GIO GLOBAL LAND LOT I€¦ · Gio-GL Lot1, GMES Initial Operations Date Issued: 12.12.2014 Issue:...

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)



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)



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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



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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



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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


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


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


(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





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Figure 13: RMSE between NDVI V2, MODIS NDVI and GEOV1 NDVI: A. Monthly NDVI V2



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




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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


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



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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


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


(1)

Ozone climatology

(2)

AOD climatology(3)

Pressure from DEM


(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.

http://landsaf.meteo.pt/

http://landsaf.meteo.pt/algorithms.jsp;jsessionid=2A3128EDCE442ADB16298ABF5BC40287?seltab=22&starttab=22

http://landsaf.meteo.pt/algorithms.jsp;jsessionid=2A3128EDCE442ADB16298ABF5BC40287?seltab=22&starttab=22

http://vip.arizona.edu/documents/MODIS/MODIS_VI_UsersGuide_01_2012.pdf



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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

http://calvalportal.ceos.org/web/olive/site-description#Garrigues_et_al__2008

http://lpvs.gsfc.nasa.gov/LPV_CS_gen.html



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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



Issue: I1.21



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:



Issue: I1.21



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.



Issue: I1.20



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.

A

.

B

.

A

.

B

.



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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.

A

.

B

.

A

.

B

.



Issue: I1.20



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.



Issue: I1.20




(blue) and GEOV1 (green). Examples of two sites where GEOV1 provides spurious NDVI values at the end of the growing season.


(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)

A

.

B

.

A

.

B

.



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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

.

B

.



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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



Issue: I1.20



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.

0

20

40

60

80

100

2008 2009 2010 2011 2012

% g

oo

d o

bse

rvat

ion

s

NDVI V2 AVHRR NDVI GEOV1 NDVI



Issue: I1.20



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.

0

20

40

60

80

100

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

% g

oo

d o

bse

rvat

ion

s

NDVI V2 MODIS NDVI GEO V1



Issue: I1.20



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.

0

10

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40

50

60

70

80

90

100

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

%

NDVI

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35

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NDVI

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%

NDVI

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%

NDVI

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9%

NDVI



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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).


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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Issue: I1.20



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.


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.

0

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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

0

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NDVI

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NDVI



Issue: I1.20



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).


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.



Issue: I1.20



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).



Issue: I1.20



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).



Issue: I1.20



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.


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.



Issue: I1.20



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.



Issue: I1.20



NDVI V2(X) – GEOV1(Y) NDVI V2(X) – MODIS(Y)

BE

F

BD

F

NLF



Issue: I1.20




SH

R

HE

R

CU

L



Issue: I1.20




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.


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’).



Issue: I1.20



NDVI V2 - AVHRR

Sampling scheme ‘all constraints’

NDVI V2 - AVHRR

Sampling scheme ‘noMask’

NDVI V2 - GEOV1


0

0,005

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AC



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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.

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Issue: I1.20



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.



Issue: I1.20



NDVI V2 - AVHRR


NDVI V2 - AVHRR


NDVI V2 - GEOV1


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


NDVI V2 - AVHRR


NDVI V2 - GEOV1


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


NDVI V2 - AVHRR


NDVI V2 - GEOV1




Issue: I1.20



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.



Issue: I1.20




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.



Issue: I1.20



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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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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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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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.



Issue: I1.20



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.


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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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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9. REFERENCES

Atzberger, C, Meroni, M., Leo, O., and Sghaier, N., Intercomparison of BioPar & MARSOP

VGT products. Part IV: analysis of the full archive. CROP CIS user utility assessment of

BioPar products, Issue 1.0 (2012). http://web.vgt.vito.be/documents/BioPar/g2-GC-RP-

BioParUtilityAssessment-Part4-I1.00.pdf .

Eerens, H., Baruth, B., Bydekerke, L., Deronde, B., Dries, J., Goor, E., … Verheijen, Y.

(2009). Ten-daily global composites of METOP-AVHRR. In Proc. of the 6th International

Symposium on Digital Earth, 9-12 September.

Fernandes, R., Plummer, S., Nightingale, J., et al. (2014). Global Leaf Area Index Product

Validation Good Practices. CEOS Working Group on Calibration and Validation - Land

Product Validation Sub-Group. Version 2.0: Public version made available on LPV

website.

Kogan, F. (1990).Remote sensing of weather impacts on vegetation in non-homogeneous

areas. International Journal of Remote Sensing, 11: 1405-1411.

Kogan, F. (1994). Droughts of the late 1980's in the United States as derived from NOAA

polar-orbiting satellite data. Bulletin of the American Meteorological Society, 76(5),

655−668.

Kogan, F. (1995). Application of vegetation index and brightness temperature for drought

detection. Advances in Space Research, 15, 91−100.

Rojas O, Vrieling A, Rembold F (2011). Assessing drought probability for agricultural areas in

Africa with coarse resolution remote sensing imagery. Remote Sensing of Environment,

vol. 115, 343–352.

Sannier, C. a. D., Taylor, J. C., Du Plessis, W., & Campbell, K. (1998). Real-time vegetation

monitoring with NOAA-AVHRR in Southern Africa for wildlife management and food

security assessment. International Journal of Remote Sensing, 19(4), 621–639.

Walthall, C. L., Norman, J. M., Welles, J. M., Campbell, G., and Blad, B. L. (1985), Simple equation to approximate the bi-directional reflectance from vegetative canopies and bare soil surfaces, Applied Optics, 24(3):383-387.

http://web.vgt.vito.be/documents/BioPar/g2-GC-RP-BioParUtilityAssessment-Part4-I1.00.pdf

http://web.vgt.vito.be/documents/BioPar/g2-GC-RP-BioParUtilityAssessment-Part4-I1.00.pdf

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