Transcript Comparing MERIS with MODIS data

PhD Dissertation Defense
Analysis, Improvement and Application of
the MODIS LAI/FPAR Product
Wenze Yang
Department of Geography and Environment, Boston University
Dissertation Committee
Ranga B. Myneni
Yuri Knyazikhin
Nathan Philips
Crystal B. Schaaf
Jeffrey T. Morisette
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Outline
1. Introduction
2. Objectives
3. Research Topics
1.
2.
3.
4.
Analysis of global MODIS LAI and FPAR products
Products validation and algorithm refinement
Prototyping C5 LAI products from Terra and Aqua
Case study: LAI seasonal swings in the Amazon
4. Concluding Remarks
5. Future Work
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1. Introduction
• Definition
– LAI: one sided green leaf area per unit ground area in broadleaf canopies,
and half 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 the vegetation.
• Scientific Importance
–
–
–
–
Description of vegetation canopy structure.
Hydrology, energy balance, carbon cycle, nutrient dynamics.
Geographical distribution.
Climate response and feedback, climate change science.
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MODIS LAI Production
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2. Objectives
• Part 1 – Analysis of global LAI products
1. To understand product quality with respect to version, algorithm, snow
and cloud conditions.
• Part 2 – Products validation and algorithm refinement
1. To summarize the experience of several collaborating investigators on
validation of MODIS LAI products and activities.
2. To demonstrate the close connection between product validation and
algorithm refinement.
• Part 3 - Terra and Aqua products
1. 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 study
1. To track phenological leaf area changes in the Amazon rainforests.
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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
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).
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Introduction
• 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).
Part One
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Data
• 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
Part One
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Changes in Biome Classification Map
At-launch AVHRR-based (for C3 LAI)
18%
C3 MODIS-based (for C4 LAI)
18%
23%
18%
5.2%
25%
7.4%
16%
12%
26%
10%
21%
Shrubs
Grasses/
Cereal Crops
Savannas
Broadleaf
Crops
Needleleaf Forests
Broadleaf
Forests
Part One
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Retrieval Index
Main algorithm retrievals increased from 55 percent to 67 percent globally,
but remained low in broadleaf forests.
Part One
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Time Series of Global LAI
Collection 4 LAI values are more realistic based on comparisons to field
measurements.
Part One
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Main vs. Backup Retrievals
 L A I  L A I M ain  L A I B ackup
Near linear relationship between delta LAI and main algorithm LAI.
Backup algorithm underestimates LAI over dense broadleaf forests.
Part One
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Snow and Cloud Conditions
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.
Part One
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Conclusions
• 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.
Part One
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Research Topics
Part Two
MODIS Leaf Area Index Products:
From Validation to Algorithm Improvement
Yang, Tan et al. (2006b). MODIS leaf area index products: from validation to
algorithm improvement. IEEE Trans. Geosci. Remote Sens. (accepted in Nov
2005).
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Global Validation Activities
Shrubs
Grasses/
Cereal Crops
Savannas
Broadleaf
Crops
Needleleaf Forests
Broadleaf
Forests
Summarize the experience of several collaborating investigators on LAI validation.
Part Two
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Validation Procedure
• 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.
Part Two
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Example Validation Results
Alpilles, Broadleaf Crops
[Tan et al., 2005]
30
Percentage, in %
25
20
Ruokolahti, Needleleaf Forests
[Wang et al., 2004]
15
10
5
0
-3
-2
-1
0
1
2
3
Mongu, Shrub/Woodland
[Huemmrich et al., 2005]
Difference
C4
Mongu, Shrub/Woodland
[Privette et al., 2002]
C4B
Wisconsin, Broadleaf Forests
[Ahl et al., 2005]
Part Two
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Accuracy of C4 MODIS LAI
7
C4 MODIS LAI
6
5
RMSE=0.66LAI
4
3
2
y = 1.12x + 0.12
2
R = 0.87
RMSE=0.66LAI
1
0
0
1
2
3
4
5
6
Field LAI
Grasses & Cereal Crops
Shrubs
Broadleaf Crops
Savannas
Broadleaf Forests
Needleleaf Forests
b1-b4
all
one-one
b1-b4,b6
7
Part Two
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From Validation to Algorithm Refinement
• 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)
Part Two
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Landcover Misclassification
Collection 3
Biome
Classification
Map
At launch
Biome
Classification
Map
Tile: h10v05
Site: Konz
Shrubs
Grasses/
Cereal Crops
Savannas
Broadleaf
Crops
Needleleaf Forests
Broadleaf
Forests
Part Two
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Input Surface Reflectance Uncertainties
Coefficient of variation of surface reflectance
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.
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Surface Reflectance (NIR)
Algorithm Path (percent)
Model Uncertainties
Surface Reflectance (Red)
Mismatch between model simulated and observed surface reflectance leads to
low main algorithm retrievals in broadleaf forests.
Part Two
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MODIS LAI Product Flow and Research Work
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Conclusions
• 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.
Part Two
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Research Topics
Part Three
Analysis of Prototype Collection 5 Products of
Leaf Area Index from Terra and Aqua
MODIS Sensors
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).
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Introduction
• 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
.
Part Three
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Data
• 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
subdivided into deciduous and evergreen subclasses.
Part Three
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Consistency Between Terra/Aqua Products
• Continental Scale: North America, 201-208, 2003
0.0
0.3
0.8
Terra LAI
1.6
2.8
4.2
5.0
7.0
Combined LAI
Terra Aqua
Coverage
Terra and Aqua LAI are consistent at a large scale
Part Three
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Temporal Compositing
• 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)
Part Three
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Comparison of Terra 8-day and
Combined 8-day Products over North America
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)
*Statistics of combined 8-day product are in pareses.
Combined 8-day product helps to increase rate of main algorithm retrievals
over woody vegetation.
Part Three
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Seasonal Variations
• Tile Scale: h12v04, Broadleaf Forests, 2004
Combined 4-day product poses slightly higher composite-to-composite
variability. However it improves resolution of seasonal cycle.
Part Three
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Cloud and Aerosol Series
Combined, Clouds
Combined, Aerosols
Terra, Clouds
Terra, Aerosols
• Tile Scale: h12v04, Broadleaf Forests, 2004
Main
No clouds
Main
With Clouds
Back-Up
No Clouds
Backup
With Clouds
Main
No Aerosols
Main
With Aerosols
Back-Up
No Aerosols
Backup
With Aerosols
Part Three
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Conclusions
• 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.
Part Three
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Research Topics
Part Four
Large Seasonal Swings in Leaf Area of
Amazon Rainforests
Myneni, Yang et al. (2006). Large seasonal swings in leaf area of Amazon
rainforests. (in preparation).
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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
BCM
LAI
Forests
Other
Savannas
Non-vegetated
# of
Dry Months
0.0
0.3
0.8
1.6
2.8
4.2
5.0
7.0
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 Forests
Solar
Radiation
(W/m²)
1000
5.5
950
5.0
900
850
4.5
800
4.0
Precipitation
(mm /mo) 750
300
Leaf
200
Area
100
Index
2000
2001
2002
*Dry seasons are in grey shaded bars.
2003
2004
2005
2006
The phase-shift between LAI and solar radiation suggests
rainforests’ adaptation to anticipating more sunlight.
Part Four
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Time Series for Amazon Savannas
3.0
Solar
Radiation
(W/m²)
1000
2.5
900
2.0
800
1.5
Precipitation 700
(mm /mo)
300
Leaf
200
Area
100
Index
2000
2001
2002
*Dry seasons are in grey shaded bars.
2003
2004
2005
2006
The phase of LAI time series indicates a close relationship
between LAI and water availability.
Part Four
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Dry – Wet Season LAI Difference
58 percent of the forest area displays distinctive green up in dry season.
Part Four
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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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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.
This research can be seen as a roadmap for evaluation of future versions of
similar products.
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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 paircorrelation 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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