1. Xiaoyang Zhang1 Mark Friedl2
Science Review Panel Meeting
Biosphere 2, Tucson, AZ - January 4-5, 2011
Vegetation Phenology and Vegetation Index Products from Multiple Long Term Satellite Data Records
Phenology Science Algorithm
Xiaoyang Zhang Mark Friedl2
1ERT at NOAA/NESDIS/STAR
2Department of Geography and Environment, Boston University
NASA MEASURES #NNX08AT05A

2. Outline
Background
Algorithms for developing the MEASURES global land surface vegetation phenology (LSVP) product
Preprocessing temporal vegetation index (VI) and determining VI value of background surface
Fitting time series vegetative VI trajectory and detecting vegetation phenological metrics
Assessing MEASURES global LSVP quality --precision and confidence of phenological product
Summary
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3. Background

4. Seasonal Canopy Variation in Deciduous Forests
From Baldocchi et al., 2001.
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5. Temporal Vegetation Index from Satellite and its Corresponding Phenological Events
Greenup Onset
Maturity Onset
Senescence Onset
Dormancy Onset
Greenup Phase
Senescent Phase
Maturity Phase
Dormant Phase
Maximum VI
VI at greenup on set
VI increase rate
VI decrease rate
Growing season length
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6. Biophysically Understanding Temporal Trajectory of Satellite Vegetation Index (VI) in Greenup Phase – an example
Plant growing (VIv)
VIVIb
VI=VIv+VIb VI
Time
Full snow cover (VIs)
Partialy snow cover (snow melting)
Background (VIb)
Plant growing (VIv)
VIVIs VIs=0 if without snow cover
VI=VIs+VIb VIs=0 if without snow cover
VIVIb
VI=VIv+VIb VI
Time
VIa-atmospheric/cloudy contribution
+VIa
Background
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7. Algorithms for Developing the MEASURES Land Surface Vegetation Phenology (LSVP) Product
LTDR AVHRR
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8. Preprocessing Time Series VI and Determining VI Value of Background Surface
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9. LTDR AVHRR QA in NA QA0- good quality QA1-dark vegetation
QA2- partially cloudy QA3- cloudy shadow
QA4- partially cloudy & cloudy shadow QA5-dark vegetation & partially cloudy
QA6- dark vegetation & cloudy shadow QA7-dark vegetation & partially cloudy & cloudy shadow
QA8- cloudy QA9-unknown poor quality situation, not processed here.
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10. Temporal LTDR AVHRR Data (3-day Composite) for Phenology Detection
Note all data except the QA3, QA8, QA9, and QA11 are used for phenology detection because of the limited QA0 data in the time series. Note that QA11 represents snow flag that obtains from snow data.
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11. Reconstruction of Time Series Vegetative VI Trajectory
(1) Identify background VI value (VIb)
Using snow flag and winter land surface temperature (<278K) to determine winter period and background VI (based on data in every two winters)
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12. Determination of Background VI Value
AVHRR NDVI
Land Surface Temperature LST
NDVI (x10000)
LST
Average maxima NDVI during two winters (LST <278K)
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13. Ancillary Data for the Determination of Background VI: Snow Cover
Jan. 2001
Example:
GVIx snow flag in NA
(a) NOAA GVIx snow flag (7 day 4 km), global coverage
(b) AVHRR daily snow cover inNorthern Hemisphere ( )(Zhao, H. and Fernandes, R., 2009) The up left pixel is at 90N, 180W. The up right pixel is at 90N, 180E The down left pixel is at 29.5N, 180W. The down right pixel is at 29.5N, 180E.
(c) Snow detection from LTDR (U of Arizona)
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14. Ancillary Data for the Determination of Background VI: LST
LST for the determination of winter period:
(1) NCEP North America Regional Reanalysis (NARR) (Mesinger et al., 2006) from 3-hourly LST data at a spatial resolution of 32 km
(2) Global 8-day LST averaged from Terra and Aqua MODIS LST from 2003 to 2009
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15. Average Background AVHRR NDVI in NA
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16. Reconstruction of Time Series Vegetative VI Trajectory
(2) Remove atmospheric/cloud contamination (VIa)
Removal of large spurious values
Large gap determination and filling using ecosystem rules:
Forests: Span between two cycle peaks NOT less than 4 months
Shrubland/grassland: Span between two cycle peaks NOT less than 2 months
Cropland: Span between two peaks NOT less than 1.5 months
Removal of irregular local small values
Time series smoothing using Savitzky_Golay filter
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17. Removal of Pseudo Plant Cycles
NOT two peaks
Ecosystem rules:
Forests: Span between peaks >4 month
Shrubland/grassland: Span between peaks >2 months
Cropland: Span between peaks > 1.5 months
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18. Fitting Time Series Vegetative VI Trajectory and Identifying Vegetation Phenological Metrics
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19. Determination of NDVI Increase and Decrease Sections and Growing Cycles: Fourier Algorithm
Fourier algorithm was tried to determine the cycles of vegetation growth. While it works well in deciduous forests, it fails in shrublands with one large cycle and one small cycle.
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20. Determination of NDVI Increase and Decrease Trajectories and Vegetation Growing Cycles: Local Moving Slope Algorithm
Moving slope is very flexible and is capable of identifying growing cycles from complex temporal trajectories
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21. Fitting Temporal Vegetative Trajectory Using Pieceswise Logistic Model
Varying a and b
Greenup phase
t is time in days
VI(t) is the VI value at time t
VIv is the VI value in vegetation growth
a and b are vegetation growth parameters
c+VIb is the maximum VI value
VIb is the background VI value
Varying c
Greenup phase
Zhang et al., 2003
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22. Types of Temporal Trajectories in Vegetation Index Across Globe
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23. Identifying Phenological Transition Dates
The rate of curvature change
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24. Temporal Trajectory of AVHRR NDVI and Field Observations at Harvard Forest.
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25. Greenup Onset from AVHRR in 1996b
Recorded in the first cycle
Recorded in the second cycle
The first greenup onset for the year (combined from the first and the second cycles) c

26. Dormancy Onset from AVHRR in 1996b a
Recorded in first cycle
Recorded in second cycle
The first greenup onset for the year (combined from the first and the second cycles) c

27. Assessment of MEASURES Global LSVP Quality --Precision and Confidence of Phenological Product (for both AVHRR and MODIS/VIIRS)
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28. Assessment of Fitted Temporal Vegetative VI Trajectory
Quality of curve fitting using Willmott’s index of agreement (Willmott, 1982 ):
where n is number of observations with good (or better) quality during vegetation growing season
P(i) are the fitted values
O(i) are the observations with good quality
Ō is the mean observed value.
This index of agreement provides a measure of relative error in model estimates
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29. Assessment of Good Quality Observations in the Time Series
Proportion of good quality observations during a growing season:
Pqa0=Nqa0/T
T—is the total number of 3-day VI during a growing season
Nqa0—is the number of good quality observation within a growing season. If there is one good value within a moving window of 3 3-day VI, it is counts as one good observation. This is due to the fact that the error in phenology detection is lowest at temporal resolution of 6 or 8 days (Zhang et al., 2009).
Zhang et al., 2009
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30. Determination of Quality in Phenology Detection by Combining the Willmott’s Index of Agreement and the Proportion of Good Quality Data in Satellite Observations
Willmott’s index of agreement
1996
Proportion of good quality observations
1996
Quality assurance
1996
QA=0 (processed, good quality) if Pqa0 >0.6 and d>0.8
QA=1 (processed, other quality) if Pqa0 >0.3 and d>0.8
QA=2 (not processed, clod) if Pqa0 <0.3
QA=4 (not processed, other ) if growing season amplitude in VI<0. 08 in forests and <0.02 in other ecosystems

31. Conclusions
Reconstruction of vegetative seasonal trajectory (including the identification of background VI value, the removal of atmospheric/cloud contamination, and the determination of growing cycles) is the most important part in phenology detection.
Pieceswise logistical model is robust in fitting various complex trajectories of vegetation growth. It’s curvature change rate is effective for the identification of phenological transition dates and other metrics.
The quality of phenological detection could be assessed using the combination of good quality satellite observations during a vegetation growing season and the quality of curve fitting (quantified using Willmott’s index of agreement).
The developed algorithm is tested using AVHRR data which are of relatively poor quality. The algorithm would produce better phenology detections from MODIS/VIIRS data which are high quality.
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32. THANKS
Next : ESDR Specification

33. ESDR specification

34. Land Surface Vegetation Phenology Science Data Sets (SDS)
DataField Name Format
DataField_1 Onset_Greenness_Increase UINT16
DataField_2 Onset_Greenness_Maximum UINT16
DataField_3 Onset_Greenness_Decrease UINT16
DataField_4 Onset_Greenness_Minimum UINT16
DataField_5 VI_Onset_Greenness_Minimum UINT16
DataField_6 VI_Onset_Greenness_Maximum UINT16
DataField_7 VI_Area UINT16
DataField_8 Growing_Season_Length UINT16
DataField_9 Rate_Greenness_Increase UINT16
DataField_10 Rate_Greenness_Decrease UINT16
DataField_11 Dynamics_QC UINT16
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35. Land Surface Vegetation Phenology Product Specification
Product name convention
LSP12C2.A _ hdf
LSP12C2.A: product identification
_ : time period of vegetation
phenology detection
001: the data product version
: time of product processing
.hdf: the output file is in HDF
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36. Land Surface Vegetation Phenology Product Specification (continue)
Grid Structure: LSP_Grid_LSP
Dimensions:
Dimension Dimension Name Value
Dimension_1 Ydim:MGLSP_Grid_LSP Data Rows
Dimension_2 Xdim:MGLSP_Grid_LSP Data Columns
Dimension_3 Num_QC_Words:MGLSP_Grid_LSP Num_QC_Words
Dimension_4 Num_Modes:MGLSP_Grid_LSP Num_Modes
global attributes:
:WestBoundingCoordinate = ;
:EastBoundingCoordinate = ;
:NorthBoundingCoordinate = 90.0 ;
:SouthBoundingCoordinate = ;
:PixelSize = 0.05deg ;
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37. Land Surface Vegetation Phenology Product Specification (continue)
Format for SDS of Onset Greenness Increase, Onset Greenness Maximum, Onset Greenness Decrease, and Onset Greenness Minimum
DataField Name Data Type Dimensions
DataField_1 Onset_Greenness_Increase UINT16 Dimension_1
Dimension_2
Dimension_3
Description: Onset of greenness Increase ---
Days starting from January 1, 1980
Note: Word 1 is first Mode of the year, Word 2 is second mode
of the year (other possible modes are not reported)
Data conversions:
DOY=file data - (given year-1979)*366
DataField_1 HDF Attributes:
long_name STRING 1 PGE "Onset_Greenness_Increase"
units STRING 1 PGE "day"
valid_range uint PGE 1, 32766
_FillValue uint PGE
scale_factor float PGE
add_offset float PGE
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38. Land Surface Vegetation Phenology Product Specification (continue)
Format for SDS of VI Onset Greenness Increase and VI Onset Greenness Maximum
DataField_5 VI_Onset_Greenness_Increase UINT Dimension_1
Dimension_2
Dimension_3
Description: VI value at onset of greenness increase during a growth cycle
Note: Word 1 is first Mode of year, Word 2
is second mode of year (other possible
modes are not reported
DataField_5 HDF Attributes:
long_name STRING 1 PGE "VI_Onset_Greenness_Increase"
units STRING 1 PGE "VI value"
valid_range uint PGE 0, 10000
_FillValue uint PGE
scale_factor float PGE
add_offset float PGE
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39. Land Surface Vegetation Phenology Product Specification (continue)
DataField_8 Growing_Season_Length UINT16 Dimension_1
Dimension_2
Dimension_3
Description: Number of days in a growing cycle
Note: Word 1 is first Mode of year, Word 2
is second mode of year (other possible
modes are not reported
DataField_7 HDF Attributes:
long_name STRING 1 PGE "Growing_Season_Length"
units STRING 1 PGE "day"
valid_range uint PGE 0, 32766
_FillValue uint PGE
scale_factor float PGE
add_offset float PGE
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40. Land Surface Vegetation Phenology Product Specification (continue)
Format for SDS of greenness increase rate and greenness decrease rate
DataField_9 Rate_Greenness_Increase UINT16 Dimension_1
Dimension_2
Dimension_3
Description: Average rate of VI increase during a greenup phase
Note: Word 1 is first Mode of year, Word 2
is second mode of year (other possible
modes are not reported
DataField_9 HDF Attributes:
long_name STRING 1 PGE "Rate_Greenness_Increase"
units STRING 1 PGE "VI/day"
valid_range uint PGE 0, 32766
_FillValue uint PGE
scale_factor float PGE
add_offset float PGE
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41. Land Surface Vegetation Phenology Product Specification (continue)
DataField_11 Dynamics_QC UINT Dimension_1
Dimension_2
Description: Quality flags for vegetation phenology
DataField_11 HDF Attributes:
Note: First Word:
the first two bits are Mandatory QA
0=processed, good qual
1=processed, other qual
2=not processed, cloud
3=not processed, other
the next two bits are TBD
the 5-8 bits are Land Water mask
(as passed down from NBARS)
0 = Shallow ocean
1 = Land (Nothing else but land)
2 = Ocean coastlines and lake shorelines
3 = Shallow inland water
4 = Ephemeral water
5 = Deep inland water
6 = Moderate or continental ocean
7 = Deep ocean
the 9-16 bits are Phenology__Assessment
0-100 = Assessment values
255= FillValue
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42. Backup slides

43. AVHRR NDVI Quality

44. 3-day VI Time Series from Daily LTDR AVHRR Data for the Development of MEASURES LSVP Products
The priority in the generation of 3-day time series is:
QA0-good quality QA1-dark vegetation QA2-partially cloudy  QA4-partially cloudy & cloudy shadow 
QA5-dark vegetation & partially cloudy QA6-dark vegetation & cloudy shadow  QA7-dark vegetation & partially cloudy & cloudy shadow QA3-cloudy shadow QA8- cloudy  QA9-unknown poor quality situation, not processed
In the composite, the highest VI is selected if the QA is the same for a 3-day period

45. Maturity Onset from AVHRR in 1996b
Recorded in the first cycle
Recorded in the second cycle
The first greenup onset for the year (combined from the first and the second cycles) c

46. Senescent Onset from AVHRR in 1996b
Recorded in the first cycle
Recorded in the second cycle
The first greenup onset for the year (combined from the first and the second cycles) c