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Analysis, Improvement and Application of Analysis, Improvement and Application of the MODIS LAI/FPAR Productthe MODIS LAI/FPAR Product

Dissertation Committee

Ranga B. Myneni

Yuri Knyazikhin

Nathan Philips

Crystal B. Schaaf

Jeffrey T. Morisette

PhD Dissertation Defense

Wenze Yang

Department of Geography and Environment, Boston University

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1. Introduction

2. Objectives

3. Research Topics1. Analysis of global MODIS LAI and FPAR products

2. Products validation and algorithm refinement

3. Prototyping C5 LAI products from Terra and Aqua

4. Case study: LAI seasonal swings in the Amazon

4. Concluding Remarks

5. Future Work

Outline

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• Definition– LAI: one sided green leaf area per unit ground area in broadleaf canopies, and h

alf the total needle surface area per unit ground area in coniferous canopies.

– FPAR: fraction of photosynthetically active radiation (0.4-0.7 m) absorbed by t

he vegetation.

• Scientific Importance– Description of vegetation canopy structure. – Hydrology, energy balance, carbon cycle, nutrient dynamics.– Geographical distribution. – Climate response and feedback, climate change science.

1. Introduction

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MODIS LAI Production

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• Part 1 – Analysis of global LAI products1. To understand product quality with respect to version, algorithm, snow and cloud conditions.

• Part 2 – Products validation and algorithm refinement1. To summarize the experience of several collaborating investigators on validation of MODIS LAI products and activi

ties.

2. To demonstrate the close connection between product validation and algorithm refinement.

• Part 3 - Terra and Aqua products1. To analyze consistency of Terra and Aqua MODIS surface reflectances and LAI/FPAR products.

2. To explore potentials of combining Terra and Aqua data to improve quality and temporal resolution of LAI/FPAR products.

• Part 4 - Amazon Case study1. To track phenological leaf area changes in the Amazon rainforests.

2. Objectives

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Yang, Huang et al. (2006a). Analysis of leaf area index and fraction of PAR absorbed by vegetation products from the Terra MODIS sensor: 2000-2005. IEEE Trans. Geosci. Remote Sens. (accepted in Nov 2005).

3. Research Topics

Part One

Analysis of Leaf Area Index and Fraction of PAR Absorbed by Vegetation Products from

the Terra MODIS Sensor: 2000-2005

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• Main vs. backup algorithms used in MODIS LAI/FPAR products – Different physical basis.

– Different input quality.

• Anomalies in Collection 3 products– Unrealistically high LAI/FPAR values in herbaceous vegetation.

– Reflectance saturation and too few main algorithm retrievals in broadleaf forests.

– Spurious seasonality in needle leaf LAI/FPAR fields.

• Changes from Collection 3 to 4 – Surface reflectance (better atmospheric correction).

– BCM (AVHRR-based to MODIS-based).

– LUT (SeaWiFS-based to MODIS-based).– Compositing scheme (maximum FPAR only to hierarchical).

Introduction

Part One

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• MODIS LAI/FPAR products– 8-day temporal resolution, 1-km spatial resolution

– Collection 3: Nov 2000 to Dec 2002 (157 GB) – Collection 4: Feb 2000 to Sep 2005 (396 GB)

– Self-contained quality information, such as algorithm path, cloud state, aerosols, snow

• Biome classification maps (BCM)– At-launch and C3 BCMs

– 6 biome type scheme• Grasses and cereal crops• Shrubs• Broadleaf Crops• Savannas• Broadleaf Forests• Needleleaf Forests

Data

Part One

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Changes in Biome Classification Map

Part One

At-launch AVHRR-based (for C3 LAI) C3 MODIS-based (for C4 LAI)

Grasses/Cereal Crops

Shrubs

Broadleaf Crops

Savannas

BroadleafForests

Needleleaf Forests

18% 18%23%

7.4%

10%16%

25%

5.2%

26%

21%

18%

12%

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Main algorithm retrievals increased from 55 percent to 67 percent globally, but remained low in broadleaf forests.

Retrieval Index

Part One

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Time Series of Global LAI

Part One

Collection 4 LAI values are more realistic based on comparisons to field measurements.

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Main vs. Backup Retrievals

Near linear relationship between delta LAI and main algorithm LAI.

Backup algorithm underestimates LAI over dense broadleaf forests.Part One

Main BackupLAI LAI LAI

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Snow and Cloud Conditions

Part One

Low main algorithm retrievals under snow or cloud conditions.

Spurious seasonality due to snow.

The difference between LAI retrievals under cloud-free and cloudy condition is not negligible.

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• Retrievals from the main algorithm increased from 55 percent in C3 to 67 percent in C4.

• Anomalously high LAI/FPAR values in C3 product in herbaceous vegetation were corrected in C4.

• The problem of reflectance saturation and too few main algorithm retrievals in broadleaf forests persisted in C4.

• The spurious seasonality in needle leaf LAI/FPAR fields was traced to fewer reliable input data and retrievals during the boreal winter period.

• About 97 percent of the snow covered pixels were processed by the backup algorithm.

• Similarly, a majority of retrievals under cloud conditions were obtained from the backup algorithm.

Conclusions

Part One

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Yang, Tan et al. (2006b). MODIS leaf area index products: from validation to algorithm improvement. IEEE Trans. Geosci. Remote Sens. (accepted in Nov 2005).

Research Topics

Part Two

MODIS Leaf Area Index Products:From Validation to Algorithm Improvement

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Global Validation Activities

Summarize the experience of several collaborating investigators on LAI validation.

Part Two

Grasses/Cereal Crops

Shrubs

Broadleaf Crops

Savannas

BroadleafForests

Needleleaf Forests

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• Field sampling representative of LAI spatial distribution and dynamic range within each major land cover type at a validation site.

• Development of a transfer function between field measured LAI and high resolution satellite data to generate a reference LAI map over an extended area.

• Comparison of MODIS LAI with the aggregated reference LAI map at patch scale in view of geo-location and pixel shift uncertainties.

Validation Procedure

Part Two

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Example Validation Results

-3 -2 -1 0 1 2 30

5

10

15

20

25

30

Per

cen

tage

, in

%

Difference

C4 C4B

Alpilles, Broadleaf Crops

[Tan et al., 2005]

Ruokolahti, Needleleaf Forests

[Wang et al., 2004]

Mongu, Shrub/Woodland

[Huemmrich et al., 2005]y = 0.98x + 0.25

R2 = 0.79n=8

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0

Ground-Measured LAI

1:1 Line

Part Two

Mongu, Shrub/Woodland

[Privette et al., 2002]

Wisconsin, Broadleaf Forests

[Ahl et al., 2005]

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Accuracy of C4 MODIS LAI

RMSE=0.66LAI

Part Two

y = 1.12x + 0.12

R2 = 0.87RMSE=0.66LAI

0

1

2

3

4

5

6

7

0 1 2 3 4 5 6 7

Field LAI

C4

MO

DIS

LA

I

Grasses & Cereal Crops ShrubsBroadleaf Crops SavannasBroadleaf Forests Needleleaf Forestsb1-b4 allone-one b1-b4,b6Linear (one-one) Linear (all)

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• Steps – Diagnose the annual course of LAI – Identify anomalies – Trace the anomalies in inputs and LUT – Refine the operational algorithm

• Collection 3 anomalies– Summer product LAI higher than in situ LAI in herbaceous vegetation– Too few main algorithm retrievals during summer– Seasonality differences between main and backup algorithms

• Sources of uncertainties – Landcover data (KONZ, Alpilles)– Surface reflectance (AGRO, KONZ)– Model (HARV)

From Validation to Algorithm Refinement

Part Two

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Landcover MisclassificationAt launch

Biome

Classification

Map

Collection 3

Biome

Classification

Map

Part Two

Grasses/Cereal Crops

Shrubs

Broadleaf Crops

Savannas

BroadleafForests

Needleleaf Forests

Tile: h10v05

Site: Konz

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Input Surface Reflectance Uncertainties

Part Two

The quality of retrieved LAI depends linearly on surface reflectance beyond a threshold value;

There is an upper limit to LAI accuracy, which is determined by model uncertainty.

Coefficient of variation of surface reflectance

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Model Uncertainties

Part Two

Mismatch between model simulated and observed surface reflectance leads to low main algorithm retrievals in broadleaf forests.

Surface Reflectance (Red)

Su

rfac

e R

efle

ctan

ce

(NIR

)

Alg

orit

hm

Pat

h (

per

cen

t)

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MODIS LAI Product Flow and Research Work

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• Field measurements should be compared to MODIS retrievals through the use of a fine resolution map.

• MODIS LAI product is an overestimate by about 12 percent (RMSE=0.66LAI) when all six biomes are taken into account.

• Three key factors influencing the accuracy of LAI retrievals have been identified.

• This strategy of validation efforts guiding algorithm refinements has led to progressively more accurate LAI products.

Conclusions

Part Two

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Yang, Shabanov et al. (2006c). Analysis of prototype collection 5 products of leaf area index from Terra and Aqua MODIS sensors. Remote Sens. Environ. (accepted in Mar 2006).

Research Topics

Part Three

Analysis of Prototype Collection 5 Products of Leaf Area Index from Terra and Aqua

MODIS Sensors

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• Two Open Questions– Consistency between Terra and Aqua products.

– Potential for combining retrievals from the two sensors to derive improved products.

• Environmental conditions

• Temporal compositing period

.

Introduction

Part Three

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• C4 atmospherically corrected surface reflectance products from Terra and Aqua MODIS sensors– 8-day temporal resolution, 1-km spatial resolution.

– North American continent, 45 tiles, 201-208 (July 20-27), 2003.

– Tiles h12v04, h11v04 and h12v03, whole year 2004.

– 7x7 km subsets for 3 sites: AGRO, NOBS and HARV.

• Biome classification maps (BCM)– C4 BCMs.

– 8 biome type scheme: broadleaf and needle leaf forests classes were s

ubdivided into deciduous and evergreen subclasses.

Data

Part Three

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Consistency Between Terra/Aqua Products

Terra LAI Combined LAI Coverage

Terra and Aqua LAI are consistent at a large scale

Part Three

0.0 0.3 0.8 1.6 2.8 4.2 5.0 7.0 Terra Aqua

• Continental Scale: North America, 201-208, 2003

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• Using daily Terra and Aqua observations retrieve daily LAI

• Treat daily Terra and Aqua LAI retrievals as equal

• Composite daily data using standard compositing scheme to generate Terra, Aqua and Combined products

• A suite of products is proposed for Collection 5– 8-day Terra (MOD15A2)

– 8-day Aqua (MYD15A2)

– 8-day Combined (MCD15A2)

– 4-day Combined (MCD15A3)

Temporal Compositing

Part Three

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Grasses and Cereal Crops

Shrubs Broadleaf Crops

Savannah Broadleaf Forests

Needle Leaf Forests

LC, % 17 27 8 9 14 25

Red 0.049 (0.047) 0.045 (0.045) 0.035 (0.035) 0.034 (0.034) 0.025 (0.025) 0.024 (0.025)

NIR 0.296 (0.304) 0.282 (0.279) 0.381 (0.381) 0.236 (0.238) 0.338 (0.340) 0.219 (0.221)

LAI 1.5 (1.7) 1.1 (1.2) 2.0 (2.0) 1.5 (1.6) 4.3 (4.6) 2.7 (3.0)

Main,% 89 (90) 94 (97) 88 (94) 91 (96) 11 (11) 69 (70)

Saturation, % 4 (7) <1 (<1) 2 (3) <1 (<1) 32 (44) 11 (18)

Back-Up, % 7 (3) 6 (3) 10 (4) 8 (4) 58 (45) 20 (12)

Terra : Aqua, % 53 : 47 53 : 47 57 : 43 53 : 47 50 : 50 57 : 43

Clouds, % 15 (17) 13 (12) 17 (15) 18 (16) 32 (25) 11 (8)

Aerosols, % 60 (64) 59 (61) 63 (65) 59 (63) 46 (52) 51 (54)

Comparison of Terra 8-day and Combined 8-day Products over North America

Part Three

*Statistics of combined 8-day product are in pareses.

Combined 8-day product helps to increase rate of main algorithm retrievals over woody vegetation.

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• Tile Scale: h12v04, Broadleaf Forests, 2004

Seasonal Variations

Part Three

Combined 4-day product poses slightly higher composite-to-composite variability. However it improves resolution of seasonal cycle.

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• Tile Scale: h12v04, Broadleaf Forests, 2004 Cloud and Aerosol Series

Part Three

Back-UpNo Aerosols

MainNo Aerosols

BackupWith Aerosols

MainWith Aerosols

MainNo clouds

Back-UpNo Clouds

BackupWith Clouds

MainWith Clouds

Ter

ra, C

lou

ds

Ter

ra, A

eros

ols

Com

bin

ed, C

lou

ds

Com

bin

ed,

Aer

osol

s

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• There are no significant discrepancies between large area (from continent to MODIS tile) averages of the Terra and Aqua 8-day LAI and surface reflectance products.

• The Terra-Aqua combined 8-day product helps to increases the number of high quality retrievals by 10-20 percent over woody vegetation, while 4-day product greatly improves resolution of the seasonal cycle.

Conclusions

Part Three

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Myneni, Yang et al. (2006). Large seasonal swings in leaf area of Amazon rainforests. (in preparation).

Research Topics

Part Four

Large Seasonal Swings in Leaf Area of Amazon Rainforests

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Introduction

• Amazon basin has an area of about 7 million km2, hosting more than 5 million plant species.

• Former research on the timing of phenological events– Leaf life span and synchrony for the trees of tropical rainforests. – Several agents as herbivory, water stress, day length, light intensity, etc are identified as proximate cues for leafing and abscission.

• Necessity for remote sensing.

• We focus on leaf area change over large area.

Part Four

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Basic Information about Amazon

Forests Savannas

Other Non-vegetated

BCM

0.0 0.3 0.8 1.6 2.8 4.2 5.0 7.0

LAI

# of

Dry Months

1-2 3-4 5 6 7 8 9-10 11-12

Part Four

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Data

• Leaf area index– Terra, MODIS, C4, Mar 2000 – Sep 2005, 8 km.

• Landcover– Terra, MODIS, C3, 8km.

• Precipitation– TRMM, 3B43, V6, Mar 2000 – Aug 2005, 0.25 degree.

• Radiation– Terra, CERES, SFC, R4V3, Mar 2000 – May 2005, 1 degree.– Ascertained through comparison to similar data from ISCCP.

• Aerosol• Surface reflectance

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Time Series for Amazon ForestsL

eaf

Are

a

Inde

x

5.5

5.0

4.5

4.0

300

200

100

Precipitation

(mm /mo)

1000

950

900

850

800

750

Sola

r

Rad

iati

on

(W

/m²)

2000 2001 2002 2003 2004 2005 2006

Part Four

*Dry seasons are in grey shaded bars.

The phase-shift between LAI and solar radiation suggests rainforests’ adaptation to anticipating more sunlight.

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Time Series for Amazon Savannas

Precipitation

(mm /mo)

1000

900

800

700

300

200

100

Lea

f

Are

a

Inde

x

3.0

2.5

2.0

1.5

Sola

r

Rad

iati

on

(W

/m²)

2000 2001 2002 2003 2004 2005 2006

Part Four

*Dry seasons are in grey shaded bars.

The phase of LAI time series indicates a close relationship between LAI and water availability.

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Dry – Wet Season LAI Difference

Part Four

58 percent of the forest area displays distinctive green up in dry season.

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Impact of Radiation and Precipitation

Part Four

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Uncertainty Analysis

• Validation

• Clouds

• Aerosols

• Reflectance saturation

• Changes in leaf spectra with age and epiphylls

Part Four

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Conclusions• We found seasonal swings in green leaf area of about 25 percent in a majority of the Amazon rainforests. That is, leaf area equivalent to nearly 28 percent the size of South America appears and disappears each year in the Amazon.

• These leaf area changes are critical to – initiation of the transition from dry to wet season; – seasonal carbon balance between photosynthetic gains and respiratory losses; – litterfall nutrient cycling in moist tropical forests.

Part Four

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This research can be seen as a roadmap for evaluation of future versions of

similar products.

4. Concluding Remarks• Large volume global LAI/FPAR products have been analyzed with the goal of understanding product quality.

• The close connection between product validation and algorithm refinement has been demonstrated.

• A better quality 8-day and 4-day Terra-Aqua combined product suite has been proposed.

• Seasonal swings in green leaf area of about 25 percent in a majority of the Amazon rainforests have been revealed.

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5. Future Work• Devise improved remote sensing methods from knowledge gained through my studies together with recent developments in radiative transfer theory, such as the pair-correlation function and notion of spectral invariance.

• Investigate the scaling problem which may lead to better validation schemes and linkages between data of different resolutions.

• Use models to explore possible leaf area changes in Amazon rainforests as a function of solar radiation, but constant water availability.

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