Satellite Cross comparisonMorisette 1 Satellite LAI Cross Comparison Jeff Morisette, Jeff Privette...

Satellite Cross comparison Morisette1

Satellite LAI Cross Comparison

Jeff Morisette, Jeff Privette – MODLAND ValidationEric Vermote – MODIS Surface Reflectance

David Roy – MODIS Quality AssuranceAlfredo Huete – MODIS NDVI product

Satellite LAI Cross Comparison

Jeff Morisette, Jeff Privette – MODLAND ValidationEric Vermote – MODIS Surface Reflectance

David Roy – MODIS Quality AssuranceAlfredo Huete – MODIS NDVI product

Outline:

High resolution data at EOS Land Validation Core Site

Statistical regression analysis, initial results(comparing NDVI from ETM+, MODIS, and AVHRR)

Future plans for comparing multiple LAI products


EOS Land Validation Core Sites

http://modarch.gsfc.nasa.gov/MODIS/LAND/VA

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EOS Land Validation Core Sites

http://modarch.gsfc.nasa.gov/MODIS/LAND/VA

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Satellite imagery MODIS Subsets (EDC DAAC) ETM+ (EDC DAAC) ASTER data (EDC DAAC) MISR Local Mode (Langley DAAC) SeaWiFS Subsets (GSFC) IKONOS (SDP/GLCF) “GeoCover ’90s TM (SDP) EO-1 Ancillary layers and background information such as existing - elevation - land cover - reference layer available through UMd ESIP – GLCF

Field data graphic courtesy of the BigFoot program

Field and airborne data: archive and access through ORNL DAAC’s “Mercury System”

AERONET and FLUXNET data


High resolution data“targets”

High resolution data“targets”

• Timing – as close as possible special consideration for composite products

• Resolution – to allow integration with field measurements

• Viewing geometry – to match product or cover viewing range of product

• Spectral – as close as possible or over sampled (i.e.hyperspectral)

• Geocoding – as close as possible


Spectral CharacteristicsSpectral Characteristics

400 600 800 1000 1200 1400 1600 1800 2000 2200 2400

nanometers (nm)

5 6 7

5 7

4 5 6 7 8 9

5 75’

1 243

1 2 3 4 5 6 7 8

1 2

1 2 43Pan.

1 2 3

1 2 43 4’1’

Pan.

Pan.1 2 43

MODIS

SeaWiFS

AVHRR

ETM+

ASTER

ALI

IKONOS

Hyperion


AR

M/C

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Bar

ton

Ben

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Bon

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BO

RE

AS,

NSA

BO

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SSA

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ER

MS

Cas

cade

sLT

ER

Har

vard

For

est

How

land

Ji-

Par

ana

Jor

nada

LT

ER

Kon

za P

rari

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Kra

snoy

arsk

Man

dolg

obi

Mar

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g.

Mon

gu

SA

LSA

Sev

ille

ta L

TE

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Sku

kuza

Tap

ajos

Uar

dry

USD

A A

RS

VC

R

Wal

ker

Bra

nch

Wis

c.P

ark

Fal

ls

MODIS Subsets

SeaWiFS subsets

Ancillary report (CRESS)

Ancillary data (GLCF)

ETM+ (with # of acquisitions) 7 3 5 4 5 2 1 7 2 5 5 5

Ikonos 1-DEM 2 1 1 1 2 2 1 2 4 2 Airborne (MQUALs or ER-2, both) AT

Global Land Cover Test Sites

GeoCover: 1990 TM data

Aeronet CIMEL (planned)

EO-1 coverage - planned

Core Site data summary

Core Site data summary


NDVI over Konza PrairieNDVI over Konza Prairie

Data:

ETM+ & CIMEL11 September 2000“6S” atm. correction

MODIS daily11 September 2000

MODIS 16-day 8-21 September, 2000

AVHRR 14-day28 Aug.-12 Sept, 2000


Correlative Analysis:Issues

Correlative Analysis:Issues

• Typical inference is based on a null hypothesis of “no correlation” (i.e. correlation = 0, regression slope term = 0)

• Inference on correlation assume, among other things, independent data

• Correlation and R2 alone do not tell the whole story

• Regression analysis includes several assumptions

• Subset size and location will influence results


http://edc.usgs.gov/programs/sddm/modisdist/index.shtml

ISINISINISINISIN GeographicGeographicGeographicGeographic

MODIS Projection issuesMODIS Projection issues


Projection issuesProjection issues

30 km

MODIS daily

MODIS 16-day

AVHRR 14-day


Preliminary analysisPreliminary analysis

Histograms:General agreement between ETM+ and MODISAVHRR, somewhatlower

Variability decreaseswith spatial averaging


Variogram MapsVariogram Maps

Semi-variograms in 2-D• allows visual inspection

of Anisotropy• 1-D semivariograms can

be extracted


Preliminary analysis: Spatial structure

Preliminary analysis: Spatial structure


Regression analysis:Model selection

Regression analysis:Model selection

• NDVIY, i = b0 + b1 AVEETM+, i + ei

• NDVIY, i = b0 + b1 AVEETM+, i + b2 SDETM+, i ei

• NDVIY, i = b0 + b1 AVEETM+, i + b1 (AVEETM+, I)2 ei

Best fit, agrees with previous results*

* Klökitz, C., van Boxtel, A.; Carfagna, E. and van Deursen, W., 1998. Estimating the Accuracy of Coarse Scale Classification Using High Scale Information. Photogrammetric Engineering and Remote Sensing, 64(2)127-133.


Regression analysisRegression analysis

500m

1km

2km

MOD. Daily MOD.comp. AVHRR comp.


Regression analysis:Joint confidence intervals

Regression analysis:Joint confidence intervals

MOD09, 500m

R2 =0.6982

MOD13, 500m

R2 =0.6254

MOD09, 1km

R2 =0.9037

MOD13, 1km

R2 =0.8286

AVHRR, 1km

R2 =0.715

MOD09, 2km

R2 =0.9577

MOD13, 2km

R2 =0.9207

AVHRR, 2km

R2 =0.9011

Regression “target”:

One-to-one line


Checking Regression assumptions

Checking Regression assumptions

• Linearity (plot resids vs independent variable)• Constant Variance (plot resids vs fitted values)• Independence (plot residuals in space)• Outliers (examine residuals)• Normally distributed error term

(goodness of fit test on residuals)• Independent terms missing from model

(from Neter et al., “Applied Linear Regression Models”)


Checking Regression assumptions: map of residuals

Checking Regression assumptions: map of residuals

MOD09, 500m

MOD13, 500m

MOD09, 1km

MOD13, 1km

AVHRR, 1km

MOD09, 2km

MOD13, 2km

AVHRR, 2km


Checking Regression assumptions: spatial

independence of residuals

Checking Regression assumptions: spatial

independence of residuals

MOD09, 500m

MOD13, 500m

MOD09, 1km

MOD13, 1km

AVHRR, 1km

MOD09, 2km

MOD13, 2km

AVHRR, 2km


Sampling area and sizeSampling area and size

Method one:

Increase area, maintainingcenter pixel


Regression results:method one

Regression results:method one

Fitted parameters, +/- 3 standard deviations


Tiled subsetsTiled subsets

2.5km per side

…

10kmper side

144 subsets at 2.5km subsets, 36 @ 5km, 16 @7.5km, 9 @ 10km


Regression results:method two

Regression results:method two

Intercept parameter for shifted subsets

0

0.1

0.2

0.3

0.4

0.5

0.6

2.5 5 7.5 10

Subset size (km per side of square subset)

Slop

e pa

ram

eter

Min.

1st quartile

Median

Mean

3rd quartile

Max.

Slope Parameter for shifted subsets

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

2.5 5 7.5 10

Subset size (km per side of square subset)

Slop

e pa

ram

eter

Min.

1st quartile

Median

Mean

3rd quartile

Max.


Range in NDVI values vs slope parameter

Range in NDVI values vs slope parameter

144 subsets2.5kmcorr. = 0.29

36 subsets5kmcorr. = 0.36

16 subsets7.5kmcorr. = 0.68

9 subsets 10km Corr. = 0.69


Conclusion from correlative analysis

Conclusion from correlative analysis

• Regression analysis and the joint confidence intervals on the slope and intercept terms provide a meaningful summary for validation analysis.

• In comparing two coarse resolution products, the comparison should be made with both products at the same resolution.

• Here, the daily MODIS product is the most directly related to the averaged ETM+ data; which implies the importance of considering temporal composite issues.

• Subset location and size have an affect on the regression parameters. For this area, a 10km subset provided a stable subset size.

• These statistical methods can be directly applied to comparing high and coarse resolution LAI products.

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