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Gio-GL Lot1, GMES Initial Operations
Date Issued: 12.12.2014
Issue: I1.21
Gio Global Land Component - Lot I
”Operation of the Global Land Component” Framework Service Contract N° 388533 (JRC)
Validation Report
NDVI – VCI – VPI
Issue I1.21
Date of submission: 08.10.2014
Organisation name of lead contractor for this deliverable: VITO
Book Captain: E. Swinnen (VITO)
Contributing Authors: B. Smets (VITO)
W. Dierckx (VITO)
C. Toté (VITO)
Gio-GL Lot1, GMES Initial Operations
Date Issued: 12.12.2014
Issue: I1.21
Document-No. GIOGL1_VR_NDVI-V2 © GIO-GL Lot1 consortium
Issue: I1.21 Date: 12.12.2014 Page: 2 of 79
Dissemination Level
PU Public X
PP Restricted to other programme participants (including the Commission Services)
RE Restricted to a group specified by the consortium (including the Commission Services)
CO Confidential, only for members of the consortium (including the Commission Services)
Gio-GL Lot1, GMES Initial Operations
Date Issued: 12.12.2014
Issue: I1.21
Document-No. GIOGL1_VR_NDVI-V2 © GIO-GL Lot1 consortium
Issue: I1.21 Date: 12.12.2014 Page: 3 of 79
DOCUMENT RELEASE SHEET
Book Captain: E. Swinnen Date: 08.10.2014 Sign.
Approval: R. Lacaze Date: 12.12.2014 Sign.
Endorsement: M. Cherlet Date: 09.01.2015 Sign.
Distribution: GIO Global Land Consortium
Gio-GL Lot1, GMES Initial Operations
Date Issued: 12.12.2014
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Issue: I1.21 Date: 12.12.2014 Page: 4 of 79
Change Record
Issue/Rev Date Page(s) Description of Change Release
20.02.2014 All Initial version I1.00
I1.00 02.07.2014 13 Update after the external review I1.10
I1.10 08.10.2014 Added MODIS and GEOV1 NDVI to intercomparison; use of BELMANIP network sites for statistical analysis
I1.20
I1.20 12.12.2014 36, 76-
77 Clarifications regarding Figure 15 and Conclusions as suggested by the external review
I1.21
Gio-GL Lot1, GMES Initial Operations
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TABLE OF CONTENT
1. Introduction _______________________________________________________ 13
1.1 Executive Summary _____________________________________________________ 13
1.2 Scope and Objectives ___________________________________________________ 13
1.3 Content of the Document ________________________________________________ 13
1.4 Related Documents _____________________________________________________ 14
1.4.1 Applicable documents _____________________________________________________ 14
1.4.2 Input documents __________________________________________________________ 14
1.4.3 Output documents ________________________________________________________ 14
2. Users Requirements _________________________________________________ 15
3. Description of the NDVI, VPI and VCI products ___________________________ 16
3.1 Normalised Difference Vegetation Index (NDVI) ______________________________ 16
3.2 Vegetation Condition Index (VCI) __________________________________________ 16
3.3 Vegetation Production Index (VPI) _________________________________________ 16
4. Data sets used in the validation _______________________________________ 17
4.1 Satellite products _______________________________________________________ 17
4.1.1 NDVI V2, VPI and VCI ______________________________________________________ 18
4.1.2 METOP-AVHRR NDVI _______________________________________________________ 19
4.1.3 TERRA-MODIS NDVI _______________________________________________________ 19
4.1.4 GEOV1 NDVI _____________________________________________________________ 19
4.2 In-situ Reference Products _______________________________________________ 20
4.3 Regional/Biome Assessment _____________________________________________ 20
4.3.1 GLC2000 land cover classification ____________________________________________ 20
4.3.2 Homogeneous areas _______________________________________________________ 21
4.3.3 BELMANIP2 and DIRECT sites ________________________________________________ 21
5. Validation Procedure ________________________________________________ 23
5.1 Overall Procedure ______________________________________________________ 23
5.1.1 General validation approach ________________________________________________ 23
5.1.2 Sampling ________________________________________________________________ 23
5.1.3 Validation metrics _________________________________________________________ 25
5.1.4 Overview of the procedure __________________________________________________ 27
6. Results ___________________________________________________________ 29
6.1 Spatial consistency _____________________________________________________ 29
6.1.1 Spatial distribution of values ________________________________________________ 29
6.1.2 Spatial continuity _________________________________________________________ 36
6.1.3 Magnitude of values _______________________________________________________ 38
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6.2 Global statistical analysis ________________________________________________ 40
6.2.1 Magnitude of values _______________________________________________________ 40
6.2.2 Regression analysis ________________________________________________________ 42
6.2.3 Statistical assessment and effect of sampling scheme ____________________________ 50
6.3 Temporal consistency ___________________________________________________ 63
6.3.1 Temporal variations and realism _____________________________________________ 63
6.3.2 Temporal smoothness _____________________________________________________ 74
7. Conclusion ________________________________________________________ 76
8. Perspectives _______________________________________________________ 78
9. References ________________________________________________________ 79
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LIST OF FIGURES
Figure 1: The GLC classification aggregated into 7 classes ................................................. 21
Figure 2: Location of the three areas: Red: Sahara, Green: Congo Basin, Blue: Amazon
Forest ............................................................................................................................. 21
Figure 3: Spatial distribution of the BELMANIP and DIRECT sites. ..................................... 22
Figure 4: MPDs between NDVI V2, AVHRR NDVI and GEOV1 NDVI: A. 10-daily NDVI V2 vs
AVHRR NDVI; B. 10-daily NDVI V2 vs GEOV1 NDVI (white: no pixels selected for
comparison) calculated per pixel over the period 2008-2012. ....................................... 30
Figure 5: MPDu between NDVI V2, AVHRR NDVI and GEOV1 NDVI: A. 10-daily NDVI V2 vs
AVHRR NDVI; B. 10-daily NDVI V2 vs GEOV1 NDVI (white: no pixels selected for
comparison) calculated per pixel over the period 2008-2012. ....................................... 30
Figure 6: RMSE between NDVI V2, AVHRR NDVI and GEOV1 NDVI: A. 10-daily NDVI V2
vs AVHRR NDVI; B. 10-daily NDVI V2 vs GEOV1 NDVI (white: no pixels selected for
comparison) calculated per pixel over the period 2008-2012. ....................................... 31
Figure 7: Number of selected paired observations between NDVI V2, AVHRR NDVI and
GEOV1 NDVI: A. 10-daily NDVI V2 vs AVHRR NDVI; B. 10-daily NDVI V2 vs GEOV1
NDVI (white: no pixels selected for comparison) calculated per pixel over the period
2008-2012. ..................................................................................................................... 31
Figure 8: Temporal profile between the 10-daily composites of NDVI V2 (red), AVHRR (blue)
and GEOV1 (green), which demonstrates the lower maximum values of the GEOV1
NDVI. ............................................................................................................................. 32
Figure 9: Temporal profiles between the 10-daily composites of NDVI V2 (red), AVHRR
(blue) and GEOV1 (green). Examples of two sites where GEOV1 provides spurious
NDVI values at the end of the growing season. ............................................................. 33
Figure 10: Temporal profiles between the 10-daily composites of NDVI V2 (red), AVHRR
(blue) and GEOV1 (green) showing the large perturbation of the NDVI V2 data set by
undetected clouds (low NDVI values). ........................................................................... 33
Figure 11: MPDs between NDVI V2, MODIS NDVI and GEOV1 NDVI: A. Monthly NDVI V2
vs MODIS NDVI ‘noMask’; B. Monthly NDVI V2 vs GEOV1 NDVI ‘noMask’ (white: no
pixels selected for comparison) ..................................................................................... 34
Figure 12: MPDu between NDVI V2, MODIS NDVI and GEOV1 NDVI: A. Monthly NDVI V2
vs MODIS NDVI ‘noMask’; B. Monthly NDVI V2 vs GEOV1 NDVI ‘noMask’ (white: no
pixels selected for comparison) ..................................................................................... 34
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Figure 13: RMSE between NDVI V2, MODIS NDVI and GEOV1 NDVI: A. Monthly NDVI V2
vs MODIS NDVI ‘noMask’; B. Monthly NDVI V2 vs GEOV1 NDVI ‘noMask’ (white: no
pixels selected for comparison) ..................................................................................... 35
Figure 14: Number of paired observations between NDVI V2, MODIS NDVI and GEOV1
NDVI: A. Monthly NDVI V2 vs MODIS NDVI ‘noMask’; B. Monthly NDVI V2 vs GEOV1
NDVI ‘noMask’ (white: no pixels selected for comparison) ............................................ 35
Figure 15: Temporal profile between the monthly composites of NDVI V2 (red), MODIS
(blue) and GEOV1 (green), which demonstrates the larger difference between the
maximum values of the NDVI V2 and GEOV1 NDVI, compared to the 10-daily
composites (Figure 8). ................................................................................................... 36
Figure 16: temporal evolution of % of physical NDVI values for all ten-daily composites from
the overlapping time period between NDVI V2, AVHRR NDVI and GEOV1 NDVI. ...... 37
Figure 17: % of good observations for all monthly composites from the overlapping time
period between NDVI V2, MODIS NDVI and GEOV1 NDVI. ......................................... 38
Figure 18: Frequency distribution of the 10-day NDVI composite values (2008 –2012) for the
Sahara, Congo Basin and Amazon forest. Pairwise comparison of good observations of
NDVI V2 (red), AVHRR (blue), and GEOV1 (green). X-axis: NDVI value classes in steps
of 0.1, Y-axis: percentage of occurrence. ...................................................................... 39
Figure 19: Frequency distribution of the monthly NDVI composite values (2001 –2012) for
the Sahara, Congo Basin and Amazon forest. Pairwise comparison of good
observations of NDVI V2 (red), MODIS (blue), and GEOV1 (green). X-axis: NDVI value
classes in steps of 0.1, Y-axis: percentage of occurrence. ............................................ 40
Figure 20: Frequency distributions over 7 different biomes based on the 10-daily NDVI time
series of 2008-2012. Pairwise comparison of good observations for NDVI V2 (red),
AVHRR (blue), GEOV1 (green). X-axis: NDVI value classes in steps of 0.1, Y-axis:
percentage of occurrence. ............................................................................................. 41
Figure 21: Frequency distributions over 7 different biomes based on the monthly NDVI time
series of 2001-2012. Pairwise comparison of good observations for NDVI V2 (red),
MODIS (blue), GEOV1 (green). X-axis: NDVI value classes in steps of 0.1, Y-axis:
percentage of occurrence. ............................................................................................. 42
Figure 22: Scatterplots between NDVI V2 (x-axis) and AVHRR (y-axis) 10-daily composited
NDVI values of 2008-2012 using all constraints listed in section 5.1.2 over all land cover
types (top left) and over 7 different biomes. .................................................................. 43
Figure 23: Scatterplots between NDVI V2 (x-axis) and AVHRR (y-axis) 10-daily composited
NDVI values of 2008-2012 using the ‘noMask’ sampling scheme over all land cover
types (top left) and over 7 different biomes. .................................................................. 44
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Figure 24: Scatterplots between NDVI V2 (x-axis) and GEOV1 (y-axis) 10-daily composited
NDVI values of 2008-2012 using the ‘noMask’ sampling scheme over all land cover
types (top left) and over 7 different biomes. .................................................................. 46
Figure 25: Scatterplots between NDVI V2, GEOV1 and MODIS (see figure titles) monthly
composited NDVI values for the period 2001-2012 using the ‘noMask’ sampling
scheme. ......................................................................................................................... 47
Figure 26: Scatterplots of pairwise comparisons between NDVI V2 (X-axis), GEOV1 (Y-axis,
left column) and MODIS (Y-axis, right column) monthly composited NDVI values of the
period 2001-2012 using the ‘noMask’ sampling scheme over 7 different biomes. ........ 50
Figure 27: MSD, MPDu and MPDs (top), AC (middle) and RMSE (bottom) calculated
between the 10-daily composites of NDVI V2 and AVHRR NDVI for the sampling
schemes ‘all constraints’ (left) and ‘noMask’ (middle) and between NDVI V2 and GEOV1
(right) per land cover type and over the entire reference period. ................................... 52
Figure 28: Evolution over time of the RMSE calculated between the 10-daily composites of
NDVI V2 and AVHRR NDVI for the sampling schemes ‘all constraints’ (left) and
‘noMask’ (middle) and between NDVI V2 and GEOV1 (right) per land cover type and
over the entire reference period. .................................................................................... 54
Figure 29: Evolution over time of the AC (top) and R (bottom) calculated between the 10-
daily composites of NDVI V2 and AVHRR NDVI for the sampling schemes ‘all
constraints’ (left) and ‘noMask’ (middle) and between NDVI V2 and GEOV1 (right) per
land cover type and over the entire reference period. ................................................... 54
Figure 30: Evolution over time of the MDPs (top), MDPu (middle) and MSD (bottom)
calculated between the 10-daily composites of NDVI V2 and AVHRR NDVI for the
sampling schemes ‘all constraints’ (left) and ‘noMask’ (middle) and between NDVI V2
and GEOV1 (right) per land cover type and over the entire reference period. .............. 56
Figure 31: MSD, MPDu and MPDs (top), AC (middle) and RMSE (bottom) calculated
between NDVI V2 and MODIS (left) and between NDVI V2 and GEOV1 (right) NDVI
monthly composites per land cover type and over the entire reference period. ............ 57
Figure 32: Evolution over time of the MDPs (top), MDPu (top), and MSD (bottom) between
NDVI V2 and MODIS (left) and between NDVI V2 and GEOV1 (right) monthly
composites per land cover type. .................................................................................... 60
Figure 33: Evolution over time of the AC between NDVI V2 and MODIS (left) and between
NDVI V2 and GEOV1 (right) monthly composites per land cover type. ......................... 61
Figure 34: Evolution over time of the RMSE between NDVI V2 and MODIS (left) and
between NDVI V2 and GEOV1 (right) monthly composites per land cover type. .......... 62
Figure 35: Temporal NDVI profiles between the 10-daily composites of NDVI V2 (red),
AVHRR (blue) and GEOV1 (green) for the period 2008-2012 at the left, and between
Gio-GL Lot1, GMES Initial Operations
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monthly composites of NDVI V2 (red), MODIS (blue) and GEOV1 (green) for the period
2001-2012 at the right. Examples with a good agreement. ........................................... 69
Figure 36: Temporal NDVI profiles between the 10-daily composites of NDVI V2 (red),
AVHRR (blue) and GEOV1 (green) for the period 2008-2012 at the left, and between
monthly composites of NDVI V2 (red), MODIS (blue) and GEOV1 (green) for the period
2001-2012 at the right. Examples with a lower agreement. ........................................... 73
Figure 37: The frequency histogram of the delta, a measure of temporal smoothness, for the
period 2008-2012 for the NDVI 10-daily time series from NDVI V2, AVHRR and GEOV1
for all clear observations. ............................................................................................... 74
Figure 38: The frequency histogram of the delta, a measure of temporal smoothness, for the
period 2001-2012 for the NDVI monthly time series from NDVI V2, MODIS and GEOV1
for all clear observations. ............................................................................................... 75
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LIST OF TABLES
Table 1: Acceptable differences with existing satellite-derived NDVI products, as defined by
geoland2/BioPar project ................................................................................................ 15
Table 2: Sensor characteristics of the data sets used in the validation ................................ 17
Table 3: Processing characteristics of the data sets used in the validation .......................... 18
Table 4 Sampling schemes used to evaluate the comparison between different NDVI data
sets. The X marks when a constraint is used in a specific sampling scheme................ 24
Table 5: Regression metrics for the results presented in Figure 22 (all constraints) and
Figure 23 (noMask). ....................................................................................................... 44
Table 6: Regression metrics for the results presented in Figure 24 (noMask). .................... 45
Table 7: Regression metrics for the results presented in Figure 25 and Figure 26. ............. 47
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ACRONYMS
AC Agreement coefficient
ATBD Algorithm theoretical base document
AVHRR Advanced Very High Resolution Radiometer
CEOS Committee on Earth Observation Satellite
FAO Food and Agricultural Organization
fAPAR fraction of Absorbed Photosynthetic Active Radiation
GEOV1 First version of the Geoland2 products
GIO GMES Initial Operations
LAI Leaf Area Index
LPV Land Products Validaiton
LSA SAF Land Surface Analysis Satellite Applications Facility
Max Maximum
MBE Mean bias error
Min Minimum
MO3 Marsop 3 project
NDVI Normalized Difference Vegetation Index
NIR Near-infrared
RMSE Root mean squared error
SPOT Système Pour l’Observation de la Terre
VAA Viewing azimuth angle
VCI Vegetation Condition Index
VGT VEGETATION sensor onboard SPOT4/5
VPI Vegetation Production Index
VZA Viewing zenith angle
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1. INTRODUCTION
1.1 EXECUTIVE SUMMARY
From 1st January 2013, the Copernicus Global Land Service is operational, providing
continuously a set of biophysical variables describing the vegetation conditions, the energy
budget at the continental surface and the water cycle over the whole globe at one kilometer
resolution. Essential Climate Variables like the Leaf Area Index (LAI), the Fraction of PAR
absorbed by the vegetation (FAPAR), the surface albedo, the Land Surface Temperature,
the soil moisture, the burnt areas, the areas of water bodies, and additional vegetation
indices, are generated every hour, every day or every 10 days on a reliable and automatic
basis from Earth Observation satellite data.
This document concerns the reference validation of the Vegetation Production Index
(VPI) and the Vegetation Condition Index (VCI), through an evaluation of the index from
which these indices originate, namely the Normalized Difference Vegetation Index (NDVI).
The NDVI V2 dataset is compared to the METOP-AVHRR and GEOV1 NDVI dataset at a
ten-day time step, and compared to MODIS and GEOV1 NDVI datasets at a monthly time
step.
1.2 SCOPE AND OBJECTIVES
This document presents a quality assessment of the NDVI V2 time series from a
reference period 2008-2012 (2001-2012 for monthly products), based on three reference
data sets. Since the VCI and VPI products are derived from the NDVI V2 data set by
comparing the actual NDVI value against its corresponding long term statistic for the same
period in the year, the validation of the NDVI provides the validation of the VCI and VPI.
1.3 CONTENT OF THE DOCUMENT
This document is structured as follows:
Chapter 2 recalls the users requirements, and the expected performance
Chapter 3 summarizes the retrieval algorithms of NDVI V2, VCI and VPI
Chapter 4 describes the data sets used in the validation.
Chapter 5 presents the validation procedure
Chapter 6 summarizes the results of the exhaustive validation of the NDVI V2
archive.
Chapter 7 presents the conclusions.
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1.4 RELATED DOCUMENTS
1.4.1 Applicable documents
AD1: Annex II – Tender Specifications to Contract Notice 2012/S 129-213277 of 7th July
2012
AD2: Appendix 1 – Product and Service Detailed Technical requirements to Annex II to
Contract Notice 2012/S 129-213277 of 7th July 2012
1.4.2 Input documents
GIO-GL1-SVP : Service Validation Plan of the Global Land Service
GIO-GL1-ServiceSpecifications : Service Specifications of the Global Land Service
GIO-GL1_ATBD_NDVI-V2_I1.00 : Algorithm Theoretical Basis Document of the Normalised
Difference Vegetation Index V2 on which the VCI and VPI
is based
GIO_GL1_PUM_NDVI-V2_I1.00 : Product User Manual of the Normalized Difference
Vegetation Index V2
1.4.3 Output documents
GIOGL1_PUM_NDVI-V2
: Product User Manual of the NDVI Version 2
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2. USERS REQUIREMENTS
NDVI, VCI and VPI are not considered GCOS ECV products. Therefore, there are no
GTOS/GCOS specifications on required accuracy.
The FP7 geoland2/BioPar project defined user requirements for NDVI in terms of
absolute accuracy (see Table 1), which is related to the radiometric calibration of the
instrument and is not part of the validation or quality monitoring of the GL service.
The proposed approach is to compare the NDVI time series with an independent time
series. For VCI and VPI, no additional validation is performed, since it entirely depends on
the NDVI.
Table 1: Acceptable differences with existing satellite-derived NDVI products, as defined by geoland2/BioPar project
Source Threshold Target Optimal
geoland2/BioPar 0.15 0.10 0.05
The above requirements are indicative. They have to be adapted to the GL products and
clarified by the user board of the Global Land service.
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3. DESCRIPTION OF THE NDVI, VPI AND VCI PRODUCTS
3.1 NORMALISED DIFFERENCE VEGETATION INDEX (NDVI)
The NDVI is a dimensionless index that is indicative for vegetation density and is
calculated as a normalized difference between the Near Infrared (NIR) and Red
reflectances:
The NDVI is a sensor-specific index, because it is directly based on the reflectances that
are determined by the spectral characteristics of the sensor.
3.2 VEGETATION CONDITION INDEX (VCI)
The VCI or “Vegetation Condition Index” was first proposed by Kogan (1990, 1994, 1995)
and is computed as:
with NDVImin and NDVImax are the extreme values observed in the previous years (always
per pixel and period). The VCI logically varies between 0% and 100%. The lower and higher
values respectively indicate bad and good vegetation state conditions, while normal
situations have VCI fluctuating around 50%. The VCI is easy to compute and has been used
widely in literature, for instance by FAO’s Agricultural Stress Index System (Rojas et al.,
2011).
3.3 VEGETATION PRODUCTION INDEX (VPI)
The VPI or “Vegetation Production Index”, first suggested by Sannier (1998), is more
complex, as it tries to account for the real distribution of the past observations (per pixel and
per period). First, a fit is made of the (cumulative) histogram of these values. Then, the
actual NDVI-value is compared to it, and the VPI retrieves its probability of occurrence. VPI-
values of 0%, 50% and 100% respectively indicate that the actual observation corresponds
with the historical minimum (worst vegetation state), median (normal) or maximum (best
situation) ever observed. Contrary to Sannier (1998), the VPI product distributed by GIO GL
is expressed in % instead of five groups (0-20%, 20-40%, etc.).
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4. DATA SETS USED IN THE VALIDATION
4.1 SATELLITE PRODUCTS
The following sections briefly introduces the data sets used in the validation. Table 1 and
Table 2 provide more information on the sensor and processing characteristics of these
NDVI data sets.
Table 2: Sensor characteristics of the data sets used in the validation
SPOT-VEGETATION METOP A-AVHRR TERRA-MODIS
ORBITAL CHARACTERISTICS
Altitude (km) 832 817 705
Inclination angle (degrees)
98.7 98.7 98.1991°
Equator crossing time (LST) at descending node
10:30
9:30
10:30
Stability of the platform No orbital drift until beginning of 2013
No orbital drift No orbital drift
SPECTRAL CHARACTERISTICS
Spectral channels on which the NDVI is based
Red Ch2: 0.61 – 0.68 Ch1: 0.58 – 0.68 Ch1: 0.62-0.67
NIR Ch3: 0.78 – 0.89 Ch2: 0.725 – 1.0 Ch2: 0.841-0.876
Calibration of shortwave channels
Onboard calibration & vicarious calibration
Vicarious calibration a posteriori (Rao & Chen, 1999)
Onboard calibration & vicarious calibration
SPATIAL CHARACTERISTICS
Swath width (km) 2250 2400 2330
Total Earth scan angle (degrees)
101 110.8 110
Nominal resolution (km) 1.15 1.09 0.25
Maximum off-nadir resolution Along track direction (km) Across track direction (km)
Not specified 1.7
2.4 6.9
250, 500, 1000m Multiply along track resolution by 4
Attitude of satellite Known Known Known
PSF Broad Narrow Narrow
Scanning system Pushbroom Whiskbroom Whiskbroom
Spatial resolution after resampling
1°/112 (=0.0089285714°) at equator
1°/112 (=0.0089285714°) at equator
0.0083333° at equator
Resampling method Cubic convolution Nearest neighbour Cubic convolution
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Table 3: Processing characteristics of the data sets used in the validation
NDVI V2 GEOV1 AVHRR MODIS
Source SPOT-VGT SPOT-VGT METOP-
A/AVHRR
TERRA-MODIS
Temporal
resolution
10 days 10 days 10 days 1 month
Length of
compositing
period
10 days 30 days 10 days 30 days
Compositing
method
Maximum NDVI No actual
compositing
performed,
aggregation in
time through
normalisation.
Maximum NDVI
constrained by
VZA
A combination of
maximum NDVI
value constrained
by VZA (8-days
composite) and
averaging
(monthly
composite from 8-
days composite)
Normalisation
method
none Roujean model
inversion, applied
on 30 days of data
none Walthall model
applied on daily
data
Atmospheric
correction method
SMAC SMAC SMAC Method based on
look-up tables
Inputs for
atmospheric
correction
6-hourly water vapour from MeteoServices
(1)
Ozone climatology
(2)
AOD derived from blue band
- Pressure from
DEM
6-hourly water vapour from MeteoServices
(1)
Ozone climatology
(2)
AOD climatology(3)
Pressure from DEM
6-hourly water vapour from MeteoServices
(1)
Ozone climatology
(2)
AOD derived from climatology
(3)
Pressure from DEM
MODIS-derived values of water vapour, ozone and AOD. Pressure from DEM
(1) Identical water vapour data
(2) Identical Ozone climatology
(3) Identical AOD climatology
4.1.1 NDVI V2, VPI and VCI
The VCI and VPI are based on the NDVI V2, which is the maximum value of the NDVI of
all clear observations within a ten-daily period. It differs from the original standard S10 VGT
products, distributed by the CTIV, in the following way:
- The data are rescaled to byte range (0-250) using the formula DN =
(NDVI+0.08)/0.004
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- The status map information is summarized as flags in the NDVI images using the
range 251-255 of the image.
The archive data of the NDVI V2 comes from the MARSOP3 project. This is the data for
the period 2001-2012. There is no difference in processing with the GL service NDVI V2.
The same archive is used to derive the long term statistics, which are needed to calculate
the VCI and VPI products.
4.1.2 METOP-AVHRR NDVI
The NDVI from METOP-AVHRR is provided by the LSA-SAF (http://landsaf.meteo.pt/ ,
ENDVI product), hereafter called AVHRR NDVI. The NDVI is processed very similar to the
one of SPOT-VGT, with the same inputs for atmospheric correction and a similar
compositing method (max NDVI over a 10-days period, but additionally constraining the
viewing zenith angle to favour near nadir observations). The compositing period is 10 days.
The processing of the NDVI is described in detail by Eerens et al., (2009) and on
http://landsaf.meteo.pt/algorithms.jsp;jsessionid=2A3128EDCE442ADB16298ABF5BC40287
?seltab=22&starttab=22.
4.1.3 TERRA-MODIS NDVI
The Moderate Resolution Imaging Spectroradiometer (MODIS) is mounted on two
platforms, TERRA and AQUA. Because TERRA has a morning overpass over the equator,
the data from the TERRA-MODIS are used to compare with NDVI V2. TERRA-MODIS
provides global coverage in two days since December 1999. It is a sensor containing 36
bands with a spatial resolution of 250m (2 bands), 500m (5 bands) and 1km (29 bands). The
NDVI is provided to the user in the form of 16-day or monthly composites at various
resolutions. The data that are used in this study are the MOD13A3 data set, which is the
monthly gridded NDVI at 1km resolution. The data is normalized for viewing angles using
the simple Walthall model (Walthall et al., 1985) to standardize the reflectance data to nadir
and compute nadir-based VIs.
The MODIS sensor has been widely used in many applications in the domain of land,
ocean and atmosphere. More information of its pre-processing can be found on
http://vip.arizona.edu/documents/MODIS/MODIS_VI_UsersGuide_01_2012.pdf.
4.1.4 GEOV1 NDVI
The NDVI V1 product, hereafter called GEOV1 NDVI, is derived from directionally
corrected RED and NIR reflectances as described in the ATBD [GIOGL1-ATBD-NDVI-V1].
The product is updated every 10 days, with a temporal basis for compositing of 30 days.
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The GEOV1 is used for the validation at 10-dail and the monthly time step. For the month
time step comparison, the middle composite of the month was selected, because its
compositing period coincides the most with a calendar month. The end date of the product is
set to the 5th, 15th or 25th of the month and the start date is 30 days earlier. This means that
there is always a small shift in time with the other data sets used, which could impact the
comparison with the other monthly products. For the 10-daily comparison, it is important to
keep in mind that these products covers one month (due to the 30-days compositing) and
not exactly 10 days like the other products (from the days 1-10, 11-20 and 21-end of the
month).
Another difference between the GEOV1 NDVI and two ten-daily datasets (NDVI V2 and
AVHRR) is that it is calculated after the normalization of the spectral surface reflectances, by
inversion of a reflectance model, for a standard viewing and illumination condition (view
zenith angle at nadir, sun zenith angle corresponding to the median value of the
observations used for normalization) .
4.2 IN-SITU REFERENCE PRODUCTS
No in-situ data were used for the quality monitoring of the NDVI.
4.3 REGIONAL/BIOME ASSESSMENT
4.3.1 GLC2000 land cover classification
An aggregated version of the GLC2000 classification was used to distinguish between
major land cover classes (Figure 1). The classes were aggregated according to the following
scheme:
- Broadleaved Evergreen Forests (BEF): class 1
- Broadleaved Deciduous Forests (BDF): classes 2-3
- Needleleaved Forests (NLF): classes 4-5
- Shrubland (SHR): classes 11-12, 14
- Herbaceous cover (HER): class 13
- Cultivated areas and cropland (CUL): classes 16-18
- Bare areas (BA): class 19
Other classes than the ones listed above were not considered.
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Figure 1: The GLC classification aggregated into 7 classes
4.3.2 Homogeneous areas
The distribution of the NDVI values is compared for three areas with relatively
homogeneous land cover, located in the Sahara, Congo Basin and Amazon forest (Figure
2).
Figure 2: Location of the three areas: Red: Sahara, Green: Congo Basin, Blue: Amazon
Forest
4.3.3 BELMANIP2 and DIRECT sites
BELMANIP2.1 (BEnchmark Land Multisite ANalysis and Intercomparison of Products)
is a collection of sites originating from existing experimental networks (FLUXNET,
AERONET, VALERI, BigFoot,...) that are completed with sites selected from the
GLOBCOVER vegetation land cover map in order to obtain a global representation (see
Figure 3). The site selection was performed per 10° latitude band by keeping the same
proportion of biome types within the selected sites as within the entire latitude. Additional
constraints on the selection were that the sites are homogeneous over a 10x10km² area,
almost flat, and with a minimum proportion of urban area and permanent water bodies.
DIRECT is a collection of sites for which ground measurements are available and that
have been collected (Garrigues et al, 2008) and processed according to the CEOS-LPV
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guidelines (Figure 3). There are currently 113 data sets (sites and dates of measurements)
available.
These sites are used to evaluate the temporal variation and realism of the NDVI time
series.
Figure 3: Spatial distribution of the BELMANIP and DIRECT sites.
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5. VALIDATION PROCEDURE
5.1 OVERALL PROCEDURE
5.1.1 General validation approach
The proposed approach is to compare the NDVI time series with independent time series.
Because NDVI is a sensor dependent index, the validation objective is not the actual
agreement between the NDVI data sets, but a stable temporal evolution of all agreement /
difference metrics.
The validation is only performed on the NDVI, because VPI and VCI express the position
of the current NDVI observation against the observations from the past NDVI time series for
the same dekad. Since they are derived products of the NDVI, their quality depends entirely
on the quality of the NDVI and the length of the historical time series available. Therefore, no
additional validation for VPI and VCI is performed.
As mentioned before, the NDVI is based on the reflectance of two spectral bands, RED
and NIR and is thus by definition a sensor-specific spectral index and not a biophysical
quantity. The NDVI V2 is compared to data from two independent NDVI time series from
METOP-AVHRR (AVHRR) and TERRA-MODIS (AVHRR), which are also polar-orbiting wide
swath sensor. In addition, the NDVI V2 is also compared to the GEOV1 NDVI, which is the
former version of the NDVI of the Copernicus GL service. The exhaustive validation is
performed on the overlapping time series from 2008-2012 of AVHRR and GEOV1 at a 10-
daily step, and on the period 2001-2012 of MODIS and GEOV1 at a the monthly step. This
latter period almost covers the period on which the long term statistic of the NDVI V2 is
derived to calculate VCI and VPI.
Although NDVI is not a biophysical variable, we do follow the guidelines, protocols and
metrics defined by the Land Product Validation (LPV) group of the Committee on Earth
Observation Satellite (CEOS) for the validation of satellite-derived land products for the
indirect validation (Fernandes et al., 2014).
5.1.2 Sampling
The global images are systematically subsampled over the whole globe taking the central
pixel in a window of 21 by 21 pixels for validation. This subsample is representative for the
global patterns of vegetation and reduces considerably the processing time.
Although using an average filter of 3 by 3 pixels to reduce differences caused by small
geolocation errors is a common practice for the validation of biophysical variables, such a
filter was not applied to the NDVI prior to subsampling. In this way, the relation between the
NDVI value and its viewing and illumination geometry is retained, which have in general a
higher impact on the NDVI values, and are important for the NDVI V2 and AVHRR NDVI
data sets.
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Before the pairwise comparison between GL NDVI V2 and the AVHRR reference NDVI
dataset, an additional sampling was done based on different constraints. This is done with
the purpose of controlling the selection of clear observations with an as similar as possible
observation geometry to be able to make a judgement on the temporal evolution of the
metrics. The constraints are applied on both the GL NDVI V2 and the reference time series
and are particularly useful in order to evaluate the impact of different factors. The constraints
that were used are:
- Clear observations: use only observations that are not identified as bad observation
(cloud/shadow/snow/unreliable) in the status map
- Filtered observations: use only observations that are not affected by (undetected)
clouds, identified through temporal cloud screening. The use of the original
observations allows controlling the further sampling to the observation angles
constraints on the paired comparisons between different data sets.
- Identical day of observation: use only observations that are based on the same day
of observation (in the 10-daily compositing period)
- Viewing zenith angle (VZA) less than 30°: use only observations with off-nadir
viewing angles lower than 30°
- No mixed scatter observations: use only observations that are both in either the East
or the West direction, i.e. either in backscatter or forward scatter
Table 4 summarises the different sets of constraints that were used in the different
sampling schemes. Note that the ‘noMask’ sampling scheme is only based on the clear
observations constraint, and that therefore the sample is representative of the entire data
sets, including anisotropy effects in the NDVI time series.
Table 4 Sampling schemes used to evaluate the comparison between different NDVI data sets. The X marks when a constraint is used in a specific sampling scheme.
Name of the sampling
scheme
Constraint all noMask
Clear observations X X
Filtered observation X
Identical day of observation X
VZA < 30° X
No mixed scatter observations X
For all analysis, the number of pixels of the sample is always taken as a quality criterion.
Results based on less than 1000 pixels are omitted from the analysis.
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5.1.3 Validation metrics
5.1.3.1 The coefficient of determination (R²)
The coefficient of determination (R²) indicates agreement or covariation between two
data sets with respect to a linear regression model. It summarizes the total data
variation explained by this linear regression model. The result varies between 0 and 1
and higher R² values indicate higher covariation between the data sets. In order to
detect a systematic difference between the two data sets, the coefficients of the
regression line should be used.
With X) and Y) the standard deviation of X and Y and X,Y) the covariation of
X and Y.
The R² is provided only together with the scatterplots, because it allows a quantitative
interpretation of the scatterplots.
5.1.3.2 Geometric mean regression
Model I regression models (e.g. Ordinary Least Squares) are appropriate for predicting
one data set out another and one data set is assumed error-free. This is not the case when
comparing two similar data sets of remote sensing images, because both are subjected to
noise. In this case, model II regression models are more suited. Different regression models
II exist, such as the geometric mean (GM), orthogonal and OLS bisector regression models.
The difference between the models is in the way the errors are minimized
OLS minimizes the sum of the squared vertical distances (errors on Y) from the data
points to the regression line
GM minimizes the sum of the products of the vertical and horizontal distances (errors
on Y and X)
Orthogonal regression minimizes the sum of the squared perpendicular distance from
the data point to the line (errors on Y and X)
OLS bisector regression bisects the angle between the Y on X OLS regression line
and the X on Y OLS regression line.
Each of the model II regression analysis methods has its merits and deficiencies. A
comparison of MODIS NDVI with AVHRR NDVI in which the four different methods were
used, showed that the model II approaches results were very similar, and that the difference
was the largest compared to the simple OLS regression method (Ji, Gallo, Eidenshink, &
Dwyer, 2008). Therefore, the choice is somewhat arbitrary. Here, the GM regression model
was used because of its simplicity.
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The GM model is of the form
With
(GMRslo)
(GMRint)
X and Y : the standard deviation of X and Y
R: the correlation coefficient
Sign(): signum function that takes the sign of the variable between the brackets
: the mean value of X
The GM regression slope and intercept are also added as quantitative information related
to the scatterplots. The regression is also used to derive the agreement coefficient and to
differentiate between systematic and random differences.
5.1.3.3 The agreement coefficient (AC)
The agreement coefficient (AC) is based on the evaluation of the distances between
the actual observations and, the 1-1 line and the Geometric Mean (GM) regression line
between both data sets. It is the evaluation of three distances among the points and the
two lines:
- The distance between the point (Xi,Yi) and the 1-1 line
- The distance between the point (Xi,Yi) and the regression line. This is the
unsystematic difference between Xi and Yi.
- The distance between the 1-1 line and the linear regression line. This is the
systematic difference between Xi and Yi. The systematic difference is attributed to
the fixed difference between the two data sets.
To make the coefficient unit independent and bounded, the distance is standardized
by a quantity referred to as the potential difference, which is measured by the range of X
and Y.
or
with and the mean values of X and Y, n the number of samples, SSD is the sum
of the squared differences and SPOD is the sum of the potential differences.
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The agreement coefficient provides a better differentiation compared to the R². Small
deviations in agreement are better expressed in the AC compared to the R².
5.1.3.4 The mean squared difference (MSD)
The mean squared difference (MSD) is defined as
It can be further partitioned into the systematic mean product difference (MPDs) and
the unsystematic mean product difference (MPDu).
With and calculated using the GM regression line and n the number of samples.
Then,
The partitioning of the difference into systematic and unsystematic difference is
particularly important in the evaluation of the NDVI data sets, because the focus of the
validation is on the temporal evolution of the difference between data sets.
5.1.3.5 The root mean squared error (RMSE)
The Root Mean Squared Error (RMSE) measures how far the difference between the
two data sets is from 0 and is defined as
The RMSE includes both systematic and unsystematic differences and is a widely
used difference measure, but it lacks the differentiation between systematic and random
error. However, based on our experience, it provides a better differentiation (especially
spatially) compared to the MSD, and it is easier to interpret (same scale as inputs).
5.1.4 Overview of the procedure
Since no standard procedure exists to assess the performance of EO-derived NDVI
products, a procedure adapted from the validation of the vegetation products in Global Land
service is followed. These are:
(1) Spatial consistency analysis
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a. Spatial distribution of the NDVI values: global maps of metrics expressing the
similarity and difference between different global NDVI time series were computed.
The metrics are MPDs, MPDu and RMSE (see section 5.3).
b. Spatial continuity: the missing values or pixels flagged as invalid over land were
quantified.
c. Magnitude of values: the NDVI distribution for three areas with homogeneous land
cover (Sahara, Amazonia and Congo Basin) was evaluated for the different time
series.
(2) Global statistical analysis
a. Histograms: statistical distributions of NDVI values were computed over biomes for
the different data sets. An aggregated version of the GLC2000 land cover map was
used for this purpose (Figure 1).
b. Scatterplots between the different data sets were produced at a global scale and per
land cover type.
c. Statistical assessment per biome: metrics (see section 5.1.3) among different data
sets were computed per biome, but also per year to evaluate the time evolution of the
metrics.
(3) Temporal consistency analysis
a. Temporal variation and realism: temporal profiles are evaluated for BELMANIP and
DIRECT sites (see section 4.3.3).
b. Temporal smoothness: the smoothness was evaluated by taking three consecutive
observations and computing the absolute value of the different delta between the
centre P(dn+1) and the corresponding linear interpolation between the two extremes
P(dn) and P(dn+2) as follows:
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6. RESULTS
6.1 SPATIAL CONSISTENCY
6.1.1 Spatial distribution of values
This analysis discusses the spatial distribution of the MPDs, MPDu, and RMSE between
the different data sets. The metrics are derived per pixel from the cloud-free, paired NDVI
observations from the overlapping time series.
It should be noted that in the case of the NDVI, the metrics of agreement/disagreement
purely express the difference in NDVI, which can be due to differences in sensor
characteristics or processing choices.
First the results of the 10-daily time step comparison are discussed followed by that of the
monthly time step comparison. .
6.1.1.1 Ten-daily composites
First we present the MPDs, MPDu, and RMSE between the 10-daily NDVI values of NDVI
V2, AVHRR and GEOV1 for the overlapping time period (2008-2012). The results of only
one sampling schemes will be discussed, i.e. ‘noMask’ (see Table 4). The other sampling
scheme (only possible in the comparison to AVHRR) selects too little observations per pixel,
which would lead to non-representative and non-significant results.
Figure 4 presents the MPDs (the systematic bias) between the NDVI from NDVI V2,
AVHRR and GEOV1 for the overlapping time period 2008-2012. The majority of MPDs
values are below 0.005. This is the case both for the comparison of NDVI V2-AVHRR and
the comparison NDVI V2-GEOV1.
Figure 5 presents the MPDu (the unsystematic difference) between the same data sets.
The majority of the pixels have a MPDu below 0.01, but different patterns emerge when
comparing with AVHRR or GEOV1. In comparison to AVHRR, higher values of MPDu (up to
0.04) are found in tropical areas. In the northern hemisphere, also slightly higher values of
MPDu are found. Contrarily, the opposite pattern is found when comparing NDVI V2 with
GEOV1, where tropical areas have slightly higher values up to 0.015, but values up to 0.03
can be found in northern latitudes. The differences between NDVI V2 and GEOV1 are purely
due to processing choices, since essentially the same input data (SPOT/VGT spectral
reflectances) were used to generate them. At higher latitudes, the large MDPu and RMSE
(Figure 6) is due to a difference in the maximum NDVI values, GEOV1 having a lower
maximum value as demonstrated in Figure 8. Additionally, all temporal profiles at latitudes
above 55° N from BELMANIP2 and DIRECT sites show at least one artefact in the GEOV1
time series, where spurious NDVI values occur mainly at the end and sometimes at the
beginning of the growing seasons (see e.g. Figure 9). In the areas around the equator, the
reason for the high MDPu and RMSE values is different. Here, the NDVI V2 temporal profiles
are perturbed by a large amount of undetected clouds (see Figure 10). The GEOV1 NDVI
does not have this noise, but only few values are present in the entire time series.
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Figure 4: MPDs between NDVI V2, AVHRR NDVI and GEOV1 NDVI: A. 10-daily NDVI V2 vs AVHRR NDVI; B. 10-daily NDVI V2 vs GEOV1 NDVI (white: no pixels selected for comparison) calculated per pixel over the period 2008-2012.
Figure 5: MPDu between NDVI V2, AVHRR NDVI and GEOV1 NDVI: A. 10-daily NDVI V2 vs AVHRR NDVI; B. 10-daily NDVI V2 vs GEOV1 NDVI (white: no
pixels selected for comparison) calculated per pixel over the period 2008-2012.
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Figure 6: RMSE between NDVI V2, AVHRR NDVI and GEOV1 NDVI: A. 10-daily NDVI V2 vs AVHRR NDVI; B. 10-daily NDVI V2 vs GEOV1 NDVI (white: no
pixels selected for comparison) calculated per pixel over the period 2008-2012.
Figure 7: Number of selected paired observations between NDVI V2, AVHRR NDVI and GEOV1 NDVI: A. 10-daily NDVI V2 vs AVHRR NDVI; B. 10-daily
NDVI V2 vs GEOV1 NDVI (white: no pixels selected for comparison) calculated per pixel over the period 2008-2012.
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The RMSE in Figure 6 shows more spatial details of the differences between the data
sets. Similar opposite patterns as for the MPDu are found for RMSE: higher RMSE values in
tropical areas when comparing NDVI V2 with AVHRR and higher RMSE values at higher
latitudes in the northern hemisphere in the comparison with GEOV1. The lowest RMSE is
found between NDVI V1 and GEOV1 in barren areas. The RSME between NDVI V2 and
AVHRR is somewhat higher there, because AVHRR has a slightly higher NDVI in these
areas due to sensor differences. Unlike the VGT red band, the AVHRR red band does not
overlap with the transition zone between absorption and reflection by vegetation, leading to
lower red reflectance values and thus higher NDVIs. The NIR reflectance band of VGT is
narrower than that of AVHRR. This leads to a banana-shaped agreement between the data
sets, where in theory the similarity is highest for the mid-range NDVI values, and slightly
lower for the low and high NDVI values. In the comparison between the actual NDVI data
(see scatterplots in 6.2.2.1) there is a shift in this theoretical agreement due to the difference
in overpass time.
For the area where more than 120 observations (2/3rd of the total observations in a 5-
years period (36 dekads x 5 years)) were used in the calculation of all metrics (see Figure 7),
the majority of the pixels have an RMSE of less than 0.1. For the other third of the pixels
(mainly located in the tropics), the RMSE between NDVI V2 and AVHRR is up to 0.2 or even
higher (very localized areas). The main reason for this is the higher amount of undetected
clouds in both the NDVI V2 and AVHRR NDVI data sets.
Surprisingly, the number of good observations used in the evaluation is much lower in the
comparison with GEOV1 than with AVHRR, especially in barren, tropical and mountainous
areas. This is because the GEOV1 flags a much larger amount of pixels because a more
strict cloud screening is applied. This leads to lower differences with NDVI V2 and smoother
temporal profiles (see e.g. Figure 10).
Figure 8: Temporal profile between the 10-daily composites of NDVI V2 (red), AVHRR (blue)
and GEOV1 (green), which demonstrates the lower maximum values of the GEOV1 NDVI.
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Figure 9: Temporal profiles between the 10-daily composites of NDVI V2 (red), AVHRR
(blue) and GEOV1 (green). Examples of two sites where GEOV1 provides spurious NDVI values at the end of the growing season.
Figure 10: Temporal profiles between the 10-daily composites of NDVI V2 (red), AVHRR
(blue) and GEOV1 (green) showing the large perturbation of the NDVI V2 data set by undetected clouds (low NDVI values).
6.1.1.2 Monthly composites
The MPDs, MPDu and RMSE between the monthly NDVI values of NDVI V2, MODIS and
GEOV1 for the overlapping time period (2001-2012) are presented in Figure 11 to Figure 13.
The ’noMask’ sampling scheme, which takes only into account the Information in the status
maps, is the only one used (seeTable 4).
Figure 11 presents the MPDs between these data sets. The majority of MPDs values are below
0.005. This is the case both for the comparison of NDVI V2-MODIS and the comparison NDVI V2-
GEOV1, but with larger number of MDPs values higher than 0.005 for GEOV1 comparison.
Figure 12 presents the MPDu between the data sets. Both in comparison to MODIS and
GEOV1, the MPDu value for the majority of pixels is still below 0.005. However, with respect
to MODIS, higher values up to 0.025 can be found in tropical areas, and slightly higher
values up to 0.015 in northern latitudes. With respect to GEOV1, values up to 0.03 can be
found in northern latitudes, with no appreciable increase in tropical areas.
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Figure 11: MPDs between NDVI V2, MODIS NDVI and GEOV1 NDVI: A. Monthly NDVI V2 vs MODIS NDVI ‘noMask’; B. Monthly NDVI V2 vs GEOV1
NDVI ‘noMask’ (white: no pixels selected for comparison)
Figure 12: MPDu between NDVI V2, MODIS NDVI and GEOV1 NDVI: A. Monthly NDVI V2 vs MODIS NDVI ‘noMask’; B. Monthly NDVI V2 vs GEOV1 NDVI ‘noMask’ (white: no pixels selected for comparison)
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Figure 13: RMSE between NDVI V2, MODIS NDVI and GEOV1 NDVI: A. Monthly NDVI V2 vs MODIS NDVI ‘noMask’; B. Monthly NDVI V2 vs
GEOV1 NDVI ‘noMask’ (white: no pixels selected for comparison)
Figure 14: Number of paired observations between NDVI V2, MODIS NDVI and GEOV1 NDVI: A. Monthly NDVI V2 vs MODIS NDVI ‘noMask’; B.
Monthly NDVI V2 vs GEOV1 NDVI ‘noMask’ (white: no pixels selected for comparison)
A
.
B
.
A
.
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.
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The RMSE is shown in Figure 13. The resulting patterns are similar to that of MPDu, but
provides more spatial details.
The number of paired observations used in the analysis is shown in Figure 14, which
serves as a quality measure of the derived metrics. The more observations were used, the
more representative the results are. Again here, there are considerably less observations
used in the comparison with GEOV1, due to the presence of more flagged values in that data
set.
Comparing the differences between the NDVI V2 and the GEOV1 at the 10-daily and
monthly time step, it is observed that the RMSE is higher at high latitudes and lower in the
tropics. Both can be explained by the compositing method to create the monthly NDVI V2
data set, i.e. the maximum NDVI over the three dekads in one month was taken. The
maximum NDVI selection rule tends to select observations with a larger viewing zenith angle.
For GEOV1, the middle composite of the month was taken. This NDVI is based on
normalized reflectances over a 30-days period. This normalization reduces the extreme
values associated with large viewing zenith angles. At high latitudes, the maximum of NDVI
V2 becomes much higher than that of GEOV1, as demonstrated in Figure 15. In the tropical
areas, this leads to less undetected clouds, which also leads to less random errors (MPDu) at
the monthly time step for these areas.
Figure 15: Temporal profile between the monthly composites of NDVI V2 (red), MODIS (blue)
and GEOV1 (green), which demonstrates the larger difference between the maximum values of the NDVI V2 and GEOV1 NDVI, compared to the 10-daily composites (Figure 8).
The systematic difference is slightly higher at the monthly time step between NDVI V2 and
GEOV1 compared to the analysis at the 10-daily time step, for which an explanation lacks. It
should also be noted that for the two analysis, a different time period was compared possibly
leading to different results.
6.1.2 Spatial continuity
Figure 16 shows the percentage of physical NDVI values for the ten-daily composites of
NDVI V2, AVHRR and GEOV1 NDVI in the period 2008-2012. This means that clouds, cloud
shadows, snow and ice and bad radiometric quality was not used. The results are the same
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for the VCI and VPI data sets. The number of pixels with physical NDVI values for all data
sets is the lowest in the northern hemispherical winter, largely due to no observations due to
the low sun position or snow cover. The number of good observations for GEOV1 is lower
by 37% on average than that of NDVI V2. This can be explained by the stricter filtering of the
clouds of GEOV1 compared to that of the standard cloud filtering of the NDVI V2. The earlier
overpass time of METOP-AVHRR is responsible for the lower amount of good observations
in the northern hemispherical winter. The sun position is closer to the horizon earlier in the
morning, resulting in a longer period of no observations during there. During the summer
period, there are slightly less observations from the AVHRR NDVI time series, which can be
attributed to the better cloud masking due to the presence of thermal channels.
Figure 16: temporal evolution of % of physical NDVI values for all ten-daily composites
from the overlapping time period between NDVI V2, AVHRR NDVI and GEOV1 NDVI.
Similar results are obtained from the comparison of monthly composites of NDVI V2,
MODIS NDVI and GEOV1 NDVI in the period 2001-2012 (Figure 17). in August 2002, there
is a sudden short decrease in the number of good observations of GEOV1. When checking
the images visually, it appeared that large part of the image is flagged compared to the
previous or next one. The pattern of the missing values corresponds to cloudy observations.
It is not clear what caused this. Possibly this is due to a lower amount of input data used to
generate the composite. For this period the processing was done offline in the frame of the
Geoland2 project, so we cannot check the processing. The lower amount of good NDVI
values of MODIS can be explained by the better cloud screening compared to NDVI V2.
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2008 2009 2010 2011 2012
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Figure 17: % of good observations for all monthly composites from the overlapping time
period between NDVI V2, MODIS NDVI and GEOV1 NDVI.
6.1.3 Magnitude of values
The distribution of the NDVI values is compared for three areas with relatively
homogeneous land cover (Figure 2). The expected values for the Sahara site are low NDVI
values, and high NDVI values for the two other sites.
The NDVI distributions between ten-day composites of NDVI V2, AVHRR and GEOV1 are
shown in Figure 18. The distributions of NDVI V2 and GEOV1 are very similar.
For the Sahara and the Amazon site, NDVI V2 has a lower NDVI value compared to
AVHRR NDVI. For the site in the Congo Basin, both data sets show a similar distribution of
values. This latter site is less homogeneous in vegetation cover. The differences between the
results of the two data sets can be explained by their difference in sensor characteristics.
The Red reflectance band of VGT overlaps partly with the transition zone between
absorption and reflectance (the ‘red edge’), while this is not the case for AVHRR. This leads
to a slightly lower NDVI for barren areas and for dense vegetation.
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2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
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NDVI V2 MODIS NDVI GEO V1
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Sahara Congo Basin Amazon forest
ND
VI
V2 –
AV
HR
R
ND
VI
V2 –
GE
OV
1
Figure 18: Frequency distribution of the 10-day NDVI composite values (2008 –2012) for the Sahara, Congo Basin and Amazon forest. Pairwise comparison of good observations of NDVI V2 (red), AVHRR (blue), and GEOV1 (green). X-axis: NDVI value classes in steps of 0.1, Y-axis: percentage of occurrence.
Figure 19 shows the frequency distributions between the monthly composites of NDVI V2
compared to MODIS and GEOV1. The results are more different between NDVI V2 and
GEOV1 compared to the 10-daily time step, but the peaks are still at corresponding NDVI
classes. The similarity is high between MODIS and NDVI V2. Only for the Congo site, the
peak of NDVI values is at a lower NDVI value for MODIS compared to NDVI V2.
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Sahara Congo Basin Amazon forest
ND
VI
V2 –
MO
DIS
ND
VI
V2 –
GE
OV
1
Figure 19: Frequency distribution of the monthly NDVI composite values (2001 –2012) for the Sahara, Congo Basin and Amazon forest. Pairwise comparison of good observations of NDVI V2 (red), MODIS (blue), and GEOV1 (green). X-axis: NDVI value classes in steps of 0.1, Y-axis: percentage of occurrence.
6.2 GLOBAL STATISTICAL ANALYSIS
6.2.1 Magnitude of values
Frequency distributions of the subsampled global images for the major biomes were
derived for paired observations of NDVI V2 and the reference data sets. This was done for
the 10-daily and monthly composites. A simplified version of the GLC2000 classification,
representing 7 major land cover classes (Broadleaved Evergreen Forests, Broadleaved
Deciduous Forests, Needleleaved Forests, Shrubland, Herbaceous cover, Cultivated areas
and Bare areas) was used as stratification (see section 4.3.1, p20).
6.2.1.1 Ten-daily composites
Figure 20 presents the frequency distributions of the 10-daily NDVI time series for the
overlapping time period between NDVI V2, AVHRR NDVI and GEOV1 NDVI. The NDVI
distributions are similar for all land cover classes. NDVI V2 tends to have slightly lower
values than AVHRR.
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NDVI
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NDVI
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GEOV1 tends to have lower values, especially for the classes herbaceous (HER) and
cultivated (CUL), despite the fact that both data sets are based on identical input data.
ND
VI
V2 –
AV
HR
R
BEF
BDF
NLF
SHR
HER
CUL
BA
ND
VI
V2 –
GE
OV
1
BEF
BDF
NLF
SHR
HER
CUL
BA
Figure 20: Frequency distributions over 7 different biomes based on the 10-daily NDVI time series of 2008-2012. Pairwise comparison of good observations for NDVI V2 (red), AVHRR (blue), GEOV1 (green). X-axis: NDVI value classes in steps of 0.1, Y-axis: percentage of occurrence.
6.2.1.2 Monthly composites
Figure 21 present the same result, but for the monthly composites of NDVI V2 compared
to MODIS and GEOV1 NDVI. For the latter, we obtain the same results as for the 10-daily
composites, i.e. a good correspondence of the shape of the frequency distribution for the
forest classes and a shift to lower values for GEOV1 for the other classes.
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NDVI
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NDVI
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NDVI
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NDVI
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NDVI
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When comparing NDVI V2 with MODIS NDVI, the frequency distributions are almost
identical. The difference in correspondence with GEOV1 and MODIS is striking. GEOV1 is
derived from essentially the same input data but with a completely different processing
approach, whereas the MODIS NDVI is completely independent from the NDVI V2 in all
aspects (input data, processing approach, etc.).
ND
VI
V2 –
MO
DIS
BEF
BDF
NLF
SHR
HER
CUL
BA
ND
VI
V2 –
GE
OV
1
BEF
BDF
NLF
SHR
HER
CUL
BA
Figure 21: Frequency distributions over 7 different biomes based on the monthly NDVI time series of 2001-2012. Pairwise comparison of good observations for NDVI V2 (red), MODIS (blue), GEOV1 (green). X-axis: NDVI value classes in steps of 0.1, Y-axis: percentage of occurrence.
6.2.2 Regression analysis
This section discusses the scatterplots between the NDVI time series of NDVI V2 and the
reference time series, also based on the global subsample. The scatterplots were created for
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NDVI
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%
NDVI
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%
NDVI
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NDVI
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NDVI
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NDVI
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all the pixels aggregated and stratified in seven major land cover classes based on a
regrouping of the GLC2000 classes (see section 4.3.1).
6.2.2.1 Ten-daily composites
The scatterplots between the 10-daily NDVI composites of NDVI V2 and AVHRR are
presented in Figure 22 (sampling scheme using all constraints ‘all’) and Figure 23 (‘noMask’
sampling scheme). In the first case, only cloud-free observations with constraints on
observation angles are selected in both datasets. The scatterplot for all land cover types
(Figure 22 top left) shows that the NDVI from both sensors is very similar, and nearly linearly
related. The other scatterplots in Figure 22 show the results for the 7 biomes. As expected,
the scatter is the highest for the forest cover classes, in particular for evergreen forests. This
is probably caused by larger impact of angular dependencies on forest vegetation and a
larger amount of undetected clouds in the imagery of both sensors.
All (R² = 0.947)
BEF (R² = 0.583)
BDF (R² = 0.913)
NLF (R² = 0.880)
SHR (R² = 0.941)
HER (R² = 0.952)
CUL (R² = 0.932)
BA (R² = 0.955)
Figure 22: Scatterplots between NDVI V2 (x-axis) and AVHRR (y-axis) 10-daily composited NDVI values of 2008-2012 using all constraints listed in section 5.1.2 over all land cover types (top left) and over 7 different biomes.
It is clear that the selection criteria for the ‘all’ sampling constraints results in a more
compact scatterplot, compared to the ‘noMask’ constraints (Figure 23), which is also
reflected in a decreased R² (see Table 5). In this case, the result for Herbaceous cover
(HER) shows relatively more scatter. The large scatter originates from angular effects and
undetected clouds, which are present in both data sets. The associated metrics (RMSE, AC,
MSD, MPDu and MPDs) are presented in section 6.2.3.
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All (R² = 0.879)
BEF (R² = 0.252)
BDF (R² = 0.757)
NLF (R² = 0.787)
SHR (R² = 0.876)
HER (R² = 0.879)
CUL (R² = 0.810)
BA (R² = 0.860)
Figure 23: Scatterplots between NDVI V2 (x-axis) and AVHRR (y-axis) 10-daily composited NDVI values of 2008-2012 using the ‘noMask’ sampling scheme over all land cover types (top left) and over 7 different biomes.
The slope (GMRslo) and intercept (GMRint) of the GM regression are very comparable
between the two sampling schemes (Table 5). The largest difference in slope between the
two sampling schemes is observed for NLF and BA. The difference in slope can be explained
by angular influences and undetected clouds.
all constraints noMask
Class GMRint GMRslo R2 GMRint GMRslo R2
Global 0.068 1.005 0.947 0.041 0.994 0.879
BEF -0.034 1.107 0.583 -0.084 1.126 0.252
BDF 0.113 0.938 0.913 0.044 0.999 0.757
NLF 0.108 0.953 0.880 0.030 1.023 0.787
SHR 0.072 1.005 0.941 0.043 1.013 0.876
HER 0.077 0.994 0.952 0.050 0.996 0.879
CUL 0.084 0.963 0.932 0.046 0.970 0.810
BA 0.032 1.107 0.955 0.028 1.070 0.860
Table 5: Regression metrics for the results presented in Figure 22 (all constraints) and Figure 23 (noMask).
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The scatterplots between the 10-daily NDVI composites of NDVI V2 and GEOV1 are
presented in Figure 24 (‘noMask’ sampling scheme). While there is a good agreement for the
low and high NDVI values, for the intermediate NDVI range, GEOV1 values are substantially
lower. The scatter is larger above the 0-1 line and originates from undetected clouds and
anisotropy effects within the NDVI V2 data set. In the GEOV1 data set, the cloud screening is
done independently, and a more strict cloud screening is used. The NDVI V2 copies the
status map that is delivered as an additional layer by the processing center. The GEOV1
NDVI is based on normalized reflectances which results in smoother profiles, and this
combined with the more strict cloud screening results in lower scatter below the 0-1 line in
the scatterplots.
The associated metrics (RMSE, AC, MSD, MPDu and MPDs) are presented in section
6.2.3.
No Mask
Class GMRint GMRslo R2
Global -0.010 0.981 0.925
BEF 0.082 0.876 0.521
BDF -0.009 0.976 0.849
NLF 0.048 0.915 0.642
SHR -0.017 0.969 0.905
HER -0.015 0.971 0.923
CUL -0.002 0.954 0.906
BA 0.000 0.917 0.917
Table 6: Regression metrics for the results presented in Figure 24 (noMask).
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All (R² = 0.925)
BEF (R² = 0.521)
BDF (R² = 0.849)
NLF (R² = 0.642)
SHR (R² = 0.905)
HER (R² = 0.923)
CUL (R² = 0.906)
BA (R² = 0.917)
Figure 24: Scatterplots between NDVI V2 (x-axis) and GEOV1 (y-axis) 10-daily composited NDVI values of 2008-2012 using the ‘noMask’ sampling scheme over all land cover types (top left) and over 7 different biomes.
6.2.2.2 Monthly composites
The analysis was also repeated using the monthly NDVI composites of NDVI V2, GEOV1
and MODIS, overall (Figure 25) and per biome (Figure 26), for the period 2001-2012. In
these cases the ‘noMask’ conditions are used in the sample selection, because the
necessary information on angles and day of observation is not available for the GEOV1 NDVI
dataset.
Whereas the relationship between NDVI V2 and MODIS is nearly linear (Figure 25), this is
not the case in the comparison of NDVI V2 with GEOV1. There is a good agreement for the
low and high NDVI values, but for the intermediate NDVI range, GEOV1 is substantially
lower. Overall, the GM regression line deviates substantially from the 0-1 line in slope and
intercept (Table 7).
The same conclusions can be drawn from the analyses per major land cover type (Figure
26). The relationship between NDVI V2 and MODIS is almost linear. The GM regression line
almost coincides with the 0-1 line. Also here, there is more scatter at the side of NDVI V2,
which originates from anisotropy effects and undetected clouds, even in monthly composites.
This effect is also visible in the scatterplot between NDVI V2 and GEOV1.
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NDVI V2(X) – GEOV1(Y)
NDVI V2(X) – MODIS(Y)
Figure 25: Scatterplots between NDVI V2, GEOV1 and MODIS (see figure titles) monthly composited NDVI values for the period 2001-2012 using the ‘noMask’ sampling scheme.
NDVI V2 (X) – GEOV1 (Y) NDVI V2 (X) – MODIS (Y)
Class GMRint GMRslo R2 GMRint GMRslo R2
Global -0.030 0.948 0.944 0.000 1.008 0.927
BEF -0.156 1.124 0.807 0.009 1.022 0.543
BDF -0.081 1.020 0.884 0.016 0.977 0.836
NLF -0.088 1.046 0.707 0.099 0.877 0.722
SHR -0.034 0.936 0.920 -0.009 1.015 0.892
HER -0.035 0.943 0.933 -0.005 0.992 0.909
CUL -0.038 0.948 0.918 0.004 0.981 0.878
BA -0.002 0.862 0.898 0.003 0.999 0.881
Table 7: Regression metrics for the results presented in Figure 25 and Figure 26.
When looking at the different biomes, the relationships between NDVI V2 and the two
other NDVI data sets are variable. The impact of BRDF and undetected clouds is largest
when compared to MODIS. This issue is visible in all land cover classes but most
pronounced in BEF. Except for the class NLF, the relationship is very close to the 0-1 line.
Contrarily, when comparing GEOV1 and NDVI V2, the scatter is not large in the class BEF
(mostly due to the absence of values in the GEOV1 for this class) and the scatter is larger or
equally large for the GEOV1 for the two other forest classes (BDF and NLF) and for CUL.
The associated metrics (RMSE, R², AC, MSD) are presented in section 6.2.3.
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NDVI V2(X) – GEOV1(Y) NDVI V2(X) – MODIS(Y)
BE
F
BD
F
NLF
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NDVI V2(X) – GEOV1(Y) NDVI V2(X) – MODIS(Y)
SH
R
HE
R
CU
L
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NDVI V2(X) – GEOV1(Y) NDVI V2(X) – MODIS(Y)
BA
Figure 26: Scatterplots of pairwise comparisons between NDVI V2 (X-axis), GEOV1 (Y-axis, left column) and MODIS (Y-axis, right column) monthly composited NDVI values of the period 2001-2012 using the ‘noMask’ sampling scheme over 7 different biomes.
6.2.3 Statistical assessment and effect of sampling scheme
In this analysis, several metrics of agreement and difference described in section 5.1.3
are calculated based on a sample in time and space of the entire overlapping time series and
also for each year separate, at the global scale and per land cover.
6.2.3.1 Ten-daily composites
Figure 27 shows the results of the metrics calculated over the 2008-2012 period per land
cover class and over all land cover classes (overall) between NDVI V2 and AVHRR (two
sampling schemes: ‘all’ and ‘noMask’) and between NDVI V2 and GEOV1 (‘noMask’).
When comparing the two sampling schemes in the comparison between NDVI V2 and
AVHRR, it is observed that the agreement is higher and thus the differences are smaller for
the sampling scheme using all constraints (‘all’). The unsystematic difference (MPDu) is
lowest for ‘all’ compared to ‘noMask’. The opposite is true for the systematic difference
(MPDs). This is normal, because the ‘all’ sampling scheme is designed to compare
observations done in similar conditions. The increase in unsystematic difference between ‘all’
and ‘noMask’ is attributed to angular effects and undetected clouds, cloud shadow or snow
observations. These findings are not valid for the class BEF. The RMSE has for all land
cover classes approximately the same magnitude as for the overall sample (around 0.09 for
‘all’ and 0.10 for ‘noMask’), except for BA and BEF (only ‘noMask’).
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NDVI V2 - AVHRR
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NDVI V2 - AVHRR
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NDVI V2 - GEOV1
Sampling scheme ‘noMask’
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Figure 27: MSD, MPDu and MPDs (top), AC (middle) and RMSE (bottom) calculated between the 10-daily composites of NDVI V2 and AVHRR
NDVI for the sampling schemes ‘all constraints’ (left) and ‘noMask’ (middle) and between NDVI V2 and GEOV1 (right) per land cover type and over the entire reference period.
0
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The agreement coefficient AC is highest for ‘all’ than for ‘noMask’ and varies around 0.85
for AC and 0.95 for R for the most constrained sample (‘all’). When considering all pixels, the
agreement is slightly lower (AC=0.8 and R=0.9). For BEF, the values are considerably lower
for the reasons explained before.
Compared to GEOV1, there is almost no systematic difference with the NDVI V2. The
overall difference (MSD) is almost entirely attributed to unsystematic difference. This is to be
expected, given that the GEOV1 is based on a different processing approach. The
agreement (AC) is low for BEF and NLF. The results for BEF are also based on a small
sample size. For NLF, the agreement is significantly smaller. An important reason for this is
that in this land cover, the spurious NDVI values as demonstrated in Figure 9 occurs the
most frequent. The same is reflected in the RMSE for that class, which is also the highest. It
is also important to note that , for all biomes, the RMSE between NDVIV2 and GEOV1 is
lower than the RMSE between NDVI V2 and AVHRR (whatever the sampling). When looking
at the MPDs, it can be seen that the systematic difference is the highest between NDVI V2
and AVHRR, which is probably due to the difference in overpass time and the difference in
spectral response functions. So actually, when looking at the random error (MPDu) this is
smallest between NDVI V2 and AVHRR. Of course, due to the difference in sampling
scheme, these results cannot be directly compared to those of the comparison with GEOV1.
The larger difference between the noMask sampling scheme comparison, can certainly by a
large part be explained by the larger amount of undetected clouds in the comparison to
AVHRR.
Figure 28, Figure 30 and Figure 29 show the temporal evolution of these metrics per class
and overall and for the same data sets and sampling schemes. For most land cover classes
and over all classes (overall), the metrics are stable over time. NLF exhibits the most
variability over time. The ‘noMask’ results in the NDVI V2 – AVHRR analysis are also more
stable over time opposed to using the ‘all constraints’ sampling scheme, because these are
derived using a larger sample size.
Nevertheless, a small decreasing trend is observed in the NDVI V2 – AVHRR ‘noMask’
comparison for the metrics RMSE (Figure 28) and the systematic difference MPDs (Figure
30). This trend is not visible in the comparison with GEOV1. For the latter, this is most
pronounced for the ‘noMask’ sampling scheme, but the magnitude of the decrease in mean
difference over time is in the order of 0.001 NDVI (0.1%), which is very small. For RMSE the
decrease is less than 0.007 over the five years, which is also very small. It is not clear where
this weak trend originates from, but it is not observed in the comparison with the monthly
composites with MODIS data (see 6.2.3.2).
The agreement coefficient AC is stable over time.
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NDVI V2 - AVHRR
Sampling scheme ‘all constraints’
NDVI V2 - AVHRR
Sampling scheme ‘noMask’
NDVI V2 - GEOV1
Sampling scheme ‘noMask’
Figure 28: Evolution over time of the RMSE calculated between the 10-daily composites of NDVI V2 and AVHRR NDVI for the sampling schemes
‘all constraints’ (left) and ‘noMask’ (middle) and between NDVI V2 and GEOV1 (right) per land cover type and over the entire reference period.
NDVI V2 - AVHRR
Sampling scheme ‘all constraints’
NDVI V2 - AVHRR
Sampling scheme ‘noMask’
NDVI V2 - GEOV1
Sampling scheme ‘noMask’
Figure 29: Evolution over time of the AC (top) and R (bottom) calculated between the 10-daily composites of NDVI V2 and AVHRR NDVI for the
sampling schemes ‘all constraints’ (left) and ‘noMask’ (middle) and between NDVI V2 and GEOV1 (right) per land cover type and over the entire reference period.
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NDVI V2 - AVHRR
Sampling scheme ‘all constraints’
NDVI V2 - AVHRR
Sampling scheme ‘noMask’
NDVI V2 - GEOV1
Sampling scheme ‘noMask’
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Figure 30: Evolution over time of the MDPs (top), MDPu (middle) and MSD (bottom) calculated between the 10-daily composites of NDVI V2 and
AVHRR NDVI for the sampling schemes ‘all constraints’ (left) and ‘noMask’ (middle) and between NDVI V2 and GEOV1 (right) per land cover type and over the entire reference period.
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6.2.3.2 Monthly composites
The same analysis was done comparing the monthly composites of AVHRR and MODIS.
As for the other analysis on the monthly composites, only the ‘noMask’ sampling scheme
was used.
Figure 31 shows the results of the metrics derived per land cover class and overall.
Compared to MODIS, the difference with NDVI V2 is predominantly unsystematic, although a
small systematic difference is obtained for two forest classes BEF and NLF.
When GEOV1 is used as reference data set, the overall difference (MSD) with NDVI V2 is
of the same magnitude as in the comparison to MODIS, but there is a larger contribution of a
systematic difference (40% on average) to the total difference.
NDVI V2 – MODIS NDVI V2 – GEOV1
Figure 31: MSD, MPDu and MPDs (top), AC (middle) and RMSE (bottom) calculated between
NDVI V2 and MODIS (left) and between NDVI V2 and GEOV1 (right) NDVI monthly composites per land cover type and over the entire reference period.
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Except for BEF, the magnitude of the overall differences is smaller in the comparison to
MODIS than to GEOV1. The RMSE is consequently also higher for GEOV1 than for MODIS.
The agreement (AC) is not much affected, and is even larger for BEF and NLF for GEOV1.
This is probably due to the small sample size for these classes.
Figure 32 presents the evolution over time of the difference metrics per land cover class
and overall, Figure 33 and Figure 34 show the same for AC and RMSE.
Figure 32 show the differences of the NDVI V2 with MODIS and GEOV1. Compared to
MODIS, the difference is mainly unsystematic, whereas in the comparison with GEOV1 it is
predominantly unsystematic. There is also a large difference over the years, especially
between the years 2001-2002 and 2003-2012. In this first period data from VGT1 is used for
the NDVI V2 and GEOV1 and the second period is based on data from VGT2. Compared to
MODIS, a much larger systematic difference is observed for the VGT1 period for the forest
classes. When comparing to GEOV1, the difference is the largest for all other classes and
almost not present for the three forest classes. In GEOV1 NDVI processing, the VGT1
reflectances are spectrally corrected to fit the VGT2 reflectances. This correction is not
applied on the NDVI V2 data set, which explains the difference in systematic difference
between the data sets for most of the classes. However, in the comparison with MODIS,
which is a completely independent reference data set, the systematic difference is only
present for the forested classes, i.e. where there seems to be no systematic difference
between NDVI V2 and GEOV1, and vice versa. Also the evolution of the difference metrics
over time for the period 2003-2012 is not the same compared to MODIS or the GEOV1. For
SHR, HER and CUL, there is a small upward trend in the systematic difference. This trend is
around 0.0004 in the comparison with MODIS and somewhat larger with GEOV1 (around
0.0007), but still very small to impact the data.
The AC in Figure 33 shows a very stable result over time for most classes and compared
to the two NDVI data sets. The AC is most variable for the classes BEF and NLF. For the
class BA in the comparison with GEOV1, the AC is substantially lower for the years 2001-
2002 than for the years 2003-2012.
For RMSE (Figure 34), the same conclusions can be drawn as for the other difference
measures.
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NDVI V2 – MODIS NDVI V2 – GEOV1
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Figure 32: Evolution over time of the MDPs (top), MDPu (top), and MSD (bottom) between NDVI V2 and MODIS (left) and between NDVI V2 and
GEOV1 (right) monthly composites per land cover type.
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NDVI V2 – MODIS NDVI V2 – GEOV1
Figure 33: Evolution over time of the AC between NDVI V2 and MODIS (left) and between NDVI V2 and GEOV1 (right) monthly composites per
land cover type.
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NDVI V2 – MODIS NDVI V2 – GEOV1
Figure 34: Evolution over time of the RMSE between NDVI V2 and MODIS (left) and between NDVI V2 and GEOV1 (right) monthly composites per
land cover type.
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6.3 TEMPORAL CONSISTENCY
6.3.1 Temporal variations and realism
Figure 35 shows a number of temporal profiles which have a good agreement, and Figure
36 shows some examples where the temporal profiles differ more. The red line is the NDVI
V2, the green line the GEOV1 NDVI, and the blue line the AVHRR NDVI in case of 10-daily
composites (left column) and MODIS in case of monthly composites (right column).
The period for which the data are plotted is 2008-2012 for the 10-daily composites and
2001-2012 for the monthly composites.
The Belmanip 218 in Figure 35 is a barren soil pixel located in the Sahara. Although there
is a constant difference between the NDVI of AVHRR and VGT, their temporal evolution
agrees very well. There is almost no difference with the GEOV1 NDVI. Compared to MODIS,
the NDVI V2 differs with a small constant difference. For the monthly composites, there is a
sudden shift between the years 2001-2002 and the period 2003-2012, i.e. the period where
the data input shifted from VGT1 to VGT2. The same shift is observed for the Belmanip 160
(BDF) and Direct 61 and 75 (both SHR) in Figure 35, and for the Belmanip 296 (BDF), 284
(HER) and 1 (SHR) in Figure 36.
Belmanip 74 (BDF) is a broadleaf deciduous forest. It shows good agreement, both in
temporal evolution as in range (min, max) over the seasons, compared to AVHRR and to
MODIS. The peak values are slightly lower for the GEOV1 data set, which is probably due to
the length of compositing period (30 days instead of 10 days) which tends to reduce the
extreme values.. Compared to MODIS, the peak values are lower for NDVI V2 monthly
composites for the first two years, i.e. also the change between VGT1 and VGT2. This is also
the case for Belmanip 159 and 260 (BDF) in Figure 35 and for Belmanip 155 (BEF) in Figure
36.
Belmanip site 159 and 160 are also Broadleaf Deciduous forest sites. They show a good
general agreement, although the AVHRR NDVI is somewhat higher and the GEOV1 is often
lower. There is a very good agreement with the MODIS NDVI. And we see clearly on 10-
days comparison that the GEOV1 profile is smoother than the NDVI V2 and AVHRR profiles.
Belmanip 332 and 106 are examples for cultivated land. The agreement with AVHRR is
very high, and a bit lower with MODIS. The peak values of MODIS are lower, especially for
Belmanip 332. For this site, the GEOV1 NDVI has always a lower peak value than NDVI V2.
The difference between the time series of the various data sets is smaller for Belmanip 106.
Belmanip 58, an herbaceous site in Texas, shows a less regular temporal pattern, which
is reflected in all products. Local differences in the NDVI values are present. AVHRR is in
general higher than NDVI V2, which is also slightly higher than GEOV1. The agreement with
MODIS is the best, but here NDVI V2 is sometimes slightly higher than MODIS NDVI.
Belmanip 260 is a needleleaf forest site where a good agreement over time is found
between all products.
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The two shrubland sites (Direct 61 and 75 ) show very good temporal agreement. For the
Direct 75, AVHRR is almost with a constant factor higher than NDVI V2 and GEOV1. This
difference is not present for the Direct 61, where NDVI V2 and AVHRR NDVI are almost
identical apart from the short term variation. For the monthly composites, the agreement with
MODIS is largest for the Direct 75, and slightly less for Direct 61. But overall, the temporal
patterns are similar.
In general, the temporal patterns of the different data sets agree well.
Besides the seasonal patterns, the temporal profiles give also information on the high
frequency quality of products. All these graphs show that, following this criteria, GEOV1
display the less shaky variations.
Figure 36 show some examples with deviant temporal patterns, or where the difference
between the data sets is larger.
The Belmanip 296 is a broadleaf deciduous forest site. It shows a good temporal
correspondence for the 10-daily composites, although there is a large difference between the
datasets of around 0.05 to 0.1. AVHRR has the highest NDVI and GEOV1 the lowest. For
the monthly composites, the temporal agreement between NDVI V2, GEOV1 and MODIS is
less. There seems to be less variation in peak values for MODIS compared to NDVI V2 and
GEOV1, and the latter two also show sometimes very different patterns (e.g. in the year
2009).
Direct 59 (BDF) and Belmanip 1 (SHR) show very different temporal profiles of AVHRR
compared to the other data sets. Here, the AVHRR does not agree with NDVI V2 or with
GEOV1, and shows a much higher peak in NDVI in the middle of the year. The same is true
for Direct site 10 (CUL), , and to a lesser extent also for Belmanip 129 (BDF), 284 (HER), 98
(NLF), and 79 (NLF) and Direct site 70 (CUL). These are either sites at high latitudes with
forest cover or sites located in mountainous areas. The difference in overpass time of the
AVHRR sensor could be an explanation for these differences, also because these
differences are only present in the AVHRR data set.
When comparing the monthly composites for these sites, the results are more variable.
For Direct 59 (Southwest Australia), the NDVI V2 has higher peaks compared to MODIS for
the time series 2005-2012. The GEOV1 is much lower than the NDVI V2, but also follows a
similar temporal pattern. For the Belmanip 129, the agreement with MODIS for the period
2003-2012 is large, although more low NDVI noise (spikes) are present in the NDVI V2 time
series. The GEOV1 is lower than NDVI V2 and the growing season peak often differs in
shape.
For the Direct 10 (CUL), the agreement with MODIS is very high, except for the last 2
years. Up to 2007, NDVI V2 and GEOV1 differ substantially in the magnitude of the peak, but
from 2008 on, the temporal profile is very similar.
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The NDVI V2 is considerably higher than that of MODIS for Belmanip 284 (HER) and 1
(SHR). Here, the GEOV1 data is more similar to MODIS than to the NDVI V2 temporal profile
for the period 2003-2012.
For Direct 70 (CUL), the temporal pattern is very different between NDVI V2, MODIS and
GEOV1. This is also the case for Belmanip 155 (BEF), but here, there is a larger agreement
in mean value between NDVI V2 and MODIS. GEOV1 is lower compared to these two, and
has larger spikes. Belmanip 28 and 155 are both from evergreen broadleaf forest, and in a
region where cloud cover is high. All data sets suffer in this region from undetected clouds.
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10-daily composites Monthly composites
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Figure 35: Temporal NDVI profiles between the 10-daily composites of NDVI V2 (red), AVHRR (blue) and GEOV1 (green) for the period 2008-2012
at the left, and between monthly composites of NDVI V2 (red), MODIS (blue) and GEOV1 (green) for the period 2001-2012 at the right. Examples with a good agreement.
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10-daily composites Monthly composites
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Figure 36: Temporal NDVI profiles between the 10-daily composites of NDVI V2 (red), AVHRR (blue) and GEOV1 (green) for the period 2008-2012
at the left, and between monthly composites of NDVI V2 (red), MODIS (blue) and GEOV1 (green) for the period 2001-2012 at the right. Examples with a lower agreement.
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6.3.2 Temporal smoothness
Figure 37 presents the frequency distribution of a measure of smoothness, delta (see
section 5.1.4) for the 10-daily NDVI time series of NDVI V2, AVHRR and GEOV1 for the
period 2008-2012. Both NDVI V2 and AVHRR exhibit a small delta, with half or more of the
observations having a delta lower than 0.05. For GEOV1, even 67% of observations have
such a low delta. This higher temporal smoothness can be explained because the GEOV1
compositing period is based on 30 days instead of 10 days, because it benefits from an
additional detection of residual contaminated pixels and because it is based on normalized
reflectances that reduce extreme values.
Figure 37: The frequency histogram of the delta, a measure of temporal smoothness, for the
period 2008-2012 for the NDVI 10-daily time series from NDVI V2, AVHRR and GEOV1 for all clear observations.
Figure 38 shows the frequency histogram of the delta for the monthly NDVI time series of
NDVI V2, MODIS and GEOV1 for the period 2001-2012. The smoothness of NDVI V2 and
GEOV1 is roughly equal, because now both data sets are based on a 30-day compositing
period which eliminates better cloudy observations. All three datasets have small delta, with
60% or more of observations having a delta lower than 0.1.
0
10
20
30
40
50
60
70
80
0 0.2 0.4 0.6 0.8 1
%
delta
NDVI V2
AVHRR NDVI
GEOV1
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Figure 38: The frequency histogram of the delta, a measure of temporal smoothness, for the
period 2001-2012 for the NDVI monthly time series from NDVI V2, MODIS and GEOV1 for all clear observations.
0
10
20
30
40
50
60
0 0.2 0.4 0.6 0.8 1
%
delta
NDVI V2
MODIS NDVI
GEOV1
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7. CONCLUSION
The validation of the NDVI V2 was performed by comparison with other NDVI data sets.
These are the GEOV1 NDVI, which was also distributed by the GL Service (NDVI V1), the
10-daily composites of METOP-AVHRR from the LSA-SAF, and the monthly TERRA-MODIS
composites. The NDVI, being a sensor-dependent indicator of vegetation greenness, cannot
be compared to in situ data, but its behavior in relation to other NDVI data sets is assessed.
The purpose of the validation is on the consistency over time in the agreement with the
reference data sets, rather than the absolute accuracy of the NDVI.
Although NDVI is not a biophysical variable, we do follow the guidelines, protocols and
metrics defined by the Land Product Validation (LPV) group of the Committee on Earth
Observation Satellite (CEOS) for the indirect validation of satellite-derived land products
(Fernandes et al., 2014).
The validation is performed for the entire data set taking only into account the status map
of the products, but also by constraining the paired observations to same day of registration,
similar near nadir viewing conditions and with an additional temporal filtering to remove
undetected clouds. This latter approach could only be applied on the comparison with the
METOP-AVHRR NDVI data set.
The requirements of the NDVI is defined as the difference between the NDVI and existing
satellite derived NDVI products, with a threshold of 0.15, target of 0.10 and optimal of 0.05.
Theis requirement definition needs to be treated with caution, because the NDVI is a sensor-
dependent index and can therefore differ strongly based on spectral characteristics of the
sensors.
The validation results showed that the NDVI V2 is similar to the other products and is
most similar to the TERRA-MODIS NDVI (overall difference of 0.005, systematic difference <
0.00002 and random difference of 0.005). It should be noted that this analysis was done at
the monthly time step. When comparing to 10-daily METOP-AVHRR NDVI composites, the
overall difference is 0.009, with a random difference of 0.0076 taking into account all cloud
free paired observations. When comparing similar observation conditions, the overall
difference is reduced to 0.008, with a random difference of 0.003. The difference with the
GEOV1 NDVI is overall 0.005 of which the majority is a random difference 0.0047. All figures
are well within the requirements.
The difference among data sets is not homogeneous over the globe and differs with
vegetation type and location. The largest differences are found in mountainous and tropical
areas where persistent cloud cover affects the observations. The biomes for which the
highest difference was found are the broadleaved evergreen forest (overall difference with
GEOV1 of 0.0088, with AVHRR of 0.025, and with MODIS of 0.0071) and the needleleaf
forest (overall difference with GEOV1 of 0.011, with AVHRR of 0.012, and with MODIS of
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0.0096). The former is located in tropical areas. The needleleaf forest predominantly occurs
at higher latitudes in the northern hemisphere. Here, the agreement is lower because of
undetected snow cover and the lower amount of observations available due to the limited
illumination in the northern hemispherical winter.
Compared to GEOV1 NDVI, the NDVI V2 has generally higher values. This results from
the fact that the NDVI V2 is based on the selection of the maximum NDVI within a 10-daily
period. This favours the selection of NDVI values associated with a larger viewing zenith
angle. The GEOV1 NDVI is based on normalized reflectances to nadir viewing conditions
estimated over a period of 30 days. This leads to limit the higher NDVI values. This does not
impact the calculation of the VCI and VPI products, which have always been based on the
NDVI V2.
The obtained difference and agreement metrics are stable over the time periods
evaluated. There is one exception, where a large difference over time was observed. This
was in the comparison of the monthly composites of NDVI V2 to MODIS NDVI and to the
GEOV1 NDVI for the overlapping period 2001-2012. A large systematic difference was
observed in the comparison to MODIS for the forest classes only between the years 2001-
2002 and 2003-2012. In February 2003 the data switched from VGT1 to VGT2, which are
almost identical sensors. This caused a jump in the NDVI values of NDVI V2. In the
comparison with the GEOV1 data set, which is corrected for the difference between VGT1
and 2, this large difference in the metrics was not observed for the forest classes, but for the
other classes. Because NDVI V2 is not corrected for the change in sensors, this difference is
observed. Nevertheless, the jump in the values is also still visible in a number of the temporal
profiles of the GEOV1 NDVI.
The change from VGT1 to VGT2 is thus present in the NDVI V2 data set, but currently
these years are not distributed through the GL service. A reprocessing of the entire VGT
archive (VGT1 and 2) is foreseen for next year by the processing center. Once this is
completed, the issue of spectral correction between the NDVI of VGT1 and 2 will be
investigated.
The spatial coverage of the NDVI V2 is season dependent and lower in the northern
hemispherical winter (70% compared to 95% in summer). The coverage agrees well with the
NDVI from other AVHRR (95% in summer and 58% in winter) and MODIS (90% in summer
and 58% in winter). The coverage of the NDVI V2 data set is substantially higher than that of
the GEOV1 NDVI data (35% in winter and 57% in summer).
The temporal profiles show an agreement that is consistent over time.
Overall, we conclude that the NDVI V2 is performing well, without any trends compared to
other NDVI products if the data from the VGT1 and VGT2 are investigated separately.
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8. PERSPECTIVES
The validation will be updated once the VGT archive has been reprocessed. For the timing of
this activity, we are depending on the reprocessing speed of the processing centre.
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