copernicus global land operations
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Copernicus Global Land Operations – Lot 1 Date Issued: 12.02.2020 Issue: I1.00
Document-No. CGLOPS1_SQE2019_NDVI300m-V1 © C-GLOPS Lot1 consortium
Issue: I1.00 Date: 12.02.2020 Page: 1 of 43
Copernicus Global Land Operations
“Vegetation and Energy” ”CGLOPS-1”
Framework Service Contract N° 199494 (JRC)
SCIENTIFIC QUALITY EVALUATION
NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI)
COLLECTION 300M
VERSION 1
Issue I1.00
Organization name of lead contractor for this deliverable: VITO
Book Captain: Else Swinnen
Contributing Authors: Carolien Toté
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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: Else Swinnen
Sign Date 12.02.2020
Approval: Roselyne Lacaze
Sign Date 24.02.2020
Endorsement: Michael Cherlet Sign Date
Distribution: Public
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Change Record
Issue/Rev Date Page(s) Description of Change Release
12.02.2020 All First issue I1.00
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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 300m v1 quality .................................................................. 17
4 Scientific Quality Evaluation Method ................................................................................. 18
4.1 Overall procedure ................................................................................................................. 18
4.1.1 Global analysis ............................................................................................................................................ 19
4.1.2 Regional analysis: Europe ........................................................................................................................... 20
4.1.3 Specific events ............................................................................................................................................ 22
4.2 Validation metrics ................................................................................................................. 22
4.2.1 The coefficient of determination (R²) ......................................................................................................... 23
4.2.2 Geometric mean regression........................................................................................................................ 23
4.2.3 The root mean squared difference (RMSD) ................................................................................................ 23
4.2.4 Random and systematic differences ........................................................................................................... 24
4.3 Other Reference Products ...................................................................................................... 24
5 Results .............................................................................................................................. 26
5.1 Global and regional analysis .................................................................................................. 26
5.1.1 Product completeness ................................................................................................................................ 26
5.1.2 Spatial consistency ...................................................................................................................................... 28
5.1.3 Statistical consistency ................................................................................................................................. 32
5.1.4 Temporal consistency ................................................................................................................................. 34
5.2 Specific events ...................................................................................................................... 37
5.2.1 Temporal profiles ........................................................................................................................................ 37
5.2.2 Visual inspection ......................................................................................................................................... 38
6 Conclusions ....................................................................................................................... 42
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7 References ........................................................................................................................ 43
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List of Figures
Figure 1: The CGLS LC100 classification aggregated into 8 classes ............................................ 20
Figure 2:The GLC classification aggregated into 8 classes over the Europe ROI .......................... 21
Figure 3: Temporal evolution of the amount of valid values (%) in NDVI300 V1 for 2019 (blue) and
2014 (orange) at global scale (left) and calculated over the Europe ROI (right) ..................... 26
Figure 4: Spatial distribution of the amount of missing values (%) of NDVI300 V1 for 2019 (top) and
2014 (bottom) at global scale (left) and for the Europe ROI (right) ......................................... 27
Figure 5: Frequency distribution of the gap length (in dekads) in NDVI300 V1 for 2019 (green) and
2014 (red) at global scale (left) and calculated over the Europe ROI (right) ........................... 27
Figure 6: RMSD, RMPDs and RMPDu between NDVI300 V1 of 2014 and 2019 at global scale (left)
and over Europe (right) .......................................................................................................... 28
Figure 7: Histogram of the difference between NDVI300 V1 of 2019 and 2014 (2019 minus 2014)
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 NDVI300 V1 product at global
scale. Pairwise comparison of NDVI values for 2019 (green) and 2014 (red). X-axis: NDVI
values in steps of 0.02, Y-axis: percentage of occurrence. ..................................................... 31
Figure 9: Frequency distributions over different biomes based on the NDVI300 V1 product over
Europe. Pairwise comparison of NDVI values for 2019 (green) and 2014 (red). X-axis: NDVI
values in steps of 0.02, Y-axis: percentage of occurrence. ..................................................... 32
Figure 10: Scatterplots between NDVI300 V1 of 2019 (X) and 2014 (Y) over all land cover types
(top left) and per biome at global scale .................................................................................. 33
Figure 11: Scatterplots between NDVI300 V1 of 2019 (X) and 2014 (Y) over all land cover types
(top left) and per biome, over Europe. .................................................................................... 34
Figure 12: Frequency histogram of delta, a measure of temporal smoothness, for 2019 (green) vs
2014 (red), at global scale (left) and over Europe (right) ........................................................ 35
Figure 13: Temporal profiles of average NDVI 300m V1 for 2019 (red) compared to the 2014-2017
mean (grey) for France, Germany, Lithuania, Poland and Ukraine over cultivated areas (left)
and herbaceous cover (right) ................................................................................................. 36
Figure 14: Temporal profiles of NDVI 300m for 2019 (red) and 2014 (blue) over sites with specific
events in 2019. Green dashed lines indicate the fire event date............................................. 38
Figure 15: 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. Event 8 is shown in
Figure 16. .............................................................................................................................. 40
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Figure 16: Maps of the NDVI (top) and the absolute difference NDVI 2019 minus 2014 (bottom)
over the Zambia ROI (event 8 in Table 4). ............................................................................. 41
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List of Tables
Table 1: Overall procedure for the Scientific Quality Evaluation of the NDVI 300m Version 1 ....... 19
Table 2: Aggregation scheme for CGLS Land Cover 100m classes into 8 major biomes and
proportion of each biome at global scale ................................................................................ 20
Table 3: Aggregation scheme for CGLS Land Cover 100m classes into 8 major biomes and
proportion of each biome over Europe ROI. ........................................................................... 21
Table 4: Specific events analysed in this SQE .............................................................................. 22
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List of Acronyms
AD Applicable Document
ASAP Anomaly hotSpot of Agricultural Production
ATBD Algorithm Theoretical Basis Document
BA Bare areas
CEMS Copernicus Emergency Management Service
CEOS Committee on Earth Observing Satellites
CGLS Copernicus Global Land Service
CUL Cultivated areas
DBF Deciduous Broadleaf Forest
DN Digital Number
EBF Evergreen Broadleaf Forest
EDO European Drought Observatory
EFFIS European Forest Fire Information System
EPSG European Petroleum Survey Group
GCOS Global Climate Observing System
GDO Global Drought Observatory
GIO GMES Initial Operations
GM Geometric Mean
GMR Geometric Mean Regression
HER Herbaceous cover
JRC Joint Research Centre
LPV Land Product Validation subgroup
MARS Monitoring Agricultural ResourceS
MODIS MODerate resolution Imaging Spectroradiometer
MPD Mean Product Difference
MSD Mean Squared Difference
MVC Mean Value Composite
MXF Mixed Forest
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
QAR Quality Assessment Report
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
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SHR Shrubland
SSD Service Specifications Document
SQE Service Quality Evaluation
SVP Service Validation Plan
V Version
VZA Viewing Zenith Angle
WGS World Geodetic System
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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) 300m Version 1 product. The SQE is part of the
Copernicus Global Land Service operations activities. The quality evaluation is performed on data
of 2019 (36 dekads), at global scale, on a large region of interest (ROI) located in Europe, and on
point locations with specific events reported in 2019. The products of 2019 are compared to
products of 2014, validated in a quality assessment analysis. The objective is to check that the
quality of NDVI 300m Version 1 2019 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 2019.
The results of the SQE indicate that the NDVI 300m Version 1 products of 2019 are performing
similarly to those of 2014.
The new cloud screening method implemented in PROBA-V Collection 1 from 5th December 2016
results is a higher amount of gaps in 2019 compared to 2014 (Toté et al., 2018). The spatial
distribution and the gap length frequency distribution are similar for 2019 and 2014. Differences in
NDVI values between 2019 and 2014 are small: 65% (global scale) or 50% (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 June-August in France, Germany, Lithuania, Poland and
Ukraine related to drought conditions. Crops were affected by heatwaves and rain deficit in most
western and northern-central European countries (JRC EDO, 2019; JRC MARS, 2019a), as most
parts of Europe were affected by unusually early and intense heatwaves (JRC MARS, 2019b), in
June (NASA Earth Observatory, 2019a) and July (NASA Earth Observatory, 2019b)
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 2019 compared to 2014.
Temporal profiles of point locations and visual inspection (so-called quicklooks) of small areas
around specific events reported in 2019 indicate most of the events were well captured by the
NDVI. Temporal profiles show sharp drops (wildfires, floods) or positive evolution after the events
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(floods). Visual inspection showed that the effect of most events is detectable in the NDVI
quicklooks.
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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 300m Version 1 products. The quality evaluation is performed on data of 2019 (36
dekads), at global scale, on a large region of interest (ROI) located in Europe, and on point
locations with specific events reported in 2019. The objective is to check that the quality of NDVI
Collection 300m Version 1 product keeps stable over time. In order to do so, the products of 2019
are compared to products of 2014 which were validated in an exhaustive validation exercise
[GIOGL1_QAR_NDVI300m-V1].
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 results of the NDVI 300m V1 quality assessment
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.
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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_NDVI300m-V1 Algorithm Theoretical Basis Document of the NDVI
Collection 300m Version 1 product
GIOGL1_QAR_NDVI300m-V1 Quality assessment report containing the results of
the scientific validation of the NDVI Collection 300m
Version 1 product
GIOGL1_VR_NDVI-VCI-VPI1km-
V2.2
Validation report of the NDVI Collection 1km Version
2.2 products
CGLOPS1_ATBD_LC100m-V2.0 Algorithm Theoretical Basis Document of the global
Land Cover Collection 100m map.
1.3.3 Output
Document ID Descriptor
GIOGL1_PUM_NDVI300m-V1 Product User Manual summarizing all information about the
NDVI Collection 300m Version 1 product
1.3.4 External documents
Document ID Descriptor
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
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2 REVIEW OF USERS REQUIREMENTS
According to the applicable document [AD2] and [AD3], the user’s requirements relevant for NDVI
Collection 300m are:
Definition:
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
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: 1/336° - 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 is not an Essential Climate Variable, 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
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3 REVIEW OF THE NDVI COLLECTION 300M V1 QUALITY
An inter-comparison between the NDVI Collection 300m V1, the NDVI V2 (Collection 1km) and
MODIS 250m was performed for two 10° x 10° tiles (1 in Western Europe, 1 in East Africa) over
the year 2014. The details of the analysis are given in the Quality Assessment Report
[GIOGL1_QAR_NDVI300m-V1]. A summary of the results is added below.
To assess the quality of the NDVI 300m, the product was compared to the NDVI V2 expanded to
the same resolution and to MODIS product re-sampled to 300m. A large similarity between the
NDVI 300m and the NDVI V2 was observed (RMPDs below 0.02, R² above 0.8 depending on the
sampling scheme used). The differences found relate to higher spatial detail in the NDVI 300m
product and a difference in Maximum Value Compositing (MVC) approach.
The higher spatial detail impact is visible when comparing the results obtained over the European
tile and the East African tile. The landscape in the first tile is more fragmented with sharp
boundaries between the patches (e.g. agricultural fields) whereas, in East Africa, large parts of the
area consist of smoothly varying landscape (e.g. savannah area). The similarity between the 9
pixels of NDVI 300m covering one NDVI 1km pixel is then higher.
In the compositing of the PROBA-V NDVI 300m input data, there is a preferential selection of
observations with a view zenith angle (VZA) smaller than 40°, a constraint that is not used for the
PROBA-V NDVI 1km V2 products. Given that, in general, off nadir observations lead to higher
NDVI values due to anisotropy of the surface and a longer atmospheric path, these observations
are often selected in the NDVI 1km V2 product. Due to the VZA constraint, the MVC approach for
NDVI 300m will select data from another day with more nadir viewing, but with a lower NDVI value.
The difference that is observed is of the magnitude of 0.02 NDVI.
Overall, the comparison between PROBA-V NDVI 300m and MODIS NDVI
[GIOGL1_QAR_NDVI300m-V1] confirmed the results that were obtained previously on the
comparison between NDVI 1km V2 and MODIS [GIOGL1_VR_NDVI-VCI-VPI1km-V2.2].
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4 SCIENTIFIC QUALITY EVALUATION METHOD
4.1 OVERALL PROCEDURE
The SQE of 2019 is based on the comparison of the NDVI Collection 300m Version 1 product of
2019 (36 dekads) with the same product of 2014 (36 dekads), since the data of 2014 was validated
in the validation report. 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 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 §0. 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.
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Table 1: Overall procedure for the Scientific Quality Evaluation of the NDVI 300m Version 1
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 2019 and 2014 at global and at regional scale
Frequency distribution (in %) of the length of the gaps (in dekads) in the products of 2019 and 2014 at global and at regional scale
Spatial consistency Spatial distribution of the validation metrics expressing the similarities/differences between 2019 and 2014 at global and at regional scale
Histogram of bias between 2019 and 2014, overall and per biome at global and at regional scale
Distribution of values per biome for 2019 and 2014 at global and at
regional scale
Statistical consistency Scatterplots (incl. GMR equation, R²) between 2014 and 2019, 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 2019 are compared to temporal profiles of the 2014-2017 mean for cultivated and herbaceous vegetation types in a selection of European countries.
Temporal variations and realism: temporal profiles of NDVI are extracted for specific locations, and 1° x 1° NDVI maps before and after the 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 51 by 51 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. For the statistical analyses (regression plots and
histograms), an additional 30% random sampling was done to further reduce processing time.
An aggregated version of the CGLOPS Global Land Cover at 100m, epoch 2015 (Buchorn et al.,
2019) 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. The map at 300m spatial resolution
is generated from the CGLOPS-LC100 discrete classification map. For each of the 60 global UTM
zones, an upscaling from 100m to 300m was performed by aggregating following the mode of the
discrete classes. In case of equal occurrence of discrete classes, a set of expert rules based on
cover fractions which can be retrieved from the CGLOPS-LC100 documentation is applied
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[CGLOPS1_ATBD_LC100m-V2.0]. A new global tiling grid at 300m resolution in EPSG:4326
(WGS84) is created to which each of the UTM zones is transformed following the best-available-
pixel approach.
Figure 1: The CGLS LC100 classification aggregated into 8 classes
Table 2: Aggregation scheme for CGLS Land Cover 100m classes into 8 major biomes and
proportion of each biome at global scale
Abbreviation Name CGLOPS-LC100 classes
Proportion at global scale (%)
EBF Evergreen broadleaf forest 112, 122 7.6
DBF Deciduous broadleaf forest 114, 124 6.2
NLF Needleleaf forest 111, 113, 121, 123 10.4
MXF Mixed forest 115, 125 1.4
SHR Shrubland 20 7.5
HER Herbaceous 30 21.2
CRO Crop 40 10.9
BA Bare/sparse vegetation 60, 100 15.3
Other (not considered in the analyses)
0, 50, 70, 80, 90, 116, 126, 200
19.4
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
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vegetation and considerably reduces the processing time. For the statistical analyses (regression
plots and histograms), an additional 30% random sampling was done to further reduce processing
time.
As is the case for the global analysis (see above), an aggregated version of the CGLOPS Global
Land Cover at 100m, epoch 2015 (Buchorn et al., 2019) was used to distinguish between major
land cover classes over Europe (Figure 2). The classes were aggregated according to the scheme
in Table 3.
Figure 2: The GLC classification aggregated into 8 classes over the Europe ROI
Table 3: Aggregation scheme for CGLS Land Cover 100m classes into 8 major biomes and
proportion of each biome over Europe ROI.
Abbreviation Name CGLOPS-LC100 classes Proportion (%)
EBF Evergreen broadleaf forest 112, 122 0.0
DBF Deciduous broadleaf forest 114, 124 11.0
NLF Needleleaf forest 111, 113, 121, 123 16.6
MXF Mixed forest 115, 125 4.9
SHR Shrubland 20 2.4
HER Herbaceous 30 16.0
CRO Crop 40 26.9
BA Bare/sparse vegetation 60, 100 3.3
Other (not considered in the analyses)
0, 50, 70, 80, 90, 116, 126, 200
18.9
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In addition, statistics were extracted from the Europe ROI, based on the 8 biomes as defined in the
aggregated CGLS Land Cover 100m, and the country borders. This allows comparison of the
temporal evolution of the NDVI 300m V1 of 2019 with the (2014-2017) 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.
In order to evaluate temporal consistency, temporal profiles of NDVI over point locations were
extracted. Also 1° x 1° NDVI 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 Torre del Español,
Catalonia Wildfire Spain
41.2659° N 0.5919° E 26/06/2019
2 Karabaglar, Izmir Wildfire Turkey 38.2760° N 27.0190° E 21/08/2019
3 Mação Wildfire Portugal 39.7315° N 8.1255° W 22/07/2019
4 Kontodespoti/Makrimalli,
Evia Wildfire Greece
38.6409° N 23.6430° E 13/08/2019
5 Valsesia Wildfire Italy 45.6522° N 8.2957° E 05/04/2019
6 Border region Bolivia,
Paraguay and Brazil Wildfire
Bolivia,
Paraguay,
Brazil
19.637° S 58.061° W 08/2019
7 Queensland, New South
Wales Wildfire Australia 33.207° S 150.697° E 10/2019
8 Southern province Drought Zambia 15.0° - 18.2° S 24.8° - 29.1° E from
02/2019
9 Arkansas River Flood USA 35.7651° N 95.2589° W 05/2019
10 Queensland Flood Australia 18.781° S 140.813° E 01-02/2019
4.2 VALIDATION METRICS
In the following paragraphs and equations, X refers to the product under evaluation (i.e. 2019), and
Y to the reference product (i.e. 2014).
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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., 2016a).
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. By applying an
eigen decomposition to the covariance metrics of X and Y, two eigenvectors are obtained that
describe the principal axes of the point cloud (Duveiller et al., 2016b), i.e. the regression line with
equation:
Eq. 2
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. 3
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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. 4
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. 5
With and estimated using the GM regression line and the number of samples. Then,
Eq. 6
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
“More rain needed in southern Europe”, JRC MARS Bulletin – Crop monitoring in Europe, March
2019. Available online at: https://ec.europa.eu/jrc/en/mars/bulletins. (JRC MARS, 2019d)
“Extent and impacts of the heatwaves in Europe”, JRC MARS Bulletin – Crop monitoring in Europe,
June 2019. Available online at: https://ec.europa.eu/jrc/en/mars/bulletins. (JRC MARS, 2019b)
“Weakened yield outlook for summer crops”, JRC MARS Bulletin – Crop monitoring in Europe,
August 2019. Available online at: https://ec.europa.eu/jrc/en/mars/bulletins. (JRC MARS, 2019a)
“Wetter-than-usual in large parts of Europe”, JRC MARS Bulletin – Crop monitoring in Europe,
August 2019. Available online at: https://ec.europa.eu/jrc/en/mars/bulletins. (JRC MARS, 2019c)
“Heatwave Scorches Europe”, NASA Earth Observatory, 29 June 2019. Available online at:
https://earthobservatory.nasa.gov/. (NASA Earth Observatory, 2019a)
“A Second Scorching Heatwave in Europe”, NASA Earth Observatory, 27 July 2019. Available online
at: https://earthobservatory.nasa.gov/. (NASA Earth Observatory, 2019b)
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“Drought in Europe – August 2019”, JRC European Drought Observatory. Available online at:
https://edo.jrc.ec.europa.eu/. (JRC EDO, 2019)
European Forest Fire Information System (EFFIS), Fire News. Copernicus Emergency Management
Service. Available online at: http://effis.jrc.ec.europa.eu.
“Fire Burns in Paraguay, Bolivia and Brazil”, NASA Earth Observatory, August 2019. Available online
at: https://earthobservatory.nasa.gov/. (NASA Earth Observatory, 2019c)
“Wildfires in New South Wales, Australia”, The Copernicus Emergency Management Service
monitors impact of fires in Australia. (CEMS, 2019)
“Aussie Smoke Plumes Crossing Oceans”, NASA Earth Observatory, 21 November 2019. Available
online at: https://earthobservatory.nasa.gov/.
“Drought in Southern Africa”, JRC Global Drought Observatory Analytical Report, 14 August 2019.
(JRC GDO, 2019)
“Flooding Along the Arkansas River”, NASA Earth Observatory, 30 May 2019. Available online at:
https://earthobservatory.nasa.gov/. (NASA Earth Observatory, 2019d)
“Summer Floods in Australia”, NASA Earth Observatory, 13 February 2019. Available online at:
https://earthobservatory.nasa.gov/. (NASA Earth Observatory, 2019e)
“Severe crop damages in parts of Southern Africa hit by Cyclone Idai and crop failure due to drought
in Zambia’s Southern Province”, JRC MARS Special Focus, March 2019. Available online at:
https://mars.jrc.ec.europa.eu/asap/. (JRC ASAP, 2019)
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5 RESULTS
5.1 GLOBAL AND REGIONAL ANALYSIS
5.1.1 Product completeness
In order to evaluate the product completeness, the amount of valid pixels, i.e. not flagged as
‘invalid’, over land is quantified. Figure 3 illustrates the temporal evolution of the product
completeness of the NDVI 300m Version 1 product for 2019 compared to 2014 at global scale and
over the Europe ROI. At global scale, the temporal variation of missing values is very similar for
2014 and 2019, but 2019 shows on average 14% more gaps. Over Europe, the percentage of
missing values fluctuates between 4% in Summer and 70% in winter for 2019, and between 1%
and 50% for 2014. The larger amount of missing values in 2019 is linked to the new cloud
screening method that was applied in PROBA-V Collection 1 from December 2016 onwards,
resulting in a higher amount of gaps.
Global scale Europe
Figure 3: Temporal evolution of the amount of valid values (%) in NDVI300 V1 for 2019 (blue) and
2014 (orange) at global scale (left) and calculated over the Europe ROI (right)
Figure 4 compares the amount of missing values per pixel over 2014 and 2019 at global scale and
over the Europe ROI. In both years, a larger number of missing values is observed at high latitudes
and in the tropics, but in 2019 more missing values are observed compared to 2014. As explained
above, this is related to a more stringent cloud screening method applied from December 2016
onwards. Due to an artefact in the monthly land cover information used in the cloud detection
algorithm, a horizontal striping pattern is visible in the 2019 maps (see also http://proba-
v.vgt.vito.be/en/quality/known-issues).
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Global scale Europe
2019
2014
Figure 4: Spatial distribution of the amount of missing values (%) of NDVI300 V1 for 2019 (top) and
2014 (bottom) at global scale (left) and for the Europe ROI (right)
Global scale Europe
Figure 5: Frequency distribution of the gap length (in dekads) in NDVI300 V1 for 2019 (green) and
2014 (red) at global scale (left) and calculated over 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
2019 and 2014, a gap length of one dekad is the most frequent. The gap length frequency
distribution is similar for both years, although 2019 displays more gaps, as expected due to the
more stringent cloud screening, with a larger impact on ‘short’ gaps (1 to 6 dekads long) at global
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scale. Over the Europe ROI, the impact is less pronounced and mostly limited to the shortest gaps
(1 or 4 dekads).
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
300m Version 1 for 2019 and 2014 (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 2019 vs. 2014 (e.g. minor shifts in the development of the growing season (Figure 13)). The
systematic difference between the two years is low.
Global scale Europe
RM
SD
RM
PD
s
RM
PD
u
Figure 6: RMSD, RMPDs and RMPDu between NDVI300 V1 of 2014 and 2019 at global scale (left) and
over Europe (right)
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5.1.2.2 Histograms of bias
Bias was analysed between all paired estimates of the NDVI 300m Version 1 for the 36 dekads in
2019 and 2014. The histogram of the difference (2019 minus 2014), 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 ± 0.01 (25% of the pixels at global scale; 12% of the
pixels over Europe). Over 65% (global scale) or 50% (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 2014 and 2019. Both at global scale and over
Europe, the histogram is slightly skewed towards positive values, i.e. 2019 showing slightly higher
NDVI values compared to 2014. The histograms per biome show similar behaviour, but it is
especially notorious for bare areas (BA). Apart from different vegetation conditions in both years,
the difference is also related to subtle changes in absolute calibration between PROBA-V
Collection 0 and Collection 1 (used as input from December 2016 onwards), although the
magnitude of the effect on the NDVI remains below 0.015 (Toté et al., 2018).
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Global scale Europe
Figure 7: Histogram of the difference between NDVI300 V1 of 2019 and 2014 (2019 minus 2014) at
global scale (left) and over Europe (right). Top: all land cover, bottom: per major biome.
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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 300m Version 1 values of 2019 and 2014 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 2019 seem slightly higher for evergreen
broadleaf (EBF), deciduous broadleaf forests (DBF) and cropland (CRO). Over Europe, the 2019
NDVI is slightly lower for needleleaf forest (NLF) and mixed forest (MXF), and slightly higher for
shrubland (SHR), herbaceous cover (HER) and bare areas (BA).
Figure 8: Frequency distributions over different biomes based on the NDVI300 V1 product at global
scale. Pairwise comparison of NDVI values for 2019 (green) and 2014 (red). X-axis: NDVI values in
steps of 0.02, Y-axis: percentage of occurrence.
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Figure 9: Frequency distributions over different biomes based on the NDVI300 V1 product over
Europe. Pairwise comparison of NDVI values for 2019 (green) and 2014 (red). X-axis: NDVI values in
steps of 0.02, Y-axis: percentage of occurrence.
5.1.3 Statistical consistency
This section discusses the statistical consistency between the NDVI 300m Version 1 of 2019 and
2014. 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, both at global scale and over the Europe ROI, the GM regression line is
almost 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
2019 vs. 2014) and differences between PROBA-V Collection 0 and Collection 1 (as previously
explained), 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. Only the EBF biome
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shows an important deviation, with a slope well above 1. This is related to a larger amount of high
NDVI values, as also seen in Figure 8. The difference is possibly related to a larger amount of
undetected clouds in the 2014 dataset2 (see also § 5.1.1). Over Europe, the scatterplot for BA
shows a relatively large amount of pixels where the NDVI in 2019 is notoriously larger than in
2014.
Figure 10: Scatterplots between NDVI300 V1 of 2019 (X) and 2014 (Y) over all land cover types (top
left) and per biome at global scale
2 This is not observed in the SQE2019 for the NDVI V2.2 (1km) products, since the NDVI V2.2 is entirely
based on PROBA-V Collection 1, while this is not the case for NDVI300m V1.
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Figure 11: Scatterplots between NDVI300 V1 of 2019 (X) and 2014 (Y) over all land cover types (top
left) and per biome, over Europe.
5.1.4 Temporal consistency
5.1.4.1 Temporal smoothness
The delta (Table 1), expressing the temporal smoothness for the NDVI 300m Version 1 of 2014
and 2019 is presented in Figure 12, both at global scale and over the Europe ROI. The temporal
smoothness is nearly identical (slightly higher) in 2019 compared to 2014.
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Global scale Europe
Figure 12: Frequency histogram of delta, a measure of temporal smoothness, for 2019 (green) vs
2014 (red), 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 2019 was an
exceptionally warm and, in many places, dry year, with several heat waves during summer, and a
month of November that was wetter than usual in large parts of the continent (JRC MARS, 2019c).
The heat waves negatively impacted crop production during the growing season in many places,
while the wet conditions had diverse implications for winter crops sowing and establishment, and
harvest of summer crops.
In July 2019, several European countries broke temperature records in a scorching heatwave
(NASA Earth Observatory, 2019b). Also June 2019 was very hot (NASA Earth Observatory,
2019a), as most parts of Europe were affected by unusually early and intense heatwaves (JRC
MARS, 2019b). Crops were affected by heatwaves and rain deficit in most western and northern-
central European countries (JRC EDO, 2019; JRC MARS, 2019a).
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 2019 (in
red) with the 2014-2017 mean (in grey). The graphs for cultivated areas show the effect of the heat
and drought in the June-July 2019, with a relative strong decline of the NDVI during this period,
most noticeable for Lithuania and Poland. For herbaceous cover the trend is less pronounced. The
graphs over Lithuania show very low NDVI in January and February, related to very low
temperatures (well below average) in this period (JRC MARS, 2019d).
…
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Cultivated areas Herbaceous cover
Fra
nce
Ge
rma
ny
Lith
ua
nia
Po
land
Ukra
ine
Figure 13: Temporal profiles of average NDVI 300m V1 for 2019 (red) compared to the 2014-2017
mean (grey) for France, Germany, Lithuania, Poland and Ukraine over cultivated areas (left) and
herbaceous cover (right)
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5.2 SPECIFIC EVENTS
5.2.1 Temporal profiles
Figure 14 illustrates the temporal profiles of NDVI 300m for 2019 (red) and 2014 (blue) over 10
sites with specific events in 2019, as specified in Table 4. For all events except event 8, the profile
is based on one pixel. The graph for the drought event in Zambia (event 8) is based on average
values over cropland in Zambia. The dates of the events are indicated by dashed green lines.
For the wildfire events in Spain (event 1), Turkey (event 2), Portugal (event 3), Greece (event 4)
and Australia (event 7; CEMS, 2019; NASA Earth Observatory, 2019f), the drop in NDVI from
values between 0.5 and 0.8 (hence relatively dense vegetation) to values around 0.2-0.3 is very
clear from the temporal profiles. The negative NDVI in the time profile for the Australia fire event is
probably related to very dark burnt surface. In case of the wildfire in the border region between
Bolivia, Paraguay and Brazil (event 6; NASA Earth Observatory, 2019c), the drop in NDVI is also
well pronounced. Also in 2014, the location was affected by a wildfire. In case of the wildfire event
in Italy (event 5), the wildfire does not result in a drop of NDVI but seems to result in a later start of
the growing season and a lower maximum NDVI over the season following the event. The effect of
the drought period in Southern province of Zambia from February onwards (event 8; JRC ASAP,
2019; JRC GDO, 2019) results in slightly lower NDVI values compared to 2014 (which was an
average year). The flooding events 9 (USA; NASA Earth Observatory, 2019d) and 10 (Australia;
NASA Earth Observatory, 2019e) result in a period of negative NDVI values, due to water
presence on the ground, but also cause the NDVI to peak around 2-3 months after the event. The
effect gradually decreases afterwards.
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Figure 14: Temporal profiles of NDVI 300m for 2019 (red) and 2014 (blue) over sites with specific
events in 2019. Green dashed lines indicate the fire event date.
5.2.2 Visual inspection
The effect of specific events on the NDVI 300m was also visually checked by looking at 1° x 1°
images around the point locations listed in Table 4, except for the drought event in Zambia (event
8), see below. Figure 15 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
events 9 and 10, the last map shows the moment when vegetation reaches a maximum state after
the event. The location and extent of the event is highlighted using a red circle.
The location and the extent of the damage caused by the fire events are clearly visible for events 1
(Spain), 2 (Turkey), 3 (Portugal), 4 (Greece), 5 (Italy), 6 (border region Bolivia, Paraguay and
Brazil, NASA Earth Observatory, 2019c) and 7 (Australia; CEMS, 2019; NASA Earth Observatory,
2019f). The effect of the flood events 9 (USA; NASA Earth Observatory, 2019d) and 10 (Australia;
NASA Earth Observatory, 2019e) is also clearly visible in the NDVI quicklooks with negative NDVI
values when the water is present on the ground.
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Nb Before After
1
2
3
4
5
6
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Nb Before After
7
9
10
Figure 15: 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. Event 8 is shown in Figure 16.
The drought period in Southern province in Zambia (event 8), is visually inspected based on the
NDVI and the absolute difference between the NDVI in 2019 and 2014, as this affects the whole
region, not limited to a specific delineated area or point (Figure 16). Based on the NDVI only, it is
hard to visually delineate the effect of drought in the Southern province of Zambia (event 8; JRC
ASAP, 2019; JRC GDO, 2019), as it is merely a general decline of the NDVI. This is clearly visible
in the NDVI absolute difference between 2019 and 2014 (Figure 16).
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Nb Before After
8
NDVI
NDVI diff. 2019-2014
Figure 16: Maps of the NDVI (top) and the absolute difference NDVI 2019 minus 2014 (bottom) over
the Zambia ROI (event 8 in Table 4).
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6 CONCLUSIONS
The SQE of 2019 is based on the comparison of the NDVI Collection 300m Version 1 products of
2019 (36 dekads) with the same product of 2014.
The new cloud screening method implemented in PROBA-V Collection 1 from 5th December 2016
results is a higher amount of gaps in 2019 compared to 2014 (Toté et al., 2018). The spatial
distribution and the gap length frequency distribution are similar for 2019 and 2014. Differences in
NDVI values between 2019 and 2014 are small: 65% (global scale) or 50% (Europe) of the pixels
show an absolute 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 during the growing season in France, Germany, Lithuania,
Poland and Ukraine related to heat waves and rain deficit (JRC EDO, 2019; JRC MARS, 2019a).
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 overall 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. For evergreen broadleaf forest, the scatterplots indicates some higher
values in 2019 compared to 2014. Also over bare areas in Europe, the scatterplot shows a
relatively large amount of pixels where the NDVI in 2019 is notoriously larger than in 2014. The
temporal smoothness is nearly identical in 2019 compared to 2014.
Temporal profiles of point locations and visual inspection (so-called quicklooks) of small areas
around specific events reported in 2019 indicate most of the events were well captured by the
NDVI. Temporal profiles show sharp drops (wildfires, floods) or positive evolution after the events
(floods). Visual inspection showed that the effect of most events is detectable in the NDVI
quicklooks.
To conclude, the NDVI Collection 300m Version 1 products of 2019 perform similarly to those of
2014, with however differences due to natural inter-annual variations of environmental conditions.
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7 REFERENCES
Buchorn, M., Smets, B., Bertels, L., Lesiv, M., Tsendbazar, N.-E., Herold, M., Fritz, S., 2019. Copernicus Global Land Service: Land Cover 100m: epoch 2015: Globe. https://doi.org/10.5281/zenodo.3243509
CEMS, 2019. EMSR408 Wildfires in New South Wales, Australia.
Duveiller, G., Fasbender, D., Meroni, M., 2016a. Supplementary information for: Revisiting the concept of a symmetric index of agreement for continuous datasets. Sci. Rep. 6, 19401. https://doi.org/10.1038/srep19401
Duveiller, G., Fasbender, D., Meroni, M., 2016b. Revisiting the concept of a symmetric index of agreement for continuous datasets. Sci. Rep. 6, 1–14. https://doi.org/10.1038/srep19401
JRC ASAP, 2019. Severe crop damages in parts of Southern Africa hit by Cyclone Idai and crop failure due to drought in Zambia’ s Southern Province. Special Focus - March 2019.
JRC EDO, 2019. Drought in Europe – August 2019. EDO Analytical Report.
JRC GDO, 2019. Drought in Southern Africa - August 2019. GDO Analytical Report.
JRC MARS, 2019a. Weakened yield outlook for summer crops. JRC MARS Bulletin - Crop monitoring in Europe, August 2019.
JRC MARS, 2019b. Extent and impacts of the heatwaves in Europe. JRC MARS Bulletin - Crop monitoring in Europe, June 2019.
JRC MARS, 2019c. Wetter-than-usual in large parts of Europe. JRC MARS Bulletin - Crop monitoring in Europe, November 2019.
JRC MARS, 2019d. More rain needed in southern Europe. JRC MARS Bulletin - Crop monitoring in Europe, March 2019.
NASA Earth Observatory, 2019a. Heatwave Scorches Europe. Image of the Day, 29 June 2019.
NASA Earth Observatory, 2019b. A Second Scorching Heatwave in Europe. Image of the Day, 27 July 2019.
NASA Earth Observatory, 2019c. Fire Burns in Paraguay, Bolivia, and Brazil.
NASA Earth Observatory, 2019d. Flooding Along the Arkansas River. Image of the Day, 30 May 2019. https://doi.org/10.1126/science.357.6351.560-f
NASA Earth Observatory, 2019e. Summer Floods in Australia. Image of the Day, 13 February 2019.
NASA Earth Observatory, 2019f. Aussie Smoke Plumes Crossing Oceans. Image of the Day, 21 November 2019.
Toté, C., Swinnen, E., Sterckx, S., Adriaensen, S., Benhadj, I., Iordache, M.-D.D., Bertels, L., Kirches, G., Stelzer, K., Dierckx, W., Van den Heuvel, L., Clarijs, D., Niro, F., 2018. Evaluation of PROBA-V Collection 1: Refined Radiometry, Geometry, and Cloud Screening. Remote Sens. 10, 1375. https://doi.org/10.3390/rs10091375