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Stakeholders Needs: User Needs Analysis and Earth Observation

Infrastructure State of the Art Assessment

Deliverable n° WP2 - D2.1

Grant Agreement number 687490

Call identifier EO-1-2015

Project Acronym ONION

Project title Operational Network of Individual Observation Nodes

Funding Scheme Collaborative project

Project Starting date 01/01/2016

Project Duration 24 months

Project Coordinator Thales Alenia Space France (TASF)

Deliverable reference number and full name

D2.1 - Stakeholders needs

Delivery Date 30/04/2016

Issue V1.0

Document produced by

SKO Team: H. Matevosyan, I. Lluch, C.A. Moreno, A. Lamb, R. Akhtyamov, G. De Angelis ACRI Team: O. Lesne, A. Mangin DEI Team: A. Sousa STP Team: U. Pica UPC Team: A. Camps

Document verified by WP2 Leader Alessandro Golkar [SKO]

Document authorised by WP2.1 Coordinator

Armen Poghosyan [SKO]

Dissemination Level PU *

* Please indicate the dissemination level using one of the following codes:

PU = Public,

PP = Restricted to other programme participants (including the Commission Services).

RE = Restricted to a group specified by the consortium (including the Commission Services).

CO = Confidential, only for members of the consortium (including the Commission Services).

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Abstract

This report surveys and analyses user and stakeholder Earth Observation (EO) needs

and the related technological gaps in fulfilment of ONION’s project deliverable 2.1. The

ONION project explores fractionated and federated space architectures to enhance EO

capabilities. Such architectures can complement and enhance the European EO

infrastructure –Copernicus- on specific areas, which this document identifies.

The analysis methodology is based upon a comprehensive analysis of the value chain

elements in the European EO infrastructure. It leverages on an extensive database

(DB) built to support the analysis. The DB consists of several EO entities called users,

needs, services, products, measurements, instruments and missions. This report

describes the different attributes to each entity and implementation of a relational

database in MS Access ®, which holds mapping tables to connect the entities, like

user to needs, needs to services, and so on. Said DB has been populated with 63 EO

users, 37 explicit needs, the 6 Copernicus services, 95 EO products, 92

measurements, 427 instruments, and 312 missions. The data is based on

Copernicus/GMES requirements documents, FP7 and H2020 EO research projects,

the CEOS database [1], the WMO OSCAR database [2], and contributions by ONION

consortium partners.

A quantitative methodology has been applied to select promising use cases that

emerge from the combination of pressing needs and technological gaps. The results

section introduces two different assessment perspectives on said use cases. First, an

analysis on the technical maturity of the corresponding EO service is provided. Then,

radar plots have been used to breakdown the score of top use cases assessed by the

database. Based on this information, we selected 10 use cases for further evaluation.

Those are climate for ozone layer and UV assessment, land for basic mapping: risk

assessment, marine for weather forecast, atmosphere for weather forecast, fishing

pressure, land for infrastructure status assessment, sea ice monitoring, agriculture

(hydric stress), natural habitat & protected species monitoring, and sea ice melting

emissions.

The technological state of the art of the selected use cases has been analysed in depth

based upon the measurements, instruments and missions components of the EO value

chain. The use cases description at the end of this report includes metrics on revisit

time, historic measurement gaps and best achievable resolution to identify

technological areas for improvement in support of the following system requirements

generation tasks in the ONION project.

Potential technical contributions to the EO infrastructure by ONION include update

frequency, revisit time, and horizontal and vertical resolutions. In particular the EO

products for marine operations and navigation would benefit from reductions of revisit

time (from 24h to 1h), land products for risk assessment (landslide, floods) can benefit

from enhanced land cover (1000 km by 1000 km) and ice monitoring products would

benefit from enhanced vertical resolution.

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List of participants

Participant No

Participant organisation name Country

1 (Coordinator) Thales Alenia Space France (TASF) FR

2 Thales Alenia Space España S.A. (TASE) ES

3 Deimos Engenharia (DEI) PT

4 ACRI-ST (ACRI) FR

5 Universitat Politecnica de Catalunya (UPC) ES

6 Skolkovo Institute of Science and Technology (Skoltech) (SKO)

RU

7 Politechnika Warszawska (WUT) PL

8 SpaceTec Partners SPRL (STP) BE

No part of this work may be reproduced or used in any form or by any means (graphic,

electronic, or mechanical including photocopying, recording, taping, or information storage and

retrieval systems) without the written permission of the copyright owner(s) in accordance with

the terms of the ONION Consortium Agreement (EC Grant Agreement 687490).

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Applicable documents

Ref. / Document Title Ref Date

ONION Grant Agreement 687490-ONION 13/10/2015

Document Change Record

Issue Date Page / paragraph affected

V0.1 10/03/2016 First draft, partial

V0.5 23/04/2016 Extensive changes in all

sections and new sections

added

V0.7 27/4/2016 Revision by consortium

V1.0 29/4/2016 Consolidated final version

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Table of contents

1 INTRODUCTION ................................................................................................. 11

1.1 Report structure ............................................................................................ 12

2 DEFINITIONS ...................................................................................................... 13

3 APPROACH ........................................................................................................ 16

3.1 Scoring methods for user needs and EO services assessment ................ 16

3.1.1 Introduction: Value Chain Analysis .............................................................. 16

3.1.2 Scores by Entities ....................................................................................... 18

3.1.3 Limitations .................................................................................................. 20

3.2 Method for the technological assessment of use cases ............................ 21

4 IMPLEMENTATION ............................................................................................ 23

4.1 Relational Database Overview ...................................................................... 23

4.2 Database Information Gathering Process.................................................... 25

5 FIRST PHASE. USER NEEDS AND EO SERVICES ASSESSMENT ................. 27

5.1 Weight System Sensitivity Analysis............................................................. 29

5.2 Service Technical Maturity Breakdown by Products .................................. 31

5.3 Score Breakdown: Radar Plots .................................................................... 34

6 SECOND PHASE: TECHNOLOGY ASSESSMENT ............................................ 41

7 RESULTS DISCUSSION ..................................................................................... 53

8 CONCLUSIONS .................................................................................................. 57

9 BIBLIOGRAPHY ................................................................................................. 59

10 APPENDICES ..................................................................................................... 62

10.1 Appendix A. Example SQL Querie Implemented in the database .............. 62

10.2 Appendix B. User Weights ............................................................................ 63

10.3 Appendix C. Needs Description Tables. ...................................................... 66

10.4 Appendix E. Missions Considered in the Analysis ..................................... 67

10.5 Appendix D. Scored use cases. .................................................................... 75

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List of Tables

Table 1. Acronyms ........................................................................................................ 8

Table 2. Description of entities and attributes in the DB .............................................. 13

Table 3. User attributes and related numerical score .................................................. 18

Table 4. Need attributes and related numerical score ................................................. 19

Table 5. Product attributes and scoring mechanism. ................................................... 20

Table 6. Top use cases ranking .................................................................................. 27

Table 7. Agriculture & Forestry: Hydric Stress user case table .................................... 42

Table 8. Marine for Weather Forecast user case table ................................................ 43

Table 9. Sea Ice Monitoring: Extent/Thickness user case table .................................. 44

Table 10. Fishing Pressure & Fish Stock Assessment user case table ....................... 45

Table 11. Land for Infrastructure Status Assessment user case table ......................... 46

Table 12. Land for Mapping: Risk Assessment user case table .................................. 47

Table 13. Sea Ice Melting Emissions user case table ................................................. 48

Table 14. Climate for Ozone Layer and UV Assessment use case table ..................... 49

Table 15. Natural Habitat and Protected Species Monitoring use case table .............. 50

Table 16. Atmosphere for Weather Forecast use case table ....................................... 51

Table 17 Atmosphere for Weather Forecast use case table (continued) ..................... 52

Table 18. Master summary of the analysis by use cases. FPBI is the fraction of

Products that would benefit from an improvement in the corresponding characteristics

................................................................................................................................... 53

Table 19. Master summary table of technical improvement lines for the EO

infrastructure ............................................................................................................... 55

Table 20. Users in the DB and weighting schemes ..................................................... 63

Table 21. Needs in the DB and their description ......................................................... 66

Table 22. Missions considered in the analysis ............................................................ 67

Table 23. List of all scored use-cases applications (triad weight system). Items in red

are analysed further in the results section due their high scores. ................................ 75

List of Figures

Figure 1. Overview of the two-phased approach. ........................................................ 16

Figure 2. Copernicus Space Component Value Chain Analysis .................................. 17

Figure 3. The relational database diagram. ................................................................. 24

Figure 4. Screenshot from the Table “User”, showing the drop-down list for the attribute

“Reference Market Area”. ........................................................................................... 24

Figure 5. Implemented database infographic .............................................................. 26

Figure 6. Sensitivity of Marine for Weather Forecast to Weighting Scheme Type ....... 29

Figure 7. Sensitivity of sea ice monitoring to Weighting Scheme Type ........................ 29

Figure 8. Sensitivity of Land for infrastructure status assessment to Weighting Scheme

Type ........................................................................................................................... 29

Figure 9. Sensitivity of Marine for Fish Stock Management to Weighting Scheme Type

................................................................................................................................... 29

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Figure 10. Sensitivity of Agriculture and Forestry to the weighting Scheme Type ........ 30

Figure 11. Sensitivity of Atmosphere for Weather Forecast to Weighting Scheme Type

................................................................................................................................... 30

Figure 12. Sensitivity Sea Ice Melting emissions to Weighting Scheme Type ............. 30

Figure 13. Sensitivity of Climate for Ozone Layer & UV to Weighting Scheme Type ... 30

Figure 14. Sensitivity of Land Basic mapping: Risk Assessment sensitivity to Weighting

Scheme Type ............................................................................................................. 30

Figure 15. Sensitivity of Natural Habitat and Protected Species Monitoring to Weighting

Scheme ...................................................................................................................... 30

Figure 16. Normalized Scores of Copernicus Services ............................................... 31

Figure 17. Distribution of Product Gaps for the Copernicus Atmosphere Services ...... 32

Figure 18. Distribution of Product Gaps for the Copernicus Climate Services ............. 32

Figure 19. Distribution of Product Gaps for the Copernicus Emergency Services ....... 32

Figure 20. Distribution of Product Gaps for the Copernicus Land Services ................. 32

Figure 21. Distribution of Product Gaps for the Copernicus Marine Services .............. 32

Figure 22. Score breakdown of use-case Atmosphere for Weather Forecast .............. 35

Figure 23. Score breakdown for Marine for Weather Forecast use case ..................... 35

Figure 24. Score breakdown for sea ice monitoring use case. .................................... 36

Figure 25. Score breakdown of fishing pressure and fish stock assessment use case.

................................................................................................................................... 36

Figure 26. Score breakdown for Natural Habitat and Protected Species Monitoring ... 37

Figure 27. Score breakdown for land for infrastructure status assessment use case. . 37

Figure 28. Score breakdown of Land for basic mapping: Risk assessment use case. . 38

Figure 29. Score breakdown for agriculture and forestry: hydric stress use case. ....... 38

Figure 30. Score breakdown for climate for ozone layer and UV use case. ................. 39

Figure 31. Score breakdown for sea ice melting emissions. ........................................ 39

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Table 1. Acronyms

Acronyms

BOL Beginning Of Life

CSC Copernicus Space Component

Copernicus The European Earth observation programme (previously GMES)

DB Database

DG AGRI European Directorate General for Agriculture

DG CLIMA European Directorate General for Climate

DG DEV European Directorate General for International Cooperation and

Development

DG ECHO European Directorate General for Humanitarian Aid and Civilian

Protection

DG RELEX European Directorate General for External Relations

EC European Commission

EC JRC EC Joint Research Center

EEA European Environment Agency

EFAS European Flood Awareness System

EFFIS European Forest Fire Information System

EOL End Of Life

ESA European Space Agency

EU European Union

EUB End-Users Board

FAO Food and Agriculture Organization of the United Nations

FOS Flight Operations Segment

FP (7) Framework Programme (7th)

GBIF Global Biodiversity Information Facility

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Acronyms

GMES Global Monitoring for Environment and Security

H2020 Horizon 2020. EU research and innovation program

HIGHRES High resolution optical imager

IRS Cross Nadir Scanning IR sounder

LSS Limb-Scanning Sounder

MD Metadata

MODRES Moderate Resolution Optical Imager

MSI Multi Spectral Imager

MST Management Support Team

MWISC Microwave Imaging/sounding radiometer (Conical scanning)

MWISCT Microwave Imaging/sounding radiometer (Cross-Track scanning)

NASA National Aeronautics and Space Administration

NDVI Normalized Data Vegetation Index

ONION Operational Network of Individual Observation Nodes

OSPAR Convention for the Protection of the Marine Environment of the North-

East Atlantic

PDGS Payload Data Handling and Ground Storage

PK Primary Key

PMB Project Management Board

PMP Project Management Plan

RA Radar Altimeter

REA Research Executive Agency

RGB Red Green Blue

RS Radar Scaterometter

S-1 Sentinel-1

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Acronyms

S-2 Sentinel-2

SAB Security Advisory Board

SAR Synthetic Aperture Radar – Imaging radar

SQL Structured Query Language

SR Special Scanning or non-scanning microwave radiometer

STAB Scientific and Technical Advisory Board

SWIRS SW and IR Sounder

SWS Cross-nadir SW Sounder

UNCBD United Nations Convention on Biological Diversity

UNCCD United Nations Convention to Combat Desertification

UNEP United Nations Environment Program

UNFCCC United Nations Framework Convention on Climate Change

UNHABITAT United Nations Humans Settlements Program

UNHCR United Nations Refugee Agency

UNICEF United Nations Children’s Right & Emergency Relief Organization

UNDP United Nations Development Program

UNESCO United Nations Educational, Scientific and Cultural Organization

UNSD United Nations Statistics Division

WFP World Food Program

WCMC World Conservation Monitoring Centre

WP Work Package

WP2 Work Package 2

WPC Work Package Committee

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1 INTRODUCTION

The Operational Network of Individual Observation Nodes (ONION) project pursues to

identify the opportunities and challenges for the application of fractionated and

federated satellite concepts to Earth Observation (EO). The application of these

paradigms can complement and enhance the value of the European EO infrastructure -

Copernicus- on niche areas.

Fractionated [3] and Federated [4] satellite concepts are novel space systems

architectural paradigms based upon distribution. The fractionated spacecraft concept is

based upon breaking down a conventional monolithic spacecraft into closely flying, yet

physically separated subsystems. Such an arrangement allows for decoupling of

design constraints for different instruments, increased system upgradability and

responsiveness, at the cost of increased design complexity. In the case of Federated

Satellite Systems (FSS) conventional spacecraft establish a network to exchange

resources (such as bandwidth and computing power) for mutual benefit. Yet they retain

their independent goals and operational independence. For a detailed survey on FSS

and Fractionated Spacecraft, see this project’s state of the art survey (deliverable 2.2).

Both of these novel distribution concepts can support the future of EO by bringing new

sensing capabilities to the table (interferometry, distributed aperture, bi-static radar…),

and making the EO infrastructure more responsive and resilient. The potential benefits

come with challenges at all levels, from design and architecting to specific

technologies.

In order to identify potential applications and technological areas for complementing

Copernicus, this first report of the ONION project examines the user community, their

needs, and technological gaps in the European Earth Observation (EO) infrastructure.

The scope of this report is fundamentally European, notwithstanding the consideration

of non-European beneficiaries of the European infrastructure and coordination between

European and third-party EO missions.

The approach followed is to start from a comprehensive, broad identification and

analysis of user’s needs and European EO services performance in the light of

potential ONION applications. This leads to a selection of EO use cases, based upon a

quantitative assessment methodology. After narrowing the scope to the use cases, we

perform a deeper technological assessment of the EO infrastructure applicable to the

use cases. The identification of use cases and related areas for technological

improvement paves the way to derive mission and system requirements for the ONION

project.

The missions and instruments included in the technological assessment are those of

Copernicus and contributing missions [5] together with other missions traditionally

available to European users, such as ESA sponsored, European national agencies

(DLR, CNES, ASI, CDTI…), NASA, JAXA, NOAA, CSA and commercial imagery

missions. The time scope of the analysis is from nowadays to 2039.

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1.1 Report structure

This report first introduces the definitions used across the document and the

implemented database. The methodology to assess user needs and EO infrastructure

performance, which is based upon two distinct phases, is introduced in the approach,

section 3. Section 4 discusses the implementation details of the methodology on a

relational DB of EO value chain entities. After discussing the implementation, section 5

introduces the results of the first phase of the approach as a way to select the top use

cases for deeper technological assessment.

Taking it from there, section 6 discusses in more detail each of the use cases and

provides a technological evaluation of the instruments, missions and measurements

supporting the use case as explained in the approach. Section 7 consolidates the

results of the report and points to improvement directions for the EO infrastructure

addressable by ONION. Finally, section 8 draws conclusions to pave the way for the

following ONION work packages, specifically WP2.3 on system requirements.

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2 DEFINITIONS

Table 2 lists and defines the attributes and entities used through this document and in

the database (DB) that supports the analysis process.

Table 2. Description of entities and attributes in the DB

Term Description Status

Accuracy Refers to the performance of a given product in regards to its information content. Depending on the related product, it can be map scale, vertical or horizontal resolution, or percentage accuracy on a geophysical measurement.

Product attribute

Beneficiary Users who receive benefits from the Earth Observation infrastructure

User attribute

BOL Beginning of Life Mission attribute

Closest Copernicus Product

A Copernicus product, which fulfils a certain product expectation, as expressed by the user. A Copernicus product is the result of retrieving and processing Copernicus Space data. It may also be a result of data fusion from different sources.

Product attribute

Emergence Shows whether the need is new, foreseeable or increasingly trending towards space-based solutions

Need attribute

Entity Refers to individual components of the Copernicus value chain

EOL End of Life Mission attribute

Event Duration The duration of an emergency event Deprecated. Replaced by “Temporal Coverage”

Horizontal Coverage (offered)

Area covered by an implemented EO product, in km2

Attribute of product

Horizontal Coverage (required)

Area to be covered by an EO product, in km2, as expressed by users

Attribute of product

Instrument Instruments carried on-board missions Entity

Maturity Shows whether a user and related market are mature

Attribute of user

Measurement Measurements carried out by the instruments

Entity

Mission A spacecraft mission within the Copernicus Space Segment

Entity

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Need The attribute of interest to the user. Needs can be necessities, overall desires or wants, or wishes for something which is lacking

Entity

Product The result of retrieving and processing space data for a particular application

Entity

Product Access (offered)

Implemented Product delivery time, in hours Product attribute

Product Access (required)

Expected product delivery time, in hours, as expressed by users

Product attribute

Reference Market Area

Shows whether the market the user is part of is from EU, outside EU or EU & Outside EU

User attribute

Reference Market Size Shows the market size the user is part of. User attribute

Relevance of Space Solution

The applicability of a space solution to solve a need.

Need attribute

Revisit time The time interval between two recurrent measurements on the same target, based upon the set of missions/instruments capable to perform such measurement.

Measurement attribute

Service A portfolio of products addressing a specific user need

Entity

Space Awareness The perception by the user that space assets can cover his or her needs.

Substitutes “willingness to use space data”

Stakeholder Users who have interest, investment and/or stake in space-based EO

User attribute

Temporal Coverage The time extent, in hours, a given mission is capable to cover a particular event.

Attribute of Mission, substitutes “Event duration”.

Update Frequency (offered)

Corresponds to the time, in hours, between two recurrent provisions of an implemented product to a user.

Product attribute

Update Frequency (required)

Corresponds to the time difference, in hours, between the recurrent provisions of a dataset to users, as required by them.

Product attribute

User Either stakeholders or beneficiaries of the European Earth Observation infrastructure

Entity

Use Case In this report, a use case is application of a Copernicus service to a particular user need. The service application is further specified by the set of measurements required to fulfil the user’s need.

Intersection of a service and a need

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Willingness to use space data

The desire of a user to fulfil her/his

information needs with data generated by

space missions.

Deprecated for “space awareness”.

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3 APPROACH

The aim of this task is identify key technological areas and applications for the ONION

project to focus on. For this, we use a quantitative approach starting from a broad

perspective on the EO users, their needs, and EO services & products performance.

On this first phase of the approach, we apply a quantitative method to rank interesting

intersections of EO services and needs, where use cases emerge.

The analysis is then narrowed down to the top use cases as ranked by the

methodology. On the selected use-cases, we perform a deeper technology

assessment, which corresponds to the phase II of the approach. The technology

assessment again broadens the scope by considering a large set of missions and

instruments.

Figure 1. Overview of the two-phased approach.

The rest of this section describes in detail the methodologies for both phases.

3.1 Scoring methods for user needs and EO services assessment

The methodology to analyse user needs and the status of the corresponding EO

infrastructure according to phase I of the approach is introduced herewith.

3.1.1 Introduction: Value Chain Analysis

As a first step towards establishing an information database structure for the user

needs analysis, we perform a bottom-up value chain analysis of the Copernicus Space

Component (Figure 2).

The value chain in Figure 2 starts from the end users of Copernicus services.

● End users (e.g. farmers) have needs to be satisfied by Copernicus services

(e.g. to optimize fertilizer use).

o Needs are characterized by specific need attributes (e.g. crop

monitoring).

o Services are characterized by specific service attributes (e.g. service

update frequency: monthly).

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o Users are characterized by specific user attributes (e.g. reference

market area: EU).

● Services are delivered through the creation and dissemination of products (e.g.

Vegetation index maps).

o Products are characterized by specific product attributes (e.g.

accuracy with respect to ground truth).

● Products are developed by Copernicus’ PDGS from data and metadata (e.g.

downlinked S-2 MSI data and associated metadata) retrieved by Copernicus’

FOS. Data is downlinked from the associated Copernicus spacecraft, which

takes measurements of the subject of interest (e.g. RGB observations of the

subject of interest).

o Measurements are characterized by specific measurement attributes

(e.g. ground spatial resolution).

● Measurements are taken by spacecraft instruments.

o Instruments are characterized by specific instrument attributes.

● Instruments are carried on-board spacecraft missions.

o Missions are characterized by specific mission attributes.

● A coordinated set of space missions compose the Copernicus Space

Component.

Figure 2. Copernicus Space Component Value Chain Analysis

By ensuring the operations of the value chain described above, the CSC delivers value

to the users and satisfies their needs.

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This section describes a method for scoring and ranking EO need-service

intersections, or use cases. The result of this scoring process is called ONION Use

Case Interest Score (OUCIS). OUCIS is a traceable measure aggregating several

relevant evaluation metrics as will be defined shortly. Items with large OUCIS scores

represent combinations of users, needs, services and related products which are more

appealing for ONION to address, in terms of economic impact, societal impact and

technology gaps. This analysis filters the use cases for deeper technological

assessment in phase II and informs system requirements activities in ONION WP2.3.

The OUCIS is based upon the first four elements of the value chain (users, needs,

services, products). OUCIS contains products and service technical maturity

information, but for a detailed assessment on the technologies side we will use the

second part of the value chain (measurements, instruments, missions).

The approach presented here is the implementation of the previous methodological

documentation iterated between the partners [6]. The proposed methodology has

known limitations which are discussed at the end of this section.

3.1.2 Scores by Entities

This section describes the mechanisms to assign numerical scores to qualitative data

on the user, need, service and product domains.

First, we define a User Table where we list users, identified from multiple

documentation sources listed in the bibliography. For each user we evaluate their

attributes of interest. The definitions of such attributes can be found in Table 2. Then,

numerical scores are assigned to each user attribute. Table 3 shows the user attributes

and numerical scoring methods.

Table 3. User attributes and related numerical score

User Attribute Numerical score

1 2 3

Market Size Niche Mass

Market Area Outside EU EU EU & outside EU

Maturity & Awareness to use space data

Mature, Low Awareness

Not Mature, High Awareness

Mature, moderate Awareness

Not Mature, Moderate Awareness

Mature, High Awareness

Not mature, Low Awareness

Note that user maturity and awareness to use space data can both be low, moderate or

high, and their values are composed as illustrated in the table above. This scoring

method intends to assign lower OUCIS to users and related markets that are

developed and convinced that space data does not fill their needs. This recognizes the

difficulty on penetration and disrupting of well-established markets. A high score is

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assigned to markets that are not developed, neither well educated in space data

availability, as potential new markets for space data. Also high score and attention is

paid to developed markets which already use space data (Mature, High Awareness

combination).

A user scoring for each user is derived by multiplying the attribute scores as per

equation 1.

Equation 1. User Score Formula

𝑈𝑠𝑐𝑜𝑟𝑒 = 𝑚𝑎𝑟𝑘𝑒𝑡𝐴𝑟𝑒𝑎𝑠𝑐𝑜𝑟𝑒 × 𝑚𝑎𝑟𝑘𝑒𝑡𝑆𝑖𝑧𝑒𝑠𝑐𝑜𝑟𝑒 × 𝑓�𝑚𝑎𝑡𝑢𝑟𝑖𝑡𝑦, 𝑎𝑤𝑎𝑟𝑒𝑛𝑒𝑠𝑠

Then, we define a Needs Table which consists of EO space data needs listing as

identified from the documentation. In addition, we define a User-Need Mapping Table

where we map users to needs. Any need can be mapped to any user. Needs have only

two attributes, which are relevance of the space solution (see Table 2) and emergence.

Table 4 summarizes the possible values and associated numerical scores for the

needs.

Table 4. Need attributes and related numerical score

Need Attribute Numerical score

0 1 2

relevance of the space solution not relevant* low relevance high relevance

*e.g, in-situ data collection.

Note the emergence attribute has been included for informative purposes but does not

change the scoring.

The need score is then the aggregation of users, with corresponding weights, who

expressed that need, multiplied by the relevance of a space solution to that need.

Equation 2. Need Score Formula

𝑁𝑒𝑒𝑑𝑠𝑐𝑜𝑟𝑒 = 𝑁𝑒𝑒𝑑𝑅𝑒𝑙𝑒𝑣𝑎𝑛𝑐𝑒 × � (𝑊𝑖

𝑢𝑠𝑒𝑟𝑠

𝑖

× 𝑈𝑠𝑒𝑟𝑠𝑐𝑜𝑟𝑒 )

Subsequently, we define a product table, listing the space data products and

assigning them attributes as per

Table 5. Each product is scored on the basis of the technical performance gaps

between what users expect (documented in Copernicus and GMES-era user

documentation and research project reports) and what is implemented in Copernicus.

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Table 5. Product attributes and scoring mechanism.

Product attribute Numerical score

1 (better than required)

2 (equal to required) 3 (worse than required)

Product Access (req. & offered) (h)

if req>offered if req=offered if req<offered

Update Frequency (Req. & offered) (h)

if req>offered if req=offered if req<offered

Horizontal coverage (Req. & offered) (km

2)

if req<offered if req=offered if req>offered

Accuracy (Req. & offered) if req>offered if req=offered if req<offered

The product score is the multiplication of the scores obtained across attributes as per

Table 5. For horizontal coverage, defined in table 1, if the product requirement is larger

than the actual product offer, the score is 3, pointing to larger gaps. The rationale is the

same for all product attributes, that is, to assign large scores to products with poor

technical performance as means to point to technical gaps. For access, frequency and

accuracy attributes, defined in Table 2, smaller magnitudes mean better performance

(resolution, map scale, delivery periods…) therefore the scoring mechanism assigns

them maximum score when the requirement is smaller than the offer.

Next, we define a Service-Product matrix mapping Copernicus services (e.g.

Emergency, Land,...) to their suite of products, or product portfolio. Each service is

assigned a quantitative score as the average of product scores of across the portfolio.

Equation 3. Service Score Formula

𝑆𝑒𝑟𝑣𝑖𝑐𝑒𝑠𝑐𝑜𝑟𝑒 = � 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑠𝑐𝑜𝑟𝑒

𝑁𝑖

𝑁 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑠

The overall Onion Use Cases interest score (OUCIS) can be then derived as the

multiplication of each need score to the related service score, following a Need-

Service mapping table.

Equation 4. Overall Onion Use Cases Interest Score

scorescoreServiceNeedOUCIS

All the scores introduced in this section, including the OUCIS, are to be normalized.

Section 4 details the normalization procedures.

3.1.3 Limitations

The quantitative methodology to user need analysis introduced here has several

limitations to be aware of. Mitigation strategies for each are included.

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● A well-known limitation of this method is to aggregate quantitative scores,

which have the same numerical values but are rather categorical (ordinal)

values. That is, a “2” in User Score may have a different meaning or relevance

to ONION than a “2” in relevance of a space solution. The classical remedy to

this shortcoming is the use of user-specified normalized weights for each score.

Weights provide a quantitative ranking of the different components to the overall

score with each other. A weight system for users have been implemented,

however, weights are very difficult to estimate in an objective way.

● Results may vary significantly with varying weights. Sensitivity analysis has

been performed to show the variation of results to changing weights.

● Another well-known limitation of this method is the omission of consideration of

synergies among products. A user may derive less value by the exploitation of

two disaggregated products, than two related products (for example offered by

a data fusion service). This is very important to keep in mind as one of the key

features of ONION is related to the ability of fusing data coming from multiple

observing sources. Very obvious synergies can be captured manually by

defining new “merged” products in the Service-Product table.

Results obtained with this method, which Section 5 introduces, are to be validated

following a two-step approach. In the first step, results are shared and discussed

among the consortium. Once internal validation is deemed successful, results are

shared and discussed with the ONION User Advisory Board.

3.2 Method for the technological assessment of use cases

The phase I methodology described above is used to filter a set of use cases, for which

in phase II of the approach we perform a detailed technological assessment. The goal

of phase II assessment is to characterize the technical state of the art of space EO in

relation to each particular use case.

For this, we turn now our attention into the three final elements of the value chain

described in Figure 2, named the infrastructure layer. Measurements, instruments and

missions define the technical capabilities of space-based EO. First, we need to connect

the top use cases to a set of supporting measurements, a knowledge-intensive task

based upon expert contribution by consortium partners. After assigning measurements

to the each use cases, we can proceed to analyse the uses cases through their

measurements. By using the data of the downstream section of the value chain, we

derive 3 analyses on the measurement level:

● The best available performance of all instruments, current and planned until

2039, capable of capturing a given measurement. Usually it is embodied by a

spatial resolution. Note that this depends also on the mission entities carrying

that instrument. The spatial resolution can be horizontal, vertical, or both, when

applicable.

● The occurrence and duration of measurement continuity gaps in the 2016-

2039 horizon, when mission planning is available.

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● The current revisit time, i.e. the time interval between two recurrent captures

of a measurement on a particular target, taking into account the full set of

missions that possess instruments capable of taking said measurement. Note

that the best possible revisit time does not necessarily correspond to the best

available performance metric, that is, not all of missions contributing to the

revisit will have instruments matching the state of the art performance.

The result of the complete approach described in this section is a list of high-ranking

use cases, a detailed description of each, and the performance, continuity and revisit

analyses of all measurements related to a given use-case.

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4 IMPLEMENTATION

This section covers details about the gathering, organizing and analysing datasets

representing the entities described in section 3. It includes also details about the

implementation of the scoring methodology.

4.1 Relational Database Overview

The chain of value delivery to the user includes 7 different components, as shown in

the Figure 2. Representation of those components in its essence is independent from

one another. They are connected to each other with relationships in the context of

Copernicus value delivery model. This means that the data gathering and definitions of

relationships for each of the category of components can be carried out independently.

Hence, a decision to use a relational database [7] was made. Each of the components

in the value chain was implemented as a standalone entity (or table), without having

any direct relationship with others. For the implementation of the relationships among

different entities of the value chain, the paradigm of primary keys (PK) [8] in relational

databases has been heavily utilized. All the relationships were implemented in

separate tables, representing a mapping between primary keys of entities being

connected. Each record in the relation tables contains pair of PKs connected with that

relation. The resulting structure is represented in the Figure 3. For example, the entities

“User” and “Need” with all their attributes are represented in separate tables and the

relationship between them is represented in the table “User-Need Mapping”, with the

pairs of PKs “User ID” and “Need ID”. This concept allows a compact representation of

the data and relationships, without a need for repeating all the values of the attributes

in new records of relationships.

The database also provides embedded information about the attributes described in

section 3. Possible values of discrete attributes are confined to the predefined sets in

the scoring methodology. This allows having immediate data validation when inputting

new records to the database. For example, in the Figure 4 the field “Reference Market

Area” is confined to the data set {EU; Outside EU; EU and outside EU} as described in

the scoring methodology. Same applies to the calculated fields: they are read-only,

meaning it is impossible to update them manually, and they get their values by

calculated formulas from the scoring methodology. Same applies also to the fields with

normalized scores. They are calculated by dividing the score to the maximal theoretical

score in the database. For example, the user score is a product of 3 different attributes

with maximum values of 3, hence the normalized score is the result of division of the

calculated user score divided by 33 = 27.

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Figure 3. The relational database diagram.

Figure 4. Screenshot from the Table “User”, showing the drop-down list for the attribute “Reference Market Area”.

Apart from the attributes described in the scoring methodology, the table “User” also

has embedded weighting system. There are 4 types of weighting schemes in the

database:

● Equal Weights, all the users are considered equal and the weights are not

affecting the normalized score.

● Priority Weights, the users are grouped in two categories, one with high priority

and the other one with low. The ones with high priority are assigned weight 2

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and ones with low priority are assigned weight 1. For normalization, the scores

are divided by the maximum weight, which in this case is 2. High priority

corresponds to users with high decision power in the economical, industrial and

security domains; such as EU directorates, industry, and governments.

● Triad Weights, this type of weights is similar to the Priority Weights, but it adds

more granularity to the weighting. Instead of 2 types of weights, there are 3.

Hence for normalization, the score is divided by 3.

● Global Challenges Weights, this is also a ternary weighting system and the

normalization process is similar to the Triad Weights system. In this case, the

priority is given to the users that tackle relevant global challenges humanity is

facing, especially in terms of societal and environmental impact. These users,

such as humanitarian relief organizations, education and research initiatives,

and development programmes might be underrepresented in the priority and

triad scoring systems.

The values of the weights across all the schemes are presented in Table 20 in

appendix B.As described in the scoring scheme, there are both independent and

aggregate scores. For example, the user score depends solely on its attributes, hence

it is independent. But the score of the need already depends on the scores of the users

connected to that need, hence it is an aggregate score. The calculation of independent

scores is done in the same table the data is recorded in. The case of aggregate scores

is more complicated, as it needs to reconcile information from different tables. For

example, the score of a need is the aggregate of user scores related to that need. This

cases are solved using database queries [7], implemented in special-purpose language

SQL (Structured Query Language) [8]. The complete list of queries is provided in the

Appendix A.

4.2 Database Information Gathering Process

The database described above has been populated mainly through publicly available

documents (see bibliography at the end of this document) referring to the Copernicus

programme policies and requirements, GMES, and related H2020 and FP7 research

projects which developed EO products.

The database includes 63 EO users, with 37 corresponding needs, the 6 Copernicus

services, 95 EO products, 92 measurements, 427 instruments, and 312 missions.

There are 467 relations between users and needs, 60 need-service mappings, and 132

service-product mappings, as some of marine and atmospheric products also

contribute to the climate change service portfolio. As per measurements, 1286

connections have been established between them and instruments, 861 relations

connect instruments and missions, since some instruments have been flown in several

missions and vice versa.

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The data acquisition sequence started from the Emergency domain, then covered Land

and Marine, and closed with Atmospheric and Climate. The security domain is

underrepresented since not much data was publicly available.

The data gathering procedure was re-iterated at each step, as any new data was re-

mapped to previous data in the database, if applicable, through all mapping tables.

This means that the database is not only a cohesive juxtaposition of literature, but also

a survey of the relations among EO applications.

Figure 5. Implemented database infographic

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5 FIRST PHASE. USER NEEDS AND EO SERVICES

ASSESSMENT

The analysis methodology for user needs and EO services performance described in

section 3 was applied on the database implementation. This leads to a list of scored

use cases, which emerge at the intersection of a need with a service. Note a given

need might be linked to more than one service. The list of needs wit descriptions can

be found in Appendix C. Needs Description Tables. The full list of need-service

intersections can be found in Appendix D. From those, the intersections that achieved

a high OUCIS receive a specific use-case name (e.g. fishing pressure at the

intersection of fish stock management need and marine service).

The top scoring items are climate for ozone layer and UV assessment, land for basic

mapping: risk assessment, marine for weather forecast, atmosphere for weather

forecast, fishing pressure, land for infrastructure status assessment, sea ice

monitoring, agriculture (hydric stress), Marine for Air Quality and Atmospheric

Composition, Atmosphere for Marine Operations Safety, natural habitat & protected

species monitoring, sea ice melting emissions, and ice extent/thickness monitoring.

The latter, a use case at the intersection of climate service and marine operations

safety, was consolidated with sea ice monitoring for a single use-case related to high-

latitude, ice-aware safe navigation and climate science. Table 6 lists this cases scored

by triad weights score. The chosen ones are shown in bold.

Table 6. Top use cases ranking

Use Case name Service-need intersection Score (triad weights)

Marine for Weather Forecast Marine for Weather Forecast 1

sea ice monitoring Marine for Marine Operations Safety

0.9916

Sea ice extent/Thickness (reconciled with sea ice monitoring)

Climate for Marine Operations Safety 0.8263

fishing pressure, stock assessment Marine for Fish Stock Management 0.774

Land for Infrastructure Status Assessment


0.7556

agriculture (hydric stress) Land for Agriculture, Rural Development and Food Security

0.7535

Land for Basic Maps Land for Basic Maps 0.6842

Sea Ice melting emissions Climate for Emissions and Surface Fluxes Assessment

0.6739

Atmosphere for Weather Forecast Atmosphere for Weather Forecast 0.6667

Climate for Ozone Layer & UV Climate for Ozone Layer & UV 0.6666

Atmosphere for Marine Operations Safety

Atmosphere for Marine Operations Safety

0.6611

Marine for Air Quality and Atmospheric Composition

Marine for Air Quality and Atmospheric Composition

0.6608

natural habitat monitoring, protected species monitoring

Climate for Biodiversity Assessment

0.652

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It is worth mentioning here, that these use-cases correlate with the results of a paper

by Zell et al. [9]. Two high-scoring potential use cases, Atmosphere for Marine

Operations Safety and Marine for Air Quality and Atmospheric Composition, have not

been considered for further analysis to avoid over-representing the marine service and

since they are partially covered by 3 other selected use-cases related to weather

forecast and ice monitoring. Instead, the climate for biodiversity assessment use case

has been added –called natural habitat and protected species monitoring- to add this

perspective to the assessment.

The scores presented above correspond to the triad weighting scheme introduced in

the approach section, more details on the sensitivity of the results to the user weight

scheme are discussed in the next section. The subsequent sections analyse the

service technical maturity part of the OUCIS score, and conclude with a score

breakdown analysis of the top-scoring cases.

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5.1 Weight System Sensitivity Analysis

Figure 6 to Figure 15 show the OUCIS score results of the use-cases highlighted in the

previous section.

0.93870.9764 1

0.6792

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Equal Weights Priority Weights Triad Weights Global ChallengesWeights

Marine for Weather Forecast

Figure 6. Sensitivity of Marine for Weather Forecast to Weighting Scheme Type

1 1 0.9916

0.6981

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


Sea Ice Monitoring: Extent, Thickness

Figure 7. Sensitivity of Sea Ice Monitoring to Weighting Scheme Type

0.7256 0.70110.7556

0.5346

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1



Figure 8. Sensitivity of Land for infrastructure status assessment to Weighting Scheme Type

0.79580.7382

0.774

0.8775

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


Fishing Pressure and Fish Stock Assessment

Figure 9. Sensitivity of Marine for Fish Stock Management to Weighting Scheme Type

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0.8842

0.7541 0.7535

1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


Agriculture and Forestry: Hydric Stress

Figure 10. Sensitivity of Agriculture and Forestry to the Weighting Scheme Type

0.6258 0.6509 0.6667

0.4528

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


Atmosphere for Weather Forecast

Figure 11. Sensitivity of Atmosphere for Weather Forecast to Weighting Scheme Type

0.74810.7146

0.6739

0.7702

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


Sea Ice Melting Emissions

Figure 12. Sensitivity Sea Ice Melting emissions to Weighting Scheme Type

0.6972 0.70480.6666

0.7783

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


Climate for Ozone Layer & UV

Figure 13. Sensitivity of Climate for Ozone Layer & UV to Weighting Scheme Type

0.74840.6905 0.6842

0.6478

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


Land for Basic Mapping: Risk Assessment

Figure 14. Sensitivity of Land Basic mapping: Risk Assessment Sensitivity to Weighting Scheme Type

0.8842

0.6743 0.652

0.9906

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


Natural Habitat and Protected Species Monitoring

Figure 15. Sensitivity of Natural Habitat and Protected Species Monitoring to Weighting Scheme

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As portrayed above, the change on the results across weight systems is in general not

dramatic. While there are obviously changes in magnitudes, the ordinality of the items

is mostly preserved. Items like climate for ozone layer & UV (Figure 13) present higher

scores in the global challenges weight system as they are connected to users like

universities, NGOs, humanitarian relief and research institutes. Conversely, marine for

weather forecast (Figure 6) and Sea ice monitoring (Figure 7) experience a strong

decrease when using the global challenges weight system. These items are connected

to state, industry and security users, which attain high weights in the triad and priority

weight systems instead.

Fishing pressure and stock management (Figure 9), and agriculture (Figure 10) always

score above 0.7 in all weight systems, and marine for weather forecast (Figure 6) is the

top item in priority and triads weight systems. High scores are achieved due a

connection to highly relevant users and to a technically underperforming service. A

detailed discussion on the services technical maturity across products can be found in

the following section. The breakdown and interpretation of the scores here can be

found in the radar plots section. Besides the items presented here, there are another

48 potential use cases in the database, their sensitivities not analysed here for brevity.

5.2 Service Technical Maturity Breakdown by Products

This section presents detailed information about the technical maturity of the services.

The final normalized scores, calculated according to the scoring method presented on

the approach section, are presented on Figure 16. Services represent a portfolio of

products and their score is an aggregation of the product scores. As mentioned above,

the products are scored depending on four categories: how timely the product can be

delivered to the user, represented by “Product Access Score”, how wide is the

horizontal coverage of the product represented by “Product Horizontal Coverage

Score”, how frequently is it updated represented by “Product Update Frequency Score”

and lastly how accurate is the information provided by the product represented by the

“Product Accuracy Score”.

1

0.83

0.67 0.67

0.580.5

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

CopernicusMarineServices

CopernicusClimateServices

CopernicusAtmosphere

Services

CopernicusLand Services

CopernicusSecurityService

CopernicusEmergency

Service

Normalized Service Score

Figure 16. Normalized Scores of Copernicus Services

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0.250

0.000

0.188

0.4380.375

1.000

0.250 0.250

0.375

0.000

0.563

0.313

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000

Distribution ofAccess Scores

Distribution ofHorizontal

Coverage Scores

Distribution ofUpdate Frequency

Scores

Distribution ofAccuracy Scores

Copernicus Atmosphere Services

Better Than Required Equal to Required Worse Than Required

Figure 17. Distribution of Product Gaps for the Copernicus Atmosphere Services

0.1200.040

0.3200.280

0.320

0.880

0.3200.280

0.560

0.080

0.3600.440

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000



Coverage Scores


Scores


Copernicus Climate Services


Figure 18. Distribution of Product Gaps for the Copernicus Climate Services

0.111

0.222 0.185

0.815

0.037 0.074

0.852

0.037

0.889

0.741

0.000

0.185

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000



Coverage Scores


Scores


Copernicus Emergency Services


Figure 19. Distribution of Product Gaps for the Copernicus Emergency Services

0.0310.094

0.219

0.4690.406

0.563 0.594

0.406

0.563

0.344

0.1880.125

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000



Coverage Scores


Scores


Copernicus Land Services


Figure 20. Distribution of Product Gaps for the Copernicus Land Services

0.179

0.000

0.143

0.2500.250

1.000

0.321

0.107

0.571

0.000

0.536

0.643

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000



Coverage Scores


Scores


Copernicus Marine Services


Figure 21. Distribution of Product Gaps for the Copernicus Marine Services

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Figure 16 shows that the Copernicus marine services are the most promising ones to

be complemented by ONION, followed by climate, and portrays the emergency as the

most mature one. Note that Copernicus climate services are still not operational and

user requirements not solidly grounded, but their product portfolio is based upon

atmospheric and ocean products which are available in other services therefore their

performance can be analysed. The drawback of this estimate for climate is that the

product performance requirements in the DB are worst-case (based on atmospheric

and ocean users, which might have more stringent access and frequency

requirements) hence the climate services scores might be higher than should.

To analyse the overall service scores and describe the main driving parameters,

histograms of product performance distribution for those 4 categories were created.

The histograms of all the 5 services analysed are presented in Figure 17 to Figure 21.

The green bars represent the fraction of products performing better than required on

the corresponding attribute, the orange bars show the fraction of products performing

equally well as the requirement, and the red bars show the fraction of products

performing worse than what is required from them. For example, in Figure 17, for the

atmosphere services, 0.25 fraction of the products (25%) perform better than required

on the “Product Access” attribute.

As it might be seen from the figures below, one of the attributes that the ONION can

enhance in Copernicus is the access time, meaning that users would benefit of a

quicker pace of product delivery. This is an aggregate effect of both limited temporal

coverage of the space component and of the data processing capacities on the ground.

For example, the least performing service in the category “Product Access” is the

emergency service (Figure 19), as some products inside the portfolio require human

analyst time [10]. The accuracy of Copernicus services, especially for land and the

emergency, exceeds user expectations.

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5.3 Score Breakdown: Radar Plots

This section details on the main parameters affecting the OUCIS score of use cases.

Radar plots of the high-ranking use cases are introduced below. The chosen

parameters are as follows:

● Need Score, as defined in the section 3.

● Fraction of users related to the need, that is, the number of users related to the

need divided by the total number of users in the DB. This complements the

need score.

● Service Score, as defined in section 3.

● Fraction of Products in Upper 25% of Score Values. This accounted as the

fraction of the relevant products that score above the 75% of the maximum

score of all products within the service portfolio.

● Fraction of Products in Upper 25% of Score Values. Same but below 25%. This

number accompanied with the previous one give hints about the products’ gap

distribution, as the previous one accounts for the top worst performing ones and

this one accounts for the top best performing products.

The above-mentioned parameter choices were driven by the methodology itself. One of

the cornerstones of the scoring scheme is the “Need Score”. Along with the number of

users related to that need it gives hints also about the importance of the users related

to the need. The next parameter of the radar plots is the “Service Score”, the next

building block of the OUCIS. To understand the rationale behind a particular service

score, we accompanied it with two more parameters, one showing the normalized

number of products that perform the worst (biggest gap) and the other showing the

normalized number of products performing the best (smallest gap) within the given

service portfolio. All the five parameters together give an overall picture about the

OUCIS.

Figure 22 to Figure 31 illustrate the score breakdown of the use cases selected. The

figures are grouped by services. Therefore, the parameters related to the services have

the same values. For example, in Figure 23 - Figure 25 the use cases of marine

service are presented and they all share the same values except from the need score

and normalized number of users that are service independent. Each of the radar plots

contain 4 data points for the need score corresponding to all the 4 types of weighting

schemes discussed above. All other parameter values are independent from the

weighting scheme hence they remain constant.

The formula of OUCIS has two main components, a need related one and a service

related one. Therefore, the use-cases can score high when both the service and the

need score are high and also when those scores complement one another. The highest

scoring service included in the analyses is marine service. As it can be seen in Figure

23 to Figure 25, more than half of all the products within the Marine product portfolio

are underperforming and only a reduced subset meet expectations. This justifies the

high score of the Marine service.

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0.708

0.67

0.188

0

0.22

0.8630.882

0.453

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

Need Score

Service Score

Fraction of Products inUpper 25% of Score

Values

Fraction of Products inLower 25% of Score

Values

Fraction of Users

Atmosphere for Weather ForecastEqual User Weights Priority User Weights Triad User Weights Global Challenges Weights

Figure 22. Score breakdown of use-case Atmosphere for Weather Forecast

0.7078

1

0.5

0.036

0.222

0.8631

0.8823

0.4528

0

0.2

0.4

0.6

0.8

1

Need Score

Service Score


Values


Values

Fraction of Users

Marine for Weather ForecastEqual User Weights Priority User Weights Triad User Weights Global Challenges Weights

Figure 23. Score breakdown for Marine for Weather Forecast use case

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0.754

0.5000

0.0360

0.2381

0.8840.875

0.465

1.0000

0.000

0.200

0.400

0.600

0.800

1.000

Need Score

Service Score


Values


Values

Fraction of Users

Sea Ice Monitoring: Extent, ThicknessEqual User Weights Priority User Weights Triad User Weights Global Challenges Weights

Figure 24. Score breakdown for sea ice monitoring use case.

0.600

0.8300

0.400

0.040

0.19

0.6530.683

0.585

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

Need Score

Service Score


Values


Values

Fraction of Users

Fishing Pressure and Fish Stock AssessmentEqual User Weights Priority User Weights Triad User Weights Global Challenges Weights

Figure 25. Score breakdown of fishing pressure and fish stock assessment use case.

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0.800

0.83

0.4

0.04

0.29

0.715

0.690

0.793

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

Need Score

Service Score


Values


Values

Fraction of Users

Natural Habitat and Protected Species MonitoringEqual User Weights Priority User Weights Triad User Weights Global Challenges Weights

Figure 26. Score breakdown for Natural Habitat and Protected Species Monitoring

0.821

0.6700

0.1560.062

0.27

0.930

1.000

0.535

0.000

0.200

0.400

0.600

0.800

1.000

Need Score

Service Score


Values


Values

Fraction of Users

Land for Infrastructure Status Assessment Equal User Weights Priority User Weights Triad User Weights Global Challenges Weights

Figure 27. Score breakdown for land for infrastructure status assessment use case.

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0.846

0.67

0.1560.062

0.27

0.916

0.906

0.648

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000

Need Score

Service Score


Values


Values

Fraction of Users

Land for Basic Mapping: Risk AssessmentEqual User Weights Priority User Weights Triad User Weights Global Challenges Weights

Figure 28. Score breakdown of Land for basic mapping: Risk assessment use case.

1.000

0.67

0.1560.062

0.38

1.0000.997

1.000

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000

Need Score

Service Score


Values


Values

Fraction of Users

Agriculture and Forestry: Hydric StressEqual User Weights Priority User Weights Triad User Weights Global Challenges Weights

Figure 29. Score breakdown for agriculture and forestry: hydric stress use case.

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0.631

0.83

0.4

0.04

0.22

0.748

0.706

0.623

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

Need Score

Service Score


Values


Values

Fraction of Users

Climate for Ozone Layer & UVEqual User Weights Priority User Weights Triad User Weights Global Challenges Weights

Figure 30. Score breakdown for climate for ozone layer and UV use case.

0.677

0.83

0.4

0.04

0.24

0.758

0.714

0.616

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

Need Score

Service Score


Values


Values

Fraction of Users

Sea Ice Melting EmissionsEqual User Weights Priority User Weights Triad User Weights Global Challenges Weights

Figure 31. Score breakdown for sea ice melting emissions.

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The need scores are an effect of both the users associated to a need, and their score

and weight. For instance, in the case of agriculture (Figure 29), the need score is very

high, regardless the weighting scheme, as it has the biggest user base, 0.45 (or 45%)

of the overall of user base. So, this need score is not sensitive to the weighting scheme

since it has plenty of users and the user weight importance gets diluted. On the other

hand, the marine for weather forecast (Figure 23) use case has a smaller number of

users (22% of users) but the users have high weights in 3 of the weighting schemes,

hence the score is in the upper half.

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6 SECOND PHASE: TECHNOLOGY ASSESSMENT

In the first phase of the approach, we have analysed in terms of users, needs and

broad technological maturity a set of 58 use cases. From those, we have downselected

10 use-cases to be studied in more depth. Those are climate for ozone layer and UV

assessment, land for basic mapping: risk assessment, marine for weather forecast,

atmosphere for weather forecast, fishing pressure, land for infrastructure status

assessment, sea ice monitoring, agriculture, sea ice melting emissions and natural

habitat and protected species monitoring.

This section performs a technological assessment by means of mapping

measurements to the list of use cases. Then, each measurement is assessed in terms

of best available resolution in the 2016-2039 horizon, a specific user requirement for

that measurement [11], the current revisit time achievable, and the measurement

continuity gaps. The best resolution and the best revisit time are prohibitive. Both

cannot be achieved simultaneously. The missions considered in the assessment are

listed in the Table 22.

This technology information on the use cases interesting to ONION will inform the

system requirements activities of WP2.3. Based on this information, ONION

architectures can be designed to bridge the continuity, revisit and/or performance gaps

of the EO infrastructure. The results are organized in the use-case tables that follow

(Table 7 – Table 16). Each use case table summarizes the measurements necessary

to support the use case.

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Table 7. Agriculture & Forestry: Hydric Stress user case table

User Case Agriculture & Forestry: Hydric Stress

Need and Copernicus Service Related

Land for Agriculture, Rural Development and Food Security, Copernicus climate service

Use case description

Methods enabling precision agriculture, efficient irrigation, fires prevention and forest protection, and impacts on hydrological basin support agronomic research and production, assessment of population food security and sovereignty, and environmental impact evaluation. Main objectives: (i) Develop new applications based on high-detail soil moisture data; (ii) Further review the information requirements of resource managers and policy-makers dealing with water-energy-food issues; (iii) Assure that this information is readily available to practitioners and policy makers.

List of related users Agronomic Research Entities ; DG AGRI; DG DEV; DG ECHO/DG RELEX; DG ENV; Donor Governments; EEA; Farms ; General Public; Health Organizations; Industry; International Humanitarian Relief Organizations (red cross); Irrigation Associations ; National Environment Agencies; NGOs (for humanitarian aid); Providers of Location-Based Services; Research Organizations; UN Food: WFP, FAO; UN Stats: UNSD; Universities; World Meteorological Organization

List of related measurements (CEOS)

Land surface topography Land surface imagery

Soil moisture at the surface

Soil moisture in the roots region

Land surface temperature

Chlorophyll Fluorescence from Vegetation on Land

Leaf Area Index (LAI)

Elicited needs 250 m horizontal

resolution, 5 years revisit time, 1 m accuracy

N/A 50 km horizontal

resolution, 24 h revisit time, 0.01 m3/m3

N/A 1 km horizontal

resolution, 1 h revisit time, 1 K accuracy

100 m horizontal resolution, 2 h revisit

time

0.25 km horizontal resolution, 24 h

revisit time, 20 % accuracy

State of the art (EU) Spatial resolution [m]

0.41 horizontal 0.01 vertical

0.25 0.8 N/A 10 10500 5.3

State of the art revisit time [min]

13.1 5.8 17.9 N/A 14.5 750.8 27.1

Continuity Gaps None None None N/A None None None

No missions available after

1-Jan-33 1-Jan-38 1-Jan-38 N/A 1-Dec-39 1-Jan-23 1-Jan-38


Normalized Differential Vegetation Index (NDVI)

Soil type Vegetation Canopy

(cover) Vegetation Canopy

(height) Vegetation type

Fractionally absorbed PAR (FPAR)

Photosynthetically Active Radiation

(PAR)

Elicited needs 2 km horizontal

resolution, 24 h revisit time, 5 % accuracy

2 km horizontal resolution, 24 h revisit

time, 5 % accuracy 0.07/classes accuracy requirement not available

10 m horizontal resolution, 7 days

revisit time, 0.02/classes accuracy

0.25 km horizontal resolution, 24 h revisit

time, 5 % accuracy

requirement not available


0.41 0.8 1 1 0.41 5.3 5.3


13.8 58.5 N/A 750.8 7.4 29.5 104.2

Continuity Gaps None None None 1-apr-2016 To 1-dec-

2016 244 days None None None


1-Dec-39 1-Jan-38 1-Jan-27 1-Jan-27 1-Dec-39 1-Jan-33

1-Jan-29

http://database.eohandbook.com/measurements/instruments.aspx?measurementTypeWMOID=183

























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Table 8. Marine for Weather Forecast user case table

Use case Marine for Weather Forecast

Need and

Copernicus

Service Related

Weather forecast, Copernicus marine service

Use case

description

This use-case is covering marine weather related measurements such as wind parameters, wave parameters, etc. This information is of paramount importance to a wide variety of activities, from

tourism to fishing, oil and gas exploration and exploitation.

List of related

users

Aviation; General Public; Industry; Maritime Transport Industry; National Coast guards; National Environment Agencies; Oil and Gas; OSPAR, national coastal and marine monitoring agencies; Port

Managers; Providers of Location-Based Services; Tourism Operators ; Transport & Logistics ; Weather prediction centers; World Meteorological Organization

List of related

measurements

(CEOS)

Sea Surface

Temperature

Ocean

Dynamic

topography

Ocean

Surface

Currents

Sea Level

Wind speed

over sea

surface

(horizontal)

Wind

stress

Dominant wave

direction

Dominant

Wave Period

Sea State

Wavelength

Significant

wave height

Wave directional

energy

frequency

spectrum

Elicited needs

10 km

horizontal

resolution, 24

h revisit time,

0.1 K accuracy

25 km

horizontal

resolution, 24

h revisit time,

1 cm accuracy

25 km

horizontal

resolution, 24

h revisit time

25 km

horizontal

resolution, 1

week revisit

time, 1 cm

accuracy

10 km

horizontal

resolution, 3 h

revisit time,

0.5 m/s

accuracy

N/A

15 km horizontal

resolution, 1 h

revisit time, 10

degrees accuracy

15 km

horizontal

resolution, 1 h

revisit time,

0.25 s

accuracy

N/A

25 km

horizontal

resolution, 3

h revisit time,

0.1 m

accuracy

50 km horizontal

resolution, 6 h

revisit time


10 1 horizontal 0.01 vertical

1 300 horizontal 0.01 vertical

3 horizontal 0.3 vertical

N/A 9 9 N/A 3 horizontal 0.3 vertical

18000


12.6 83.3 104.2 104.2 23.2 N/A 320.7 320.7 N/A 69.0 N/A

Continuity Gaps None

1-Jan-27 to 1-

Jan-30 1096

days

None None None N/A None None N/A None

No missions

available until

1-Jan-18


1-Dec-39 1-Jan-33 1-Jan-27 1-Jan-30 1-Jan-38 N/A 1-Jan-26 1-Jan-26 N/A 1-Jan-30 1-Jan-21




























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Table 9. Sea Ice Monitoring: Extent/Thickness user case table

Use case Sea Ice Monitoring: Extent/Thickness


Marine Operations Safety, Copernicus marine service, Copernicus climate service & Polar regions


This use-case covers a wide range of measurements, that are of high relevance to marine operations and polar regions follow-up. Main objectives: (i) to provide real-time sea-ice data to ensure navigation safety in polar shipping routes; (ii) Improve the precision of ice thickness measurements; (iii) Provide EO information on arctic sea-ice that allows for improved understanding of climate change; (iv) Use satellite elevation data to determine the ice thickness near the grounding line; (v) Increase operational monitoring capability of polar regions as they significantly influence global climate.

List of related users

Civil Protection Agencies; Maritime Transport Industry; National Coast guards; National Environment Agencies; National Fishery Agencies; National Geographic agencies; National Marine Research Institutes; NGOs (for humanitarian aid); Oil/Gas/Mining Companies ; Port Managers; Providers of Location-Based Services; Tourism Operators ; Transport & Logistics ; Weather prediction centers; World Meteorological Organization


Ocean Imagery and

water leaving

radiance

Ocean Surface Currents

Sea Surface Temperature

Sea Ice Sheet

Topography

Iceberg fractional

cover

Iceberg height

Sea-ice concentration

Sea-ice cover

Sea-ice drift

Sea-ice surface

temperature

Sea-ice thickness

Sea-ice type

Elicited needs

4 km horizontal resolution, 24 h revisit time, 5 % accuracy

25 km

horizontal

resolution,

24 h revisit

time

10 km horizontal

resolution, 24 h revisit time,

0.1 K accuracy

10 m horizontal

resolution, 1 year revisit time, 10 cm

accuracy

30 m horizontal resolution,

1 year revisit time,

5 % accuracy

30 m horizontal

resolution, 1 year revisit time, 10 cm

N/A


N/A

5 km horizontal

resolution, 3 h revisit

time, 0.5 K accuracy


time, 0.1 cm accuracy

10 km

horizontal

resolution, 3

h revisit

time,

0.25/classes

accuracy


1 1

10 0.7

horizontal 1 vertical

1 15 N/A

0.41 N/A

15 3 horizontal

1 vertical 0.8

horizontal


83.3 104.2

12.6 0.0 10.9 38.9 N/A

0.0 N/A

14.9 0.0 0.0

Continuity Gaps

0.0 None

None None None None N/A

None N/A

None None None


1-Oct-38 1-Jan-27

1-Dec-39 1-Jan-33 1-Jan-33 1-Jan-33 N/A

1-Dec-39 N/A

1-Dec-39 1-Jan-33 1-Jan-38




























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Table 10. Fishing Pressure & Fish Stock Assessment user case table

Use case Fishing Pressure & Fish Stock Assessment


Marine Copernicus service

Use case description Accurate information of the health and evolution of fish stock on all of world’s fishing areas is fundamental for the fishing industry, analysis and forecasting of fish stocks, the related food security and sovereignty institutions, supports the analysis of biodiversity and environmental agencies, and informs government policies. Main objectives: (i) to improve understanding of fish stock resilience and vulnerability to natural and anthropogenic factors (e.g. climatic versus over-fishing effects); (ii) surveillance and control of marine resources for enhanced fisheries protection; (iii) Improve coupling of dynamical models with satellite-based and in situ observations; (iv) Support the integration of satellite-based data in the practices of monitoring and

policy enforcement centers.

List of related users World Meteorological Organization; Decision Makers / Governments; General Public; Health Organizations; Industry; National Fishery Agencies; National Marine Research Institutes; UN Bio: UNCBD, GBIF, UNCCD; UN Environment: UNEP, WCMC, UNFCC; UN Food: WFP, FAO; UN Stats: UNSD; Wildlife Preservation Organizations (WWF, etc.)


Sea surface temperature Ocean chlorophyll concentration

Ocean imagery and water leaving radiance

Color dissolved organic matter (CDOM)

Elicited needs 10 km horizontal resolution, 24 h revisit time, 0.1 K accuracy

1 km horizontal resolution, 24 h revisit time, 0.05 mg/m3 accuracy


100 km horizontal resolution, 24 h revisit time, 5/m accuracy

State of the art (EU) Spatial resolution [m] 10 15 1 200

State of the art revisit time [min] 12.6 69.0 0.0 195.0

Continuity Gaps None None None None

No missions available after 1-Dec-39 1-Jan-38 1-Oct-38 1-Jan-29






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Table 11. Land for Infrastructure Status Assessment user case table

Use case Land for Infrastructure Status Assessment


Copernicus land service

Use case description Assessing the state, location and amount of infrastructures such as ports, airports, roads, and railways assists the public and private entities developing and managing them, and supports international cooperation and policy making. Moreover, live updates and evaluation of infrastructure supports population’s safety and security.

List of related users Aviation; Civil Protection Agencies; Decision Makers / Governments; Defense, intelligence and security ; DG DEV; Industry; Infrastructure Management Entities ; Insurance Companies ; EU Mayors-Adapt program; Monument Preservation Entities; NGOs (for humanitarian aid); Oil/Gas/Mining Companies ; Port Managers; Providers of Location-Based Services; Road Management ; UN Habitat: UNHABITAT


Land surface topography Land surface imagery Surface Coherent Change Detection

Vegetation Cover Vegetation type

Elicited needs 250 m horizontal resolution, 5 years revisit time, 1 m accuracy

N/A NA 10-30 m horizontal resolution, 5 years revisit time, 0.05/classes

accuracy

10 m horizontal resolution, 7 days revisit time, 0.02/classes accuracy



0.25 3 0.45 0.41


13.1 5.8 750.7 27 7.4

Continuity Gaps None None None None None


1-Jan-33 1-Jan-38 1-Jan-38 1-Jan-36 1-Dec-39






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Table 12. Land for Mapping: Risk Assessment user case table

Use case Land for Mapping: Risk Assessment


Copernicus land service, Copernicus climate service

Use case description To support that decision making and planning, the use-case provides a bunch of measurements to make the analysis possible. Climate change is infiltrated many areas of environmental research, including ecology and forest management. The monitoring at different scales can quickly detect changes in e.g. the forests status and health. Remote sensing can facilitate the early detection and mapping of disasters (e.g. desertification) threatening the ecosystems, and is particularly useful in remote areas with a gap of systematic surveys. As an example, mountain forests account for one third of the total forest area in the EU and are essential to the natural landscape as they help in soil protection and regulating water supply.

List of related users Civil Protection Agencies; DG DEV; DG ECHO/DG RELEX; Donor Governments; EEA; General Public; Industry; Infrastructure Management Entities ; International Humanitarian Relief Organizations (red cross); Monument Preservation Entities; NGOs (for humanitarian aid); Poverty Alleviation Entities; Providers of Location-Based Services; Research Organizations; Road Management ; UN Habitat: UNHABITAT; UN Humanitarian: UNHCR, UNICEF, UNDP, UNESCO; UN Stats: UNSD; Universities

List of related measurements (CEOS) Land surface

topography

Land surface imagery

Surface Coherent Change Detection

Downwelling (Incoming) short-wave radiation at the Earth surface

Downwelling (Incoming) long-wave radiation

at the Earth surface




Vegetation Cover

Vegetation type

Elicited needs 250 m

horizontal resolution, 5 years revisit

time, 1 m accuracy

N/A N/A

100 km horizontal

resolution, 3 h revisit time, 1

W/m2 accuracy

100 km horizontal

resolution, 3 h revisit time, 1

W/m2 accuracy

50 km horizontal resolution, 24 h revisit time, 0.01

m3/m3


m3/m3

0.25 km horizontal

resolution, 24 h revisit time,

20 % accuracy

10-30 m horizontal

resolution, 5 years revisit

time, 0.05/classes

accuracy

10 m horizontal

resolution, 7 days revisit

time, 0.02/classes

accuracy



0.25 3 275 500 horizontal

250 vertical 0.8 0.8 5.3 0.45 0.41

State of the art revisit time [min] 13.1 5.8 750.7 44.5 39.6 17.9 17.9 27.1 27 7.4

Continuity Gaps None None None None None None None None None None

No missions available after 1-Jan-33 1-Jan-38 1-Jan-38 1-Oct-38 1-Dec-39 1-Jan-38 1-Jan-38 1-Jan-38 1-Jan-36 1-Dec-39


























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Table 13. Sea Ice Melting Emissions user case table

Use case Sea Ice Melting Emissions


Copernicus marine service, Copernicus climate service & Polar regions


Melting Arctic sea ice accelerates methane emissions. Changes in the Arctic Ocean can affect ecosystems located far away on land. Bright sea ice reflects most sunlight, while open water absorbs most sunlight. Less sea ice, therefore, leads to more absorbed heat, and higher temperatures throughout the North Pole region. This stimulates the production of methane by microorganisms in permafrost soils, which also drives the change towards a warmer climate. Main objectives: (i) Provide EO information on arctic sea-ice that allows for improved understanding of climate change; (ii) Increase operational monitoring capability of polar regions as they significantly influence global climate.

List of related users National Environment Agencies; National Geographic agencies; National Marine Research Institutes; Oil/Gas/Mining Companies ; Weather prediction centers; World Meteorological Organization.


Ocean Surface Currents

Sea Surface Temperature

Sea Ice Sheet Topography

Glacier cover

Atmospheric Chemistry - CH4 (column / profile)

Glacier area Glacier motion

Elicited needs

25 km horizontal

resolution, 24 h

revisit time

10 km horizontal


time, 0.1 K accuracy

10 m horizontal

resolution, 1 year revisit

time, 10 cm accuracy

30 m horizontal

resolution, 1 year revisit

time, 5 % accuracy

5-10 km horizontal resolution, 5

km vertical resolution, 4 h revisit

time, 10 ppb accuracy

N/A requirement not

available


1 10 0.7 horizontal

1 vertical 0.41

2300 horizontal

100 vertical N/A 0.8

State of the art revisit

time [min] 104.2 12.6 0.0 0 0 N/A 0

Continuity Gaps None None None None None N/A None


1-Jan-27 1-Dec-39 1-Jan-33 1-Jan-33 1-Dec-37 N/A 1-Jan-27


Iceberg height Sea-ice concentration Sea-ice cover Sea-ice surface

temperature Sea-ice thickness Sea-ice type Permafrost

Elicited needs

30 m horizontal resolution, 1

year revisit time, 10 cm

12 km horizontal


time, 5 % accuracy


time, 5 % accuracy

5 km horizontal resolution, 3 h revisit time, 0.5 K accuracy

100 km horizontal resolution, 24 h revisit time, 0.1 cm accuracy

10 km horizontal


time, 0.25/classes

accuracy

0.25 km horizontal resolution, 24 h revisit time, 5

accuracy


15 N/A

0.41 15 3 horizontal

1 vertical 0.8 horizontal 3


38.9 N/A

0.0 14.9 0.0 0.0 0

Continuity Gaps None N/A

None None None None None


1-Jan-33 N/A

1-Dec-39 1-Dec-39 1-Jan-33 1-Jan-38 1-Jan-33


















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Table 14. Climate for Ozone Layer and UV Assessment use case table

Use case Climate for Ozone Layer and UV Assessment


Atmosphere service, Climate Change service

Use case description Assessing the Ozone current concentrations and the historical data supports a wide range of climate and meteorological models. Timely and reliable long-term information for assessment, monitoring and verification purposes; Satellite-based transboundary predictions of future conditions – in both the short- and long term

List of related users DG AGRI; DG ENV; EEA; General Public; Health Organizations; National Environment Agencies; National Meteorological Offices; NGOs (environmental); Providers of Location-Based Services; Research Organizations; UN Environment: UNEP, WCMC, UNFCC; Universities; Weather prediction centers; World Meteorological Organization


Ozone profile


Short-wave cloud reflectance

Short-wave Earth surface bi-directional

reflectance

Solar spectral irradiance

Upwelling (Outgoing) short-wave radiation at the Earth surface

Upwelling (Outgoing) short-wave radiation

at TOA

Upwelling (Outgoing)

spectral radiance at TOA

Elicited needs

20-50 km horizontal resolution (1-5 km vertical

resolution), 4 h revisit time, 10 % accuracy

100 km horizontal resolution, 3 h revisit time, 1

W/m2 accuracy



N/A N/A


time, 1 W/m2 accuracy

Requirement not available


250 horizontal 1000 vertical

275 275 10 N/A N/A

275 10


16.9 44.5 320.7 23.2 N/A N/A

58.5 750.8

Continuity Gaps none None None None N/A N/A

None None


1-Dec-39 1-Oct-38 1-Oct-19 1-Dec-39 N/A N/A

1-Jan-38 01-Jan-27
























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Table 15. Natural Habitat and Protected Species Monitoring use case table

Use case Natural Habitat and Protected Species Monitoring


Biodiversity Assessment, Copernicus climate service


Climate change is a critical topic today, as we live to see how the world around us changes. Those changes heavily affect both fauna and flora. This use-case takes care of measurements that analyse the state of biodiversity and its change, for a long-term perspective of the evolution of ecosystems and habitats.


Agronomic Research Entities ; DG DEV; DG ENV; EC JRC; EEA; Farms ; Industry; Irrigation Associations ; National Environment Agencies; National Fishery Agencies; National Marine Research Institutes; Research Organizations; UN Bio: UNCBD, GBIF, UNCCD; UN Environment: UNEP, WCMC, UNFCC; UN Stats: UNSD; Universities; Wildlife Preservation Organizations (WWF, etc); World Meteorological Organization



Downwelling (Incoming) long-wave radiation at the Earth surface

Atmospheric Chemistry - CH4 (column/profile)

Ozone profile

Fractionally absorbed PAR

(FPAR)

Photosynthetically Active Radiation

(PAR)

Land surface imagery Vegetation Cover Vegetation type

Elicited needs


W/m2 accuracy


W/m2 accuracy

5-10 km horizontal resolution, 5 km

vertical resolution, 4 h revisit time, 10

ppb accuracy

20-50 km horizontal

resolution (1-5 km vertical resolution), 4 h revisit time, 10

% accuracy

0.25 km horizontal resolution, 24 h revisit time, 5 %

accuracy

N/A N/A

10-30 m horizontal resolution, 5 years

revisit time, 0.05/classes

accuracy

10 m horizontal resolution, 7 days

revisit time, 0.02/classes

accuracy


275 500 horizontal

250 vertical 2300 horitzonal

100 vertical 250 horizontal 1000 vertical

5.3 5.3 0.25 0.45 0.41


44.5 39.6 0 16.9 29.5 104.2 5.8 27 7.4

Continuity Gaps None None None none None None None None None


1-Oct-38 1-Dec-39

1-Dec-37 1-Dec-39 1-Jan-33 1-Jan-29 1-Jan-38 1-Jan-36 1-Dec-39


Surface Coherent Change Detection


Soil moisture in the roots

region


Permafrost


Normalized Differential Vegetation Index (NDVI)

Vegetation Canopy (cover)

Vegetation Canopy (height)

Elicited needs N/A


m3/m3

N/A


m3/m3

0.25 km horizontal resolution, 24 h revisit time, 5

accuracy

0.25 km horizontal resolution, 24 h


2 km horizontal resolution, 24 h revisit time, 5 %

accuracy

0.07/classes accuracy

requirement not available


3 0.8 N/A

0.8 3 5.3 0.41 1 1


750.7 17.9 N/A

17.9 0 27.1 13.8 N/A 750.8

Continuity Gaps None None N/A

None None None None None 1-apr-2016 To 1-dec-

2016 244 days


1-Jan-38 1-Jan-38 N/A

1-Jan-38 1-Jan-33 1-Jan-38 1-Dec-39 1-Jan-27 1-Jan-27








































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Table 16. Atmosphere for Weather Forecast use case table

Use case Atmosphere for Weather Forecast


Weather forecast, Atmosphere service for Air quality and atmospheric monitoring


Climate and meteorological models support the assessment of air quality and pollution monitoring. Main objectives: (i) Continuous large-scale monitoring with advanced satellite-based systems; (ii) Satellite-based transboundary predictions of future conditions in both the short- and long term; (iii) Timely and reliable long-term information for assessment, monitoring and verification purposes


Aviation; Decision Makers / Governments; DG ENV; EEA; Health Organizations; National Environment Agencies; National Meteorological Offices; NGOs (environmental); Providers of Location-Based Services; Research Organizations; UN Environment: UNEP, WCMC, UNFCC; Universities; World Meteorological Organization; Research Organizations; UN Environment: UNEP, WCMC, UNFCC; Universities; World Meteorological Organization


Wind speed over sea surface (horizontal)

Wind stress

Wind vector over sea surface (horizontal)

Volcanic ash

Aerosol single scattering

albedo

Aerosol optical depth

(column/profile)

Aerosol layer height

Aerosol Extinction / Backscatter

(column/profile)

Aerosol absorption optical depth

(column/profile)

Atmospheric specific humidity (column/profile)

Elicited needs

10 km horizontal resolution, 3 h revisit time, 0.5 m/s accuracy

N/A

10 km horizontal resolution, 3 h revisit time, 0.5 m/s accuracy

0.5 km horizontal

resolution, 1-2 h revisit time,

N/A


accuracy

N/A

100-200 km horizontal (1 km vertical

resolution) resolution, 1 week revisit time, 10%

accuracy


accuracy

25 km horizontal resolution, 4 h



3 horizontal 0.3 vertical

N/A 1 30 N/A 30 horizontal

30 vertical N/A 66 horizontal

30 vertical 30 horizontal


0.3 vertical


23.2 N/A 137.0 50.7 N/A 21.6 N/A

35.6 21.6 7.7

Continuity Gaps

None N/A None None N/A None N/A

None None None


1-Jan-38 N/A 1-Jan-33 1-Dec-39 N/A 1-Oct-2038 N/A

1-Jan-38 1-Oct-38 1-Dec-39




























of 77

Table 17 Atmosphere for Weather Forecast use case table (continued)


Downwelling (Incoming) short-wave

radiation at the Earth surface

Downwelling (Incoming) long-wave radiation

at the Earth surface

Atmospheric pressure (over sea surface)

Atmospheric temperature

(column/profile)

Atmospheric stability index

Precipitation Profile (liquid or

solid)

Cloud optical depth

Cloud liquid water

(column/profile)


Elicited needs


revisit time, 1 W/m2 accuracy


revisit time, 1 W/m2 accuracy

Requirement not

available

25 km horizontal resolution (1 km

vertical

resolution), 4 h revisit time, 0.5 K

accuracy

Requirement not

available


revisit time, 0.1 mm accuracy



50 km horizontal resolution (0.3 km

vertical

resolution), 1 h revisit time, 10 %

accuracy


revisit time, 1 K accuracy


275 500 horizontal



0.3 vertical 40 horizontal




10


44.5 39.6 195.0 12.1 39.6 44.5 58.5 35.6 14.5

Continuity Gaps None None None None None None None None None


1-Oct-38 1-Dec-39 1-Jan-38

1-Jan-38 1-Dec-39 1-Oct-38 1-Dec-39 1-Oct-38 1-Dec-39

On the following, the measurements associated to the use-cases are analysed. Note that the revisit time metric refers to a moderate

latitude location (Island of Malta, 35 deg N) except for the measurements linked to sea ice monitoring and sea ice melting, for which a

high latitude (Greenland seashore, 80 deg N ) has been assumed to compute the revisit time.
























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7 RESULTS DISCUSSION

The previous sections discussed in depth the analysis procedure, and the results of the

different assessment perspectives. This section summarizes and consolidates the

analyses to identify the areas where ONION can complement Copernicus.

Section 5 introduced and discussed in depth the ranking of use cases, and the reasons

for the score of each use case. Detailed breakdowns of the score are found in the radar

plots (Figure 22 to Figure 31). Table 18 summarizes all of scores breakdown. For each

service, we include the Fraction of Products that would benefit from Improvement

(FPBI) under a specific characteristic (horizontal coverage, accuracy, update

frequency, access time).

Table 18. Master summary of the analysis by use cases. FPBI is the fraction of Products that would benefit from an improvement in the corresponding characteristics

Use Case name N

users

Related need score

Related Service score Final Score normalized FPBI

coverage FPBI

accuracy FPBI freq.

FPBI access

Service score

Marine for Weather Forecast

14 0.8823 <10% 60-70% 50-60% 50-60% 1 1

Sea ice monitoring

15 0.8749 <10% 60-70% 50-60% 50-60% 1 0.9916

Fishing pressure, stock

assessment 12 0.6829 <10% 60-70% 50-60% 50-60% 1 0.774

Land for Infrastructure

Status Assessment

17 1 30-40% 10-20% 10-20% 50-60% 0.67 0.7556

Agriculture (hydric stress)

24 0.9972 30-40% 10-20% 10-20% 50-60% 0.67 0.7535

Land for Basic Maps

18 0.9055 30-40% 10-20% 10-20% 50-60% 0.67 0.6842

Sea Ice melting emissions

15 0.7135 <10% 60-70% 50-60% 50-60% 1 0.6739

Atmosphere for Weather Forecast

14 0.8823 <10% 30-40% 50-60% 30-40% 0.67 0.6667

Climate for Ozone Layer &

UV 14 0.7058 <10% 40-50% 30-40% 50-60% 0.83 0.6666

Natural habitat monitoring, protected species

monitoring

18 0.6903 <10% 40-50% 40-40% 50-60% 0.83 0.652

As shown in Table 18, use cases in the marine domain promise new opportunities for

ONION to pursue. In terms of accuracy, update frequency and access time, 50% of the

products in the marine service can obtain benefits from ONION distributed space

architectures. In contrast, the Copernicus land service notably meets current

application requirements. The only technical area of potential improvement for land

services would be the products access time. Basic maps, infrastructure assessment

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and agricultural information needs to be delivered quickly to the users (50-60% if

products would benefit from enhanced timeliness), but does not require large

improvements in the actual frequency of the measurement (10-20%) therefore the

potential areas for improvement reside in the data pipeline. Federated and fractionated

ONION architectures could speed up data delivery via in-orbit relays complementing

the Copernicus Space Segment.

The Copernicus atmospheric services have already dozens of products, and even

though most are on pre-operational phase, they promise adequate performance levels.

Potential areas for the enhancement of this service remain in the update frequency;

Better update frequencies to inform users of atmospheric composition and air quality

would enhance this very relevant EO service. Improving measurement update

frequencies can be supported by ONION architectures.

The climate change service is still not well established in Copernicus; however, it is

mostly a combination of atmospheric and marine products and therefore the same

discussion presented above applies to this case. Additionally, some of the needs are

very pressing, including the evaluation of anthropogenic effects on climate (climate

forcing and surface emission monitoring) and the effects of climate change on

biodiversity. Improving the related atmospheric and marine products to support high

fidelity climate models is necessary. Hence, it would be interesting to increase update

frequencies for both portfolios through ONION.

Finally, emergency services are not directly represented in the use cases selection, but

still deserve a mention through its highest scoring service application, emergency for

thematic mapping, which analyses the temporal and spatial variation of a theme

(mainly infrastructures, urbanization) for disaster prevention and vulnerability

assessment (see Appendix C in the section 10.3 for a description of all DB). This use

case is mature in terms of product accuracy, but as for all emergency applications, any

improvement on delivery time and update frequency will be well received. Whatever

improvements to the data pipeline that could be originated in space would greatly

enhance this service application, as is the case for atmospheric, marine and land.

To cover the specific gaps on the use cases and services discussed above,

improvements on the different technical attributes of the EO infrastructure can be

deployed. We now turn our attention to the technology assessment to evaluate what is

the magnitude of the potential improvements of interest. Table 19 summarizes the

extent of these improvements, with their connections to critical products and use cases.

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Table 19. Master summary table of technical improvement lines for the EO infrastructure

Product

reference

Related

Instrument set

(Table 1.

Acronyms)

Use cases

reference

Horizontal

coverage Accuracy

Update

frequency

Access

time

Landslide

maps,

infrastructure

vulnerability

mapping

HIGHRES,

SAR, IRS,RA

Land for

Infrastructure

Status Assessment

user case table

Land for Mapping:

Risk Assessment

user case table

Agriculture &

Forestry: Hydric

Stress user case

table

From small area

maps (50x50

km2) to 400x400

km2

From 20

days to 9 h

Flood mapping HIGHRES,MOD

RES, SAR

From small area

maps (50x50

km2) to

1500x1500 km2

Improvement of

map scale of

about 10 times

Water

reservoirs,

ground water

mapping

HIGHRES,

MODRES, SAR

Improvement of

map scale of

about 10 times

From 10

days to 3

days

Sea level and

anomalies, sea

oxygen, sea

surface winds

RA,RS,SAR,

MWISC,MODR

ES, Wind stress

assessment has

no

corresponding

instruments

Marine for Weather Forecast user case table, Fishing Pressure & Fish Stock Assessment user case table

From 10 km

horizontal

resolution to 1-2

km

Sea Ice sheet

topography,

thickness

RA, RS, SAR,

MWISC,

MODRES,

HIGHRES

Sea Ice Monitoring: Extent/Thickness user case table, Sea Ice Melting Emissions user case table

From 1 m

vertical

resolution to 10

cm

Ozone profiles

and ozone

profiles

forecast

IRS,SWS,

MODRES,LSS,

MWISCT

Climate for Ozone Layer and UV Assessment use case table

From 20 km to

10 km horizontal

resolution

Sea

chlorophyll

content, bio/

geo chemicals

SWIRS,

HIGHRES,

MODRES

Fishing Pressure & Fish Stock Assessment user case table, Natural Habitat and Protected Species Monitoring use case table

From 24h -48h

to 1h revisit

Sea wave

topography

SAR, RA,

MWISC, Wind

stress

assessment has

no

corresponding

instruments

Marine for Weather Forecast user case table

From 24h -48h

to 1h revisit

From 24h

access to

1h

Soil Moisture,

FAPAR, LAI,

Land surface

albedo

SAR, RA,

MWISC, RS,

SR, MODRES,

HIGHRES

Agriculture & Forestry: Hydric Stress user case table)

From 10 days to

1 day revisit

The Marine services and its applications (sea ice monitoring, weather, sea ice melting)

would benefit from a reduction in access time and revisit, up to 1h. The current

Copernicus infrastructure delivers about 24-48h latency for the corresponding

measurements. Intensive usage of NOAA, JAXA, ESA, CSA, NASA and commercial

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missions together with Copernicus can reduce this latency up to about 320 minutes for

sea wave direction, period, and height measurements as shown in

Table 8. This is still not enough to support near-real time marine operation. This is an

interesting direction for ONION project to pursue. Moreover, the dynamic ocean

topography measurement has a discontinuity in the 2027-2030 period. ONION

distributed mission concepts can support low-cost dedicated platforms for meeting

niche needs like this one inside a bigger network of monolithic missions. Vertical

resolution for ice thickness should also be improved for sea ice monitoring applications,

from current 1 m to 1 cm.

Improved horizontal resolutions (from 20 to 10 km) for atmospheric measurements

would support ozone profiling for the challenging climate applications.

Potential improvements also include reducing product delivery time for ground water

mapping in risk and infrastructure assessments. However, a reduction from the current

10-20 days to 3 days concerns the ground segment and data analysis procedures and

is not directly in the ONION scope.

ONION distributed architectures can also improve responsiveness in a cost-effective

fashion in the event of landslides and floods where the extent of coverage and

accuracy required, during the event, is not achievable by current EO infrastructure.

Needed improvements in land cover are on the order of 1000 km by a 1000 km and for

map scales, 10 times better accuracy is desirable.

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8 CONCLUSIONS

This report introduced a relational database on Earth Observation users, needs,

services, products and the EO infrastructure in order to understand the potential areas

where ONION architectures can complement and enhance Copernicus services.

The information in the database is classified and characterized using several attributes

and a quantitative scoring system. The results have been consolidated in scored list of

need-service correspondences, termed use-cases. The score of each use case, called

Onion Use Case Interest Score (OUCIS) depends on the amount and relevance of

users who expressed that need, and the technical maturity of the service that is

connected to that need. The latter depends on the technical performance of the product

in said service portfolio.

After gathering information from user and technical requirements of Copernicus/GMES,

FP7/H2020 research connected to Copernicus, and business intelligence from the

ONION consortium, the database contains 63 EO users, 37 explicit needs, the 6

Copernicus services, 95 EO products, 92 measurements, 427 instruments, and 312

missions. The scoring exercise revealed 10 use cases most interesting for ONION.

These are climate for ozone layer and UV assessment, land for basic mapping: risk

assessment, marine for weather forecast, atmosphere for weather forecast, fishing

pressure, land for infrastructure status assessment, sea ice monitoring, agriculture

(hydric stress), sea ice melting emissions and natural habitat and protected species

monitoring.

The bilateral analysis of both user needs and specific technology gaps can support

further decision making in the areas and applications to develop. Some of the most

important characteristics that all the Copernicus services can benefit from are higher

update frequency and lower revisit time. Specifically, reductions of both revisit and

product delivery in the marine service from 24-48h to 1h would support marine services

enabling polar navigation, enhanced marine weather forecast and real-time monitoring,

oil and gas exploration and oil spill combat, among others. This quick pace of product

delivery to the users can be achieved by ONION architectures by 1) deploying

distributed, large numbers of small observation nodes, and 2) networking the space

segment through FSS approaches for reduced latency and real-time access to space

data.

Moreover, the related sea ice monitoring needs would be better supported by increases

in vertical resolution from 1m to 1cm. It remains to be assessed if distributed

instrumentation in the ONION frame can provide such an improvement. Atmospheric

sensing (especially for ozone) would benefit from horizontal resolution improvements

from 20 to 10 km.

Improved ground coverage with sustained resolution (about 1000 km by 1000 km) is

desirable for infrastructure assessments, risk mapping and flood situations. ONION

architectures can support this need by distributed synthetic apertures relying on several

cooperating sensing nodes, further enhancing the value of Copernicus.

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The results of the work package were presented to the UAB board during the

Federated and Fractionated Satellite Systems Workshop in Rome, on 11 October

2016. The UAB concluded that the methodology chosen is rather complicated, but

gives reasonable results. The four priority cases to be considered, Marine weather

forecast, Artic sea ice monitoring, agricultural hydrological stress and Fishery pressure

and aquaculture, are well representative of classes of requirements which call for

complex satellite architectures to which the ONION contribution might be beneficial.

The presented report covers the requirements for the WP2.1. The conclusions here

prepare the ONION system requirements exercise for WP2.3.

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9 BIBLIOGRAPHY

[1] Commitee on Earth Observation Satellites, “CEOS MISSION, INSTRUMENTS AND MEASUREMENTS DATABASE ONLINE.” .

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non-Life Insurance Sector,” 2012.

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[26] A. Mangin, “SAFI: Support to Aquaculture and the Fishery Industry,” Dec. 2014.

[27] Ciais, Dolman, Dargaville, Barrie, Bombelli, Butler, Canadell, and Moriyama, “GEO carbon strategy,” GEO secretariat Geneva, Rome, 2010.

[28] COPERNICUS Marine Service, “Catalogue of products,” Jan. 2016.

[29] D. Quintart, “Data Warehouse Requirements - Version 2.0,” May 2014.

[30] ESA and GMES, “GSE Land Information Services,” 2011.

[31] Food and Agriculture Organization of the United Nations and USAID, “Land Cover Mapping and Change Assessment,” Rome, 2005.

[32] Francoise Villete, “Copernicus Emergency Service (overview),” May 2015.

[33] G. Balsamo, B. Raoult, H. Hersbach, and P.Poli, “Copernicus Workshop on climate observation requirements,” Jul. 2015.

[34] G. Campbell, “MarCoast Final Report,” Jan. 2011.

[35] Gil Denis, “RISK-EOS 2: A cornerstone of the GMES Emergency Response Service,” Aug. 2010.

[36] G. Joyanes, “Satellite Data Requirements - Copernicus Security requirements focused on Support to EU external actions,” Brussels, 2013.

[37] J. Dorandeu, R. Santoleri, G. Larnicol, S. Labroue, L.A. Breivik, F. Dinessen, H. Roquet, A. Stoffelen, and L. Crosnier, “Hearing on the satellite data requirements for the COPERNICUS programme: Copernicus Marine requirements,” Brussels, May 2013.

[38] Jean-Noel Thepaut, “Copernicus Climate Change service. Climate Data Store Workshop,” Jul. 2015.

[39] P. Albert, “Marine User Requirements,” May 2014.

[40] Peter Albert et al., “Hearing on the satellite data requirements for the COPERNICUS programme: Copernicus Marine requirements,” Brussels, Mar. 2014.

[41] T. Hausler, S. Gomez, G. Ramminger, and R. Ngamabou, “GMES service element forest monitoring,” 2009.

[42] T.Holzer-Popp, L. Kluser, and F. Schnell, “User Requirements Document v6.3. MACC III: Monitoring atmospheric composition and climate III.,” DLR, 2015.

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[43] “EMS Early Warning. Flood and Fire Alerts,” Copernicus, 2015.

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[45] “Floods. GIO EMS - Mapping,” GMES.

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10 APPENDICES

10.1 Appendix A. Example SQL Querie Implemented in the database

Here we present an SQL query that calculates services’ scores. The query joins the

tables “Services” and “Products” using the relationship table “Service-Product

Mapping”. Using the information from the resulted table, we calculate the average

product scores having related to the service.

SELECT services.id,

services.[service name],

Round(Avg([normalize product score]), 2) AS [Service Score]

FROM services

INNER JOIN (product

INNER JOIN [service-product mapping]

ON product.id =

[service-product mapping].[product id])

ON services.id = [service-product mapping].[service id]

GROUP BY services.id,

services.[service name];

The resulting datasheet is presented in the Figure 32.

Figure 32. DB output of an example SQL query

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10.2 Appendix B. User Weights

The table below shows the weights for all the users in different weighting schemes.

Table 20. Users in the DB and weighting schemes

User name User Source Document Equal Weights

Priority Weights

Triad Weights

Global Challenges

Weights

Agronomic Research Entities

Space-TEC-Agriculture [12]

1 1 2 3

Aviation PROMOTE [13] 1 2 2 1

Civil Protection Agencies TERRAFIRMA [14] 1 2 2 1

Decision Makers / Governments

Phare/Globalland [15] [16]

1 2 3 2

Defense & Intelligence Surrey-EOAPPS [17] 1 2 3 1

DG AGRI Globalland 1 2 2 1

DG CLIMA EU climate action [18] 1 1 2 3

DG DEV Globalland 1 1 2 3

DG ECHO/DG RELEX RESPOND [19] 1 1 1 3

DG ENV Globalland 1 1 2 3

Disaster Alleviation Entities

ESA-EOGG [20] 1 2 3 3

Donor Governments RESPOND 1 2 2 3

EC JRC STP/Globalland 1 1 2 3

EEA Globalland 1 1 2 3

EFAS STP 1 1 2 2

EFFIS STP 1 1 2 2

Farms Space-TEC-Agriculture / ESA-EOGG

1 1 1 2

General Public Phare/Globalland 1 2 3 3

Geotechnical Institutes TERRAFIRMA 1 1 1 2

Health Organizations PROMOTE 1 2 2 3

Industry TERRAFIRMA 1 2 3 1

Infrastructure Management Entities

Surrey-EOAPPS 1 2 3 2

Insurance Companies Space-TEC-Insurance [21] 1 1 2 1

International Humanitarian Relief Organizations (red cross)

RESPOND 1 1 1 3

Irrigation Associations Space-TEC-Agriculture / ESA-EOGG

1 1 1 2

Logistics management agencies

Inquiring survey (input from IHI)

1 1 1 1

Maritime Transport Industry

MYO UAR URD [19] 1 2 3 1

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Mayors-Adapt EU climate action 1 1 1 3

Military and Safety Surrey-EOAPPS 1 2 3 1

Monument Preservation Entities

Surrey-EOAPPS 1 1 2 3

National Coast guards MYO UAR URD 1 1 2 1

National Environment Agencies

TERRAFIRMA/MYO UAR URD [22]

1 2 2 3

National Fishery Agencies MYO UAR URD 1 2 2 2

National Geographic agencies

MYO UAR URD [23] 1 1 1 1

National Marine Research Institutes

MYO UAR URD 1 1 1 2

National Meteorological Offices

PROMOTE/AOPC 1 2 1 2

National Security organization


1 2 3 1

NGOs (environmental aid)

ESA-EOGG 1 1 1 3

NGOs (for humanitarian aid)

RESPOND 1 1 1 3

Oil/Gas/Mining Companies

Space-TEC-OILGAS/ESA-EOGG [24]

1 2 3 1

OSPAR, national coastal and marine monitoring agencies

MYO UAR URD 1 1 1 1

Pan European Federations

TERRAFIRMA 1 1 1 1

Police Forces Various 1 2 3 1

Port Managers Space-TEC-Water [25] 1 1 1 1

Poverty Alleviation Entities

ESA-EOGG 1 1 1 3

Providers of Location-Based Services

Various 1 2 3 2

Renewable Energies' Companies

Space-TEC-Renewables/ ESA-EOGG

1 1 2 3

Research Organizations Globalland 1 2 2 3

Road Management Surrey-EOAPPS 1 2 2 2

Ship building companies Inquiring survey (input from IHI)

1 1 1 1

Shipping operating agencies


1 1 2 1

Tourism Operators https://artes-apps.esa.int/projects/theme/tourism

1 1 2 2

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Transport & Logistics Space-TEC-Water + https://artes-apps.esa.int/projects/theme/transport-logistics

1 2 3 1

UN Bio: UNCBD, GBIF, UNCCD

Globalland 1 1 2 3

UN Environment: UNEP, WCMC, UNFCC

Globalland 1 1 2 3

UN Food: WFP, FAO RESPOND/Globalland 1 1 3 2

UN Habitat: UNHABITAT Globalland 1 1 2 2

UN Humanitarian: UNHCR, UNICEF, UNDP, UNESCO

RESPOND/Globalland 1 1 1 3

UN Stats: UNSD Globalland 1 1 1 1

Universities TERRAFIRMA 1 1 1 3

Weather prediction centers

MYO UAR URD 1 2 3 1

Wildlife Preservation Organizations (WWF, etc.)

WWF site 1 1 2 3

World Meteorological Organization

PROMOTE 1 2 3 2

Other resources include [23], [26][27], [28], [29], [30], [31],[32], [33],[34], [35],[36], [37],

[38], [39], [40], [41], [42], [43], [44], [45].

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10.3 Appendix C. Needs Description Tables.

The table below presents all the needs from the database with their descriptions.

Table 21. Needs in the DB and their description

Need name Need Description

Agriculture, Rural Development and Food Security

Estimates of crop production, water satisfaction index, early warning of harvest shortfalls

Air Quality and Atmospheric Composition

The quality of air that one directly breathes at the surface

Alerting Service Alert of an ongoing crisis

Animal Migration Maps Track for animal migration

Assessment of Renewable Energies' potential

Provide Meteorological (cloud, water vapour) and atmospheric (aerosol, ozone) data; and solar irradiance maps

Basic Maps Base layer information with key geographical features

Biodiversity Assessment Vegetation indices, information on habitat deterioration, evolution of vegetation parameters

Climate Evolution Assess long term climate evolution

Climate Forcing Monitoring human-forced climate change

Climate Policy Development Informing policy development to protect citizens from climate-related hazards such as high-impact weather events

Communication/Reporting resources Context/supporting and justifying operations

Crisis and Damage mapping Updated (24h) geographical information

Emissions and Surface Fluxes Assessment

Anthropogenic emissions, Greenhouse gases

Fish Stock Management Analysis and forecasting of fish stocks

Forest Resources Assessment Deforestation rates, forest intactness

In-field Data collection Locally sampled info

Infrastructure Status Assessment Roads, Railroads, Buildings, Power Lines, Pipelines and others

Inland Water Management Maps Measure quantity, quality (acidity) and track for algae.

Land Degradation and Desertification Assessment

Degradation risk index, degradation hot spots, etc

Maintenance information Estimation of the required ship maintenance date

Marine Operations Safety Oil Spill combat, ship routing, weather forecasting, defense, search and rescue

Mining Focused on information for mining industry

Mitigation and Adaptation Improving planning of mitigation and adaptation practices for key human and societal activities;

Ocean Color Maps Track for algae, bloom, toxicity, "Red Tide" and acidity

Oil and Gas Assessment Focused on information retrieval for oil and gas industry

On time operation Optimized routing and ship speed

Ozone Layer & UV Archive and forecast information on ozone layer and UV

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Ports Monitoring monitoring of ports and facilitate traffic management

Refugee support mapping Snapshot of temporary Settlements and Internally displaced people

Ship positioning mapping Monitoring ship positions and information

Situation Mapping After crisis mapping

Solar Radiation The amount of solar radiation coming to Earth

Thematic Mapping Focused on spatial variation of a theme

Urban and Regional Development Monitoring of settlements, land losses or gain

Water Quality Water quality and pollution both in high seas and coast

Water Resources Erosion risk maps, average water available for watershed

Weather Forecast Climate monitoring, ice seasonal forecast

10.4 Appendix E. Missions Considered in the Analysis

In this section we list all the missions that were considered in the technology

assessment part with their corresponding BOL and EOL.

Table 22. Missions considered in the analysis

Mission Name BOL EOL

3D Winds 1/1/2030 1/1/2033

ACE 1/1/2022 1/1/2023

ADM-Aeolus 12/1/2016 12/1/2020

AISSat-1 7/12/2010 12/1/2019

AISSat-2 7/8/2014 6/1/2017

AISSat-3 4/1/2016 7/1/2019

ALOS-2 5/24/2014 5/1/2019

AMAZONIA-1 12/1/2017 12/1/2020

Aqua 5/4/2002 10/1/2019

ASCENDS 1/1/2022 1/1/2025

Aura 7/15/2004 10/1/2019

BIOMASS 1/1/2020 1/1/2025

CALIPSO 4/28/2006 9/1/2017

CARTOSAT-1 5/5/2005 6/1/2016

CARTOSAT-2 1/10/2007 12/1/2016

CARTOSAT-2A 4/28/2008 4/1/2016

CARTOSAT-2B 7/12/2010 7/1/2016

CARTOSAT-2E 7/1/2017 7/1/2022

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CARTOSAT-3 1/1/2018 1/1/2023

CAS500-1 5/1/2019 5/1/2023

CATS-on-ISS 1/22/2015 1/1/2018

CBERS-4 12/6/2014 12/1/2017

CBERS-4A 1/1/2018 1/1/2021

CDARS 1/1/2020 1/1/2024

CFOSAT 1/1/2018 1/1/2021

CLARREO Pathfinder-on-ISS 1/1/2019 1/1/2020

CloudSat 4/28/2006 9/1/2017

COMS 6/26/2010 3/1/2018

COSMIC-1 FM1 4/14/2006 9/1/2019

COSMIC-1 FM2 4/14/2006 12/1/2018

COSMIC-1 FM4 4/14/2006 12/1/2018

COSMIC-1 FM5 4/14/2006 12/1/2018

COSMIC-1 FM6 4/14/2006 12/1/2018

COSMIC-2A (Equatorial) 1/1/2016 1/1/2021

COSMIC-2B (Polar) 1/1/2018 1/1/2023

COSMO-SkyMed 1 6/8/2007 6/1/2016

COSMO-SkyMed 2 12/9/2007 12/1/2016

COSMO-SkyMed 3 10/25/2008 10/1/2016

COSMO-SkyMed 4 11/6/2010 11/1/2017

CryoSat-2 4/8/2010 2/1/2017

CSG-1 12/1/2016 12/1/2023

CSG-2 12/1/2017 12/1/2024

CYGNSS 10/1/2016 12/1/2018

DESIS-on-ISS 1/1/2016 1/1/2019

Diademe 1&2 2/15/1967 12/1/2050

DMSP F-14 4/4/1997 12/1/2016

DMSP F-15 12/12/1999 5/1/2016

DMSP F-16 10/18/2003 10/1/2016

DMSP F-17 11/4/2006 12/1/2015

DMSP F-18 10/18/2009 12/1/2015

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DMSP F-19 4/3/2014 3/1/2019

DMSP F-20 1/1/2020 1/1/2025

DSCOVR 2/11/2015 1/1/2020

EarthCARE 8/1/2018 8/1/2021

ECOSTRESS-on-ISS 1/1/2017 1/1/2018

EnMAP 1/1/2018 1/1/2023

ePOP on CASSIOPE 9/29/2013 5/1/2017

EPS-SG-a 1/1/2021 1/1/2028

EPS-SG-b 1/1/2022 1/1/2030

GACM 1/1/2030 1/1/2033

GCOM-C 12/1/2016 12/1/2021

GCOM-C2 1/1/2020 1/1/2025

GCOM-C3 1/1/2024 1/1/2029

GCOM-W 5/18/2012 5/1/2017

GCOM-W2 1/1/2017 1/1/2022

GCOM-W3 1/1/2020 1/1/2025

GEDI-on-ISS 1/1/2018 1/1/2019

GEO-CAPE 1/1/2023 1/1/2026

GEO-KOMPSAT-2A 5/1/2018 5/1/2028

GEO-KOMPSAT-2B 11/1/2018 4/1/2025

GISAT 12/1/2017 12/1/2026

GOES-13 5/24/2006 6/1/2021

GOES-14 6/27/2009 6/1/2024

GOES-15 3/4/2010 6/1/2025

GOES-R 3/1/2016 9/1/2025

GOES-S 6/1/2017 10/1/2028

GOES-T 4/1/2019 7/1/2033

GOES-U 10/1/2024 10/1/2038

GOSAT 1/23/2009 3/1/2018

GOSAT-2 1/1/2018 1/1/2023

GPM Core 2/27/2014 5/1/2017

GRACE 3/17/2002 9/1/2017

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GRACE FO 8/1/2017 11/1/2022

GRACE-II 1/1/2030 1/1/2033

Himawari-8 10/7/2014 12/1/2029

Himawari-9 1/1/2016 1/1/2031

HRWS SAR 1/1/2022 1/1/2028

HyspIRI 1/1/2020 1/1/2023

ICESat-II 10/1/2017 12/1/2020

Ingenio 1/1/2018 1/1/2025

INSAT-3A 4/10/2003 11/1/2015

INSAT-3D 7/26/2013 7/1/2020

INSAT-3DR 7/1/2016 7/1/2023

Jason-3 1/1/2016 1/1/2019

JPSS-1 1/1/2017 3/1/2024

JPSS-2 7/1/2021 7/1/2028

JPSS-3 1/1/2026 1/1/2034

JPSS-4 1/1/2031 1/1/2038

KALPANA-1 9/12/2002 12/1/2016

KOMPSAT-2 7/27/2006 12/1/2015

KOMPSAT-3 5/18/2012 5/1/2016

KOMPSAT-3A 3/26/2015 3/1/2019

KOMPSAT-5 8/22/2013 8/1/2017

KOMPSAT-6 6/1/2019 6/1/2024

LAGEOS-1 5/4/1976 5/1/2052

LAGEOS-2 10/22/1992 10/1/2052

Landsat 7 4/15/1999 1/1/2021

Landsat 8 2/11/2013 5/1/2023

Landsat 9 1/1/2023 1/1/2033

LARES 2/13/2012 2/1/2052

LIS-on-ISS 2/1/2016 2/1/2018

LIST 1/1/2030 1/1/2033

MEGHA-TROPIQUES 10/12/2011 12/1/2016

MERLIN 1/1/2019 1/1/2022

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Meteosat-10 7/5/2012 1/1/2022

Meteosat-11 7/15/2015 7/1/2025

Meteosat-7 9/2/1997 3/1/2017

Meteosat-8 8/28/2002 1/1/2019

Meteosat-9 12/22/2005 1/1/2021

Metop-A 10/19/2006 8/1/2018

Metop-B 9/17/2012 9/1/2017

Metop-C 10/1/2018 10/1/2023

MTG-I1 (imaging) 6/1/2019 12/1/2027

MTG-I2 (imaging) 6/1/2023 12/1/2031

MTG-I3 (imaging) 12/1/2026 7/1/2034

MTG-I4 (imaging) 6/1/2031 12/1/2039

MTG-S1 (sounding) 7/1/2021 12/1/2029

MTG-S2 (sounding) 7/1/2029 12/1/2037

MTSAT-1R 2/26/2005 12/1/2015

MTSAT-2 2/18/2006 1/1/2017

NISAR 1/1/2020 1/1/2025

NMP EO-1 11/23/2000 9/1/2016

NOAA-15 5/1/1998 12/1/2015

NOAA-18 5/20/2005 12/1/2015

NOAA-19 2/4/2009 12/1/2015

NORSAT-1 3/1/2016 8/1/2019

NORSAT-2 12/1/2016 6/1/2019

OCEANSAT-2 9/24/2009 9/1/2016

OCEANSAT-3 1/1/2018 1/1/2023

OCO-2 7/2/2014 10/1/2016

OCO-3-on-ISS 1/1/2016 1/1/2020

Odin 2/20/2001 12/1/2016

Oersted 11/21/1999 12/1/2015

OSTM (Jason-2) 6/20/2008 10/1/2017

PACE 1/1/2020 1/1/2024

PATH 1/1/2030 1/1/2033

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PAZ 12/1/2015 12/1/2020

PCW-1 1/1/2021 1/1/2036

PCW-2 1/1/2021 1/1/2036

Pleiades 1A 12/17/2011 12/1/2016

Pleiades 1B 12/2/2012 12/1/2017

PRISMA 12/1/2017 12/1/2022

PROBA 10/22/2001 12/1/2015

PROBA-V 5/7/2013 5/1/2016

QuikSCAT 6/19/1999 10/1/2015

RADARSAT C-1 7/1/2018 11/1/2025

RADARSAT C-2 7/1/2018 11/1/2025

RADARSAT C-3 7/1/2018 11/1/2025

RADARSAT-2 12/14/2007 4/1/2019

RapidEye 8/29/2008 8/1/2019

RapidScat-on-ISS 9/20/2014 9/1/2016

RASAT 8/17/2011 8/1/2016

RESOURCESAT-2 4/20/2011 4/1/2016

RESOURCESAT-2A 7/1/2016 7/1/2021

RESOURCESAT-3 1/1/2019 1/1/2023

RISAT-1 4/26/2012 4/1/2017

RISAT-1A 1/1/2019 1/1/2023

RISAT-2 4/20/2009 4/1/2016

SAC-E/SABIA_MAR-A 1/1/2018 1/1/2023

SAC-E/SABIA_MAR-B 1/1/2019 1/1/2024

SAGE-III-on-ISS 2/1/2016 5/1/2017

SAOCOM 1A 12/1/2016 12/1/2021

SAOCOM 1B 12/1/2017 12/1/2022

SAOCOM-2A 1/1/2021 1/1/2025

SAOCOM-2B 1/1/2022 1/1/2027

SARAL 2/25/2012 12/1/2018

SARE-2A (S1) 1/1/2019 1/1/2024

SARE-2A (S2) 1/1/2019 1/1/2024

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SARE-2A (S3) 1/1/2020 1/1/2025

SARE-2A (S4) 1/1/2020 1/1/2025

SCATSAT-1 1/1/2016 1/1/2020

SCD-1 2/9/1993 12/1/2015

SCD-2 10/22/1998 12/1/2015

SCISAT-1 8/12/2003 3/1/2018

SCLP 1/1/2030 1/1/2033

Sentinel-1 A 4/3/2014 1/1/2021

Sentinel-1 B 2/1/2016 4/1/2023

Sentinel-1 C 1/1/2019 1/1/2026

Sentinel-2 A 6/23/2015 7/1/2022

Sentinel-2 B 7/1/2016 5/1/2023

Sentinel-2 C 1/1/2020 1/1/2027

Sentinel-3 A 12/1/2015 12/1/2022

Sentinel-3 B 5/1/2017 1/1/2024

Sentinel-3 C 1/1/2020 1/1/2027

Sentinel-4 A 1/1/2021 1/1/2029

Sentinel-4 B 1/1/2029 1/1/2037

Sentinel-5 precursor 4/1/2016 12/1/2020

Sentinel-5A 1/1/2021 1/1/2028

Sentinel-5B 1/1/2022 1/1/2030

Sentinel-6 A 1/1/2020 1/1/2025

Sentinel-6 B 1/1/2025 1/1/2030

SMAP 1/31/2015 6/1/2018

SMOS 11/2/2009 2/1/2017

SORCE 1/25/2003 10/1/2019

STARLETTE 2/6/1975 12/1/2050

STELLA 9/30/1993 12/1/2050

STSAT-3 11/22/2013 11/1/2016

Suomi NPP 10/28/2011 9/1/2020

Swarm 11/22/2013 11/1/2016

SWOT 1/1/2020 1/1/2024

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TanDEM-X 6/21/2010 12/1/2019

TCTE 11/19/2013 12/1/2017

TEMPO 12/1/2021 12/1/2023

Terra 12/18/1999 10/1/2019

TerraSAR-X 6/15/2007 12/1/2019

TSIS-on-ISS 1/1/2017 1/1/2023

TSX-NG 1/1/2018 1/1/2025

UK-DMC2 7/29/2009 7/1/2016

VENUS 12/1/2016 12/1/2019

Deimos-2 6/19/2014 12/1/2024

DubaiSAT-2 11/21/2013 12/1/2018

Geoeye-1 9/6/2008 12/1/2016

IKONOS 9/24/1999 12/1/2016

SPOT-6 9/9/2012 12/1/2022

SPOT-7 6/30/2014 12/1/2024

TH-1A 8/24/2010 12/1/2016

TH-1B 5/6/2012 12/1/2016

TH-1C 10/26/2015 12/1/2018

Worldview-1 9/18/2007 12/1/2016

Worldview-2 10/1/2009 12/1/2016

Worldview-3 8/13/2014 12/1/2021

Worldview-4 9/15/2016 12/1/2023

Deimos-1 7/29/2009 12/1/2019

DMC-2 7/30/2009 12/1/2016

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10.5 Appendix D. Scored use cases.

Table 23. List of all scored use-cases applications (triad weight system). Items in red are analysed further in the results section due their high scores.

Copernicus Services

DB Needs Emergency Land Marine Atmosphere Security/maritime

surveillance Climate change

Fish stock management

Fishing pressure

[0.77]

0.65

Crisis and Damage Mapping 0.51

Forest Resources assessment

0.37

0.46

Agriculture, rural development and food security

Agriculture (hydric stress)

[0.75]

Biodiversity Assessment

0.52

Natural habitat and protected species monitoring [0.65]

Land Degradation and desertification assessment

0.46

0.58

Emissions and surface fluxes assessment

0.54

Sea ice melting emissions [0.67]

Water Quality assessment (sea)

0.50

Marine Operations Safety

Sea ice monitoring

[0.99] 0.66 0.58

0.83(consolidated with sea ice monitoring)

Climate Evolution

0.39

Thematic Mapping 0.42 0.56

Oil and gas assessment

0.06

Air Quality and atmospheric composition

0.66 0.44

Assessment of renewable energie's potential

0.38 0.57 0.38

0.47

Climate forcing

0.37

0.46

Infrastructure Assessment 0.57

Land for infrastructure

status assessment

[0.76]

Weather forecast

Marine for weather forecast

[1]

Atmosphere for weather

forecast [0.67]

Ports Monitoring 0.21

0.42

0.35

Animal migration maps

0.14 0.21

Ocean color maps

0.50

Inland Water Management Maps

0.26

Mitigation and Adaptation to climate change

0.44

Climate Policy Development

0.33

Solar Radiation

0.48

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Ship Maintenance information

0.03

Ship On time operation

0.04

Ship positioning mapping

0.30

Urban and Regional Development

0.53

Water resources (land)

0.49

Mining

0.10

In-field Data collection

Communication/Reporting resources

0.22

Refugee support mapping 0.33

Situation Mapping 0.23

Basic Maps 0.51

Land for mapping: risk assessment

[0.68]

Ozone layer & UV assessment

0.53

Climate for ozone layer and UV

assessment [0.67]

Alerting service 0.28

0.37

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END OF DOCUMENT

stakeholders needs: user needs analysis and earth observation ... · infrastructure state of the...

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