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Hyperspectral techniques to extract LAI from Hyperspectral techniques to extract LAI from medium resolution MERIS superspectral datamedium resolution MERIS superspectral data

Francis Canisius, Richard Fernandes and Raymond SofferCanada Center for Remote Sensing

Natural Resources Canada

Francis Canisius, Richard Fernandes and Raymond SofferFrancis Canisius, Richard Fernandes and Raymond SofferCanada Center for Remote SensingCanada Center for Remote Sensing

Natural Resources CanadaNatural Resources Canada

22ndnd MERIS/(A)ATSAR User Workshop MERIS/(A)ATSAR User Workshop

2222ndnd to 26to 26thth September September –– ESA/ESRIN Frascati (Rome) ItalyESA/ESRIN Frascati (Rome) Italy

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OutlineOutline

IntroductionOverall methodologyMERIS as a source of hyperspectral informationField LAI measurementLAI and spectral responseMERIS HS LAI algorithmMERIS HS LAIComparison with TOA algorithmConclusion

IntroductionOverall methodologyMERIS as a source of hyperspectral informationField LAI measurementLAI and spectral responseMERIS HS LAI algorithmMERIS HS LAIComparison with TOA algorithmConclusion

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Global Climate Observation System requires Leaf Area Index (LAI) asmapped at ~250m resolution as an essential climate variable.

Current global LAI products do not consistently meet GCOS specification for accuracy in part due to sensitivity to atmospheric effects, variability in soils and land cover.

MERIS has sufficient spatial resolution to meet GCOS requirements and it provides unique spectral sampling with 15 narrow bands.

Current MERIS LAI algorithms are based on various multi-spectral approaches and somehow results are not up to the GCOS requirement.

In this study we assess the potential for using MERIS for LAI retrieval using red edge parameters/derivatives estimated by first approximating full spectral reflectances curves using standard MERIS sampling.

IntroductionIntroduction

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MERIS level 1p data acquired on 3rd July 2006

Wide Wide swathswath fine fine resolutionresolution MERISMERIS

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Overall methodologyOverall methodology

In-situ LAI(04/07/2006)

MERIS Level 1P(03/07/2006)

Smile correction

TOC reflectance

Spline Interpolation

MERIS TOA LAI algorithm

Narrowband NDVI

Product intercomparison

Rededge NDVI

SMAC correction

Single band

LAI

LAI

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Spectral signatures Spectral signatures withwith MERISMERIS

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400 500 600 700 800 900

Wavelength (nm)

ME

RIS

Ref

lect

ance

ForestGrassCornSoybeanMERIS Bands

Water vapour, land1090015Atmosphere corrections1088514Vegetation, water vapour reference2086513Atmosphere corrections15778.7512Oxygen absorption R-branch3.75760.6211Vegetation, cloud7.5753.7510atmospheric corrections10708.759Chlorophyll fluorescence peak7.5681.258Chlorophyll absorption106657Suspended sediment106206Chlorophyll absorption minimum105605Suspended sediment, red tides105104Chlorophyll and other pigments104903Chlorophyll absorption maximum10442.52Yellow substance and pigments10412.51

Potential ApplicationsWidth (nm)

Centre(nm)Band

MERIS bands

MERIS reflectance spectrum

Interpolated wide swath MERIS bands(linear spline interpolation)

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MERIS vs HyperspectralMERIS vs Hyperspectral

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Wavelength (nm)

Ref

lect

ance

(cor

n)

MERIS reflectance

Modeled reflectance

y = 0.935x + 0.0346R2 = 0.9977

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Modeled Reflectance (corn)

MER

IS R

efle

ctan

ce (c

orn)

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Wavelength (nm)

Ref

lect

ance

(cor

n)

MERIS reflectance

Calibrated (model) ref lectance

Comparison of MERIS (July 04, 2006) and Modeled (Profliar) reflectance of a corn field

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Field LAI Field LAI measurementsmeasurements

The field site is The field site is located in located in Nepean (45:18 N, 75:45 W) Nepean (45:18 N, 75:45 W) close to Ottawa (the capital close to Ottawa (the capital city Canada).city Canada).

The fields were large and The fields were large and homogeneous (average 20 homogeneous (average 20 ha larger than a FR MERIS ha larger than a FR MERIS pixel)pixel)

Corn, soybean and Corn, soybean and grass/pasture were the main grass/pasture were the main agriculture practicesagriculture practices

Broadleaf dominant forest Broadleaf dominant forest was present in isolated was present in isolated patches as well as a larger patches as well as a larger tracttract

LAI was estimated using LAI was estimated using Digital Hemispherical Digital Hemispherical PhotographsPhotographs (DHP) method DHP) method during 2006 growing season during 2006 growing season (4/5th of July).(4/5th of July).

Sharpened true color image of July 30, 2006 Landsat TM scene

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LAI from MERIS TOA algorithmLAI from MERIS TOA algorithm

Comparison between the LAI values derived from field LAI measurements to the corresponding LAI estimates from the MERIS TOA LAI algorithm

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MER

IS L

AI

(MER

IS T

OA

LA

I Alg

orith

m) Corn

SoybeanGrassForest

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MERIS based spectra and LAIMERIS based spectra and LAI

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660 680 700 720 740 760 780 800

Wavelength (nm)

MER

IS R

efle

ctan

ce (A

vera

ge)

LAI 0 to 1LAI 1 to 2LAI 2 to 3LAI 3 to 4LAI 4 to 5LAI 5 to 6

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670 690 710 730 750 770

Wavelength (nm)

ME

RIS

Ref

lect

ance

(SD)

LAI 0 to 1LAI 1 to 2LAI 2 to 3LAI 3 to 4LAI 4 to 5LAI 5 to 6

LAI class - average reflectance LAI class - standard deviation

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LAI and MERIS LAI and MERIS narrownarrow band band VIsVIs

0.58540.4610550, 670, 800MCARI2

0.48880.3636550, 670, 800MTVI1

0.47280.3417550, 670, 750TVI

0.57940.4608670, 800SAVI

0.63540.5269670, 800MSR

0.72340.6343670, 800NDVI

0.73790.6572670, 800SR

R2 with LAIeR2 with LAI Wavebands (nm)Indices

Coefficient of determination (R2) between MERIS HS VIs and field LAI

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LAI and MERIS red edge NDVI LAI and MERIS red edge NDVI

Band 2 (nm)690700710720730740750760770(B2-B1)/(B2+B1) vs

LAI

0.18000.26890.38180.48850.56040.60410.62890.63980.64236800.33430.45220.55450.61850.65600.67680.68590.6881690

0.55840.64340.69060.71740.73210.73850.74007000.70790.73700.75410.76380.76800.7688710

0.75890.77040.77710.77980.77957200.77930.78380.78480.7825730

0.78720.78520.77617400.77380.7292750

0.4113760

Band1(nm)

Band 2 (nm)690700710720730740750760770(B2-B1)/(B2+B1) vs

LAIe

0.35250.44730.54910.63080.67920.70620.72050.72660.72816800.50670.60060.66880.7060.72610.73660.74110.7422690

0.66240.70330.72320.73410.740.74250.74307000.70950.71660.72160.72470.72590.7256710

0.70750.70890.710.70980.70827200.70170.70060.69860.6944730

0.6940.68840.67657400.66930.6219750

0.3268760

Band1(nm)

Coefficient of determination (R2) between MERIS red edge NDVI and field LAI

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LAI and single bands LAI and single bands estimatedestimated withwith MERISMERIS

0.04750.1549730

0.35050.5548720

0.62360.7902710

0.73630.8173700

0.76100.7635690

0.75050.7089680

R2 with LAIeR2 with LAI Wavebands (nm)

Coefficient of determination (R2) between MERIS HS bands and field LAI

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LAI regression modelsLAI regression models

y = 0.1569e4.0416x

R2 = 0.6343

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NDVI (670, 800 nm)

LAI

ForestGrassCornSoybean

y = 19.618e-19.051x

R2 = 0.8173

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Reflectance (700 nm)

LAI

ForestGrassCornSoybean

y = 0.1919e17.198x

R2 = 0.7838

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0.08 0.13 0.18NDVI (730, 750 nm)

LAI

ForestGrassCornSoybean

NDVI (670, 800 nm) and LAI

NDVI (730, 750 nm) and LAI

Band (700 nm) and LAI

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LAI ProductsLAI Products

0

6

LAI

LAI image (MERIS TOA LAI estimate) LAI image (MERIS HS LAI estimate)

210 km

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Product intercomparisonProduct intercomparison

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MER

IS L

AI

(MER

IS T

OA

LA

I Alg

orith

m) Corn

SoybeanGrassForest

MERIS HS LAI estimate vs MERIS TOA LAI estimate

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What is the appropriate narrow band What is the appropriate narrow band NDVINDVI--LAI relationship?LAI relationship?

( )( ) ( )[ ]↑↑↑↓↓

↓↑ −+−

+−−

−= ccb

b

LLc p

qp ρτττρω

ωω

τωρ 111

11

( )LAIbbq 1exp15.0 0−−+≈

( )LAIam ap 1exp10

−−≈( ) ( )

( )θθθ

τ cosexpLAIG Ω

−≈

Backgound and leaf single scattering albedo

Escape probability (sensitive to geom)

Recollision probability (not sensitive to geom)

Transmittance (sensitive to geometry)

Lb ωω ,

1212

12

2 LLLL

LLbs p

NDVIωωωω

ωω−+−

=

Black Soil NDVI has minimal sensitivity to acquisition geometry.

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ln(nbNDVIln(nbNDVI) linearly related to ) linearly related to ln(LAIln(LAI))

y = 0.003Ln(x) + 0.0106R2 = 0.9818

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0.012

0.014

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0 2 4 6 8LAI

ND

VIbs

( ) bsNDVIccLAI 10ln +≈ ( )bsNDVIddNDVI ln10 +≈

Ln(LAI) ~linear function of NDVIbsLn(NDVIbs) ~linear function of NDVI

y = 0.019Ln(x) + 0.0955R2 = 0.9901

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0.007 0.009 0.011 0.013 0.015

NDVIbs

ND

VI

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Verification over field sitesVerification over field sites

00.10.20.30.40.50.60.70.80.9

1

R-squared Median AbsoluteResidual

Median RelativeResidual

R2

OR

LA

I res

idua

l or %

resi

dual

NDVI(670nm,800nm) NDVI(730nm,750nm)700nm exp(NDVI(730nm,750nm);lnLAI

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Current ‘multispectral’ MERIS LAI algorithms show typical problems of land cover sensitivity and saturation at LAI>4.

MERIS spectral sampling may be sufficient to retrieve LAI sensitive parameters specially red edge indices but our approach could be made more physically realistic.

Theory suggests that a red edge NDVI will be related to LAI and leaf albedo but minimally to soil albedo and acquisition geometry. (Sensitivity to LAI > sensitivity to leaf albedo)

Our data verifies that red edge based indices tend to reduce sensitivity to land cover type and minimize saturation at high LAI.

We did not test sensitivity to atmosphere or acquisition geometry or understory variability. The use of additional spectral bands to address background reflectance variability needs to be investigated.

ConclusionsConclusions

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Thank You

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InIn--situ LAIsitu LAI

InIn--situ LAI was estimated using situ LAI was estimated using Digital Hemispherical PhotographsDigital Hemispherical Photographs(DHP) method during 2006 growing DHP) method during 2006 growing season (4/5season (4/5thth of July). of July).

CANEYE version 3.6 softwareCANEYE version 3.6 software was was used for the DHP processing. used for the DHP processing.

Each field represents the average Each field represents the average LAI value of two transects of a plot LAI value of two transects of a plot and average values of the plots and average values of the plots within the field.within the field.

LAI in the forest areas were derived LAI in the forest areas were derived from Landsat 5 TM image based on from Landsat 5 TM image based on the forest plots. the forest plots.

MERIS pixels which have at least 75 MERIS pixels which have at least 75 % overlap with field were identified % overlap with field were identified for further analysis.for further analysis.

2.22.784F8-1Forest2.43.1100F7-1Forest3.24.0100F6-1Forest3.34.2100F5-1Forest3.64.6100F2-1Forest3.24.0100F1-12Forest2.83.586F1-11Forest3.13.996F1-9Forest3.44.3100F1-8Forest3.13.8100F1-7Forest3.44.2100F1-6Forest3.44.3100F1-5Forest2.22.8100F1-4Forest4.05.0100F1-3Forest2.43.0100F1-2Forest3.13.8100F1-1Forest0.81.099GBF13-1Beans0.70.998GBF25E-1Beans0.80.977CFIA05-3Beans0.80.979CFIA05-2Beans0.80.991CFIA05-1Beans3.04.891CFIA16-1Corn3.66.089CFIA14-1Corn2.24.088CFIA11-1Corn2.14.387CFIA06-1Corn2.13.580CFIA04-1Corn1.73.088CFIA03-1Corn1.94.298CFIA02-1Corn2.83.195CFIA12-2Grass/pasture2.83.188CFIA12-1Grass/pasture2.62.985CFIA07-2Grass/pasture2.62.995CFIA07-1Grass/pasture2.42.575CFIA01-2Grass/pasture2.42.5100CFIA01-1Grass/pastureLAIeLAI% OverlapPixelLand use

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Scatter plot of data in regression space

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1.03 1.05 1.07 1.09

exp(ndvi 730,750)

LAI

SoybeanCornMixed ForestPasture

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NDVI Sensitivity to LAI > NDVI Sensitivity to leaf albedo

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0.020.240.510.831.21.72.33.24.89

Lω

Lb

b

ddNDVIdLdNDVIω

LAI