Copernicus Global Land Operations Vegetation and Energy - ICDC · List of Figures Figure 1: Flow...

Copernicus Global Land Operations – Lot 1

Date Issued: 11.03.2019

Issue: I1.33

Copernicus Global Land Operations

”Vegetation and Energy” “CGLOPS-1”

Framework Service Contract N° 199494 (JRC)

PRODUCT USER MANUAL

LEAF AREA INDEX (LAI)

FRACTION OF ABSORBED PHOTOSYNTHETICALLY ACTIVE RADIATION (FAPAR)

FRACTION OF VEGETATION COVER (FCOVER)

COLLECTION 1KM

VERSION 2

Issue 1.33

Organisation name of lead contractor for this deliverable: VITO

Book Captain: Bruno Smets (VITO)

Contributing Authors: Aleixandre Verger (CREAF)

Fernando Camacho (EOLAB)

Roxane Van der Goten (VITO)

Tim Jacobs (VITO)



Issue: I1.33

Document-No. CGLOPS1_PUM_LAI1km-V2 © C-GLOPS Lot1 consortium

Issue: I1.33 Date: 11.03.2019 Page: 2 of 56

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)



Issue: I1.33



Document Release Sheet

Book captain: Bruno Smets Sign Date 11.03.2019

Approval Roselyne Lacaze Sign Date 19.03.2019

Endorsement: Michael Cherlet Sign Date

Distribution: Public



Issue: I1.33



Change Record

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

11.04.2016 All First version for SPOT/VGT Version 2 products I1.00

I1.00 10.01.2017

Chapter 3

Chapter 4

Chapter 6

Chapter 7

Included PROBA-V input data

Updated technical characteristics

Added summary of quality assessment results

Updated product usage

I1.10

I1.10 13.01.2017 Editorial updates after JRC review I1.20

I1.20 16.01.2017 52-54

28;48

Move Users’ requirements to Annex

Add one recommendation for efficient use I1.21

I1.21 23.03.2017 All Updated after external review I1.30

I1.30 12.09.2017 28-37 Update of attributes I1.31

I1.31 12.10.2017 42-43 Add section 4.2 I1.32

I1.32 11.03.2019 2.4

3.2.1

More details about algorithm limitations

More details about quality flag I1.33



Issue: I1.33



TABLE OF CONTENTS

Executive Summary .................................................................................................................. 11

1 Background of the document ............................................................................................. 12

1.1 Scope and Objectives............................................................................................................. 12

1.2 Content of the document....................................................................................................... 12

1.3 Related documents ............................................................................................................... 12

1.3.1 Applicable document .................................................................................................................................. 12

1.3.2 Input ............................................................................................................................................................ 12

1.3.3 External documents .................................................................................................................................... 13

2 Algorithm .......................................................................................................................... 14

2.1 Introduction .......................................................................................................................... 14

2.2 Definition of the Variables ..................................................................................................... 14

2.2.1 Leaf area index (LAI) ................................................................................................................................... 14

2.2.2 Fraction of photosynthetically active radiation absorbed (FAPAR) ............................................................ 15

2.2.3 Fraction of vegetation cover (FCover) ........................................................................................................ 15

2.3 Retrieval Methodology .......................................................................................................... 16

2.3.1 Background ................................................................................................................................................. 16

2.3.2 Outline ........................................................................................................................................................ 16

2.3.3 Input data.................................................................................................................................................... 18

2.3.4 Processing steps .......................................................................................................................................... 20

2.4 Limitations ............................................................................................................................ 26

3 Product Description ........................................................................................................... 28

3.1 File Format ............................................................................................................................ 28

3.2 Product Content .................................................................................................................... 30

3.2.1 Data Files ..................................................................................................................................................... 30

3.2.2 Quicklook or browse image ........................................................................................................................ 38

3.3 Product Characteristics .......................................................................................................... 39

3.3.1 Projection and grid information ................................................................................................................. 39

3.3.2 Spatial information ..................................................................................................................................... 39

3.3.3 Temporal information ................................................................................................................................. 39

3.4 Data Policies ......................................................................................................................... 40

3.5 Contacts ................................................................................................................................ 41



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4 Differences with previous versions ..................................................................................... 42

4.1 Algorithm .............................................................................................................................. 42

4.2 Minor versions and production re-runs .................................................................................. 43

5 Validation ......................................................................................................................... 45

6 Product Usage ................................................................................................................... 48

6.1 Analysis ................................................................................................................................ 48

6.2 Assimilation in models ........................................................................................................... 48

6.3 Gap Filling ............................................................................................................................. 48

6.4 Data Continuity ..................................................................................................................... 49

7 References ........................................................................................................................ 51

8 Annex: Users Requirements ............................................................................................... 54



Issue: I1.33



List of Figures

Figure 1: Flow chart the three processing branches (A, B and C). First, daily S1 top of canopy

reflectance products are transformed into daily estimates of LAI, FAPAR and FCover using

specific neural networks (NNTs) for Evergreen Broadleaf Forest (EBF) or non_EBF pixels

(Branch A). Second, dedicated filtering, smoothing, gap filling and compositing temporal

techniques are applied. The inputs of this second step are the daily estimates, the sun zenith

angle of the observations, the pixel’s latitude, a climatology of LAI, FAPAR and FCover, and

the EBF and Bare Soil (BS) landcover classes derived from the climatology. The outputs are

the final dekadal V2-H estimates when processing historical time series (Branch B) and V2-RT

in near real time (Branch C). .................................................................................................. 17

Figure 2: Left to right: Chronograph showing the several periods considered and the associated

branches (C-, B, C+) used to process the data. Top to bottom: The compositing used for near

real time estimation from RT0 (in the top) to the final consolidation RT6 (in the bottom).dx

refers to the dekad being processed. dx+n corresponds to the n dekads after the dekad dx

required for compositing the final consolidated RT6. .............................................................. 18

Figure 3: Spectral Response Functions of VGT1, VGT2, and the three PROBA-V cameras,

superimposed with a spectrum of green grass (Blue, Red, NIR and SWIR bands, respectively).

.............................................................................................................................................. 19

Figure 4: Illustration of the 3-iterations of TSGF filtering (continuous line) to eliminate contaminated

data (filled circles). Empty circles correspond to valid data. ................................................... 23

Figure 5: Temporal profile of Version 2 VGT (black solid line) over two typical sites of crops and

shrubs. Daily estimates derived from VGT-S1 products are indicated by the dots: black

squares correspond to outliers. Empty circles to the valid LAI estimates used to compute the

Version 2 VGT product. The dashed green line corresponds to the Version 1 VGT climatology.

The solid green line to the CACAO estimates. The red line corresponds to Version 1 VGT

product. .................................................................................................................................. 25

Figure 6: Idem as Figure 5 for three forest sites. ........................................................................... 25

Figure 7: Regions of Version 2 product. ........................................................................................ 29

Figure 8: Colour coding for quicklook images of LAI, FAPAR and FCover .................................... 39

Figure 9 : Example of climatology fill issue for PROBA-V time series, in V2.0.1 (left) and solved in

V2.0.2 (right) for Belmanip Site #16 (Evergreen Broadleaf Forest) ......................................... 44

Figure 10 : LAI-QFLAG 20050710 (red: not observed, blue: seq (485), yellow:filled (>4095)) ....... 49



Issue: I1.33



List of Tables

Table 1: Spectral characteristics of SPOT/VGT and PROBA-V sensor ......................................... 19

Table 2: Spectral conversion coefficients (Bx and Bx) between VEGETATION and PROBA-V

sensors. ................................................................................................................................. 21

Table 3: Coefficients used to rescale the neural network PROBA-V outputs to VGT-like estimates.

.............................................................................................................................................. 21

Table 4: Definition of the continental tiles. ..................................................................................... 30

Table 5: Range of values and scaling factors of biophysical variables. ......................................... 30

Table 6: Range of values and scaling factors of quality metrics. ................................................... 31

Table 7: Description of Quality Flag of biophysical variables. ........................................................ 32

Table 8: Examples of Quality Flag coding. .................................................................................... 33

Table 9: Description of netCDF file attributes ................................................................................ 35

Table 10: Description of netCDF layer attributes ........................................................................... 36

Table 11: Description of netCDF coordinate variable attribute for longitude .................................. 37

Table 12: Description of netCDF grid mapping variable attributes ................................................. 38

Table 13: Algorithm differences between Version 2 and Version 1 products. ................................ 42

Table 14 : Overview version 2 releases ........................................................................................ 43

Table 15: Summary of Product Evaluation (V2 SPOT/VGT). The plus (minus) symbol means that

the product has a good (poor) performance according to each evaluated criterion. Period of

analysis: 2004-2005. .............................................................................................................. 46

Table 16: Summary of Product Evaluation (V2 PROBA-V). The plus (minus) symbol means that the

product has a good (poor) performance according to each evaluated criterion. Period of

analysis: October 2013- September 2014. ............................................................................. 47

Table 17: GCOS requirements for LAI and FAPAR as Essential Climate Variables (GCOS-154,

2011) ..................................................................................................................................... 55

Table 18: CGLOPS uncertainty levels for FAPAR and FCover products ....................................... 55

Table 19: WMOs requirements for global LAI and FAPARproducts (From http://www.wmo-

sat.info/oscar/requirements); G=goal, B=breakthrough, T=threshold. .................................... 56



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ACRONYMS

AFRI Africa continent

ATBD Algorithm Theoretical Basis Document

B0, B2, B3 Spectral bands of VEGETATION and PROBA-V sensors in blue, red and near infrared, respectively

BELMANIP Benchmark Land Multi-site Analysis and Intercomparison of Products

BRDF Bidirectional reflectance distribution function

BS Bare Soil

CACAO Consistent Adjustment of Climatology to Actual Observations

CCD Charge Coupled Device

CEOS Committee on Earth Observation System

CGLS Copernicus Global Land Service

CNES Centre National d’Etudes Spatiales (French Space Agency)

CREAF Centre de Recerca Ecològica I Aplicacions Forestals

CYCLOPES Carbon cYcle and Change in Land Observational Products from an Ensemble of Satellites

DN Digital Number

EBF Evergreen Broadleaf Forest

EOLAB Earth Observation Laboratory (Spanish small enterprise)

FAPAR Fraction of photosynthetically active radiation absorbed by the vegetation

Fcover Fraction of green vegetation cover

GAI Green Area Index

GCOS Global Climate Observing System

geoTIFF Geographic Tagged Image File Format

GIO GMES Initial Operations

H Offline processing of historical time series

JRC Joint Research Center

LAI Leaf Area Index

LPV Land Product Validation group of CEOS

MODIS Moderate Resolution Imaging Spectroradiometer

NetCDF-CF Network Common Data Form Climate and Forecast metadata convention

NIR Near Infrared

NNT Neural Network

NOBS Number of observations

PROBA-V Vegetation instrument on board of PROBA satellite

PUM Product User Manual



Issue: I1.33



QAR Quality Assessment Report

QFLAG Quality Flag

R&D Research and Development

RMSE Root Mean Square Error

RTx Consolidation mode from RT0 (near real time) to RT6 (after six dekads)

S1 Daily synthesis

SOAM South America continent

SPOT Système Pour l’Observation de la Terre

SVAT Soil-Vegetation-Atmosphere Transfer model

SWIR Short Wave Infrared band

TOC Top Of Canopy

TSGF Temporal Smoothing Gap Filling

VGT VEGETATION sensor onboard SPOT4/5

VITO Vlaamse Instelling voor Technologisch Onderzoek (Flemish Institute for Technological Research), Belgium

VR Validation Report

WMO World Meteorological Organisation

XML Extensible Markup Language

XSL Extensible Stylesheet Language



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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.

From 1st January 2013, the Copernicus Global Land Service is providing a set of biophysical

parameters that describe the vegetation dynamics, such as the first version of Leaf Area Index

(LAI), the fraction of absorbed photosynthetically active radiation (FAPAR) and the fraction of

vegetation cover (FCover) products

This document describes the LAI, FAPAR and FCover Collection 1km Version 2 products derived

from SPOT/VEGETATION and PROBA-V data. The products are provided every 10 days, with a

temporal basis for compositing between ±15 and ±60 days depending on the number of available

valid observations. They are delivered with a maximum of 3 days lag in Near Real Time, followed

by consolidations in the course of the next 6 dekads (V2-RT). They are complemented by the

constitution of long term time series from 1999 (V2-H).

Similarly to Version 1 [CGLOPS1_PUM_LAI[FAPAR/FCOVER]1km-V1], the Version 2 products

capitalize on the development and validation of already existing products: CYCLOPES version 3.1

and MODIS collection 5 and the use of neural networks(Baret et al. 2013; Verger et al. 2008). The

version 2 products are derived from top of canopy daily (S1-TOC) reflectances instead of

normalized top of canopy 30-day composited reflectances as in the Version 1. Again compared to

version 1, the compositing step is performed at the biophysical variable level instead of reflectance

level. This allows reducing sensitivity to missing observations and avoiding the use of a BRDF

model. Smoothing and gap filling is achieved over a compositing temporal window that may be

dissymmetric depending on the number of valid daily estimates. These version 2 products have a

high consistency with version 1 products (90% of samples within GCOS requirements for LAI and

80% for FAPAR and FCover) and a similar accuracy as evaluated over the limited ground

measurements but provide an improved continuity (no missing data in Version 2 due to

climatological gap filling) and smoothness and include a Near Real Time computation. The

consistency between V2-H and V2-RT was evaluated over the overlapping period between both

sensors showing good overall results but local discrepancies (higher values of V2-RT) over some

regions with growing vegetation that could have an impact for users dealing with anomalies (see

Quality Assessment reports [GIOGL1_QAR_LAI[FAPAR/FCOVER]1km-VGT-V2] and

CGLOPS1_QAR_LAI[FAPAR/FCOVER]1km-PROBAV-V2 for details).

Version 2 products are delivered with global coverage in netCDF4 CF-1.6 format. In addition to the

LAI, FAPAR and FCover values, the products include quality information on the number of daily

estimates used in the composition, the length of the compositing period and the RMSE between

the final dekadal value and the valid daily estimates in the compositing period.



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1 BACKGROUND OF THE DOCUMENT

1.1 SCOPE AND OBJECTIVES

The Product User Manual (PUM) is the primary document for users to read before handling the

products.

It gives an overview of the product characteristics, in terms of algorithm, technical characteristics,

and main validation results. This issue of the PUM focuses on the LAI, FAPAR, FCover Collection

1km Version 2 products derived from SPOT/VGT and PROBA-V data.

1.2 CONTENT OF THE DOCUMENT

This document is structured as follows:

Chapter 2 presents a description of the algorithm.

Chapter 3 describes the technical characteristics of the product.

Chapter 4 describes the main differences with previous version.

Chapter 5 summarizes the validation procedure and the results.

Chapter 6 presents some basic usage and gives recommendations on efficient use of

products

Users’ requirements are recalled in Annex.

1.3 RELATED DOCUMENTS

1.3.1 Applicable document

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

151-277962 of 7thAugust 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 7thAugust 2015

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

Requirements Proposal –SPB-GIO-3017-TUG-SS-004 –Issue I1.0 –26thMay 2015

1.3.2 Input

Document ID Descriptor

CGLOPS1_ATBD_LAI[FAPAR/FCOVER]1km-

V2

Algorithm Theoretical Basis Document for

Collection 1km LAI, FAPAR and FCover Version

2 products



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GIOGL1_QAR_LAI[FAPAR/FCOVER]1km-

VGT-V2

Quality Assessment Report of the Collection 1km

LAI, FAPAR and FCover Version 2 derived from

SPOT/VGT

CGLOPS1_QAR_LAI[FAPAR/FCOVER]1km-

PROBAV-V2

Quality Assessment Report of the Collection 1km

LAI, FAPAR and FCover Version 2 derived from

PROBA-V

CGLOPS1_PUM_LAI[FAPAR/FCOVER]1km-

V1

Product User Manual of Collection 1km LAI,

FAPAR, FCover Version 1 products

GIOGL1_VR_LAI[FAPAR/FCOVER]1km-V1 Validation Report of Collection 1km LAI, FAPAR

and FCover Version 1 derived from SPOT/VGT

These documents are available on the respective variable page on

http://land.copernicus.eu/global/products/[LAI,FAPAR,FCover] on the tabs for 1km.

1.3.3 External documents

SPOT-VGT http://www.spot-vegetation.com/index.html

PROBA-V http://proba-v.vgt.vito.be/

PROBAV_PUM PROBA-V Products User Manual

Available at http://proba-v.vgt.vito.be/sites/proba-v.vgt.vito.be/files/Product_User_Manual.pdf

http://land.copernicus.eu/global/products/%5bLAI,FAPAR,FCover

http://www.spot-vegetation.com/index.html



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2 ALGORITHM

2.1 INTRODUCTION

Vegetation state and dynamics play a key role in global climate-carbon cycle. The importance of

continuously monitoring the Earth's surface was recently recognized by Global Climate Observing

System (GCOS 2010). A set of Essential Climate Variables was identified as being both accessible

from remote sensing observations and intervening within key processes (GCOS 2010). Among

those related to land surfaces, the leaf area index (LAI) and the fraction of absorbed photosynthetic

active radiation (FAPAR) may be derived from observations in the reflective solar domain. These

vegetation biophysical variables are crucial in several processes, including photosynthesis,

respiration and transpiration. In addition to LAI and FAPAR, the fraction of green vegetation

(FCover) as seen from nadir appears also as a very pertinent variable for partitioning contributions

between soil and vegetation in land surface models.

2.2 DEFINITION OF THE VARIABLES

2.2.1 Leaf area index (LAI)

LAI is defined as half the developed area of photosynthetically active elements of the vegetation

per unit horizontal ground area. It determines the size of the interface for exchange of energy

(including radiation) and mass between the canopy and the atmosphere. This is an intrinsic canopy

primary variable that should not depend on observation conditions. LAI is strongly non linearly

related to reflectance. Therefore, its estimation from remote sensing observations will be scale

dependentover heterogeneous landscapes(Garrigues et al. 2006; Weiss et al. 2000). When

observing a canopy made of different layers of vegetation, it is therefore mandatory to consider all

the green layers. This is particularly important for forest canopies where the understory may

represent a very significant contribution to the total canopy LAI. The derived LAI corresponds

therefore to the total green LAI, including the contribution of the green elements of the understory.

It is termed GAI (Green Area Index) but the LAI term will be kept in this document for the sake of

simplicity. There is a strong debate within the scientific community about the usefulness and actual

possibility to derive the true LAI corresponding to the above definition. Remote sensing estimates

accesses mainly an effective LAI, i.e. the LAI that would provide the same radiometric response

with the assumptions on canopy architecture (which may depart from the actual architecture)

included in the inversion algorithm. Further, the inversion process will also impact the retrieved LAI

values. The clumping factor is used to express the ratio between the effective and the actual LAI.

Clumping may appear at a range of scales, from the shoot level (groupings of leaves or needles

around the fine branches), at the plant scale (grouping of shoots within trees), at the stand scale

(grouping of trees within a vegetation patch) and at the landscape scale (grouping of vegetation

patches). The Collection 1km LAI Version 2 product derives from CYCLOPES V3.1 (Baret et al.

2007) and MODIS collection 5 (Myneni et al. 2002) products that correspond to different

assumptions on canopy architecture. The resulting LAI product is thus relatively consistent with the



Issue: I1.33



actual LAI for low LAI values and ‘non-forest’ surfaces; while for forests, particularly for needle leaf

types, significant departures with the true LAI are expected.

2.2.2 Fraction of photosynthetically active radiation absorbed (FAPAR)

FAPAR corresponds to the fraction of photosynthetically active radiation absorbed by the green

elements of the canopy. The FAPAR value results directly from the radiative transfer model in the

canopy which is computed instantaneously. It depends on canopy structure, vegetation element

optical properties and illumination conditions. FAPAR is very useful as input to a number of primary

productivity models based on simple efficiency considerations (McCallum et al. 2009; Prince 1991).

Most of the primary productivity models using this efficiency concept are running at the daily time

step. Consequently, the product definition should correspond to the daily integrated FAPAR value

that can be approached by computation of the clear sky daily integrated FAPAR values as well as

the FAPAR value computed for diffuse conditions. To improve the consistency with other FAPAR

products that are sometimes considering the instantaneous FAPAR value at the time of the

satellite overpass under clear sky conditions (e.g. MODIS), a study investigated the differences

between alternative FAPAR definitions. Results show that the instantaneous FAPAR value at

10:00 (or 14:00) solar time is very close to the daily integrated value under clear sky conditions.

The FAPAR Version 2 derives from CYCLOPES V3.1 (Baret et al. 2007) and MODIS/TERRA

collection 5 (Myneni et al. 2002) products. Those FAPAR products are defined as the

instantaneous black-sky FAPAR at the time of TERRA overpass (10:30) (MODIS) or at 10:00

(CYCLOPES). The difference on FAPAR value is marginal and the resulting Version 1 FAPAR

product therefore corresponds to the instantaneous black-sky around 10:15 which is a close

approximation of the daily integrated black-sky FAPAR value.

FAPAR is relatively linearly related to radiometric signal, and is little sensitive to scaling issues

(Hilker et al. 2010; Weiss et al. 2000). Note also that the FAPAR refers only to the green parts of

the canopy.

2.2.3 Fraction of vegetation cover (FCover)

FCover is defined as the fraction of ground surface covered by green vegetation as seen from the

nadir direction. It is used to separate vegetation and soil in energy balance processes, including

temperature and evapotranspiration. It is computed from the leaf area index and other canopy

structural variables and does not depend on variables such as the geometry of illumination as

compared to FAPAR. For this reason, it is a very good candidate for the replacement of classical

vegetation indices for the monitoring of green vegetation. Because of the linear relationship with

radiometric signal, FCover will be only marginally scale dependent (Weiss et al. 2000). Note that

similarly to LAI and FAPAR, only the green elements will be considered, either belonging both to

the overstory and understory.



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2.3 RETRIEVAL METHODOLOGY

2.3.1 Background

The Version 2 algorithm [CGLOPS1_ATBD_LAI[FAPAR/FCOVER]1km-V2] (Verger et al. 2014a)

which is dedicated to the estimation of LAI, FAPAR and FCover provides improved products as

compared to Version 1 although derived from the same sensors SPOT/VEGETATION and

PROBA-V observations. Version 2 should have the same temporal sampling frequency of 10 days.

Similarly to Version 1, Version 2 capitalizes on the development and validation of already existing

products: CYCLOPES version 3.1 and MODIS collection 5, and the use of neural networks (Baret

et al. 2013; Verger et al. 2008). The basic underlying assumption is that a strong link exists

between VEGETATION and PROBA-V observations and the fused product resulting from

CYCLOPES and MODIS products. Products should also be associated with quality assessment

flags as well as quantified uncertainties. The algorithm runs at the pixel level. The main

improvements targeted for Version 2 as compared to Version 1 are:

Version 2 offers near real time (RT) estimation, in addition of historical (H) data series.

Version 2 improves the smoothness as compared to Version 1. Despite being one of the

smoothest available products, Version 1 still include some problems, particularly over

cloudy areas [GIOGL1_VR_LAI[FAPAR/FCOVER]1km-V1] (Camacho et al. 2013).

Version 2 includes less missing data than Version 1

[CGLOPS1_ATBD_LAI[FAPAR/FCOVER]1km-V2] (Verger et al. 2014b).

2.3.2 Outline

The Version 2 algorithm [CGLOPS1_ATBD_LAI[FAPAR/FCOVER]1km-V2] (Verger et al. 2014a)

for the estimation of global LAI, FAPAR, and FCover at 1 km spatial resolution and 10-day step

(dekad) in near real time as well as in offline mode (time series from 1999 to 2013) from

VEGETATION and PROBA-V data consists of two main steps:

(1) neural networks are used to provide estimates from daily S1 top of the canopy reflectances

(Branch A in Figure 1).

(2) filtering, smoothing, gap filling and temporal compositing techniques are applied to ensure

consistency and continuity of the LAI, FAPAR and FCover time course every 10 days. The

outputs are the final dekadal V2-H estimates when processing historical time series (Branch B)

and V2-RT in near real time (Branch C):

The historical time series (Branch B in Figure 1) corresponds to the past period

where, for a given dekad ‘dx’ to be processed, the ‘n’ dekads before ‘d’ and after ‘d’ in

the time series are available. ‘n’ is the number of dekads required for the convergence

of LAI, FAPAR and FCover values (see Figure 2). ‘n’ is fixed to 6 (cf. section 2.3.4.3).

The near real time products (Branch C in Figure 1) are derived for the most recent

limited season (the last 2 months) using similar principles as those for the past-time



Issue: I1.33



series. Note that, each time a new dekad is processed (real time estimates), the recent

past values of the variables are updated (Figure 2). This results in successive updates

of the LAI, FAPAR, FCover that converge towards the past time series values after the

‘convergence period’ (up to 2 months) with major improvement after two dekads (RT2).

Figure 1: Flow chart the three processing branches (A, B and C). First, daily S1 top of canopy

reflectance products are transformed into daily estimates of LAI, FAPAR and FCover using specific

neural networks (NNTs) for Evergreen Broadleaf Forest (EBF) or non_EBF pixels (Branch A). Second,

dedicated filtering, smoothing, gap filling and compositing temporal techniques are applied. The

inputs of this second step are the daily estimates, the sun zenith angle of the observations, the

pixel’s latitude, a climatology of LAI, FAPAR and FCover, and the EBF and Bare Soil (BS) landcover

classes derived from the climatology. The outputs are the final dekadal V2-H estimates when

processing historical time series (Branch B) and V2-RT in near real time (Branch C).

S1 TOC reflectances

A

CB

Instantaneousproducts

Historic time

series

Near Real-Time

Dekadal GEOV2-H LAI, FAPAR, FCover

Dekadal GEOV2-RT LAI, FAPAR, FCover

Filtering, Smoothing,Gap filling,

Compositing

EBF landcover

BS landcoverLatitude

Sun zenithangle

EBF or

BS?

LAI, FAPAR,FCoverclimatology

NNTs

Daily products

Dekadal V2-H LAI, FAPAR, FCover

Dekadal V2-RT LAI, FAPAR, FCover



Issue: I1.33



Figure 2: Left to right: Chronograph showing the several periods considered and the associated

branches (C-, B, C+) used to process the data. Top to bottom: The compositing used for near real

time estimation from RT0 (in the top) to the final consolidation RT6 (in the bottom).dx refers to the

dekad being processed. dx+n corresponds to the n dekads after the dekad dx required for compositing

the final consolidated RT6.

Note that for the first past dekads (from d1 to d1+n), no past data is available (Figure 2). Here,

Branch C is run in reverse mode (it is called C- as opposed to the forward mode for real time

estimation called C+). In Figure 2, d1 and dx correspond to the first and last dekad in the time

series at the time of implementation of Version 2 algorithm. n is the number of dekads required for

convergence.

2.3.3 Input data

The input data of the processing line are the daily synthesis (S1) Top of Canopy (TOC)

reflectances in 3 bands (B2, B3, MIR) products generated and provided by the SPOT

VEGETATION and PROBA-V programmes through VITO (http://www.vito-eodata.be).

The Version 2 algorithm was set-up using Collection 3 of SPOT/VGT data and Collection 0 of

PROBA-V data.

The disseminated Version 2 products are generated from Collection 3 of SPOT/VEGETATION

data, from Collection 0 of PROBA-V data from 2014 to December 2016 and from Collection 1 of

PROBA-V data afterwards.

Convergence PeriodConsolidated PeriodHistoric Period

dx+1dx dx+n

BC-

Past Projection

C+

d1 d1+n dx-n

http://free.vgt.vito.be/



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Table 1: Spectral characteristics of SPOT/VGT and PROBA-V sensor

Acronym Centre (nm) Width (nm) Potential Applications

VGT PROBA-V VGT PROBA-V

B0 450 463 40 46 Continental Ecosystem -Atmosphere

B2 645 655 70 79 Continental Ecosystem

B3 835 845 110 144 Continental Ecosystem

SWIR 1665 1600 170 73 Continental Ecosystem

From April 1998 to May 2014, the VEGETATION sensor has been operational on board the SPOT

4 and 5 Earth observation satellite systems. It provides a global observation of the world on a daily

basis. The instrumental concept relies on a linear array of 1728 CCD detectors with a large field of

view (101°) in four optical spectral bands described in Table 1 and Figure 3. Although very similar,

some differences between VEGETATION 1 and VEGETATION 2 instruments have to be noticed

[SPOT-VGT], particularly regarding the spectral sensitivity (Figure 3).

The spatial resolution is 1.15 km at nadir and presents minimum variations for off-nadir

observations. The 2200 km swath width implies a maximum off nadir observation angle of 50.5°.

About 90% of the equatorial areas are imaged each day, the remaining 10% being imaged the next

day. For latitudes higher than 35° (North and South), all regions are acquired at least once a day.

The multi-temporal registration is about 300 meters.

Figure 3: Spectral Response Functions of VGT1, VGT2, and the three PROBA-V cameras,

superimposed with a spectrum of green grass (Blue, Red, NIR and SWIR bands, respectively).



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The PROBA-V sensor has been launched on 6th May 2013 onboard the PROBA platform. The

satellite operates at 820 km altitude on a circular helio-synchronous orbit with a local overpass time

at launch of 10:45 h. Because the satellite has no onboard propellant, the overpass time is

expected to gradually differ from the at-launch value. After launch, the local overpass time first

increased to 10:50h in October 2014, followed by a decrease to 10:45h in June 2016. By end-of-

mission in March 2020, the Local Time of Descending Node will be at ~09:30h.

It provides a global observation on a daily basis for latitudes higher (lower) than 35° (-35°). The

instrument is viewing the surface under a 102.6° field of view, providing a swath of about 2295 km.

The ground sampling distance that varies across the swath from 100m up to 350m at the

extremities of the swath. The SWIR domain is providing a ground sampling distance about twice as

that in the VIS-NIR.

The PROBA-V bands are relatively close to those of the VEGETATION instruments, with however

some slight shifts (particularly for the blue and SWIR bands) as shown in Table 1. Spectral

response functions of PROBA-V are shown in Figure 3.

The PROBA-V processing is described in Sterckx et al. (2014) and Dierckx et al. (2014). The

description of the PROBA-V S1 TOC products is detailed in the Product User Manual

[PROBAV_PUM].

2.3.4 Processing steps

2.3.4.1 Daily LAI, FAPAR, FCover estimates

The derivation of the daily biophysical estimates of LAI, FAPAR and FCover is based on neural

networks (NNT) trained using VGT-S1 reflectance data and fused MODIS and CYCLOPES LAI,

FAPAR, FCover products similarly as in Version 1 (Baret et al. 2013). Two sets of specific neural

networks for EBF and nonEBF were calibrated for each of the 3 variables considered (LAI, FAPAR,

and FCover).

The inputs of the NNTs are:

(1) the top of the canopy daily S1 reflectances in red, NIR and SWIR spectral bands from VGT

and PROBA-V.

(2) the cosine of the three angles (view zenith, sun zenith and relative azimuth angles)

characterizing the acquisition geometry

The output is the corresponding daily value of the biophysical variable (LAI, FAPAR or FCover).

To make the training process computationally tractable, it was achieved for the 2003-2007 period

over the Benchmark Land Multi-site Analysis and Intercomparison of Products (BELMANIP2)

(Baret et al. 2006) sub-sample of sites which are representative of surface types and conditions

over the Earth. To be consistent with the Version 1 of the algorithm, the LAI and FAPAR outputs in



Issue: I1.33



the training dataset were computed similarly by fusing CYCLOPES version 3.1 (Baret et al. 2007)

and MODIS collection 5 products (Yang et al. 2006). The weighting of CYCLOPES and MODIS

was designed to enhance the specific advantage of each product while limiting their deficiencies

(Baret et al. 2013). Note that for FCover, no fusion was completed since CYCLOPES was the only

existing product.

Two sets of neural networks are used for EBF and non_EBF classes. EBF identification is

achieved using the Version 1 climatology computed as the inter-annual average of Version 1 VGT

time series for 1999-2010 period (Verger et al. 2015). For the pixels identified as EBF, the

corresponding flag QFLAG(11) in Table 7 is set to 1.

The neural network architecture is the same for all the variables and the two biomes (one hidden

layer with 5 neurons with a tangent sigmoid function, and a linear output layer with a single

neuron).

Since the training of neural networks was based on VGT S1 TOC data, VGT-like reflectances are

required for the application of neural networks. Because the spectral characteristics of PROBA-V

sensor are slightly different from those of VGT (Table 1), a spectral conversion was applied on the

actual PROBA-V TOC reflectances as:

where is the converted PROBA-V TOC reflectance and is the TOC PROBA-V

reflectance and is the conversion coefficient for band (Table 2).

Table 2: Spectral conversion coefficients (Bx and Bx) between VEGETATION and PROBA-V sensors.

B2 B3 SWIR

1.001869321 0.998005748 0.986722946

0.002362609 0.000112021 0.002070232

Finally, the PROBA-V Version 2 products ( ) are rescaled with respect to VGT Version 2

products using a third polynomial conversion to generate VGT-like estimates ( ):

with the coefficients of Table 3.

Table 3: Coefficients used to rescale the neural network PROBA-V outputs to VGT-like estimates.

LAI FAPAR FCover

-0.0137 -0.6921 -0.6148

0.0774 0.9837 0.7901

0.0947 -0.3290 -0.1548

-0.0640 0.0064 0.0022



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2.3.4.2 Filtering residual outliers

In spite of using atmospherically corrected S1 TOC reflectances as inputs in the NNTs, the

resulting daily product estimates are still contaminated by residual cloud and atmospheric effects

(Figure 4). The remaining outliers are filtered using a six-step process. The first three outlier

rejection steps are applied to the input data while the last three steps are applied to the daily

estimates:

(1) Air mass test: The data for which

were considered as unreliable

because of too large atmospheric BRDF effects and flagged as outliers.

(2) Soil line: The reflectance ( ) data lying below the soil line in the B2, B3 and SWIR

bands

were considered with a high probability of being contaminated by significant fraction of

water-bodies or clouds and flagged as outliers.

(3) Data are excluded if they do not belong to the definition domain of reflectances defined by

the convex hull formed by the training dataset

(4) Daily estimates are flagged as outliers if they are out of the physical range of variation of

the variable ([0, 7] for LAI, [0, 0.94] for FAPAR and [0, 1] for FCover) extended by the

tolerance limits ([-0.2, 7.2] for LAI, [-0.05, 0.99] for FAPAR, [-0.05, 1.05] for FCover). Values

that are within the tolerance limits but higher (lower) than the physical maximum (minimum)

are fixed to the physical maximum (minimum).

For the fifth and sixth outlier rejection steps, emphasis is put on LAI products that show the

highest sensitivity to possible problems in reflectance values. Therefore, when an outlier is

detected on LAI data, it is also considered as an outlier for FAPAR and FCover to keep a high

level of consistency between the three variables

(5) The Temporal Smoothing Gap Filling (TSGF) filter (Verger et al. 2011) based on Savitzky-

Golay and linear interpolation is applied. It leads to a smoothed curve fitted to the upper

envelope of values in the time series (Figure 4). Daily estimates are considered as outliers

if their minimum absolute distance to the TSGF LAI values within a ±15-day window is

greater than 0.10 and the 15% of the TSGF values. The process is repeated 3 times. To

avoid rejecting false outliers the upper values are only filtered in the last iteration of TSGF.

The more restrictive criteria imposed here to the values under TSGF upper envelope

accounts for the effect of residual clouds that yield systematically lower values than the

realistic LAI (Figure 4).



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Figure 4: Illustration of the 3-iterations of TSGF filtering (continuous line) to eliminate contaminated

data (filled circles). Empty circles correspond to valid data.

(6) A specific procedure based on prior knowledge of the expected seasonality is applied for

pixels at high latitude and for EBFs.

For the high latitude ( ) in winter time (sun zenith angle, ) (QFLAG(10) in

Table 7), some artefacts are observed due to snow cover or very poor illumination

conditions. Because of the low temperature, short days and low illumination experienced

during winter time at very high latitudes, the LAI values are expected to be relatively stable

and low. For the pixels at in winter time, , the LAI values and

are considered as outliers and rejected, with being the 5-percentile of LAI

estimates and being the default minimum value of LAI in winter time.

For pixels identified as EBF (QFLAG(11) in Table 7), a minimum seasonality and high

values of LAI are assumed. The observed artifacts in EBFs are mostly associated to the

high cloud cover observed in the equatorial and tropical latitudes which introduce a

negative bias in LAI (Figure 5, Top). For EBF cases, LAI values and are

rejected, with being the 90-percentile of daily LAI estimates and the minimum

default value of LAI in EBFs.

2.3.4.3 Temporal smoothing, gap filling and compositing

The filtered daily estimates are then composited every 10-days to generate the final products. The

temporal composition combines the TSGF filter (Verger et al. 2011) and the Consistent Adjustment

of Climatology to Actual Observations (CACAO) (Verger et al. 2013) techniques. TSGF fits a

second-degree polynomial over an asymmetric temporal window. The compositing window is made

of past and future semi-windows of adaptive length varying between 15 and 60 days. The length of

the semi-window is determined by the availability of 6 valid daily estimates in the compositing

window the closest to the date of the dekad at which the product is estimated (Verger et al. 2011).

If less than 6 daily estimates exist in a 60 day semi-window, the corresponding flag QFLAG(3) in

Table 7 is set to 1 and a gap filling procedure is activated. If the climatology is available, CACAO



Issue: I1.33



values evenly distributed every 10-days are used to fill gaps before the application of TSGF and

the corresponding flag QFLAG(13) in Table 7 is set to 1.

CACAO consists in fitting the climatology to daily estimates by scaling and shifting the climatology

values over portions of the seasonal cycle (sub-seasons). The CACAO process is applied to each

sub-season and will result in estimates at the daily time step. A sub-season is defined by the

period between two consecutive extrema in the climatology. For pixels identified as bare soil (BS)

or EBF with almost no seasonality, no sub-seasons were identified and CACAO is fitted over the

entire time series. The Version 1 climatology from 1999 to 2010 is used (Verger et al. 2015).

CACAO allows to better cope with missing and noise contaminated data as compared to standard

methods (Kandasamy et al. 2013; Verger et al. 2013). If the climatology is available for a given

pixel, the CACAO method allows filling all the gaps in the time series, even for missing data during

long periods. Indeed, CACAO is closer to the daily estimates than the original climatology because

it allows inter-annual variations of the time course (Figure 5 Top). However, the main limitation of

CACAO reconstruction method is its inability to capture underlying atypical modes of seasonality

including rapid natural and human induced disturbances in the time series that strongly differ from

the average climatology (e.g. flood or fire events, changes in the land cover) (Figure 5 Bottom)

(Verger et al. 2013). To prevent from such drawback, priority is given to TSGF smoothing since it is

closer than CACAO to the daily estimates (Figure 5, Figure 6), while CACAO is only used to fill

large gaps in the time series before the application of TSGF.

The combination of the TSGF local fitting and the projection capacity of CACAO allows to process

with the same approach both the historical time series (V2-H) (when observations are available

before and after the considered date), and in near real time (V2-RT) when only past observations

are available. In the NRT case, CACAO is applied systematically to provide estimates every dekad

in the 60-day period after the NRT date. TSGF is then applied using this 60 days semi- window in

the future, while the past semi-window spreads over 15 to 60 days length depending on the

availability of 6 valid daily estimates.

To avoid the instability in V2-RT estimates and improve the consistency with V2-H time series, the

products are updated each time a new dekad is available and processed (real time estimates).

This results in the delivery of n successive updates of the n recent past values of the products in

the convergence period. It was found that after 6 dekads the RT6 converges closely towards V2-H

processing, with major improvements after 2 dekads (Verger et al. 2014a). Therefore, products are

updated during 6 dekads until reaching convergence and then remain consolidated (i.e., the last

update after 6 dekads is considered as the consolidated product value) (Figure 2). Since the

product value after the second consolidation RT2 remains stable, the third, fourth and fifth

consolidations are not distributed but only RT0, RT1, RT2 and RT6.

The outputs are the final temporal smoothed and gap filled 10-day products: V2-H and V2-RT for

historical and near real time, respectively. When the LAI, FAPAR and FCover values are out of

range or invalid, the corresponding flag QFLAG(7), QFLAG(8) and QFLAG(9) in Table 7 are set to

1.



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Figure 5: Temporal profile of Version 2 VGT (black solid line) over two typical sites of crops and

shrubs. Daily estimates derived from VGT-S1 products are indicated by the dots: black squares

correspond to outliers. Empty circles to the valid LAI estimates used to compute the Version 2 VGT

product. The dashed green line corresponds to the Version 1 VGT climatology. The solid green line

to the CACAO estimates. The red line corresponds to Version 1 VGT product.

Figure 6: Idem as Figure 5 for three forest sites.

Daily estimates

Daily estimates



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2.3.4.4 Quality indicators

The Version 2 products are associated with quality indicators:

NOBS: the number of valid daily estimates in the composition period that are used to

compute the product value. Note that NOBS accounts only for the valid daily estimates from

actual observations and not for the climatologically-filled values. The more daily estimates

are available, the more reliable are the products. If , the corresponding QFLAG(6)

in Table 7 is set to 1.

LENGTH_BEFORE and LENGTH_AFTER: the length in days ofthe semi-periods of

composition between the dekad being processed and the outermost daily estimator

climatologically-filled value in the compositing window, which have been required to find 6

valid estimates (see section 2.3.4.3). The shorter the semi-periods of composition, the more

reliable are the products.

RMSE: the uncertainty associated to each product value was estimated as the RMSE

between the final dekadal value, , and the valid daily estimates, , in the

compositing period:

Note that only valid daily estimates from actual observations are used for the computation

of the RMSE. Climatological values are not used. The RMSE is computed only if

, otherwise it is set as a missing value. The lower the RMSE, the more reliable are the

products.

2.4 LIMITATIONS

Version 2 of algorithm capitalizes on the development and validation of already existing products:

CYCLOPES version 3.1 and MODIS collection 5 and the use of neural networks. The CYCLOPES

and MODIS products used in the training dataset, the efficacy of the training process and the

criteria used to define the input outliers will determine, respectively, the magnitude and range of

variation of the final products, their reliability and the definition domain used to remove

contaminated values.

The final product is also dependent on the criteria used to filter the output outliers, particularly for

the tropical forests and high latitudes. Outlier rejection constitutes a critical step in the algorithm.

The efficiency of the temporal methods used in the smoothing, gap filling and composition of

products from daily estimates depends on the level of noise and gaps in the time series and on the

reliability of the auxiliary data (climatology) used as a background information to fill gaps.

The main identified limitations are associated to the input and ancillary data:



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The consistency across missions of long time series of Version 2 products depends mainly

on the temporal consistency of input TOC reflectances between sensors and across the

different processing chains (e.g. cloud-aerosol screening). Since the neural networks of

Version 2 were trained over the VGT-S1 TOC inputs, the final estimates resulting from the

application of networks to PROBA-V data depend on the coefficients of the spectral

correction applied to PROBA-V reflectances to mimic VGT-S1. An adaptation of the

rescaling of Version2/PROBA-V with respect to Version2/VGT estimates will be necessary

after the planned reprocessing of PROBA-V that includes a recalibration of sensors and a

correction of cloud screening.

When the climatology derived from Version1/VGT is missing, no ancillary information is

available to fill gaps in V2-H time series or to make projections in V2-RT.

Despite the specific corrections applied to the original Version1/VGT climatology, remaining

artefacts and possible biases in the magnitude and seasonality of LAI/FAPAR/FCover in

winter time may limit the reliability of the final Version 2 products at very high northern

latitudes.

The approach used to process pixels flagged as evergreen broadleaf forests (EBF,

QFLAG(11)=1 in Table 7) constitutes an oversimplification of the reality because of the

possible seasonality of EBFs. The high uncertainty associated with the data due to poor

atmospheric correction and very high cloud occurrence in equatorial and tropical latitudes

prevented the extraction of meaningful phenology at the resolution of the individual pixels of

1 km. The high spatial and temporal resolution of Sentinel2 sensors should improve the

monitoring of vegetation in these problematic areas.

In cases of a wrong identification of a pixel as an EBF, GEOV2 only reproduces the high

values but not the actual seasonality of the pixel.

The algorithm uses a static mask for EBF based on the climatology for the period 1999-

2010. Consequently, for pixels flagged as EBF, the GEOV2 product may not capture

deforestation processes. This mask may require to be updated in the future.

V2 LAI, FAPAR and FCover variables are retrieved over inland waters not discriminated as

water in the land/sea mask (QFLAG(1)=0 in Table 7). Inland water pixels are mostly

identified as Bare Soil (BS, QFLAG(12)=1 in Table 7) based on GEOCLIM climatology.

Although it would bring an improvement to mask the inland waters, it does not exist inland

water mask reliable enough to be used without taking the risk to create side effects in the

products.

The values of LAI, FAPAR and FCover over pixels identified as BS (QFLAG(11)=1 in Table

7) are close to zero but not strictly zero. Some users may prefer forcing the values of

biophysical variables to zero for pixels flagged as BS.

The user should use the product with due attention to the quality flags values as well as the

associated uncertainties, in particular for areas with long periods of cloudiness (mainly in equatorial

areas) and snow cover (high latitudes in winter time). Note that for long periods of missing data,

near real time estimation is very challenging and the V2-RT products may be affected by the

inability of the climatology to capture underlying atypical modes of seasonality.



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3 PRODUCT DESCRIPTION

The LAI, FAPAR and FCover Collection 1km version 2 products follow the following naming

standard:

c_gls_<Acronym>[-<RTx>]_<YYYYMMDDHHMM>_<AREA>_<SENSOR>_V<Major.Minor.Run>

where

<Acronym> is the short name of the product and variable: LAI, FAPAR or FCOVER

<RTx> is an optional parameter used for Near-Real Time and during the convergence

period. For Offline products, this parameter is omitted. (Refer to ‘2.3.4 Processing steps’)

o RT0: Near Real Time product

o RTn: Consolidated Real Time product(in the convergence period), where n equals

the number of times the RT0 product was successively updated

o RT6: Final consolidated Real Time product

<YYYYMMDDhhmm> gives the temporal location (nominal date and time) of the file. YYYY,

MM, DD, hh, and mm denote the year, the month, the day, the hour, and the minutes,

respectively.

<AREA> gives the spatial coverage of the file. In this case, <AREA> is GLOBE, the name

used for the full globe.

<SENSOR> gives the name of the sensor family used to retrieve the product, so VGT

referencing SPOT-VEGETATION and PROBAV referencing PROBA-V.

<Major.Minor.Run> gives the version number of the product. “Major” increases when the

algorithm is updated. “Minor” increases when bugs are fixed or when processing lines are

updated (metadata, colour quicklook, etc...).“Run” increases whenever a new processing

run (with same major and minor version) is performed without a change in the algorithm

(e.g. due to, a change in input data). This version refers to Major = 2.

3.1 FILE FORMAT

The version 2 products are delivered in compressed Network Common Data Form version 4

(netCDF4) files with metadata attributes compliant with version 1.6 of the Climate and Forecast

(CF) conventions. The multi-layer netCDF files contain the following layers:

LAI (or FAPAR, or FCover): variable value

RMSE: root mean square error

NOBS: number of daily values used in the compositing



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QFLAG: quality flag

LENGTH_BEFORE: length in days of the semi-period before the dekadal date of the

compositing window.

LENGTH_AFTER: length in days of the semi-period after the dekadal date of the

compositing window.

Separately from the data file, the following files are available:

An xml file containing the metadata conforms to INSPIRE2.1.

A quicklook in a coloured GeoTIFF format, sub-sampled to 25% in both horizontal and

vertical directions from the LAI, FAPAR or FCover layer.

For a more user-friendly viewing of the XML metadata, an XSL transformation file can be

downloaded on

http://land.copernicus.eu/global/sites/default/files/xml/c_gls_ProductSet.xsl

This file should be placed in the same folder as the XML file.

The products are delivered as one single global image, covering 180°W to 180°E longitude, 80°N

to 60°S. The globe is processed according 36 tiles in longitude and 18 in latitude according to

Figure 7. Only the green tiles, covering land, are processed and hence integrated in the global

image. In winter season, the northern hemisphere has less observations, hence the number of

processed area could be less.

Figure 7: Regions of Version 2 product.

http://land.copernicus.eu/global/sites/default/files/xml/c_gls_ProductSet.xsl



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Table 4: Definition of the continental tiles.

Short name Continent

AFRI Africa: 30°W – 60°E, 40°N – 40°S

SOAM South America: 110°W – 30°W, 20°N – 60°S

Note that continental tiles, as defined in Table 4, for AFRI and SOAM are distributed over

EUMETCast. The distribution is limited to the real-time mode, hence products with the filename

convention c_gls_<Acronym>-RT0_*, where <Acronym> is either LAI, FAPAR or FCover.

3.2 PRODUCT CONTENT

3.2.1 Data Files

The physical ranges of LAI, FAPAR, FCover are given in Table 5. The physical values are

retrieved from Digital Number (DN) by:

PhyVal= DN*Scale_factor + Add_offset

where the scaling factor and the offset are given in the table below and in Table 10.

As example a digital LAI of 180 corresponds to a physical LAI of 6.

Table 5: Range of values and scaling factors of biophysical variables.

LAI FAPAR FCover

Minimum value 0 0 0

Maximum value 7.0 0.94 1.0

Maximum DN value 210 235 250

Missing value or data error 255 255 255

Scale_factor 1/30 1/250 1/250

Add_offset 0 0 0

The values of the quality assessment layers, provided as part of LAI, FAPAR, FCover, are given in

Table 6.

NOBS, RMSE and LENGTH_BEFORE, LENGTH_AFTER are ancillary layers describing the

quality of the product. See section 2.3.4.4 for more details.



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Table 6: Range of values and scaling factors of quality metrics.

NOBS RMSE

LAI

RMSE

FAPAR

RMSE

FCOVER

LENGTH_BEFORE LENGTH_AFTER

Minimum value 0(*) 0 0 0 5 5

Maximum value 120 7.0 0.94 1.0 60 60

Maximum DN value 120 210 235 250 60 60

Missing value 0 255 255 255 255 255

Scale_factor 1 1/30 1/250 1/250 1 1

Add_offset 0 0 0 0 0 0

(*) Note that for NOBS=0 (no observations in the ±60-day period), LAI, FAPAR and FCover values may still be provided

through the gap-filling, indicated by the highest bits of QFLAG (value >= 4096).

The quality flag (QFLAG) is coded as a 16bit (2 bytes) pattern shown in Table 7. Bit 1 is the least

significant bit (right-most).The QFLAG value 65535 is used for missing (non-processed) pixels.

Bit 1 refers to the land-sea mask based upon the GLC-2000 land cover map

(Bartholomé and Belward 2005).

Bit 3 is activated when gap-filling is applied.

Bit 6 is activated when no available observations exist in the compositing period

(NOBS=0).

Bits 7-9 are activated when LAI, FAPAR and FCover values, respectively, are not

available (out of range or invalid).

Bit 10 is activated when a specific correction is applied for northern high latitudes at

extreme illumination conditions.

Bit 11 is activated when the pixel is recognized as Evergreen Broadleaf Forest

based on GEOCLIM climatology.

Bit 12 is activated when the pixel is recognized as Bare Soil based on GEOCLIM

climatology. Note that some pixels correspond indeed to inland-water bodies not

identified in the land-sea mask (Bit 1).

Bit 13 is activated when gap-filling is based on the climatology.

Bit 14 is activated when gap-filling is based on linear interpolation.



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Table 7: Description of Quality Flag of biophysical variables.

Bit = 0 Bit = 1

Bit 1: Land/Sea Land Sea

Bit 2: Not used

Bit 3: Filled No filled The number of valid observations at (at least) one side (the left side in the RT0 case) of the ±60-day period is lower than 6 and a gap filling procedure (Bit 13-14) is applied

Bit 4 : Not used

Bit 5: Not used

Bit 6: Input status OK All reflectance data within ±60 days (-60 days in the RT0 case) are out of range or invalid

Bit 7: LAI status OK, in expected range including tolerance

Out of range or invalid

Bit 8: FAPAR status OK, in expected range including tolerance


Bit 9: FCover status OK, in expected range including tolerance


Bit 10: HLAT status No specific correction for high latitudes is applied

A specific correction for high latitudes

( ) and SZA>70º is applied

Bit 11: EBF status Pixel is not recognized as Evergreen Broadleaf Forest

Pixel is recognized as Evergreen Broadleaf Forest

Bit 12: BS status Pixel is not recognized as Bare Soil

Pixel recognized as Bare Soil

Bit 13: Climatology Not filled Filled with climatology

Bit 14: Gap filling Not filled Filled with interpolation



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A number of common coding patterns for QFLAG are listed in the following table. Refer to the

Frequently Asked Questions page of the CGLS website (http://land.copernicus.eu/global/faq) for

information about tools for reading QFLAG.

Table 8: Examples of Quality Flag coding.

QFLAG

value

QFLAG binary code Interpretation

0 0000 0000 0000 0000 Land pixel, clear observations

485 0000 0001 1110 0101 Sea pixel

512 0000 0010 0000 0000 High latitude (lat> 55° and SZA > 70)

1024 0000 0100 0000 0000 Pixel recognized as Evergreen broadleaf forest,

with enough observations

2048 0000 1000 0000 0000 Pixel recognized as Bare soil,

with enough observations

2560 0000 1010 0000 0000 Pixel recognized as Bare soil, corrected for high latitude

4100 0001 0000 0000 0100 The number of valid observations at one side of the ±60-day period is

lower than 6 and a gap filling procedure by climatology is applied

4132 0001 0000 0010 0100 Inputs are out of range or invalid and a gap filling procedure by

climatology is applied

4612 0001 0010 0000 0100 Pixel corrected forhigh latitude, filled with climatology

4644 0001 0010 0010 0100 Pixel corrected for high latitude, filled with climatology,

all inputs are invalid or out of range

5124 0001 0100 0000 0100 Pixel recognized as Evergreen Broadleaf Forest, filled with climatology

5156 0001 0100 0010 0100 Pixel recognized as Evergreen Broadleaf Forest, filled with climatology,

all inputs out of range or invalid

6148 0001 1000 0000 0100 Pixel recognized as Bare Soil, filled with climatology

6180 0001 1000 0010 0100 Pixel recognized as Bare soil, filled climatology,

inputs out of range or invalid

6660 0001 1010 0000 0100 Pixel recognized as Bare Soil, located at high latitude, filled with

climatology

6692 0001 1010 0010 0100 Pixel recognized as Bare soil, located at high latitude,

filled climatology, inputs out of range or invalid

8196 0010 0000 0000 0100 The number of valid observations at one side of the ±60-day period is

lower than 6 and a gap filling procedure by interpolation is applied

8228 0010 0000 0010 0100 Filled by interpolation, inputs out of range or invalid

8740 0010 0010 0010 0100 Pixel located at high latitude is filled by interpolation,

inputs out of range or invalid

9220 0010 0100 0000 0100 Pixel recognized as Evergreen broadleaf forest,

filled by interpolation

65535 1111 1111 1111 1111 Invalid, pixel not processed

http://land.copernicus.eu/global/faq



Issue: I1.33



The netCDF file contains a number of attributes

on the file-level, see Table 9

on the level of the variables (layers), see Table 10

at the level of the standard coordinate (dimension) variables for latitude (‘lat’) and longitude

(‘lon’), holding one value per row or column respectively: see Table 11;

at the level of the grid mapping (spatial reference system) variable (named ‘crs’): see Table

12.



Issue: I1.33



Table 9: Description of netCDF file attributes

Attribute name Description Data

Type Example(LAI)

Conventions Version of the CF-Conventions used String CF-1.6

title A description of the contents of the file String 10-daily Leaf Area Index 1KM:

GLOBE 2017-01-10T00:00:00Z

institution The name of the institution that

produced the product String VITO NV

source The method of production of the original

data String Derived from EO satellite imagery

history

A global attribute for an audit trail. One

line, including date in ISO-8601 format,

for each invocation of a program that

has modified the dataset.

String

2017-04-03 Processing line GEO2 LAI

1km using PROBA-V collection 1

input data

references

Published or web based references that

describe the data or methods used to

produce it.

String http://land.copernicus.eu/global/produ

cts/lai

archive_facility Specifies the name of the institution that

archives the product String VITO

product_version Version of the product (VM.m.r) String V2.0.2

time_coverage_start

Start date and time of the total coverage

of the data for the product. Defined as

0h UTC on the date that is 210 days

before D, but not before the start of the

PROBA-V mission data (2013-10-16).

String 2016-11-11T00:00:00Z

time_coverage_end End date and time of the total coverage

of the data for the product. String 2017-03-11T23:59:59Z

platform Name(s) of the orbiting platform(s) String Proba-V

sensor Name(s) of the sensor(s) used String VEGETATION

identifier Unique identifier for the product String

urn:cgls:global:lai_v2_1km:LAI-

RT6_201701100000_GLOBE_PROB

AV_V2.0.2

parent_identifier

Identifier of the product collection (time

series) for the product in Copernicus

Global Land Service metadata

catalogue.

String urn:cgls:global:lai_v2_1km

long_name Extended product name String Leaf Area Index

orbit_type Orbit type of the orbiting platform(s) String LEO

processing_level Product processing level String L3

processing_mode

Processing mode used when generating

the product (Near-Real Time,

Consolidated or Reprocessing)

String Consolidated

copyright

Text to be used by users when referring

to the data source of this product in

publications (copyright notice)

String Copernicus Service information 2017

http://land.copernicus.eu/global/products/lai

http://land.copernicus.eu/global/products/lai



Issue: I1.33



Table 10: Description of netCDF layer attributes

Attribute Description Data

Type

Examples for LAI layer

CLASS Dataset type String DATA

standard_name

A standardized name that references a description of

a variable’s content in CF-Convention’s standard

names table. Note that each standard_name has

corresponding unit (from Unidata’sudunits).

String leaf_area_index

long_name

A descriptive name that indicates a variable’s

content. This name is not standardized. Required

when a standard name is not available.

String Leaf Area Index

1km

scale_factor

Multiplication factor for the variable’s contents that must be

applied in order to obtain the real values. Omit in case the

scale is 1.

Float 0.033333

add_offset

Number to be added to the variable’s contents (after

applying scale_factor) that must be applied in order to

obtain the real values. Omit for offset 0.

Float 0.0

Units

Units of a the variable’s content, taken from

Unidata’sudunits library.

Empty or omitted for dimensionless variables.

Fractions should be indicated by scale_factor.

String

valid_range

Smallest and largest values for the variable.

Missing data is to be represented by one or several

values outside of this range.

Same as data

variable 0, 210

_FillValue

Single value used to represent missing or undefined

data and to pre-fill memory space in case a non-

written part of data is read back.

Value must be outside of valid_range.

Same as data

variable 255

missing_value

Single value used to represent missing or undefined

data, for applications following older versions of the

standards.

Value must be outside of valid_range.

Same as data

variable 255

grid_mapping Reference to the grid mapping variable String crs



Issue: I1.33



Table 11: Description of netCDF coordinate variable attribute for longitude


Type Example

CLASS Dataset type String DIMENSION_SCALE

DIMENSION_

LABELS Label used in netCDF4 library String lon

standard_name

A standardized name that references a description

of a variable’s content in CF-Convention’s standard

names table. Note that each standard_name has

corresponding unit (from Unidata’sudunits).

String longitude

long_name

A descriptive name that indicates a variable’s

content. This name is not standardized. Required

when a standard name is not available.

String longitude

units Units of a the variable’s content, taken from

Unidata’sudunits library. String degrees_east

axis Identifies latitude, longitude, vertical, or

time axes. String X

_CoordinateAxis

Type Label used in GDAL library String Lon



Issue: I1.33



Table 12: Description of netCDF grid mapping variable attributes


Type Example

GeoTransform

Six coefficients for the affine

transformation from pixel/line space to

coordinate space, as defined in

GDAL’sGeoTransform

String

-180.0000000000

0.0089285714 0.0

80.0000000000 0.0 -

0.0089285714

_CoordinateAxisTypes Label used in GDAL library

String,

blank

separated

GeoX GeoY

_CoordinateTransform

Type Type of transformation String Projection

grid_mapping_name Name used to identify the grid mapping String latitude_longitude

inverse_flattening

Used to specify the inverse flattening

(1/f) of the ellipsoidal figure

associated with the geodetic datum and

used to approximate the

shape of the Earth

Float 298.257223563

long_name A descriptive name that indicates a

variable’s content. String

coordinate reference

system

longitude_of_prime_m

eridian

Specifies the longitude, with respect to

Greenwich, of the prime meridian

associated with the geodetic datum

Float 0.0

semi_major_axis

Specifies the length, in metres, of the

semi-major axis of the

ellipsoidal figure associated with the

geodetic datum and used to

approximate the shape of the Earth

Float 6378137.0

spatial_ref Spatial reference system in OGC’s

Well-Known Text (WKT) format String

GEOGCS["WGS

84",DATUM["WGS_19

84",…

AUTHORITY["EPSG","

4326"]]

3.2.2 Quicklook or browse image

The quicklook is a geo-referenced tiff file (GEOTIFF). The spatial resolution is sub-sampled, using

nearest neighbour resampling, to 25% in both directions.

The quicklook is coded in 1 byte, using the same offset and scale parameters as the main data

layer (LAI, FAPAR, FCover).

The quicklook is provided with an embedded colour table (Figure 8).

http://www.gdal.org/gdal_datamodel.html



Issue: I1.33



Figure 8: Colour coding for quicklook images of LAI, FAPAR and FCover

3.3 PRODUCT CHARACTERISTICS

3.3.1 Projection and grid information

The product is displayed in a regular latitude/longitude grid (plate carrée) with the ellipsoïd WGS

1984 (Terrestrial radius=6378km). The resolution of the grid is 1/112°.

The reference is the centre of the pixel (integer degrees are in the centre of a pixel).

The product is provided at global scale. However, through a custom order, the user is able to

select either region according to his/her needs.

3.3.2 Spatial information

As shown in Figure 7, the Version 2 products are provided from longitude 180 °W (centre of pixel)

to 180°E and latitude 80°N to 60°S.

3.3.3 Temporal information

The LAI, FAPAR, FCover Version 2 products are Near-Real Time products, updated every 10 days

within a consolidated period. The temporal information “YYYYMMDDhhmm” in the filename is the

“nominal” or reference date corresponding to the product value, the last day of the dekad (10-day

period). In addition to the actual real time product (RT0), three consolidations are provided: a first

after one dekad (RT1), a second after two dekads (RT2) and finally after 6 dekads (RT6). Users

are advised to download the successive RT modes for a given nominal date.



Issue: I1.33



As the product value after the second consolidation remains stable, it has been decided not to

distribute the third, fourth and fifth consolidations, but only the final consolidation (RT6) taking

benefit of the full 60 days LENGTH_BEFORE and LENGTH_AFTER.

As an example, the following products are available for the last dekad of March 2014:

the file c_gls_LAI-RT0_201403310000_GLOBE_PROBAV_V2.0.1.nc corresponds to the

Near Real Time LAI product. The product is generated using the past observations till D

within the compositing window. The product is made available on 2nd April.


consolidated LAI product after one dekad. It is generated using the past observations till D

within the compositing window plus the observations of one dekad in the consolidated

period (between D and D+1). It is delivered one dekad after the last dekad (D+1) of March

2014 i.e. on 12th April.


consolidated LAI product after two dekads. It is generated using the past observations till D

within the compositing window plus the observations of two dekads in the consolidated

period (between D and D+2). It is delivered two dekads after the last dekad (D+2) of March

2014, i.e. on 22nd April.

The file c_gls_LAI-RT6_201403310000_GLOBE_PROBAV_V2.0.1.nc corresponds to the

consolidated LAI product after six dekads. It is generated using the past observations till D

within the compositing window plus the observations of six dekads in the consolidated

period (between D and D+6). It is delivered six dekads after the last dekad (D+6) of March

2014, i.e. on 2nd June

The temporal information is detailed in the metadata (xml file) by two fields "EX_TemporalExtent

:beginPosition" and "EX_TemporalExtent : endPosition" as “YYYY-MM-DDTHH:MM::SS” giving the

beginning and the end of compositing and consolidation time period, respectively.

3.4 DATA POLICIES

All users of the Global Land service products benefit from the free and open access policy as

defined in the European Union’s Copernicus regulation (N° 377/2014 of 3 April 2014) and

Commission Delegated Regulation (N° 1159/2013), available on the Copernicus programme’s web

site, http://www.copernicus.eu/library/detail/248). Products from legacy R&D projects are also

provided with free and open action.

This includes the following use, in so far that is lawful:

a) reproduction;

b) distribution;

c) communication to the public;

d) adaptation, modification and combination with other data and information;

http://www.copernicus.eu/library/detail/248



Issue: I1.33



e) any combination of points (a) to (d).

EU law allows for specific limitations of access and use in the rare cases of security concerns,

protection of third party rights or risk of service disruption.

By using Sentinel Data or Service Information the user acknowledges that these conditions

are applicable to him/her and that the user renounces to any claims for damages against

the European Union and the providers of the said Data and Information. The scope of this

waiver encompasses any dispute, including contracts and torts claims that might be filed in

court, in arbitration or in any other form of dispute settlement.

Where the user communicates to the public on or distributes the original LAI, FAPAR, FCover

products, he/she is obliged to refer to the data source with (at least) the following statement

(included as the copyright attribute of the netCDF file):

Copernicus Service information [Year]

With [Year]: year of publication

Where the user has adapted or modified the products, the statement should be:

Contains modified Copernicus Service information [Year]

For complete acknowledgement and credits, the following statement can be used:

"The products were generated by the Global Land Service of Copernicus, the Earth

Observation programme of the European Commission. The research leading to the current

version of the product has received funding from various European Commission Research

and Technical Development programs. The product is based on SPOT-VEGETATION 1km

data (copyright CNES and distribution by VITO).”

The user accepts to inform Copernicus about the outcome of the use of the above-mentioned

products and to send a copy of any publications that use these products to the following address

[email protected].

3.5 CONTACTS

The Collection 1km LAI, FAPAR, FCover Version 2 products are available through the Copernicus

Global Land Service website at the address http://land.copernicus.eu/global/products

Scientific & Technical Contact e-mail address: http://land.copernicus.eu/global/contactpage

Accountable contact: European Commission Directorate - General Joint Research Centre

Email address: [email protected]

mailto:[email protected]

http://land.copernicus.eu/global/products

mailto:[email protected]



Issue: I1.33



4 DIFFERENCES WITH PREVIOUS VERSIONS

The first major version of Collection 1km LAI, FAPAR and FCover products derived from

SPOT/VEGETATION and PROBA-V data within Copernicus Global Land Service

[CGLOPS1_PUM_LAI[FAPAR/FCOVER]1km-V1] are updated every 10 days, with a temporal

basis for compositing of 30 days and delivered with a 12-day lag in near real time (RT). Validation

studies showed that the algorithm outperforms other existing products both in terms of accuracy

and precision (Camacho et al. 2013).

Version 2 of the Collection 1km LAI, FAPAR, FCover products aims to comply with the Copernicus

Global Land Service technical requirements [AD1]. These products have a high consistency with

Version 1 (Figure 5) but provide an improved continuity and smoothness and include a near real

time estimate, with subsequent consolidations.

4.1 ALGORITHM

Similarly to Version 1, Version 2 capitalizes on the development and validation of already existing

products: CYCLOPES version 3.1 and MODIS collection 5, and the use of neural networks (Baret

et al. 2013; Verger et al. 2008). The differences in algorithms between the existing Version 1 and

the new Version 2 products are summarized in the table below:

Table 13: Algorithm differences between Version 2 and Version 1 products.

Version 1 Version 2

Inputs of neural

networks

Temporal composites

of Top of the Canopy

normalized surface

reflectances

Daily Top Of the Canopy reflectances, cosine of

the 3 angles of sun and view directions

Temporal

composition

Composition of input reflectances based on a 30-day temporal window with Gaussian weighting

Composition at the level of biophysical estimates

using a Savitzky-Golay filter with an adaptive

composition window within 15- and 60-day semi-

periods defined by the availability of 6valid

observations at each side of the date being

processed

Temporal

smoothing and

gap filling

Not applied Application of TSGF (Temporal Smoothing and

Gap Filling) and CACAO (Consistent adjustment

of Climatology to Actual Observations) filters

Near Real Time 12-day lag delivery Near Real Time delivery



Issue: I1.33



Both versions are expected to be consistent to each other, both spatially and temporally, with a

better representation of high LAI, FAPAR and FCover values, and a significant improvement in

terms of spatial continuity (only 1% of missing data in version 2 as compared to 20% in version 1

over the BELMANIP2 sites during the 2003-2010 period) and smoothness, especially at high

latitudes and Equatorial areas (Figure 5). This is thanks to an original temporal compositing

technique which combines climatology information and the actual observations.

More details are given in the validation reports [GIOGL1_QAR_LAI[FAPAR/FCOVER]1km-VGT-

V2] and [CGLOPS1_QAR_LAI[FAPAR/FCOVER]1km-PROBAV-V2].

4.2 MINOR VERSIONS AND PRODUCTION RE-RUNS

The current RT version is V2.0.1 (major version 2, minor version 0, first run).

An update to the minor version can be used as part of the same time series for analysis, yet

requires users to verify the impact on their application (e.g. change in file format).

A secondary run (e.g. V2.0.2) fixes temporary production issues (bugs) by reprocessing the same

product, yielding a drop-in replacement of any former runs. Subsequent products in the time series,

who were not impacted by the issue, will resume with run number 1.

Table 14 below shows an overview of all product version 2 minor versions and production re-runs.

Table 14 : Overview version 2 releases

Version Period and consolidation Change

2.0.1 1999-2014

RT6: 2017-06-10 onwards

RT0, RT1, RT2: 2017-07-20 onwards

Nominal quality, not affected

2.0.2 RT6: 2014-01-10 to 2017-05-31

RT0, RT1, RT2: 2017-06-10 to 2017-07-10

Issue in climatology-filled pixels for

PROBA-V dataset (see below)

Issue with climatology-filled pixels for PROBA-V

Unreliable values were discovered in version 2.0.1, mainly above Evergreen Broadleaf Forest

(EBF) in the PROBA-V real-time series. Values were lower than expected (up to 2 in LAI unit

compared to major Version 1) and an inconsistent seasonal variation was shown. The unreliable

behaviour appeared only with climatology-filled pixels, as indicated in the bitwise Quality Indicator

(QFLAG, bit 13, see Table 8).

The issue was fixed in the PROBA-V processing line in July 2017 and the erroneous PROBA-V

time-series was reprocessed to version 2.0.2.



Issue: I1.33



Figure 9 : Example of climatology fill issue for PROBA-V time series, in V2.0.1 (left) and solved in

V2.0.2 (right) for Belmanip Site #16 (Evergreen Broadleaf Forest)



Issue: I1.33



5 VALIDATION

The quality assessment of Collection 1km LAI, FAPAR, FCover Version 2 products has been

performed in agreement with the best practices proposed by the CEOS LPV for the validation of

LAI products and recommendations of the review board of the Global Land Service. Two different

exercises were performed, one for SPOT/VGT offline products [see

GIOGL1_QAR_LAI[FAPAR/FCOVER]1km-VGT-V2] during two years period (2004-2005) and one

for PROBA-V Real Time processing (RT0-RT6) during one year period ( 2013-2014) including

overlap period with SPOT/VGT (November 2013- March 2014) [see

CGLOPS1_QAR_LAI[FAPAR/FCOVER]1km-PROBAV-V2].

Both products have shown a good performance for most of the criteria examined, with improved

performances as compared to Version 1 in completeness, due to the gap filling applied, and

precision, and approximately similar accuracies than Version 1 showing both large overestimations

over flooded rice fields. Moreover, Version 2 removes some artefacts present in Version 1 of

products. Note that Version2/VGT inter-annual precision meets GCOS requirements on stability for

LAI (and close for FAPAR). Good consistency is achieved in Version2/VGT when moving from

VGT1 to VGT2. However, the comparison between Version2/VGT and PROBA-V shows larger

discrepancies and a positive bias (Version2/PV > Version2/VGT) for the LAI product up to 2 units

in some areas with growing and fully developed vegetation. Users are advised to use with caution

the Version2/PV in combination with Version2/VGT products until a complete validation exercise

(with at least two years of PV data) will be available. The summary of the quality assessment

results for Version2/VGT and Version2/PV is shown in Table 15 and Table 16, respectively.

With the quality assessment reports, the Version 2 of products has reached validation Stage 1 in

the CEOS LPV hierarchy.



Issue: I1.33



Table 15: Summary of Product Evaluation (V2 SPOT/VGT). The plus (minus) symbol means that the

product has a good (poor) performance according to each evaluated criterion. Period of analysis:

2004-2005.

QA

Criteria Performance Comments

Product Completeness

+ No missing values in the V2 products as a result of the gap filling applied.

Spatial Consistency

±

Good spatial consistency between SPOT/VGT V2 and SPOT/VGT V1 LAI. Most of the residuals between both are within the GCOS requirements: typically around 90% for LAI. Lower FAPAR V2 retrievals than V1. Around 72% of pixels within the GCOS requirements. Larger discrepancies between V2 and MODIS (similar discrepancies between V1 and MODIS). Good consistency when input changes from VGT1 to VGT2

Temporal Consistency

+

Consistent seasonal variations. Improvements over EBF (correction noisy profiles). Removing artifacts detected in wintertime mainly over NLF sites. Good cross-correlations between V2 and reference products.

Intra-Annual Precision

+ Very low short-time variability (smoothness), much better than V1 and MODIS.

Inter-Annual Precision

+

Median absolute anomalies (95th and 5

th percentiles) matching the GCOS

stability requirements (0.041, 2.3%) for LAI and close (0.021, 5.8%) for FAPAR. Good stability for FCover (0.022, 6.4%). Improved inter-annual precision than SPOT/VGT V1 and MODIS C5 products.

Statistical Analysis of

Discrepancies ±

Good consistency between V2 and V1 LAI and FCover (RMSE=0.51 and 0.05 respectively). V2 LAI shows higher values than V1 for medium to high LAI. For FAPAR, V2 shows lower values than V1 (Bias=-0.035), but good correlation and RMSE (R

2=0.96, RMSE=0.06).

Good consistency when moving from VGT1 to VGT2.

Accuracy ±

Good accuracy with limited ground dataset for LAI (RMSE= 0.87, B=0.30), matching the GCOS requirements in 73% of cases. Slight negative bias for FAPAR (RMSE=0.12, B=-0.04), mainly over grassland (non-concomitant), but good agreement over forest sites. More than 50% of cases within the GCOS requirements. Positive bias for FCover (RMSE=0.11, B=0.023) over forest sites. 37% of cases within optimal accuracy levels.



Issue: I1.33



Table 16: Summary of Product Evaluation (V2 PROBA-V). The plus (minus) symbol means that the

product has a good (poor) performance according to each evaluated criterion. Period of analysis:

October 2013- September 2014.

QA

Criteria Performance Comments

Product

Completeness +

No missing values in the V2 products as a result of the gap filling

applied.

Spatial

Consistency ±

Smooth and reliable distributions over the globe, and good

autocorrelation over homogeneous sites.

Overall good spatial consistency between V2 modes, with residuals

lower than 1 LAI unit (99% of samples), or 0.1 FAPAR/FCover units

(98% of samples).

Spatial inconsistencies V2/PV vs V2/VGT mainly for LAI observed over

areas with growing and fully developed vegetation (non EBF), such as

Southern Africa. Systematic differences with V2/PV LAI > V2/VGT LAI

(up to 2 units).

Spatial inconsistencies with V1 LAI (up to ±2 LAI units) and

FAPAR/FCover (up to ±0.15 units) observed with different sign in

spring (negative residual) and fall (positive residual).

Large spatial discrepancies between V2 and MODIS products, as

between V1 and MODIS.

Temporal

Consistency +

Consistent seasonal variations. Improvements as compared to V1 over EBF (correction noisy profiles), DBF (anticipated decrease in V1 LAI), NLF (artifacts in fall) and bare areas (false seasonality in deserts). Good cross-correlations between V2 and reference products.

Improved cross-correlation V2/PV vs V2/VGT as compared to V1/PV

vs V1/VGT.

Locally, slight shift in the temporal profiles at the start and end of

season, compared to V1 and MODIS product.

Intra-Annual

Precision +

Very low short-time variability (smoothness) much better than V1 and

MODIS.

Statistical

Analysis of

Discrepancies

+

Overall good consistency between V2 and V1 for LAI (90% samples

within GCOS), FAPAR (80% of samples within GCOS) and FCover

(77%).

V2 > V1 for LAI values larger than 3, V2<V1 for FAPAR over medium ranges. For FCover, V2<V1 for very high values and consistent with FAPAR.

Accuracy ±

Acceptable accuracy for LAI, matching the GCOS requirements in 65%

of cases (RMSE= 1.06, B=0.50)

Slight positive bias for FAPAR (RMSE=0.10, B=0.05), mainly over

croplands, matching GCOS requirements in 57% of cases

Positive bias for FCover (RMSE=0.17, B=0.104)

.



Issue: I1.33



6 PRODUCT USAGE

6.1 ANALYSIS

The LAI can be used for detection of change and for providing information on shifting trends or

trajectories in land use and cover change.

FCover is a very useful and understandable product since it may be directly compared with the

human perception. Therefore, its temporal evolution can be directly linked to environmental

applications such as deforestation and other types of land cover changes. FCover is relevant for a

wide range of Land Biosphere Applications such as agriculture and forestry, environmental

management, hydrology, natural hazards monitoring and management, and fire scar extent,

among others. The product is used as an input for the start of the growing season. It can also be

used for year to year comparison of vegetation status.

FAPAR can be used into light use efficiency models at the daily time step, assuming that the

radiation is coming mainly from the sun directions (Monteith, 1972). It can be related to the

biomass production of crops or grassland (Kowalik et al, 2014; Diouf et al., 2015)

6.2 ASSIMILATION IN MODELS

The green LAI, which is the main vegetation structure variable governing canopy reflectance, is

also one of the main driver canopy functioning processes. LAI represents the size of the interface

between the plant and the atmosphere for energy and mass exchanges. It is thus of prime interest

for energy balance, photosynthesis, transpiration and litter production. LAI could be used to

validate canopy photosynthesis models which simulate growth and canopy development based on

climate and environmental factors (Warnant et al., 1994; Kohlmaier et al., 1997; Kergoat, 1998). It

is also a sensitive parameter for the control of evapotranspiration in Soil-Vegetation-Atmosphere-

Transfer (SVAT) schemes within the context of General Circulation Models (Mahfouf et al., 1995;

Neilson and Drapek, 1998) and Numerical Weather Prediction models (Boussetta et al. 2013,

Boussetta et al., 2015).

Similarly the FCover is a crucial parameter of SVAT models to separate the contribution of

vegetation and soil in the surface energy balance and evapotranspiration computation.

FAPAR is essential in production efficiency models to estimate carbon fluxes (McCallum et al.

2009), among many other applications.

In this sense, the assimilation of LAI, FAPAR and FCover into Land Surface Models may be used

to improve the characterization of the annual cycle of the hydrological (van den Hurk et al. 2003)

and carbon fluxes in the global climate-carbon cycle.

6.3 GAP FILLING

To evaluate the phenological cycle of the vegetation, it is important to know if the LAI, FAPAR and

FCover value was solely computed from daily observations. As explained in §2.3.4.3, if the number



Issue: I1.33



of daily observations is too limited in the compositing period, CACAO values are derived from the

version 1 climatology dataset to fill gaps before the computation of the actual dekad value. The

QFLAG quality indicator provides information whether gap filling was applied during the

computation. An easy method is to check if the QFLAG > 4095, as shown in Figure 10 below, and

depicted in yellow colour. As one can see that gap filling is typically applied in (i) tropical areas, (ii)

higher latitude areas and (iii) snow areas.

Figure 10 : LAI-QFLAG 20050710 (red: not observed, blue: seq (485), yellow:filled (>4095))

The user should be aware that the actual dekad value could be less accurate in the gap filled

areas. Indeed, the Version 2 product may be affected by the inability of the climatology to capture

underlying atypical modes of seasonality.

More details can be found in [GIOGL1_QAR_LAI[FAPAR/FCOVER]1km-VGT-V2 and

CGLOPS1_QAR_LAI[FAPAR/FCOVER]1km-PROBAV-V2]. In a future update of the version 2

product collection, an additional non-filled data layer could be added next to the current data layer.

6.4 DATA CONTINUITY

From a user's perspective, it is important to verify that the information derived from PROBA-V can

be safely used in an operational way to replace that of SPOT-VGT and to assess the possible

uncertainties linked to this change of data source into their applications. Our analysis over the

overlapping period between both sensors shows local discrepancies for LAI, FAPAR and FCover

that could have an impact for instance in estimation of anomalies used for near real time

vegetation monitoring (Rembold et al.,2013). In particular, for LAI, systematic higher values are

obtained for PROBA-V Version 2 as compared to SPOT/VGT Version 2 over some regions in

Africa and South America in periods close to the peak of the growing season which are associated



Issue: I1.33



with high cloud contamination in the input data. User should use with care the products until a

complete validation exercise is performed analyzing the impact of the change of sensor on

anomalies during one year of data at global scale.

In a later stage, it is planned to continue data deliveries through the use of the Sentinel-3 satellite.



Issue: I1.33



7 REFERENCES

Baret , F., Hagolle, O., Geiger, B., Bicheron, P., Miras, B., Huc, M., Berthelot, B., Weiss, M., Samain, O., Roujean, J.L., & Leroy, M. (2007). LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION. Part 1: Principles of the algorithm. Remote Sensing of Enviroment, 110, 275-286 Baret, F., Morissette, J., Fernandes, R., Champeaux, J.L., Myneni, R., Chen, J., Plummer, S., Weiss, M., Bacour, C., Garrigue, S., & Nickeson, J. (2006). Evaluation of the representativeness of networks of sites for the global validation and inter-comparison of land biophysical products. Proposition of the CEOS-BELMANIP. IEEE transactions on Geoscience and Remote Sensing, 44, 1794-1803 Baret, F., Weiss, M., Lacaze, R., Camacho, F., Makhmara, H., Pacholcyzk, P., & Smets, B. (2013). GEOV1: LAI, FAPAR Essential Climate Variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production. Remote Sensing of Environment, 137, 299-309 Bartholomé, E., & Belward, A.S. (2005). GLC2000: a new approach to global land cover mapping from Earth Observation data. International Journal of Remote Sensing, 26, 1959 - 1977 Boussetta, S., Balsamo, G., Dutra, E., Beljaars, A., Albergel, C., Assimilation of surface albedo and vegetation states from satellite observations and their impact on numerical weather prediction, Remote Sensing of Environment, Volume 163, 15 June 2015, Pages 111-126. Boussetta, S., Balsamo, G., Beljaars, A., Kral, T., & Jarlan, L. (2013). Impact of a satellite-derived leaf area index monthly climatology in a global numerical weather prediction model. International Journal of Remote Sensing, 34, 3520-3542 Camacho, F., Cernicharo, J., Lacaze, R., Baret, F., & Weiss, M. (2013). GEOV1: LAI, FAPAR Essential Climate Variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products. Remote Sensing of Environment, 137, 310-329 Defourny, P., Bicheron, P., Brockmann, C., Bontemps, S., Van Bogaert, E., Vancutsem, C., Pekel, J.F., Huc, M., Henry, C., Ranera, F., Achard, F., di Gregorio, A., Herold, M., Leroy, M., & Arino, O. (2009). The first 300 m global land cover map for 2005 using ENVISAT MERIS time series: a product of the GlobCover system,. In, Proceedings of the 33rd International Symposium on Remote Sensing of Environment. Stresa (Italy) Dierckx, W., Sterckx, S., Benhadj, I., Livens, S., Duhoux, G., Van Achteren, T., Francois, M. Mellab, K. and G. Saint, 2014. PROBA-V mission for global vegetation monitoring: standard products and image quality. Int. J. Remote Sens., vol. 35, issue 7, p. 2589-2614 Diouf, A.A.A, M. Brandt, A. Verger, M. El Jarroudi, B. Djaby, R. Fensholt, J. A. Ndione, and B. Tychon, Fodder biomass monitoring in Sahelian rangelands using phenological metrics from FAPAR time series, Remote Sensing, 2015, 7, 9122-9148; doi:10.3390/rs70709122



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Garrigues, S., Allard, D., Baret, F., & Weiss, M. (2006). Influence landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing data. Remote Sensing of Enviroment, 105, 286-298 GCOS (2010). GCOS-13. : Implementation plan for the global observing system for climate in support of the UNFCCC (2010 update). In, GCOS-138 (p. 186): WMO Hilker, T., Hall, F.G., Coops, N.C., Lyapustin, A., Wang, Y., Nesic, Z., Grant, N., Black, T.A., Wulder, M.A., Kljun, N., Hopkinson, C., & Chasmer, L. (2010). Remote sensing of photosynthetic light-use efficiency across two forested biomes: Spatial scaling. Remote Sensing of Environment, 114, 2863 Kandasamy, S., Baret, F., Verger, A., Neveux, P., & Weiss, M. (2013). A comparison of methods for smoothing and gap filling time series of remote sensing observations: application to MODIS LAI products. Biogeosciences, 10, 4055-4071 Kergoat, L. (1998) A model of hydrological equilibrum of Leaf Area Index at global scale, Journal of Hydrology, 212-213: 268-286. Kohlmaier, G.H. et al. (1997) The Frankfurt Biosphere Model. A global process oriented model for the seasonal and longterm CO2 exchange between terrestrial ecosystems and the atmosphere. Part 2: Global results dor potential vegetation in an assumed equilibrum state. Climate Research, 8, p. 61-87. Kowalik, W., K. Dabrowska-Zielinska, M. Meroni, T. Urszula Raczka, A. de Wit, Yield estimation using SPOT-VEGETATION products: a case study of wheat in European countries, International Journal of Applied Earth Observation and Geoinformation, 2014, 32, 228-239; http://dx.doi.org/10.1016/j.jag.2014.03.011. McCallum, I., Wagner, W., Schmullius, C., Shvidenko, A., Obersteiner, M., Fritz, S., & Nilsson, S. (2009). Satellite-based terrestrial production efficiency modeling. Carbon Balance and Management, 4:8 Mahfouf, J.6., A. Manzi, J. Noilhan, H. Giordani and M. Déqué (1995) The land surface scheme

ISBA within the Meteo-France climate model ARPEGE. Part 1: Implementation and preliminary

results. Journal of Climate, 8, 2039-2057.

Monteith J. (1972). Solar radiation and productivity in tropical ecosystems, J. Applied Ecology, 19, pp. 747-766. Myneni, R.B., Hoffman, S., Knyazikhin, Y., Privette, J.L., Glassy, J., Tian, Y., Wang, Y., Song, X., Zhang, Y., Smith, G.R., Lotsch, A., Friedl, M., Morisette, J.T., Votava, P., Nemani, R.R., & Running, S.W. (2002). Global products of vegetation leaf area and absorbed PAR from year one of MODIS data. Remote Sensing of Environment, 83, 214-231 Neilson R. P., Drapek R. J. (1998) Potentially complex biosphere responses to transient global warming. Global Change Biology, 4:505–521. Prince, S.D. (1991). A model of regional primary production for use with coarse resolution satellite data. International Journal of Remote Sensing



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van den Hurk, B.J.J.M., Viterbo, P., & Los, S.O. (2003). Impact of leaf area index seasonality on the annual land surface evaporation in a global circulation model. Journal of Geophysical Research: Atmospheres, 108, 4191 Rembold, F., Atzberger, C., Savin, I., Rojas, O. (2013). Using low resolution satellite imagery for yield prediction and yield anomaly detection, Remote Sens., vol. 5, no. 4, pp. 1704–1733, Apr. 2013 Sterckx, S., Benhadj, I., Duhoux, G., Livens, S., Dierckx, W., Goor, E., Adriaensen, S., Heyns, W., Van Hoof, K., Strackx, G., Nackaerts, K., Reusen, I., Van Achteren, T., Dries, J., Van Roey, T., Mellab, K., Duca, R. and Zender, J. (2014). The PROBA-V mission: image processing and calibration. Int. J. Remote Sens., 35(7), 2565 – 2588. Verger, A., Baret , F., & Weiss, M. (2008). Performances of neural networks for deriving LAI estimates from existing CYCLOPES and MODIS products. Remote Sensing of Environment, 112, 2789-2803 Verger, A., Baret, F., & Weiss, M. (2011). A multisensor fusion approach to improve LAI time series. Remote Sensing of Enviroment, 115, 2460-2470 Verger, A., Baret , F., & Weiss, M. (2014a). Near real time vegetation monitoring at global scale. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 3473-3481 Verger, A., Baret, F., Weiss, M., Filella, I., & Peñuelas, J. (2015). GEOCLIM: A global climatology of LAI, FAPAR, and FCOVER from VEGETATION observations for 1999–2010. Remote Sensing of Environment, 166, 126-137 Verger, A., Baret , F., Weiss, M., Kandasamy, S., & Vermote, E. (2013). The CACAO method for smoothing, gap filling and characterizing seasonal anomalies in satellite time series. IEEE transactions on Geoscience and Remote Sensing, 51, 1963-1972 Verger, A., Baret, F., Weiss, M., Smets, B., Lacaze, R., & Camacho, F. (2014b). Near real time estimation of biophysical variables within Copernicus global land service. In, Global vegetation monitoring and modeling (available at https://colloque.inra.fr/gv2m/Poster-Sessions/Poster-S7). Avignon (France) Warnant, P., François L., Strivay D. and Robinet F. (1994) Forcing of a global model of plant productivity with climatic and remote sensing data. In "Vegetation, modelling and climatic change effects", Veroustraete F., Ceulemans R. et al. (Eds), SPB Academic Publishing bv., The Hague, The Netherlands, p179-186. Weiss, M., Baret, F., Myneni, R., Pragnère, A., & Knyazikhin, Y. (2000). Investigation of a model inversion technique for the estimation of crop charcteristics from spectral and directional reflectance data. Agronomie, 20, 3-22 Yang, W., Shabanov, N.V., Huang, D., Wang, W., Dickinson, R.E., Nemani, R.R., Knyazikhin, Y., & Myneni, R.B. (2006). Analysis of leaf area index products from combination of MODIS Terra and Aqua data. Remote Sensing of Environment, 104, 297-312



Issue: I1.33



8 ANNEX: USERS REQUIREMENTS

According to the applicable document [AD2] and [AD3], the user’s requirements relevant for

LAI/FAPAR/FCover are:

Definition:

o Leaf Area Index (LAI): One half of the total projected green leaf fractional area in the

plant canopy within a given area; Representative of the total biomass and health of

vegetation (CEOS)

o Fraction of absorbed PAR (FAPAR): Fraction of PAR absorbed by vegetation for

photosynthesis processes (generally around the “red”: PAR stands for

Photosynthetically Active Radiation).

o Fractional cover (FCover): Fractional cover refers to the proportion of a ground

surface that is covered by vegetation.

Geometric properties:

o The baseline pixel shall be 1km or 300m

o The target baseline location accuracy shall be 1/3rd of the at-nadir instantaneous

field of view

o Pixel co-coordinates shall be given for centre of pixel

Geographical coverage:

o Geographic projection: regular lat-long

o Geodetical datum: WGS84

o Pixel size and accuracy: 1/112°; accuracy: minimum 10 digits

o Coordinate position: centre of pixel

o Global window coordinates:

Upper Left:180°W-75°N

Bottom Right: 180°E 56°S

Frequency and timeliness:

o As a baseline the biophysical parameters are computed by and representative of

dekad, i.e. for ten-day periods (“dekad”) defined as follows: days 1 to 10, days 11 to

20 and days 21 to end of month for each month of the year.

o As a trade-off between timeliness and removal of atmosphere-induced noise in

data, the time integration period may be extended to up to two dekads for output

data that will be asked in addition to or in replacement of the baseline based output

data.

o The output data shall be delivered in a timely manner, I. e. within 3 days after the

end of each dekad.



Issue: I1.33



Ancillary information:

o the number of measurements per pixel used to generate the synthesis product

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:

o Baseline: 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”

(Table 17).

o Target: considering data usage by that part of the user community focused on

operational monitoring at (sub-) national scale, accuracy standards may apply not

on averages at global scale, but at a finer geographic resolution and in any event at

least at biome level.

Table 17: GCOS requirements for LAI and FAPAR as Essential Climate Variables (GCOS-154, 2011)

Variable/ Parameter

Horizontal Resolution

Vertical Resolution

Temporal Resolution

Accuracy Stability

LAI 250 m N/A 2- weekly averages

Max(20%; 0.5) Max(10%; 0.25)

FAPAR 250 m N/A

2- weekly averages

(based on daily sampling)

Max(10%; 0.05) Max(3%; 0.02)

Additionally, the Technical User Group of the Copernicus Global Land [AD3] has recommended

new uncertainty levels for FAPAR and FCover (Table 18) while for LAI the users did not come to

an agreement.

Table 18: CGLOPS uncertainty levels for FAPAR and FCover products

Optimal Target Threshold

FAPAR 5% 10% 20%

FCover



Issue: I1.33



Additional user requirements

The GCOS requirements are supplemented by application specific requirements identified by the

WMO (Table 19). These specific requirements are defined at goal (ideal), breakthrough (optimum

in terms of cost-benefit), and threshold (minimum acceptable). In most cases the GCOS

requirements satisfy threshold levels (especially considering that GCOS requirements greatly

exceed threshold spatial resolution requirements so random errors will cancel during spatial

aggregation).

Table 19: WMOs requirements for global LAI and FAPARproducts (From http://www.wmo-

sat.info/oscar/requirements); G=goal, B=breakthrough, T=threshold.

Application Variable

Accuracy

(%)

Spatial Resolution

(km)

Temporal Resolution

(days)

G B T G B T G B T

Global Weather Prediction LAI

5 10 20 2 10 50 1 5 10 FAPAR

Regional Weather

Prediction

LAI 5 10 20 1 5

40 0.5 1 2

FAPAR 20

Hydrology LAI 5 8 20 0.01 0.1 10 7 11 24

Agricultural Meteorology LAI

5 7 10 0.01 0.1 10 5 6 7

FAPAR 8 20 5 13.6 100 1 h 0.25 7

Seasonal and Inter-annual

Forecasts FAPAR 5 7 10 50 100 500 7 12 30

Climate-Carbon Modelling LAI

5 7 10 0.25 0.85 10

1 3 30 FAPAR 0.5 2

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