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MODIS Vegetation Index ProductMODIS Vegetation Index ProductMODIS Vegetation Index Productfrom C4 to C5from C4 to C5from C4 to C5
Kamel Didan & Alfredo Huete, TBRS Lab.The University of Arizona
MODIS Land Collection 5/LTDR WorkshopUniversity of Maryland University College, January 17-18, 2007
2
OutlineOutline
VI product & AlgorithmC5 changesC4 vs. C5Validation statement Future plans (C6?)
3
Algorithm and Product DependenciesAlgorithm and Product Dependencies
Daily observations (Pixel 60-1860, Tile h20v11)
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1 6 11 16 21 26 31 36 41 46 51 56 61
Observation number
Surf
. Ref
l. an
d V
Is.
Red
NIR
Blue
MIR
NDVI
EVI
Cloud LegendClearMixedCloudy
Aerosol LegendHighAverageLow
Observations after quality evaluation and weighted average procedure. (Pixel 60-1860, Tile h20v11)
00.10.20.30.40.50.60.70.80.9
1
1 3 5 7 9 11 13 15 17
Observation number
Surf
. Ref
l. an
d V
Is RedNIRBlueMIRNDVIEVI
4
CVCV--MVC compositingMVC compositingAVHRRAVHRR--MVC vs. MODISMVC vs. MODIS--MVC vs. MODIS CVMVC vs. MODIS CV--MVCMVC
0102030405060708090
Ideal d
ata
Aeroso
l/Sha
dow/View is
...
Snow/Ic
e
Cloud
MVC_avhrrMVC_modiscv_mvc
0
20
40
60
80
100
120
Ideal d
ataAero
sol/S
hadow/View
i...
Snow/Ic
e
Cloud
MVC_avhrrMVC_modiscv_mvc
5
SUMMARY of C5 changesSUMMARY of C5 changes
1. Added per pixel reliability (Second most important change)
2. Added the composite day of the year to the output
3. Replaced the NDVI_QA and EVI_QA with one VI_QA layer
4. Restructured the VI_QA data layer to eliminate redundant information
6
1. Improved input data filtering ( & added adjacent cloud filter)
2. Modified the compositing approach:1. To identify mislabeled cloud2. Modified CV-MVC to favor smaller view angle
3. Adopted a phased production for Terra and Aqua to keep the streams separate while increasing the temporal frequency (Most important change)
SUMMARY of C5 changesSUMMARY of C5 changes
7
One VI QA layer (restructured) One VI QA layer (restructured)
NDVI QA and EVI QA are identicalEliminated this redundancy
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Composite day of the yearComposite day of the year
Useful for temporal comparisons and validation work
Southeast US (h10v05)
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Composite day of the yearComposite day of the year
Composite period 2000-161
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Per pixel data reliabilityPer pixel data reliabilityEliminate the need for QA fields/binary manipulation. Ordinal number & few (4) categoriesPowerful tool for time series/Phenology analysis– Helps in extracting ‘true’ growing season profile
Snow/Ice Ideal Marginal CloudInterpolatedOnly for CMG
11
Data reliability applicationsData reliability applications
C 5 ND V I/E V I ( B ra z i/C e r ra d o )
0
0.2
0.4
0.6
0.8
1
19-Jan-00 23-M ar-00 26-M ay -00 29-Ju l-00 01-O c t-00 04-D ec -00 06-F eb-01 11-A pr-01
D a te
NDV
I/EVI
ND VI_Good E VI_Good ND VI_Marg ina l E VI_Margina lND VI_S now E VI_S now ND VI_C loudy E VI_C loudy
ND VI Pro file (T apajos)
00.2
0.40.60.8
11.2
Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 Jan-06D ate
ND
VI
NDVI_Good ND VI_Marginal NDVI_Snow ND VI_C loudy
12
E V I E x a m p le A n n u a l P ro file s
0
0 .1
0 .2
0 .3
0 .4
0 .5
0 .6
0 .7
0 .8
0 .9
1
1 4 - De c 2 - Fe b 2 4 - Ma r 1 3 - Ma y 2 - Ju l 2 1 - A u g 1 0 - O c t 2 9 - No v 1 8 - Ja n
D a te
EVI
E V I_ No r th _ C A _ C o a s t_ 1
E V I_ No r th _ C A _ C o a s t_ 2
E V I_ E l_ C e n tr o
E V I_ S ib e r ia _ 1
E V I_ S ib e r ia _ 2
E V I_ D e s e r t_ 1
E V I_ Hi_ E V I_ S ite _ 1
E V I_ S E _ A m a z_ B a s in
Data reliability applicationData reliability applicationNDVI Example Annual Profles
00.10.20.30.40.50.60.70.80.9
1
14-Dec 24-Mar 2-Jul 10-Oct 18-JanDate
ND
VI
NDVI_North_CA_Coast_1
NDVI_North_CA_Coast_2
NDVI_El_Centro
NDVI_Siberia_1
NDVI_Siberia_2
NDVI_Desert_1
NDVI_Hi_EVI_Site_1
NDVI_SE_Amaz_Basin
13
Global performanceGlobal performance
14
Phased ProductionPhased Production
Terra & Aqua data are highly identical, but slight differences & other issues makes combining them potentially problematic– Produced on the same day, so in most cases there is no
point in using both (C4 and earlier)
Improve the use efficiency without combining the two– Phased production was an ideal solution
• Increased temporal frequency• Now there is a reason to use both
Only Aqua will be phased
15
Phased ProductionPhased Production
In order for this to work there should be no bias in the selected composite day of the year.
Selected Composite DOY Distribution
0
2
4
6
8
10
12
14
16
0 11 22 33 44 55 66 77 88 99 110
121
132
143
154
165
176
187
198
209
220
231
242
253
264
275
286
297
308
319
330
341
352
363
DOY
Perc
ent (
%)
nadir aperture door was closed
MODIS-L1B data was not available
16
Phased productionPhased production
Jasp er_R id g e_B io lo g ica l_P reserve 3x3 W in d o w @ 312x348
0
0.1
0.2
0.3
0.4
0.5
2/23/2004 3/10/2004 3/26/2004 4/11/2004 4/27/2004 5/13/2004 5/29/2004D ate
EVI
T_8_Da ys A_8_Da ys T_16_Da ys A_16_Da ys
T_A_8_Da ys T_A_16_Da ys T_P _8_Da ys T_P _16_Da ys
17
C4 and C5 ComparisonsC4 and C5 Comparisons
18
Reduced BRDF
Improved data quality screening
C4 and C5 ComparisonsC4 and C5 Comparisons
19
Cloud reductionCloud reduction
C4
C5
20
NDVI comparisonsNDVI comparisons
C4- C5 NDVI Difference Distribution (23 Composite cycles)
0
2
4
6
8
10
12
14
16
18
-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2NDVI Difference
Perc
ent (
%)
Consistent & slight decrease in C5
21
EVI ComparisonsEVI Comparisons
C4- C5 EVI Difference Distribution (23 Composite cycles)
0
2
4
6
8
10
12
14
16
18
20
-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2
EVI Difference
Perc
ent (
%)
Consistent & slight decrease in C5
22
Red LSR ComparisonsRed LSR Comparisons
C4 - C5 Red Difference Distribution
0
5
10
15
20
25
30
-0.1 -0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08 0.1
Red Difference
Perc
ent (
%)
23
NIR LSR ComparisonsNIR LSR Comparisons
C4 - C5 NIR Difference Distribution
0
5
10
15
20
-0.1 -0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08 0.1
NIR Difference
Perc
ent (
%)
24
Rain Forest NDVI Error Range
00.10.20.30.40.50.60.70.80.9
1
Dec-99 Apr-01 Sep-02 Jan-04 May-05
Date
ND
VI/E
rror
AvgMinMaxSTD_DevMax_Error
Noise related Error budgetNoise related Error budget
MODIS EVI Range/ErrorBoreal Forest
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
12/6/1999 4/19/2001 9/1/2002 1/14/2004 5/28/2005
Date
EVI/E
rror
AvgMinMaxSTD_DevMedianMax_Error
25
Validation statementValidation statement
Maturity= "Validated Stage 2“
The MODIS Vegetation Index product is retrieved accurately using a state of the art MODIS specific, quality driven, constrained view, maximum value composite method. For most cloud and snow free, low to no aerosol load, the VI values are very reliable. Over inland water bodies (rivers, lakes, etc...) surface reflectance inputs and VI values are unstable and should be usedwith caution. Strong correlations are also found with the nadir-adjusted (NBAR) NDVI and EVI products. Various field validationcampaigns indicate good agreement of VI values for most biomes, and cross sensor correlations (AVHRR, Landsat, SPOT, etc…) are very strong. The VI product is particularly reliant on coherentinter-band (red and NIR) atmosphere corrections. Unstable values may be encountered over more extreme bright or dark surfaces.
26
C6 plans (towards CDR)C6 plans (towards CDR)
MODIS specific– Completely cloud free & gap filled
• It is pointless to produce/use cloudy observations• Gaps should be filled with obs. from the historical record
(similar to the CMG approach)– Dynamic composite (First good observation)
• No fixed composite cycle, but VI produced when a good pixel becomes available
General CDR– EVI2 for continuity– EVI Backward compatibility (paper to be submitted) and no-
Blue band sensors– BRDF???