ph. d. dissertation defense evaluation of the performance of the modis lai

Ph. D. Dissertation defense

Evaluation of the Performance of the MODIS LAI and FPAR Algorithm with Multiresolution

Satellite Data

Yuhong Tian Department of Geography, Boston University

Dissertation Committee

Ranga B. MyneniYuri KnyazikhinMark A. Friedl

Curtis E. WoodcockAlexander L. Marshak

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Contents

1. Introduction

2. Objectives

3. Research Topics Prototyping of the MODIS LAI/FPAR algorithm with LASUR

and Landsat data Radiative transfer based scaling of LAI/FPAR retrievals from

reflectance data of different resolutions Multiscale analysis and validation of the MODIS LAI over

Maun, Botswana

4. Concluding Remarks

5. Future Work2 of 39

1. Introduction

LAI and FPAR:

• Definition

LAI: green leaf area index, one-sided green leaf area per unit ground area.

FPAR: fraction of photosynthetically active radiation (0.4- 0.7 m) absorbed by the vegetation.

• Importance

They are key variables in land surface models for calculation of surface photosynthesis, evapotranspiration, and net primary production.

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2. Objectives

Prototyping:1. To test the physical functionality and performance of the algorithm

with MODIS like data.

Effects of spatial resolution:1. To inquire about the cause of the discrepancy between coarse and

moderate resolution output from the same algorithm.

2. To investigate the adjustment of retrieval techniques for data

resolution.

Validation:1. To derive uncertainty information on LAI/FPAR product by

comparing with field data.

2. To define a sampling strategy. 4 of 39

3. Research Topics

Part One

Prototyping of the MODIS LAI/FPAR Algorithm with LASUR and Landsat data

Tian et al., “Prototyping of MODIS LAI and FPAR algorithm with LASUR and Landsat data”. IEEE Trans. Geosci. Remote Sens. 38(5):2387-2401, 2000.

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Research Topics: Part one

Introduction

Prototyping: use data from other instruments to test the functionality of the MODIS algorithm before MODIS data are available.

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Data

• Land surface reflectances (LASUR). - spatial resolution: 1/7th of a degree - RED (572-698 nm) and NIR (716-985 nm) from July 1989

• A TM image of Northwest U.S. (Washington and Oregon). - 30 m resolution - RED (630-690 nm) and NIR (760-900 nm) from June 26, 1987.

• A biome classification map (BCM). - Grasses and cereal crops - Shrubs - Broadleaf crops - Savannas - Broadleaf forests - Needle forests

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Consistent with Physics

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Impact of Biome Misclassification

Misclassified Biome Type

Grasses and Cereal Crops

Shrubs

Broadleaf Crops

Savannas

Broadleaf Forest

Needle Forests

BCM

Biome Type

Grasses and Cereal Crops

Shrubs

Broadleaf Crops

Savannas

Small effect Large effect

Broadleaf Forest

Needle ForestsLarge effect Small effect

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Scale Dependence of the Algorithm

LANDSATFine

resolution

LASUR Coarse

resolution

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Conclusions

• Prototyping results demonstrate the ability of the algorithm to produce global LAI and FPAR fields.

• The LAI and FPAR fields follow regularities expected from physics.

• The algorithm is dependent on the spatial resolution of the data.

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Research Topics

Part Two

Radiative transfer based scaling of LAI/FPAR retrievals from reflectance data of different

resolutions

Tian, et al., “Radiative transfer based scaling of LAI/FPAR retrievals from reflectance data of different resolutions”. Remote Sens. Environ., 2001 (in

review). 12 of 39

Research Topics: Part two

Introduction

The goal of scaling: values of LAI derived from coarse resolution sensor data should equal the arithmetic average of values derived independently from fine resolution sensor data.

Scaling issues arise when

• one attempts to assemble a consistent time series of LAI/FPAR products with data from different spatial resolutions.

• one attempts to validate moderate resolution (~ 1 km) sensor products with field measurements at much finer resolutions.

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Objectives

• To investigate the effect of pixel heterogeneity on LAI/FPAR retrievals.

• To develop a physically based theory for scaling with scale dependent radiative transfer formulation.

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Data

• AVHRR land surface reflectances at 1 km resolution over North America for July 1995

- RED (580-680 nm) and NIR (725-1100 nm). - 1 km AVHRR reflectance data were aggregated to 4, 8, 16, 32 and 64 km resolutions.

• A six biome map of North America

- developed from 1 km AVHRR NDVI data of 1995 and 1996 by Lotsch et al. (2000).

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Characterizing Land Cover Heterogeneity

• Percentage function (pf). B1 B1 B4 B4

B5 B5 B5 B4

B4 B5 B5 B5

B5 B5 B1 B5

pf1=3/16; pf4=4/16; pf5=9/16

Purity of this pixel=pf5=9/16

A 4 km x 4 km resolution pixel

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The percentage occupation of subpixel biome type in a given coarse resolution pixel.

• “purity” of a pixel.

Percentage function of dominant biome type in a given coarse resolution pixel.


Purity Decreases as Spatial Resolution Decreases

Resolution (km) Resolution (km)

Biome purity > 90%

Per

cent

age

of p

ixel

s

Per

cent

age

of p

ixel

s

Biome purity < 50%

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Purity Has a Strong Effect on LAI Retrievals

Error = |LAItrue-LAIestimated|/LAItrue

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B1 B1 B4 B4

B5 B5 B5 B4

B4 B5 B5 B5

B5 B5 B1 B5

B5 B5 B5 B5

B5 B5 B5 B5

B5 B5 B5 B5

B5 B5 B5 B5

Dom

inan

t L

and

Cov

er P

uri

ty

a) Reflectance at 4 km resolution

b) Biome type

Input:


Energy conservation law as a tool to scale models

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Leaf absorption: (1-)Noi

Leaf scattering: Noi

Leaf interception: pNoi

Noi

Not

(1-p)N0i

Ni= Noi + pNoi+ (p)2Noi+…

= Noi/(1-p)

Nt= Not/(1-pt)

= 1/(1-pfsoil)jpfj

N

N = NR + Nt + (1-)Noi

: leaf albedo, the portion of radiation flux density incident on the leaf surface that the leaf transmits and reflects.p: the fraction of photons that are scattered by leaves and will interact with leaves again.


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Dom

inan

t L

and

Cov

er P

uri

ty

Improved Retrieval Accuracy

Before After

Dom

inan

t L

and

Cov

er P

uri

ty


Conclusions

• LAI retrieval errors are inversely related to the proportion of the dominant land cover in a coarse resolution pixel.

• Pixel heterogeneity must be accounted to improve accuracy in retrievals.

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Research Topics

Part Three

Multiscale analysis and validation of the MODIS LAI over Maun, Botswana

Tian, et al., “Multiscale analysis and validation of the MODIS LAI over Maun,

Botswana”. Remote Sens. Environ., 2001 (submitted in October, 2001). 22 of 39

Research Topics: Part three

Introduction

As MODIS LAI and FPAR data start to become publicly available, product quality must be ensured by validation.

Validation: the process of assessing the uncertainty of data products by comparison to reference data (e.g., in situ, aircraft, and high-resolution satellite sensor data).

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Objectives

• To develop an appropriate ground-based validation technique for assessing the uncertainties in MODIS LAI product.

• To develop sampling strategies to collect data needed for validation of the MODIS LAI product.

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Data

• LAI measured by LAI-2000 Plant Canopy Analyzer.

• Landsat ETM+ (30 m) data.

• MODIS reflectance data (1 km) simulated from ETM+.

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Pandamatenga

S 18 39.5

E 25 29.8

Maun

S 19 55.8

E 23 30.7

Okwa

S 22 24.6

E 21 42.8

Tshane

S 24 10.1

E 21 53.3

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Sampling Scheme 1000 m

1000 m

N375W 0 N375E

A375W A375E

B375W B375E

750 m

250 m250 m

300 m

250m

A

C

D

E

F

B

1 2 75 643 START POINT

25m

N


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Validation of the MODIS LAI Product At Maun

Field data ETM+ MODIS product

Problems with validation

• Only four pairs of pixels between field measurements and MODIS data.

• Spatial registration is not accurate.

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ETM+ Image Segmentation Map

12

3

4

5

6 7

89

10

1112

1314 15

Patch by Patch Comparison

Shortcomings of pixel by pixel comparison

• GPS readings are not accurate.• Measured LAI values have high variation over short

distances. 29 of 39


Consistency between LAI Retrievals and Field Measurements

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sLAI-Field Measurements

LA

I-A

lgor

ithm

Ret

riev

als


Underestimation of LAI for Coarse Resolution Data

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D1

D2

D3

Hierarchical Analysis of Multiscale Variation in LAI Data

Four scale levels: whole image > class > region > pixel

D1=D11+ D12+D13

D2=D21+ D22

D3=D31+ D32+D33

D11

D12

D13

D31D32

D33

D22

D21

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Four images: image effect, class effect, region effect, pixel effect


Distance (h)

Sem

ivar

ianc

e (

) sill

range

Semivariogram Analysis for 4 Scale Levels

)(

2)]()([)(2

1)(

hN

xZhxZhN

h

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Three Sites

Maun (Botswana)


Harvard Forest(USA)

Ruokolahti Forest (Finland)

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LAI Semivariograms

Maun


Harvard Forest Ruokolahti Forest

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Pixel EffectRegion EffectClass EffectOriginal Image

Sem

ivar

ianc

e

Sem

ivar

ianc

e

Sem

ivar

ianc

e


Conclusions

• Consistency between LAI retrievals from 30 m ETM+ data and field measurements indicates satisfactory performance of the algorithm.

• Hierarchical variance analysis shows that the LAI retrievals from ETM+ data demonstrate multiple characteristic scales of spatial variation.

1. Within the three sites, patterns of variance in the class, region, and pixel scale are different with respect to the importance of the three levels of landscape organization.

2. The spatial structure is small across the three sites. Validation needs to be performed over small areas.

3. For validation activities, patches are better than individual pixels unless sample and registration accuracy are outstanding.

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4. Concluding Remarks

• Prototyping results demonstrate the ability of the algorithm to produce global LAI and FPAR fields. The LAI and FPAR fields follow regularities expected from physics.

• LAI retrieval errors are inversely related to the proportion of the dominant land cover in a coarse resolution pixel.

• A physically based theory for scaling with a scale dependent radiative transfer formulation was developed.

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• Consistency between LAI retrievals from 30 m Landsat ETM+ data and field measurements from Maun (Botswana) indicates satisfactory performance of the algorithm.

• LAI fields demonstrate multiple characteristic scales of spatial variation. Isolating the effects associated with different scales through variograms aids the development of a new sampling strategy for validation of MODIS products.

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5. Future Work

• Use MODIS products to improve representation of the land surface in global climate models using the scaling ideas developed here.

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The end

Questions?

The MODIS LAI/FPAR algorithm

1),(),,(1

2

1

00

N

k k

vkvk dpr

N

rk: modeled BRDF

dk: satellite measured BRDF

k: uncertainties in measurements and simulations

p=[canopy, soil]

1 2 3 4 5LAI

Freq

uenc

y

Solution distribution function


Retrieval Index Depends on Quality of Surface Reflectance

Uncertainty

Retrieval Index: ratio of the number of retrieved pixels to total number of pixels.

Xijk = I + Ci + Rij + Pijk

I: the image effect; Ci: the effect associated with class i;

Rij: the effect associated with region j of class i;

Pijk: the pixel effect associated with pixel k of region j of class i.


I = (D)

Ci = (Di) - (D)

Rij = (Dij) - (Di)

Pijk = Xijk - (Dij)

Image decomposition

ph. d. dissertation defense evaluation of the performance of the modis lai

Documents

modis data

modis algorithm

data resolution

modis laifpar algorithm

prototyping of modis

field data

research topicsprototyping

definition lai