copernicus global land operations...operate “a multi-purpose service component” that provides a...

Copernicus Global Land Operations – Lot 1 Date Issued: 25.01.2019 Issue: I1.00

Document-No. CGLOPS1_SQE2018_NDVI-VCI-VPI1km-V2.2 © C-GLOPS Lot1 consortium

Issue: I1.00 Date: 25.01.2019 Page: 1 of 49

Copernicus Global Land Operations

“Vegetation and Energy” ”CGLOPS-1”

Framework Service Contract N° 199494 (JRC)

SCIENTIFIC QUALITY EVALUATION

NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI)

COLLECTION 1KM

VERSION 2.2

VEGETATION CONDITION INDEX (VCI)

VEGETATION PRODUCTIVITY INDEX (VPI)

Issue I1.00

Organization name of lead contractor for this deliverable: VITO

Book Captain: Else Swinnen

Contributing Authors: Carolien Toté




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)




Document Release Sheet

Book captain: Else Swinnen

Sign Date 25.01.2019

Approval: Roselyne Lacaze

Sign Date 29.01.2019

Endorsement: Michael Cherlet Sign Date

Distribution: Public




Change Record

Issue/Rev Date Page(s) Description of Change Release

25.01.2019 All First issue I1.00




TABLE OF CONTENTS

Executive Summary .................................................................................................................. 12

1 Background of the document ............................................................................................. 14

1.1 Scope and Objectives............................................................................................................. 14

1.2 Content of the document....................................................................................................... 14

1.3 Related documents ............................................................................................................... 14

1.3.1 Applicable documents ................................................................................................................................ 14

1.3.2 Input ............................................................................................................................................................ 15

1.3.3 Output ......................................................................................................................................................... 15

1.3.4 External documents .................................................................................................................................... 15

2 Review of Users Requirements ........................................................................................... 16

3 Review of the NDVI Collection 1km v2.2, VCI and VPI quality .............................................. 17

4 Scientific Quality Evaluation Method ................................................................................. 19

4.1 Overall procedure ................................................................................................................. 19

4.1.1 Global analysis ............................................................................................................................................ 20

4.1.2 Regional analysis: Europe ........................................................................................................................... 21

4.1.3 Specific events ............................................................................................................................................ 22

4.2 Validation metrics ................................................................................................................. 23

4.2.1 The coefficient of determination (R²) ......................................................................................................... 23

4.2.2 Geometric mean regression........................................................................................................................ 24

4.2.3 The root mean squared difference (RMSD) ................................................................................................ 24

4.2.4 Random and systematic differences ........................................................................................................... 25

4.3 Other Reference Products ...................................................................................................... 25

5 Results .............................................................................................................................. 27

5.1 Global and regional analysis .................................................................................................. 27

5.1.1 Product completeness ................................................................................................................................ 27

5.1.2 Spatial consistency ...................................................................................................................................... 28

5.1.3 Statistical consistency ................................................................................................................................. 32

5.1.4 Temporal consistency ................................................................................................................................. 33

5.2 Specific events ...................................................................................................................... 36

5.2.1 Temporal profiles ........................................................................................................................................ 36

5.2.2 Visual inspection ......................................................................................................................................... 40

6 Conclusions ....................................................................................................................... 47




7 References ........................................................................................................................ 48




List of Figures

Figure 1: The GLC classification aggregated into 7 classes .......................................................... 21

Figure 2: The GLC classification aggregated into 7 classes over the Europe ROI ......................... 22

Figure 3: Temporal evolution of the amount of missing values (%) in NDVI V2.2 for 2018 (orange)

and 2015 (blue) at global scale (left) and calculated over the Europe ROI (right) ................... 27

Figure 4: Spatial distribution of the amount of missing values (%) of NDVI V2.2 for 2018 (top) and

2015 (bottom) at global scale (left) and for the Europe ROI (right) ......................................... 28

Figure 5: Frequency distribution of the gap length (in dekads) in NDVI V2.2 for 2018 (orange) and

2015 (blue) at global scale (left) and calculated over the Europe ROI (right) .......................... 28

Figure 6: RMSD, RMPDs and RMPDu between NDVI V2.2 of 2015 and 2018 at global scale (left)

and over Europe (right) .......................................................................................................... 29

Figure 7: Histogram of the difference between NDVI V2.2 of 2018 and 2015 (2018 minus 2015) at

global scale (left) and over Europe (right). Top: all land cover, bottom: per major biome. ...... 30

Figure 8: Frequency distributions over different biomes based on the NDVI V2.2 product at global

scale. Pairwise comparison of NDVI values for 2018 (orange) and 2015 (blue). X-axis: NDVI

values in steps of 0.1, Y-axis: percentage of occurrence........................................................ 31

Figure 9: Frequency distributions over different biomes based on the NDVI V2.2 product over

Europe. Pairwise comparison of NDVI values for 2018 (orange) and 2015 (blue). X-axis: NDVI

values in steps of 0.1, Y-axis: percentage of occurrence. BEF is not present in Europe (Table

3). .......................................................................................................................................... 31

Figure 10: Scatterplots between NDVI V2.2 of 2018 (X) and 2015 (Y) over all land cover types (top

left) and per biome at global scale ......................................................................................... 32

Figure 11: Scatterplots between NDVI V2.2 of 2018 (X) and 2015 (Y) over all land cover types (left)

and per biome, over Europe. BEF is not present in Europe (see Table 3). ............................. 33

Figure 12: Frequency histogram of delta, a measure of temporal smoothness, for 2018 (orange) vs

2015 (blue), at global scale (left) and over Europe (right) ....................................................... 33

Figure 13: Temporal profiles of average NDVI 1km V2.2 for 2018 (red) compared to the long term

(1999-2017) mean (grey) for Finland, Germany, Belgium, Denmark, Estonia and Portugal over

cultivated areas (left) and herbaceous cover (right) ................................................................ 35

Figure 14: Temporal profiles of NDVI 1km for 2018 (red) and 2015 (blue) over sites with specific

events in 2018. Green dashed lines indicate the fire event date............................................. 37

Figure 15: Temporal profiles of VCI for 2018 (red) and 2015 (blue) over sites with specific events in

2018. Green dashed lines indicate the fire event date. ........................................................... 38




Figure 16: Temporal profiles of VPI for 2018 (red) and 2015 (blue) over sites with specific events in

2018. Green dashed lines indicate the fire event date. The VPI product was discontinued after

August 2018, hence events 2 (September 2018) and 10 (November 2018) are not shown. ... 39

Figure 17: Maps of the NDVI in 1°x1° focus areas around specific events (Table 4). Areas with no

data available are masked in grey, permanent water surfaces in light blue. ........................... 42

Figure 18: Maps of the VCI in 1°x1° focus areas around specific events (Table 4). Areas with no

data available are masked in grey, permanent water surfaces in light blue. ........................... 44

Figure 19: Maps of the VPI in 1°x1° focus areas around specific events (Table 4). Areas with no

data available are masked in grey, permanent water surfaces in light blue. The VPI product

was discontinued after August 2018, hence events 2 (September 2018) and 10 (November

2018) are not shown. ............................................................................................................. 46




List of Tables

Table 1: Overall procedure for the Scientific Quality Evaluation of the NDVI 1km Version 2.2 ...... 20

Table 2: Aggregation scheme for GLC2000 classes into 7 major biomes and proportion of each

biome at global scale ............................................................................................................. 21

Table 3: Aggregation scheme for GLC2000 classes into 7 major biomes and proportion of each

biome over the Europe ROI ................................................................................................... 22

Table 4: Specific events analysed in this SQE .............................................................................. 23




List of Acronyms

AD Applicable Document

AMIS Agricultural Market Information System

ATBD Algorithm Theoretical Basis Document

AVHRR Advanced Very High Resolution Radiometer

BA Bare areas

BEF Broadleaved Evergreen Forest

BDF Broadleaved Deciduous Forest

CEMS Copernicus Emergency Management Service

CEOS Committee on Earth Observing Satellites

CGLS Copernicus Global Land Service

CUL Cultivated areas

DN Digital Number

EDO European Drought Observatory

EFFIS European Forest Fire Information System

GCOS Global Climate Observing System

GEOGLAM Group on Earth Observations Global Agricultural Monitoring

GIO GMES Initial Operations

GLC Global Land Cover

GM Geometric Mean

GMR Geometric Mean Regression

HER Herbaceous cover

JRC Joint Research Centre

LPV Land Product Validation subgroup

LTS Long Term Statistics

MARS Monitoring Agricultural ResourceS

METOP METeorological OPerational satellite

MODIS MODerate resolution Imaging Spectroradiometer

MPD Mean Product Difference

MSD Mean Squared Difference

NASA National Aeronautics and Space Administration

NDVI Normalized Difference Vegetation Index

NIR Near Infra-Red

NLF Needleleaved Forest

PROBA-V Project for on-board autonomy – Vegetation instrument

PUM Product User Manual

R² Coefficient of determination

RMPDs Root of the Systematic mean product difference

RMPDu Root of the Unsystematic mean product difference

RMSD Root Mean Squared Difference

ROI Region of Interest




SHR Shrubland

SSD Service Specifications Document

SPOT Satellite Pour l’Observation de la Terre

SQE Service Quality Evaluation

SVP Service Validation Plan

V Version

VCI Vegetation Condition Index

VGT Vegetation

VPI Vegetation Productivity Index

VR Validation Report

WGS World Geodetic System




EXECUTIVE SUMMARY

The Copernicus Global Land Service (CGLS) is earmarked as a component of the Land service to

operate “a multi-purpose service component” that provides a series of bio-geophysical products on

the status and evolution of land surface at global scale. Production and delivery of the parameters

take place in a timely manner and are complemented by the constitution of long-term time series.

This document presents the results of the annual Scientific Quality Evaluation (SQE) of the

Normalized Difference Vegetation Index (NDVI) 1km Version 2.2 product and the derived

Vegetation Condition Index (VCI) and Vegetation Productivity Index (VPI). The SQE is part of the

Copernicus Global Land Service operations activities. The quality evaluation is performed on data

of 2018 (36 dekads), at global scale, on a large region of interest (ROI) located in Europe, and on

point locations with specific events reported in 2018. The products of 2018 are compared to

products of 2015, validated in an exhaustive quality assessment analysis. The objective is to check

that the quality of NDVI 1km Version 2.2 and derived VCI and VPI 2018 products keeps stable over

time and to verify whether specific natural events are well captured by the products: (1) vegetation

development in Europe, and (2) environmental events reported in 2018. Note that the VPI product

was discontinued after August 2018, hence this product is only evaluated over the period January-

August 2018.

The results of the SQE indicate that the NDVI 1km Version 2.2 product and the VCI and VPI

products are performing similarly to those of 2015. Product completeness is very similar for 2018

compared to 2015, in terms of temporal variation, spatial variation and gap length frequency.

Differences in NDVI values between 2018 and 2015 are small (63% of the pixels show a bias lower

than 0.05) and are mostly related to differences in the development of vegetation in 2018 vs. 2015

(e.g. minor shifts in the development of the growing season). This also results in large scatter in

the scatterplots, although evenly distributed on both sides of the 1:1 line. These differences can be

caused by above/below average rainfall or temperatures, delay or advance of the phenological

cycle, varying cropping intensities, land cover changes, etc. Systematic differences are very low

(Geometric Mean regression lines are close to the 1:1 line), and the NDVI distributions between

the two datasets are very similar over different land cover classes. The temporal smoothness is

nearly identical in 2018 compared to 2015. Over Europe, temporal profiles over cultivated areas

and herbaceous cover indicate below average vegetation development in the first months of 2018

in Finland, and during the growing season in Denmark, Germany and Belgium related to drought

conditions. In contrast, the warm and relatively wet weather in the South of Europe and in the

Baltic positively impacted vegetation growth, as reflected in the temporal profiles over Estonia and

Portugal. Scatterplots show large scatter, though evenly distributed on both sides of the 1:1 line,

related to expected variations in vegetation development. Systematic differences are very low (GM

regression lines are close to the 1:1 line), and the NDVI distributions between the two datasets are

very similar for all biomes.




Temporal profiles of point locations and visual inspection (so-called quicklooks) of small areas

around specific events reported in 2018 indicate most of the events were well captured by the

NDVI, VCI and VPI. Temporal profiles show sharp drops (wildfires, floods, hail storm damage) or

positive evolution after the events (floods). Visual inspection showed that the effect of most events

is detectable in both the NDVI and NDVI anomaly (VCI, VPI) quicklooks. Nevertheless, some

events are only visible in the NDVI quicklooks (wildfire in Italy) or only in the VCI and VPI

quicklooks (wildfire in Spain). The effect of rains in Saudi Arabia was not clearly distinguishable,

neither in temporal plots, nor in the quicklooks.




1 BACKGROUND OF THE DOCUMENT

1.1 SCOPE AND OBJECTIVES

From 1st January 2013, the Copernicus Global Land Service (CGLS) is continuously providing a

set of biophysical variables describing the vegetation dynamics, the energy budget at the

continental surface and the water cycle over the whole globe. The service performs the timely

production, the re-processing, the archival, and the distribution of quality-checked products.

Scientific quality evaluation is part of the Operations activities of the service.

This document presents the results of the annual Scientific Quality Evaluation (SQE) of the NDVI

Collection 1km Version 2.2, VCI and VPI products. The quality evaluation is performed on data of

2018 (36 dekads), at global scale, on a large region of interest (ROI) located in Europe, and on

point locations with specific events reported in 2018. The objective is to check that the quality of

NDVI Collection 1km Version 2.2 and the derived VCI and VPI product keeps stable over time. In

order to do so, the products of 2018 are compared to products of 2015 which were validated in an

exhaustive validation exercise [GIOGL1_VR_NDVI-VCI-VPI1km-V2.2].

Note that the VPI product was discontinued after August 2018, hence this product is only evaluated

over the period January-August 2018.

1.2 CONTENT OF THE DOCUMENT

This document is structured as follows:

Chapter 2 recalls the users requirements, and the expected performance

Chapter 3 summarizes the NDVI 1km V2.2, VCI and VPI quality assessment as defined in

the Validation Report

Chapter 4 describes the methodology for quality assessment, the metrics and the criteria of

evaluation

Chapter 5 presents the results of the analysis

Chapter 6 summarizes the main conclusions of the study

1.3 RELATED DOCUMENTS

1.3.1 Applicable documents

AD1: Annex I – Technical Specifications JRC/IPR/2015/H.5/0026/OC to Contract Notice 2015/S

151-277962 of 7th August 2015

AD2: Appendix 1 – Copernicus Global land Component Product and Service Detailed Technical

requirements to Technical Annex to Contract Notice 2015/S 151-277962 of 7th August 2015




AD3: GIO Copernicus Global Land – Technical User Group – Service Specification and Product

Requirements Proposal – SPB-GIO-307-TUG-SS-004 – Issue I1.0 – 26 May 2015.

1.3.2 Input

Document ID Descriptor

CGLOPS1_SSD Service Specifications of the Global Component of

the Copernicus Land Service.

CGLOPS1_SVP Service Validation Plan of the Global Land Service

GIOGL1_ATBD_NDVI1km-V2 Algorithm Theoretical Basis Document of the NDVI

Collection 1km Version 2.2 product

GIOGL1_ATBD_NDVI-VCI-VPI Algorithm Theoretical Basis Document of the

Collection 1km NDVI Version 2.2, VCI, VPI product

GIOGL1_VR_NDVI-VCI-VPI1km-

V2.2

Validation report containing the results of the

scientific validation of the NDVI Collection 1km

Version 2.2 product, VCI and VPI

1.3.3 Output


GIOGL1_PUM_NDVI1km-V2.2 Product User Manual summarizing all information about the

NDVI Collection 1km Version 2.2 product

GIOGL1_PUM_VCI1km-V2 Product User Manual summarizing all information about the

Collection 1 km VCI product

GIOGL1_PUM_VPI1km-V2 Product User Manual summarizing all information about the

Collection 1 km VPI product

1.3.4 External documents


PROBA-V Products User Manual User Guide of the PROBA-V data, available on http://proba-

v.vgt.vito.be/sites/proba-

v.vgt.vito.be/files/products_user_manual.pdf

http://proba-v.vgt.vito.be/sites/proba-v.vgt.vito.be/files/products_user_manual.pdf






2 REVIEW OF USERS REQUIREMENTS

According to applicable documents [AD2] and [AD3], the users’ requirements relevant for NDVI,

VCI and VPI are:

Definition:

o The Normalized Difference Vegetation Index (NDVI) is the difference between

maximum (in NIR) and minimum (round the Red) vegetation reflectance, normalized

to the summation (CEOS) 1.

o Vegetation Condition Index (VCI): per pixel NDVI value re-scaled according to the

minimum and maximum values observed over the whole time series (Kogan, 1990)

o Vegetation Productivity Indicator (VPI): per-pixel and per dekad percentile ranking of

the NDVI value against it historical range of variability (Sannier et al., 1998)

Geometric properties:

o Location accuracy shall be 1/3rd of the at-nadir instantaneous field of view

o Pixel coordinates shall be given for the centre of pixel

Geographical coverage:

o Geographic projection: regular latitude/longitude

o Geodetical datum: WGS84

o Coordinate position: centre of pixel

o Pixel size: 1/112° - accuracy: min 10 digits

o Window coordinates:

upper left: 180°W – 75°N

bottom right: 180°E – 56°S

Ancillary information:

o The per-pixel date of the individual measurements or the start-end dates of the

period actually covered

o Quality indicators, with explicit per-pixel identification of the cause of anomalous

parameter result

Accuracy requirements: wherever applicable the bio-geophysical parameters should meet

the internationally agreed accuracy standards laid down in document “Systematic

Observation Requirements for Satellite-Based Products for Climate”. Supplemental details

to the satellite based component of the “Implementation Plan for the Global Observing

System for Climate in Support of the UNFCCC”. GCOS-#154, 2011”

Since the NDVI, the VCI and VPI are not Essential Climate Variables, there are no GCOS

specifications on required accuracy.

According to [AD3], the user requirement for NDVI in terms of acceptable differences with existing

satellite-derived products is 0.05 (optimal).

1 The NDVI is calculated per pixel as the normalized difference between the red and near infrared bands.




3 REVIEW OF THE NDVI COLLECTION 1KM V2.2, VCI AND VPI QUALITY

An exhaustive scientific validation of the 1 km NDVI V2.2, VCI and VPI products, including an

evaluation of the entire archive, was performed in [GIOGL1_VR_NDVI-VCI-VPI1km-V2.2].

The time series of NDVI V2.2 includes data from different sensors: SPOT/VGT1 (May 1998-

January 2003), SPOT/VGT2 (February 2003 – December 2013) and PROBA-V (January 2014 –

present).

In the exhaustive product validation, focus was laid on the temporal stability and the consistency of

the full time series (1999-2016). Since the VCI and VPI are directly derived from the NDVI V2.2 by

comparing actual observations with long term statistics (LTS), the quality of the VCI and VPI is

directly linked to the quality of the input NDVI V2.2 series.

The NDVI V2.2 benefits from the improved cloud and snow/ice masking in both new collections

VGT C3 and PROBA-V C1. The amount of gaps is stable over time.

The NDVI V2.2 was compared to external datasets from METOP-A and MODIS. NDVI V2.2 and

METOP-A NDVI show a non-linear relation, related to the difference in overpass time of the

sensors. NDVI V2.2 is overall lower than METOP-A NDVI, except where the NDVI is high (e.g.

tropics, summer at mid-latitudes). This bias originates from the differences in spectral bands,

absolute calibration and processing. Also there is still a decreasing trend in the bias between NDVI

V2.2 and METOP-A NDVI from 2009 toward end of 2013. This is caused by the orbital drift of

VGT2, and the related changes in illumination conditions. NDVI V2.2 and MODIS NDVI show a

nearly linear relationship, and NDVI V2.2 is slightly higher than MODIS NDVI. Differences are

largest for PROBA-V (caused by difference in overpass time) and for biomes with highest NDVI

values. The comparison to external data from METOP-A/AVHRR and TERRA/MODIS has shown

that the statistical consistency, temporal consistency and stability have largely improved in NDVI

V2.2 compared to previous versions.

The spatial patterns in the VCI and VPI derived from NDVI V2.2 and METOP-A NDVI were

compared at full resolution for 2 seasons (2009 and 2015), for two 10° x 10° tiles located in

Western Europe and East Africa. Overall the same patterns are observed in the VCI and VPI

based on different NDVI datasets, although especially for 2015 the vegetation anomalies based on

METOP-A NDVI are lower, indicating below average vegetation status.

The spatio-temporal variation of the mean VCI and VPI based on NDVI V2.2 is largely consistent

with the VCI and VPI from METOP-A NDVI. Nevertheless, the vegetation anomalies still show too

positive values in 2012-2013, related to higher NDVI values caused by the orbital drift of VGT2.

Also the analysis on anomaly class agreement shows for NDVI V2.2 a better agreement with

external reference products derived from METOP-A NDVI and MODIS NDVI compared to the

previous version. This is especially the case for the VCI, which is more influenced by extreme

values in the LTS than the VPI.




To summarize, the NDVI V2.2 time series shows high stability over time. The remaining issues are

still: (1) influence in variation in illumination conditions, (2) inter-calibration of VGT2 and PROBA-V.




4 SCIENTIFIC QUALITY EVALUATION METHOD

4.1 OVERALL PROCEDURE

The SQE of 2018 is based on the comparison of the NDVI Collection 1km Version 2.2 product and

the derived VCI and VPI products of 2018 (36 dekads) with the same product of 2015 (36 dekads),

since the data of 2015 was validated in the validation report [GIOGL1_VR_NDVI-VCI-VPI1km-

V2.2]. The products are analysed at global scale (see §4.1.1), at regional scale over Europe (see

§4.1.2) and point locations where specific events were reported (see §4.1.3). The objective of the

regional analysis and the analysis over point locations is to check whether specific natural events

are well captured by the product.

Although the NDVI is not a biophysical variable, the quality monitoring is performed following 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. For the SQE, a limited set of analyses is used.

The VCI and VPI are directly derived from the NDVI V2.2 by comparing actual observations with

the long term statistics (LTS) based on the entire time series. This means that the quality of the

VCI and VPI is directly linked to the quality of the input NDVI V2.2 series. The evaluation of the VCI

and VPI focuses on the specific events.

The following paragraphs describe the methods used for the global analysis (§4.1.1), regional

analysis over Europe (§4.1.2) and analysis of specific events (§4.1.3). The validation metrics and

Geometric Mean Regression (GMR) used are described in section §4.2. Pixels flagged as ‘missing’

(DN=251), ‘cloud/shadow’ (DN=252), ‘snow/ice’ (DN=253), ‘sea’ (DN=254) or ‘background’

(DN=255) are excluded from the analyses.

Table 1 summarizes the overall procedure applied for the analysis.




Table 1: Overall procedure for the Scientific Quality Evaluation of the NDVI 1km Version 2.2

Criterium Method and/or Validation metric

Product completeness Quantification (in %) of missing values or pixels flagged as ‘invalid’ over land: temporal evolution and spatial distribution for 2018 and 2015 at global and at regional scale

Frequency distribution (in %) of the length of the gaps (in dekads) in the products of 2018 and 2015 at global and at regional scale

Spatial consistency Spatial distribution of the validation metrics expressing the similarities/differences between 2018 and 2015 at global and at regional scale

Histogram of bias between 2018 and 2015, overall and per biome at global and at regional scale

Distribution of values per biome for 2018 and 2015 at global and at

regional scale

Statistical consistency Scatterplots (incl. GMR equation, R²) between 2015 and 2018, overall and per biome, at global and at regional scale

Temporal consistency Frequency distribution of the temporal smoothness, at global and at regional scale: the temporal smoothness is 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:

Temporal variations and realism: temporal profiles of average NDVI for 2018 are compared to temporal profiles of the long term mean for cultivated and herbaceous vegetation types in a selection of European countries.

Temporal variations and realism: temporal profiles of NDVI, VCI and VPI are extracted for specific locations, and 1° x 1° NDVI, VCI and VPI maps before and after the environmental event are visually inspected.

4.1.1 Global analysis

The global images are systematically sub-sampled over the whole globe taking the central pixel in

a window of 21 by 21 pixels. This subsample is representative for the global patterns of vegetation

and considerably reduces the processing time, while retaining the relation between the observation

and its viewing and illumination geometry.

An aggregated version of the GLC2000 classification (Bartholomé and Belward, 2005) was used to

distinguish between major land cover classes at the global scale (Figure 1). The classes were

aggregated according to the scheme in Table 2.




Figure 1: The GLC classification aggregated into 7 classes

Table 2: Aggregation scheme for GLC2000 classes into 7 major biomes and proportion of each biome

at global scale

Abbreviation Name GLC2000 classes

Proportion at global scale (%)

BEF Broadleaved Evergreen Forests 1 7.1

BDF Broadleaved Deciduous Forests 2-3 7.1

NLF Needleleaved Forests 4-5 12.8

SHR Shrubland 11-12, 14 22.6

HER Herbaceous cover 13 9.7

CUL Cultivated areas and cropland 16-18 16.5

BA Bare areas 19 13.4

Other (not considered in the analyses) 6-10, 15, 20-22 10.8

4.1.2 Regional analysis: Europe

The regional analysis focuses on a Region of Interest (ROI) over Europe, with boundary

coordinates: 33° - 72° N, -12° - 49° E. The Europe images are systematically sub-sampled taking

the central pixel in a window of 11 by 11 pixels: this subsample is representative for the patterns of

vegetation and considerably reduces the processing time.

As is the case for the global analysis (see above), an aggregated version of the GLC2000

classification (Bartholomé and Belward, 2005) was used to distinguish between major land cover

classes (Figure 2). The classes were aggregated according to the scheme in Table 3.




Figure 2: The GLC classification aggregated into 7 classes over the Europe ROI

Table 3: Aggregation scheme for GLC2000 classes into 7 major biomes and proportion of each biome

over the Europe ROI

Abbreviation Name GLC2000 classes

Proportion (%)

BEF Broadleaved Evergreen Forests 1 0.0

BDF Broadleaved Deciduous Forests 2-3 9.6

NLF Needleleaved Forests 4-5 16.7

SHR Shrubland 11-12, 14 14.3

HER Herbaceous cover 13 5.5

CUL Cultivated areas and cropland 16-18 36.5

BA Bare areas 19 3.8

Other (not considered in the analyses)

6-10, 15, 20-22 13.5

In addition, statistics were extracted from the Europe ROI, based on the 7 biomes as defined in the

reclassified GLC2000, and the country borders. This allows comparison of the temporal evolution

of the NDVI V2.2 of 2018 with the long term mean.

4.1.3 Specific events

Ten specific events were selected for analysis, of which 5 are major fires in Europe reported by the

European Forest Fire Information System (EFFIS, http://effis.jrc.ec.europa.eu), and the other 5 are

from elsewhere in the world, reported by the NASA Earth Observatory

(https://earthobservatory.nasa.gov/). The events are listed in Table 4.

http://effis.jrc.ec.europa.eu/

https://earthobservatory.nasa.gov/




In order to evaluate temporal consistency, temporal profiles of NDVI, VCI and VPI over point

locations were extracted. Also 1° x 1° NDVI, VCI and VPI maps (before and after the event) were

visually analysed.

Table 4: Specific events analysed in this SQE

Nb Site/Region Type of

event Country Latitude Longitude Date of the event

1 Psachna Wildfire Greece 38.6311° N 23.6262° E 12/08/2018

2 Monte Serra Wildfire Italy 43.7497° N 10.5459° E 24-25/09/2018

3 Taipas, Monchique Wildfire Portugal 37.3964° N 8.6160° W 03/08/2018

4 Lliutxent Wildfire Spain 38.9451° N 0.3516° W 06-07/08/2018

5 Nerva Wildfire Spain 37.7303° N 6.5681° W 02-03/08/2018

6 Queensland Flooding Australia 25.2783° S 140.4690° W 03/03/2018

7 Kerala Flooding India 9.4640° N 76.7077° E 11/08/2018

8 Rub’ al-Khali Flooding Saudi

Arabia 19.7183° N 53.4005° E 25/05/2018

9 South Dakota Hail storm

damage USA 44.6981° N 100.4949° W 27/06/2018

10 California Wildfire USA 39.7922° N 121.6236° W 25/11/2018

4.2 VALIDATION METRICS

In the following paragraphs and equations, X refers to the product under evaluation (i.e. 2018), and

Y to the reference product (i.e. 2015).

4.2.1 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. A disadvantage of R² is that it only

measures the strength of the relationship between the data, but gives no indication if the data

series have similar magnitude (Duveiller et al., 2016).




Eq. 1

With X) and Y) the standard deviation of X and Y and X,Y) the co-variation of X and Y.

The R² is only provided together with the regression analysis in the global statistical analysis (see

below), because it allows a quantitative interpretation of the scatterplots.

4.2.2 Geometric mean regression

The geometric mean (GM) regression model is used to identify the relationship between two data

sets of remote sensing measurements. Because both data sets are subject to noise, it is most

appropriate to use an orthogonal (model II) regression like the GM regression. Other orthogonal

regression models exist, but Ji and Gallo (2006) demonstrated that the model II approaches results

were very similar, and that the difference was the largest compared to the simple ordinary least

squares (model I) regression method, making the choice among orthogonal models somewhat

arbitrary.

The GM regression model minimizes the sum of the products of the vertical and horizontal

distances (errors on Y and X) and is of the form:

Eq. 2

with

(GMR slope) Eq. 3

(GMR intercept) Eq. 4

X and Y : the standard deviation of X and Y

: the correlation coefficient

: signum function that takes the sign of the variable between the brackets

: the mean value of X

The GM regression slope and intercept are 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, as described in the next paragraphs.

4.2.3 The root mean squared difference (RMSD)

The Root Mean Squared Difference (RMSD) measures how far the difference between the two

data sets deviates from 0 and is defined as




Eq. 5

The RMSD is an expression of the overall difference, including random and systematic differences

(see below).

4.2.4 Random and systematic differences

The random and systematic differences are derived from the mean squared difference ( ),

defined as

Eq. 6

The is further partitioned into the systematic mean product difference ( ) and the

unsystematic or random mean product difference ( ). In order to be comparable to the RMSD

in terms of magnitude, the root of the systematic and unsystematic mean product difference are

used ( and ).

Eq. 7

With and estimated using the GM regression line and the number of samples. Then,

Eq. 8

The partitioning of the difference into systematic and unsystematic difference provides additional

information to the RMSD on the nature of the difference between two data sets.

4.3 OTHER REFERENCE PRODUCTS

European Drought Observatory (EDO), Combined Drought Indicator. Copernicus Emergency

Management Service (CEMS, 2018a). Available online at: http://edo.jrc.ec.europa.eu.

European Forest Fire Information System (EFFIS), Fire News. Copernicus Emergency Management

Service (CEMS, 2018b). Available online at: http://effis.jrc.ec.europa.eu.

“Warm April accelerates crop development”, JRC MARS Bulletin – Crop monitoring in Europe, April

2018. Available online at: https://ec.europa.eu/jrc/en/mars/bulletins. (JRC MARS, 2018a)

“Yield forecasts revised downwards”, JRC MARS Bulletin – Crop monitoring in Europe, June 2018.

Available online at: https://ec.europa.eu/jrc/en/mars/bulletins. (JRC MARS, 2018b)

http://edo.jrc.ec.europa.eu/

http://effis.jrc.ec.europa.eu/

https://ec.europa.eu/jrc/en/mars/bulletins





“Winter crop yield forecasts further down”, JRC MARS Bulletin – Crop monitoring in Europe, July

2018. Available online at: https://ec.europa.eu/jrc/en/mars/bulletins. (JRC MARS, 2018c)

“Pasture conditions in northern Europe, update as at 31 July 2018”, JRC MARS Update, August

2018. Available online at: https://ec.europa.eu/jrc/en/mars/bulletins. (JRC MARS, 2018d)

“Yield forecast revised further downwards”, JRC MARS Bulletin – Crop monitoring in Europe, August

2018. Available online at: https://ec.europa.eu/jrc/en/mars/bulletins. (JRC MARS, 2018e)

“Weather improvements too late for crops”, JRC MARS Bulletin – Crop monitoring in Europe,

September 2018. Available online at: https://ec.europa.eu/jrc/en/mars/bulletins. (JRC MARS, 2018f)

“Crop Monitor for AMIS”, GEOGLAM Crop Monitor bulletin No. 52, July 2018. Available online at:

https://cropmonitor.org/. (GEOGLAM, 2018a)

“Crop Monitor for AMIS”, GEOGLAM Crop Monitor bulletin No. 53, August 2018. Available online at:

https://cropmonitor.org/. (GEOGLAM, 2018b)

“Crop Monitor for AMIS”, GEOGLAM Crop Monitor bulletin No. 54, September 2018. Available online

at: https://cropmonitor.org/. (GEOGLAM, 2018c)

“Crop Monitor for AMIS”, GEOGLAM Crop Monitor bulletin No. 55, October 2018. Available online at:

https://cropmonitor.org/. (GEOGLAM, 2018d)

“Rain soaks the empty quarter”, NASA Earth Observatory, 16 June 2018. Available online at:

https://earthobservatory.nasa.gov/. (NASA Earth Observatory, 2018a)

“Rivers swell in Channel Country”, NASA Earth Observatory, 29 March 2018. Available online at:

https://earthobservatory.nasa.gov/. (NASA Earth Observatory, 2018b)

“Hail cuts swaths of damage across South Dakota”, NASA Earth Observatory, 12 July 2018.

Available online at: https://earthobservatory.nasa.gov/. (NASA Earth Observatory, 2018c)

“Using satellites to spot a hail scar”, NASA Earth Observatory, 19 July 2018. Available online at:

https://earthobservatory.nasa.gov/. (NASA Earth Observatory, 2018d)

“Before and after the Kerala floods”, NASA Earth Observatory, 25 August 2018. Available online at:

https://earthobservatory.nasa.gov/. (NASA Earth Observatory, 2018e)

“Camp fire adds another scar to 2018 fire season”, NASA Earth Observatory, 28 November 2018.

Available online at: https://earthobservatory.nasa.gov/. (NASA Earth Observatory, 2018f)





https://cropmonitor.org/













5 RESULTS

5.1 GLOBAL AND REGIONAL ANALYSIS

5.1.1 Product completeness

In order to evaluate the product completeness, the amount of missing values or pixels flagged as

‘invalid’ over land is quantified. The product completeness of the VCI and VPI is identical to the

one of the NDVI, since the VCI and VPI are directly derived from it. Figure 3 illustrates the temporal

evolution of the product completeness of the NDVI 1km Version 2.2 product for 2018 compared to

2015 at global scale and over the Europe ROI. At global scale, the temporal variation of missing

values is very similar for 2015 and 2018, fluctuating between 10% in the Northern hemisphere

summer period, and 45% in the Northern hemisphere winter. Over Europe, the percentage missing

values fluctuates between 1 to 2% in Summer and up to 67% in winter. The product completeness

shows a similar temporal evolution for both years, although March 2018 shows relatively high gap

frequencies for the Europe ROI, related to a positive cloud cover anomaly over Europe as

confirmed by the Copernicus State of the European Climate – March 20182.

Global scale Europe

Figure 3: Temporal evolution of the amount of missing values (%) in NDVI V2.2 for 2018 (orange) and

2015 (blue) at global scale (left) and calculated over the Europe ROI (right)

Figure 4 compares the amount of missing values per pixel over 2015 and 2018 at global scale and

over the Europe ROI. In both cases, the spatial distribution is similar for both years, although

somewhat larger amounts of missing values are observed over Spain, France and Central Europe.

2 http://surfobs.climate.copernicus.eu/stateoftheclimate/march2018.php




Global scale Europe

2018

2015

Figure 4: Spatial distribution of the amount of missing values (%) of NDVI V2.2 for 2018 (top) and

2015 (bottom) at global scale (left) and for the Europe ROI (right)

The distribution of the gap length (in number of dekads) was also evaluated in order to better

understand the impact of the missing values for monitoring temporal variations (Figure 5). For both

2018 and 2015, a gap length of one dekad is the most frequent. The gap length frequency

distribution is similar for both years, both at global scale and over the Europe ROI.

Global scale Europe

Figure 5: Frequency distribution of the gap length (in dekads) in NDVI V2.2 for 2018 (orange) and

2015 (blue) at global scale (left) and calculated over the Europe ROI (right)

5.1.2 Spatial consistency

5.1.2.1 Spatial distribution of values

This analysis discusses the spatial distribution of the RMSD, RMPDs, RMPDu between the NDVI

1km Version 2.2 for 2018 and 2015 (Figure 6). The RMSD provides the overall difference between




the data sets, whereas the RMPDs and RMPDu attribute the difference to a systematic bias and a

random difference, respectively (see also §4.2). The metrics are derived per pixel from the cloud-

free, paired NDVI values for each dekad in the year.

Although relatively large differences are observed, mostly in densely vegetated areas, this is in

large part due to unsystematic differences related to differences between vegetation development

in 2018 vs. 2015 (e.g. minor shifts in the development of the growing season (Figure 13)). The

systematic difference between the two years is very low.

Global scale Europe

RM

SD

RM

PD

s

RM

PD

u

Figure 6: RMSD, RMPDs and RMPDu between NDVI V2.2 of 2015 and 2018 at global scale (left) and

over Europe (right)

5.1.2.2 Histograms of bias

Bias was analysed between all paired estimates of the NDVI 1km Version 2.2 for the 36 dekads in

2018 and 2015. The histogram of the difference (2018 minus 2015), overall and per biome, is

shown in Figure 7, both at global scale and over the Europe ROI.

In both cases, the peak of the bias is at 0 ± 0.02 (47% of the pixels at global scale; 34% of the

pixels over Europe). Over 63% (global scale) or 52% (Europe) of the pixels shows an absolute bias

lower than 0.05. This includes invariant surfaces (e.g. bare soils) and pixels with the same

vegetation conditions at the same time of the year in 2015 and 2018. At global scale, the histogram

is slightly skewed towards negative values, i.e. 2018 showing slightly lower NDVI values compared




to 2015. The same is true for Europe, possibly linked to soil moisture deficits from June onwards in

large parts of Europe (CEMS, 2018a; JRC MARS, 2018e). The histograms per biome show similar

behaviour.

Global scale Europe

Figure 7: Histogram of the difference between NDVI V2.2 of 2018 and 2015 (2018 minus 2015) at

global scale (left) and over Europe (right). Top: all land cover, bottom: per major biome.

5.1.2.3 Distribution per biome type

Frequency distributions of the sub-sampled global images for the major biomes were derived from

paired NDVI 1km Version 2.2 values of 2018 and 2015 at global scale (Figure 8) and over the

Europe ROI (Figure 9). The NDVI distributions between the two data sets are very similar for all

land cover classes. At global scale, the values for 2018 seem slightly lower for broadleaved

deciduous forests, shrublands, herbaceous cover and cultivated areas. Over Europe, the 2018

NDVI is slightly lower for broadleaved and needle leaved forests, shrublands and cultivated lands,

and slightly higher for herbaceous cover and bare areas.




BEF

BDF

NLF

SHR

HER

CUL

BA

Figure 8: Frequency distributions over different biomes based on the NDVI V2.2 product at global

scale. Pairwise comparison of NDVI values for 2018 (orange) and 2015 (blue). X-axis: NDVI values in

steps of 0.1, Y-axis: percentage of occurrence.

BDF

NLF

SHR

HER

CUL

BA

Figure 9: Frequency distributions over different biomes based on the NDVI V2.2 product over Europe.

Pairwise comparison of NDVI values for 2018 (orange) and 2015 (blue). X-axis: NDVI values in steps

of 0.1, Y-axis: percentage of occurrence. BEF is not present in Europe (Table 3).




5.1.3 Statistical consistency

This section discusses the statistical consistency between the NDVI 1km Version 2.2 of 2018 and

2015. The scatterplots in Figure 10 and Figure 11 were created for all the pixels aggregated and

stratified in biomes (see §4.1.1).

The results show that the GM regression line is always very close to the 1:1 line, and the

relationship is linear, but the plots show a large scatter. These differences are caused by natural

variations in vegetation growth (same dekad in 2018 vs. 2015), but can also relate to different

viewing and illumination geometry in the paired observations. The scatter is evenly distributed on

both sides of the 1:1 line.

All land cover

BEF

BDF

NLF

SHR

HER

CUL

BA

Figure 10: Scatterplots between NDVI V2.2 of 2018 (X) and 2015 (Y) over all land cover types (top left)

and per biome at global scale




All land cover

BDF

NLF

SHR

HER

CUL

BA

Figure 11: Scatterplots between NDVI V2.2 of 2018 (X) and 2015 (Y) over all land cover types (left) and

per biome, over Europe. BEF is not present in Europe (see Table 3).

5.1.4 Temporal consistency

5.1.4.1 Temporal smoothness

The delta (Table 1), expressing the temporal smoothness for the NDVI 1km Version 2.2 of 2015

and 2018 is presented in Figure 12, both at global scale and over the Europe ROI. The temporal

smoothness is nearly identical in 2018 compared to 2015.

Global scale Europe

Figure 12: Frequency histogram of delta, a measure of temporal smoothness, for 2018 (orange) vs

2015 (blue), at global scale (left) and over Europe (right)




5.1.4.2 Temporal variations and realism (regional scale only)

The analysis of temporal variations and realism focuses on the Europe ROI, since 2018 was an

exceptionally warm and, in many places, dry year. This negatively impacted crop production in

many places, especially in central, eastern and northern Europe. In contrast, the warm and

relatively wet weather in the South of Europe and in the Baltic positively impacted vegetation

growth.

The Combined Drought Indicator of the European Drought Observatory (CEMS, 2018a) indicates a

drought situation in southern Finland in April 2018. The exceptional warm summer with below-

average rainfall caused drought conditions in Scandinavia, north Germany and Poland (June), and

moved south to the Netherlands, Belgium, west Germany, UK in July and then moved more south

to France and Germany from September onwards. Although the warm and relatively dry weather in

most of Europe in the first months of 2018 boosted crop development and allowed spring sowing to

accelerate (JRC MARS, 2018a), exceptionally warm and dry conditions in central, eastern and

northern Europe from April onwards negatively affected crop growth (GEOGLAM, 2018a, 2018b,

2018c, 2018d, JRC MARS, 2018b, 2018c, 2018e) and pasture conditions (JRC MARS, 2018d).

Since mid-August 2018, weather conditions have become more favourable in parts of central and

northern Europe, but these improvements were generally too small or came too late to significantly

improve the yield outlook for crops in these regions (JRC MARS, 2018f).

Figure 13 shows a comparison of temporal evolution of mean NDVI values over cultivated areas

and herbaceous cover for a selection of countries. The plot compares the evolution over 2018 (in

red) with the 1999-2017 long term mean (in grey). The graphs for Finland show the effect of the

drought in the first months of 2018, with a delay in the start of the crop season. The graphs over

cultivated areas and herbaceous cover in Germany, Belgium and Denmark show the effect of the

summer drought, with below average NDVI values from July onwards. The graphs over Estonia

and Portugal show above average NDVI values, related to warm and relatively wet conditions

during the growing season.

Cultivated areas Herbaceous cover

Fin

land




Ge

rma

ny

Be

lgiu

m

De

nm

ark

Esto

nia

Po

rtu

ga

l

Figure 13: Temporal profiles of average NDVI 1km V2.2 for 2018 (red) compared to the long term

(1999-2017) mean (grey) for Finland, Germany, Belgium, Denmark, Estonia and Portugal over

cultivated areas (left) and herbaceous cover (right)




5.2 SPECIFIC EVENTS

5.2.1 Temporal profiles

5.2.1.1 NDVI

Figure 14 illustrates the temporal profiles of NDVI 1km for 2018 (red) and 2015 (blue) over 10 sites

with specific events in 2018, as specified in Table 4. The dates of the events are indicated by

dashed green lines. For the wildfire events in Greece, Italy, Portugal, Spain (CEMS, 2018b) and

California (NASA Earth Observatory, 2018f) and the hail storm in South Dakota (NASA Earth

Observatory, 2018c, 2018d), the drop in NDVI from values between 0.6 and 0.8 (hence relatively

dense vegetation) to values around 0.2-0.3 is very clear from the temporal profiles. The flooding

event in Australia (NASA Earth Observatory, 2018b) has the opposite effect: around 2 months after

the flooding event, the NDVI peaks at around 0.5, while the normal level is 0.1. The effect gradually

decreases towards the end of the year. The flooding events in Saudi Arabia (NASA Earth

Observatory, 2018a) and India (NASA Earth Observatory, 2018e) are not clearly visible in the

NDVI profile. For the Saudi Arabia case, some missing values can be possibly attributed to

flagging of bad (cloudy) observations during the flooding event. For India, the temporal profile

contains a relatively large number of missing observations, related to persistent cloud cover during

the rainy period in this area. However, it seems that vegetation status after the 2018 flood is rather

low compared to 2015, although towards the end of the year vegetation status seems to recover to

higher NDVI values compared to 2015.




Figure 14: Temporal profiles of NDVI 1km for 2018 (red) and 2015 (blue) over sites with specific

events in 2018. Green dashed lines indicate the fire event date.

5.2.1.2 VCI and VPI

Since the VPI product was discontinued after August 2018, this product is only evaluated over the

period January-August 2018.

Pixel profiles of VCI (Figure 15) and VPI (Figure 16) of event point locations compare the temporal

behaviour for 2018 (red) and 2015 (blue). The dates of the specific events are indicated by dashed

green lines. Similar to the NDVI plots, the VCI and VPI plots show a sharp decline after the wildfire

events in Greece, Italy, Portugal, Spain (CEMS, 2018b) and (for the VCI) California (NASA Earth

Observatory, 2018f) and the hail storm in South Dakota (NASA Earth Observatory, 2018c, 2018d).

The flooding event in Australia (NASA Earth Observatory, 2018b) has the opposite effect, with

increasing VCI and VPI values after the event. The effect gradually decreases towards the end of

the year. While the flooding events in Saudi Arabia (NASA Earth Observatory, 2018a) and India

(NASA Earth Observatory, 2018e) were not clearly visible in the NDVI profiles, the VCI and VPI

profiles do show a sharp decline related to the flooding event.




Figure 15: Temporal profiles of VCI for 2018 (red) and 2015 (blue) over sites with specific events in

2018. Green dashed lines indicate the fire event date.




Figure 16: Temporal profiles of VPI for 2018 (red) and 2015 (blue) over sites with specific events in

2018. Green dashed lines indicate the fire event date. The VPI product was discontinued after August

2018, hence events 2 (September 2018) and 10 (November 2018) are not shown.




5.2.2 Visual inspection

5.2.2.1 NDVI

The effect of specific events on the NDVI 1km Version 2.2 was also visually checked by looking at

1° x 1° images around the point locations listed in Table 4. Figure 17 illustrates the NDVI at local

scale around the events for 4 consecutive dekads, with the first dekad shown being the last one

before the event. In case of the flooding event in Australia, the effect is shown based on images

some dekads after the event.

The location and the extent of the damage caused by the fire events are clearly visible for events 2

(Italy), 3 (Portugal), 4 (Spain) and 10 (California; NASA Earth Observatory, 2018f). The fire events

in Greece (event 1) and Spain (event 5) are not clearly visible, possibly because the drops in NDVI

values are relatively low (from about 0.6 to about 0.3). It is also hard to visually delineate the

flooding in Saudi Arabia (event 8; NASA Earth Observatory, 2018a), because the normal NDVI

values are very low. The effect of the flooding in Australia (event 6; NASA Earth Observatory,

2018b) is clear based on the images before, just after, 3 dekads after and 5 dekads after the event:

vegetation status peaks towards the end of April 2018, as is also seen in Figure 14. In the case of

the flooding in India (event 7; NASA Earth Observatory, 2018e), the observations are hampered by

persistent cloud cover in the rainy season, and the effect of the event starts to become visible

some dekads after the event. The extent of the hail storm scar (event 9; NASA Earth Observatory,

2018c, 2018d) is clearly visible in the NDVI quicklooks.




Nb Before After

1

2

3

4

5

6




Nb Before After

7

8

9

10

Figure 17: Maps of the NDVI in 1°x1° focus areas around specific events (Table 4). Areas with no data

available are masked in grey, permanent water surfaces in light blue.

5.2.2.1 VCI and VPI

Since the VPI product was discontinued after August 2018, this product is only evaluated over the

period January-August 2018. Visual inspection of the VCI (Figure 18) and the VPI (Figure 19)

shows the impact of the events on vegetation status, compared to the long term statistics.

As is the case for the NDVI, the location and the extent of the damage caused by the fire events

are clearly visible in the VCI and VPI quicklooks for events 3 (Portugal), 4 (Spain) and 10

(California; NASA Earth Observatory, 2018f). In contrast to the NDVI quicklooks, the VCI and VPI

quicklooks clearly show the effect of wildfire event 5 (Spain). It is however hard to delineate the

effect of the fire event in Greece (event 1) and Italy (event 2). The latter was visible in the NDVI

quicklooks, but strong negative anomalies are observed for the entire region which makes it

impossible to delineate the effect of the wildfire event. As a result of the flooding event in Australia




(event 6; NASA Earth Observatory, 2018b), the flooding results in positive VCI and VPI values

some dekads after the event. It is to be noticed that since the NDVI normally is very low, a small

deviation from the normal value results in very high VCI and VPI. Similar to the NDVI quicklooks, it

is also hard to visually delineate the flooding in Saudi Arabia (event 8; NASA Earth Observatory,

2018a). In the case of the flooding in India (event 7; NASA Earth Observatory, 2018e), the

observations are hampered by persistent cloud cover in the rainy season, and the effect of the

event starts to become visible some dekads after the event. The extent of the hail storm scar

(event 9; NASA Earth Observatory, 2018c, 2018d) is clearly visible in the VCI and VPI quicklooks.

Nb Before After

1

2

3

4

5




Nb Before After

6

7

8

9

10

Figure 18: Maps of the VCI in 1°x1° focus areas around specific events (Table 4). Areas with no data

available are masked in grey, permanent water surfaces in light blue.




Nb Before After

1

3

4

5

6

7




Nb Before After

8

9

Figure 19: Maps of the VPI in 1°x1° focus areas around specific events (Table 4). Areas with no data

available are masked in grey, permanent water surfaces in light blue. The VPI product was

discontinued after August 2018, hence events 2 (September 2018) and 10 (November 2018) are not

shown.




6 CONCLUSIONS

The SQE of 2018 is based on the comparison of the NDVI Collection 1km Version 2.2, the VCI and

the VPI products of 2018 with the same product of 2015. Since the VPI product was discontinued

after August 2018, this product is only evaluated over the period January-August 2018 (i.e. 24

dekads), while for the NDVI and VCI the evaluation is done over 36 dekads.

Product completeness is very similar, with nearly identical temporal behaviour, gap length

frequency and spatial distribution, although some differences in magnitude exist, related to cloud

cover anomalies. Differences in NDVI values between 2018 and 2015 are small: 63% (global

scale) or 52% (Europe) of the pixels show a bias lower than 0.05 and are mostly related to

unsystematic differences in vegetation status. This can be caused by above/below average rainfall

or temperatures, delay or advance of the phenological cycle, varying cropping intensities, land

cover changes, etc.

In the case of Europe, temporal profiles over cultivated areas and herbaceous cover indicate below

average vegetation development in the first months of 2018 in Finland, and during the growing

season in Denmark, Germany and Belgium related to drought conditions (CEMS, 2018a;

GEOGLAM, 2018a, 2018b, 2018c, 2018d, JRC MARS, 2018b, 2018c, 2018e, 2018d). In contrast,

the warm and relatively wet weather in the South of Europe and in the Baltic positively impacted

vegetation growth, as reflected in the temporal profiles over Estonia and Portugal. Scatterplots

show large scatter, though evenly distributed on both sides of the 1:1 line, related to expected

variations in vegetation development. Systematic differences are very low (GM regression lines are

close to the 1:1 line), and the NDVI distributions between the two datasets are very similar for all

biomes. The temporal smoothness is nearly identical in 2018 compared to 2015.

Temporal profiles of point locations and visual inspection (so-called quicklooks) of small areas

around specific events reported in 2018 indicate most of the events were well captured by the

NDVI, VCI and VPI. Temporal profiles show sharp drops (wildfires, floods, hail storm damage) or

positive evolution after the events (floods). Visual inspection showed that the effect of most events

is detectable in both the NDVI and NDVI anomaly (VCI, VPI) quicklooks. Nevertheless, some

events are only visible in the NDVI quicklooks (wildfire in Italy) or only in the VCI and VPI

quicklooks (wildfire in Spain). The effect of rains in Saudi Arabia was not clearly distinguishable,

neither in temporal plots, nor in the quicklooks.

To conclude, the NDVI Collection 1km Version 2.2 and the derived VCI and VPI products of 2018

perform similarly to those of 2015, with however differences due to natural inter-annual variations

of environmental conditions.




7 REFERENCES

Bartholomé, E., Belward, A.S., 2005. GLC2000: a new approach to global land cover mapping from Earth observation data. Int. J. Remote Sens. 26, 1959–1977. https://doi.org/10.1080/01431160412331291297

CEMS, 2018a. European Drought Observatory [WWW Document]. URL http://edo.jrc.ec.europa.eu

CEMS, 2018b. European Forest Fire Information System [WWW Document]. URL http://effis.jrc.ec.europa.eu/

Duveiller, G., Fasbender, D., Meroni, M., 2016. Supplementary information for: Revisiting the concept of a symmetric index of agreement for continuous datasets. Sci. Rep. 6, 1–5. https://doi.org/10.1038/srep19401

GEOGLAM, 2018a. Crop Monitor for AMIS - July 2018. GEOGLAM Crop Monitor.

GEOGLAM, 2018b. Crop Monitor for AMIS - August 2018.

GEOGLAM, 2018c. Crop Monitor for AMIS - September 2018. GEOGLAM Crop Monitor.

GEOGLAM, 2018d. Crop Monitor for AMIS - October 2018. GEOGLAM Crop Monitor.

Ji, L., Gallo, K., 2006. An agreement coefficient for image comparison. Photogramm. Eng. Remote Sens. 72, 823–833. https://doi.org/10.14358/PERS.72.7.823

JRC MARS, 2018a. Warm April accelerates crop development. JRC MARS Bulletin - Crop monitoring in Europe, May 2018.

JRC MARS, 2018b. Yield forecasts revised downward. JRC MARS Bulletin - Crop monitoring in Europe, June 2018.

JRC MARS, 2018c. Winter crop yield forecasts further down. JRC MARS Bulletin - Crop monitoring in Europe, July 2018.

JRC MARS, 2018d. Pasture conditions in northern Europe, update as at 31 July 2018. JRC MARS Update, August 2018. https://doi.org/10.1163/1571809042388581

JRC MARS, 2018e. Yield forecast revised further downwards. JRC MARS Bulletin - Crop monitoring in Europe, August 2018.

JRC MARS, 2018f. Weather improvements too late for crops. JRC MARS Bulletin - Crop monitoring in Europe, September 2018.

Kogan, F.N., 1990. Remote sensing of weather impacts on vegetation in non-homogeneous areas. Int. J. Remote Sens. 11, 1405–1419. https://doi.org/10.1080/01431169008955102

NASA Earth Observatory, 2018a. Rain Soaks the Empty Quarter. Image of the Day, 16 June 2018.

NASA Earth Observatory, 2018b. Rivers Swell in Channel Country. Image of the Day, 29 March 2018.

NASA Earth Observatory, 2018c. Hail Cuts Swaths of Damage Across South Dakota. Image of the Day, 12 July 2018.

NASA Earth Observatory, 2018d. Using Satellites to Spot a Hail Scar. Image of the Day, 19 July 2018.




NASA Earth Observatory, 2018e. Before and After the Kerala Floods. Image of the Day, 25 August 2018.

NASA Earth Observatory, 2018f. Camp Fire Adds Another Scar to 2018 Fire Season. Image of the Day, 28 November 2018.

Sannier, C. a. D., Taylor, 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. Int.J. Remote Sens. 19, 621–639. https://doi.org/10.1080/014311698215892

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