ASTER Land Surface Emissivity Database (NAALSED)

Collection of NCEP reanalysis TPW datasets

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Data SetsData Sets

Stations Clear Cases

(ASTER cloud mask)

Clear Cases (additional cloud filter)

Desert Rock 63 46

Bondville, IL 115 51

PennState, PA 61 20

Boulder, CO 35 13

Fort Peck, MT 12 8

Dataset Used SURFRAD data ASTER Data Data period: 2001-2007

ASTER data is courtesy by Shunlin Liang

Table: Matched ASTER Data

Down-looking PIR at 8 meter height from the ground

UP-looking PIR

Diffuse Radiometer

Down-looking PIR on the towerAt 8-m from ground

Thermometer

Anemometer

Satellite LST: MODIS LST, ASTER, and GOES LSTGround LST: Derived from SURFRAD site measurements

Candidate Ground Sites and Database

10Jul. 2011

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

Data SetsData Sets

Ground Site Broadband Emissivity

Regression of Broadband emissivity from well-developed narrowband emissivity database:

UW-Madison baseline Fit Emissivity Database

umumum cba 125.105.8

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

ProcessingProcessing

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Satellite Data

Match-up Datasets

Time Match-up

Geolocation Match-up

Ground Data Mask

Satellite Cloud Mask

Manual Cloud ControlGround LST

Estimation/Extraction

Satellite LST Calculation/Extraction

Synthetic Analysis and

Correction

Ground DataGround Data Reader

Satellite Data Reader

Indirect Comparison

Direct Comparison

Statistical Analysis

Outputs (Plots, Tables, etc.)

General Components of Validation Processing

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ProcessingProcessing

Sample Match-up Flow Chart

Note: this flow chart is specifically for GOES ImagerSimilar procedure is/will be applied for the ASTER and MODIS/VIIRS data

Additional cloud filter

GeolocationMatch-up

SURFRADData

SatelliteData

TimeMatch-up(< 5 mins)

Time SeriesSmoothness Check

(if available):Upwelling, Downwelling

Irradiances

SpatialDifference Test:

BT -- 3X3 pix STDs,Visual -- 0.5 deg

Channel BTDifference Test:

(Ts, T10mm), (T10mm, T3.9mm)(T10mm, T12mm)

MatchedDataset

ManualTuning

Cloud Mask

ProcessingProcessing

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Data Processing/Analysis Clear-sky cases analysis

Cloud and clear-sky climatology analysis (for site selection) ASTER Clear-sky swath selection from the ASTER inventory from the Warehouse

Inventory Search Tool Ground broadband emissivity regression analysis SURFRAD LST estimation from PIR measurements Spatial and temporal match-up among ground sites, ASTER scenes and

MODIS scenes Geolocation mapping of ASTER pixels as the sub-pixels of a MODIS pixel Quality-control and enhanced cloud filtering

Processing of ASTER LST QC information Processing of ASTER emissivity QC information Processing MODIS cloud masks Processing of MODIS LST QC information Surface observations Statistical testing

ProcessingProcessing

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Data Processing/Analysis (con’t)

Site representativeness analysis and site-to-pixel difference characterization Semi-variance analysis Synthetic analysis

Site-to-pixel model testing Testing with all the MODIS Terra LST swaths passing over SUFRAD sites in 2001 Testing with the MODIS LST swaths corresponding with ASTER scenes during 2001-2007

VIIRS LST case studies on NPP land LPEATE platform Development of VIIRS LST algorithm modules for flexible offline testing and

algorithm improvement Visualization tools of the analysis and results disolay

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ResultsResults

Additional cloud filtering is need for obtaining high quality satellite-ground match-up datasetLeft: ATSER cloud free dataset. Right: possible cloud contamination.

Cloud

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ResultsResults

Comparison of the temperatures calculated from synthetic pixel average (top-right), center-pixel (bottom-left), and nearest pixel (bottom-right) with the ground site temperature. Note the different colors represent for the 9 different synthetic pixels shown previously.

For this particular site the ground site location within the satellite pixel does not have significance impact to the validation process, simply because the land surface thermal emission at Desert Rock is fairly homogeneous.

SURFRAD Station: Desert Rock

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ResultsResults

Ts: LST of SURFRAD siteTa: average LST over 13x13 ASTER pixelsTc: LST of ASTER pixel nearest to the site

0.610.082.20-1.742

-1.81

Mean

Ts - Tc

0.76-0.072.21-1.88Average

0.60-0.162.37-1.978

0.65-0.262.40-2.077

0.80-0.342.30-2.156

0.98-0.242.18-2.055

0.960.062.03-1.754

0.920.201.99-1.613

0.60-0.012.26-1.821

2.46

0.690.042.13-1,780

STDSTD`MeanSTDMean

Tc-TaTs – TaCase

Site=Desert Rock, NV

Sample statistical analysis result on the Desert Rock site.

The Ta and Ts difference is tested by comparing its spatial structure to the site geographic structure.

It shows that such Ta and Ts difference matches the site geographic feature well, which implies that the synthetic pixel temperature calculation is reasonable.

Impact of pixel location bias to the ground site

Jul. 201118

1.07-0.052.05-0.642

-0.59

Mean

Ts - Tc

1.05-0.092.08-0.80Average

0.97-0.182.02-0.778

0.95-0.032.05-0.777

0.97-0.0012.12-0.626

1.10-0.092.14-0.605

1.15-0.032.10-0.684

1.27-0.052.17-0.643

1.04-0.142.01-0.731

2.01

0.92-0.072.04-0.660

STDSTD`MeanSTDMean

Tc-TaTs – TaCase

Site=Bondville, IL

Sample statistical analysis result on the Bondville site.

Impact of pixel location bias to the ground site

ResultsResults

Jul. 201119

0.91-0.272.30-1.032

-0.77

Mean

Ts - Tc

0.72-0.132.58-0.90Average

0.70-0.252.70-1.028

0.70-0.102.80-0.877

0.69-0.002.75-0.776

0.61-0.102.64-0.675

0.61-0.032.54-0.804

0.84-0.142.27-0.913

0.85-0.382.61-1.151

2.60

0.58-0.072.62-0.840

STDSTD`MeanSTDMean

Tc-TaTs – TaCase

Site=Boulder, CO

ResultsResults

Sample statistical analysis result on the Boulder site.

Impact of pixel location bias to the ground site

ResultsResults

Sample scatter plots show the linear relationship between satellite LST and ground LST.Tsat = A Tg + B + e

2104/08/2023

Site-to-Pixel Statistical Relationship

CTTA gsat )

Site A B σ

Desert Rock, NV 1.115 -33.66 1.73

Boulder, CO 1.138 -38.76 1.32

FortPeck, MT 1.089 -25.51 2.02

Bondville, IL 1.014 -3.29 2.01

PennState, PA 1.002 -0.66 1.97

WolfPoint 34, MT 0.768 65.99 1.32

WolfPoint 29, MT 0.593 114.48 3.23

Oakley, KS 1.374 -116.54 1.33

Mercury, NV 1.044 -9.77 1.14

BATTB gsat )

Site C σ

Desert Rock, NV 1.88 2.21

Boulder, CO 0.90 2.58

FortPeck, MT -0.15 2.62

Bondville,IL 0.68 2.08

PennState, PA -0.17 1.99

WolfPoint 34, MT 1.47 1.98

WolfPoint 29, MT 0.64 3.86

Oakley, KS 0.24 1.03

Mercury, NV 3.99 1.25

ResultsResults

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Site

MODIS Pixel- SURFRAD

Synthetic Pixel- SURFRAD

Nearest Aster Pixel – Synthetic pixel

Match-Up Pairs (SURFRAD, ASTER, MODIS)

Mean StdDev Mean StdDev Mean StdDev

Desert Rock, NV -0.44 1.84 2.09 1.69 0.04 0.44 34

Boulder,CO -0.49 2.08 1.49 2.90 -0.09 0.67 7

Fort Peck, MT 0.35 2.52 0.78 1.95 0.38 1.15 4

Bondville, IL -0.17 1.51 1.04 1.48 0.03 0.90 26

Penn State, PA -1.53 1.91 0.61 1.96 0.04 1.07 7

Site-to-Pixel Statistical Relationship – MODIS Proxy

The standard deviation of the difference between real MODIS pixel LST and SURFRAD LST are consistent with the standard deviation of the difference between synthetic pixel and SURFRAD.

The synthetic pixel LST is generally much warmer (about 1.5K) than SURFRAD LST, while MODIS LST is slightly cooler than SURFRAD LST.

Implication: High resolution ASTER data be used for site representativeness analysis, but a further verification of ASTER LST quality seems essential for the parameter estimation of the site-to-pixel model (e.g., A,B,C) and the application of the model in real cases.

ResultsResults

Results –SummaryResults –Summary

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• Synthetic pixel analysis model is created for analyzing ground site temperature heterogeneous feature. ASTER data are used to generate synthetic VIIRS pixel data (LST) and compared to the SURFRAD site data

• 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, so small geo-referencing (within 1Km) bias may not be an issue affecting VIIRS/ABI LST cal/val at the above SURFRAD sites.

• Application of the site-to-pixel model depends on the ASTER LST data quality.

• Directional variation of the potential sub-pixel heterogeneity is found to be consistent with the physical topographic features, even if it is small.

• 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.

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Future PlanFuture Plan

• Analysis over time scales of interest, e.g., seasonal variation.

• Analysis over sites of different surface types

• Up/down-scaling models

• Emissivity uncertainties

• Test of the scaling model using real satellite data