Copernicus Global Land Operations

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




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 12.02.2020

Approval: Roselyne Lacaze

Sign Date 24.02.2020

Endorsement: Michael Cherlet Sign Date

Distribution: Public




Change Record

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

12.02.2020 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 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




7 References ........................................................................................................................ 43




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




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




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


proportion of each biome over Europe ROI. ........................................................................... 21

Table 4: Specific events analysed in this SQE .............................................................................. 22




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




SHR Shrubland

SSD Service Specifications Document

SQE Service Quality Evaluation

SVP Service Validation Plan

V Version

VZA Viewing Zenith Angle

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) 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




(floods). Visual inspection showed that the effect of most events is detectable in the NDVI

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 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.




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


GIOGL1_PUM_NDVI300m-V1 Product User Manual summarizing all information about the

NDVI Collection 300m Version 1 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 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




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].




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.




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




[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


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




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


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




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).

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

https://earthobservatory.nasa.gov/




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




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)

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









“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)

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

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





https://mars.jrc.ec.europa.eu/asap/




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).

http://proba-v.vgt.vito.be/en/quality/known-issues

http://proba-v.vgt.vito.be/en/quality/known-issues




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




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)




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).




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.




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.




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




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.




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.




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).

…




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)




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.




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.




Nb Before After

1

2

3

4

5

6




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).




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).




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.




7 REFERENCES

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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.

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JRC MARS, 2019c. Wetter-than-usual in large parts of Europe. JRC MARS Bulletin - Crop monitoring in Europe, November 2019.

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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

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