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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).
Page 2 of 77
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.
Page 3 of 77
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).
Page 4 of 77
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
Page 5 of 77
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
Page 6 of 77
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
Page 7 of 77
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
Page 8 of 77
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
Page 9 of 77
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
Page 10 of 77
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
Page 11 of 77
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.
Page 12 of 77
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.
Page 13 of 77
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
Page 14 of 77
Term Description Status
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
Page 15 of 77
Term Description Status
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”.
Page 16 of 77
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).
Page 17 of 77
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.
Page 18 of 77
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
Page 19 of 77
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.
Page 20 of 77
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.
Page 21 of 77
● 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.
Page 22 of 77
● 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.
Page 23 of 77
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.
Page 24 of 77
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
Page 25 of 77
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.
Page 26 of 77
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
Page 27 of 77
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
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
Page 28 of 77
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.
Page 29 of 77
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
Equal Weights Priority Weights Triad Weights Global ChallengesWeights
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
Equal Weights Priority Weights Triad Weights Global ChallengesWeights
Land for Infrastructure Status Assessment
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
Equal Weights Priority Weights Triad Weights Global ChallengesWeights
Fishing Pressure and Fish Stock Assessment
Figure 9. Sensitivity of Marine for Fish Stock Management to Weighting Scheme Type
Page 30 of 77
0.8842
0.7541 0.7535
1
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
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
Equal Weights Priority Weights Triad Weights Global ChallengesWeights
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
Equal Weights Priority Weights Triad Weights Global ChallengesWeights
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
Equal Weights Priority Weights Triad Weights Global ChallengesWeights
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
Equal Weights Priority Weights Triad Weights Global ChallengesWeights
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
Equal Weights Priority Weights Triad Weights Global ChallengesWeights
Natural Habitat and Protected Species Monitoring
Figure 15. Sensitivity of Natural Habitat and Protected Species Monitoring to Weighting Scheme
Page 31 of 77
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
Page 32 of 77
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
Distribution ofAccess Scores
Distribution ofHorizontal
Coverage Scores
Distribution ofUpdate Frequency
Scores
Distribution ofAccuracy Scores
Copernicus Climate Services
Better Than Required Equal to Required Worse Than Required
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
Distribution ofAccess Scores
Distribution ofHorizontal
Coverage Scores
Distribution ofUpdate Frequency
Scores
Distribution ofAccuracy Scores
Copernicus Emergency Services
Better Than Required Equal to Required Worse Than Required
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
Distribution ofAccess Scores
Distribution ofHorizontal
Coverage Scores
Distribution ofUpdate Frequency
Scores
Distribution ofAccuracy Scores
Copernicus Land Services
Better Than Required Equal to Required Worse Than Required
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
Distribution ofAccess Scores
Distribution ofHorizontal
Coverage Scores
Distribution ofUpdate Frequency
Scores
Distribution ofAccuracy Scores
Copernicus Marine Services
Better Than Required Equal to Required Worse Than Required
Figure 21. Distribution of Product Gaps for the Copernicus Marine Services
Page 33 of 77
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.
Page 34 of 77
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.
Page 35 of 77
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
Fraction of Products inUpper 25% of Score
Values
Fraction of Products inLower 25% of Score
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
Page 36 of 77
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
Fraction of Products inUpper 25% of Score
Values
Fraction of Products inLower 25% of Score
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
Fraction of Products inUpper 25% of Score
Values
Fraction of Products inLower 25% of Score
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.
Page 37 of 77
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
Fraction of Products inUpper 25% of Score
Values
Fraction of Products inLower 25% of Score
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
Fraction of Products inUpper 25% of Score
Values
Fraction of Products inLower 25% of Score
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.
Page 38 of 77
0.846
0.67
0.1560.062
0.27
0.916
0.906
0.648
0.000
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0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
Need Score
Service Score
Fraction of Products inUpper 25% of Score
Values
Fraction of Products inLower 25% of Score
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
Fraction of Products inUpper 25% of Score
Values
Fraction of Products inLower 25% of Score
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.
Page 39 of 77
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
Fraction of Products inUpper 25% of Score
Values
Fraction of Products inLower 25% of Score
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
Fraction of Products inUpper 25% of Score
Values
Fraction of Products inLower 25% of Score
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.
Page 40 of 77
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.
Page 41 of 77
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.
Page 42 of 77
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
List of related measurements (CEOS)
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
State of the art (EU) Spatial resolution [m]
0.41 0.8 1 1 0.41 5.3 5.3
State of the art revisit time [min]
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
No missions available after
1-Dec-39 1-Jan-38 1-Jan-27 1-Jan-27 1-Dec-39 1-Jan-33
1-Jan-29
Page 43 of 77
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
State of the art (EU) Spatial resolution [m]
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
State of the art revisit time [min]
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
No missions available after
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
Page 44 of 77
Table 9. Sea Ice Monitoring: Extent/Thickness user case table
Use case Sea Ice Monitoring: Extent/Thickness
Need and Copernicus Service Related
Marine Operations Safety, Copernicus marine service, Copernicus climate service & Polar regions
Use case description
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
List of related measurements (CEOS)
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
12 km horizontal resolution, 24 h revisit time, 5 % accuracy
N/A
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
resolution, 3
h revisit
time,
0.25/classes
accuracy
State of the art (EU) Spatial resolution [m]
1 1
10 0.7
horizontal 1 vertical
1 15 N/A
0.41 N/A
15 3 horizontal
1 vertical 0.8
horizontal
State of the art revisit time [min]
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
No missions available after
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
Page 45 of 77
Table 10. Fishing Pressure & Fish Stock Assessment user case table
Use case Fishing Pressure & Fish Stock Assessment
Need and Copernicus Service Related
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.)
List of related measurements (CEOS)
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
4 km horizontal resolution, 24 h revisit time, 5 % 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
Page 46 of 77
Table 11. Land for Infrastructure Status Assessment user case table
Use case Land for Infrastructure Status Assessment
Need and Copernicus Service Related
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
List of related measurements (CEOS)
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
State of the art (EU) Spatial resolution [m]
0.41 horizontal 0.01 vertical
0.25 3 0.45 0.41
State of the art revisit time [min]
13.1 5.8 750.7 27 7.4
Continuity Gaps None None None None None
No missions available after
1-Jan-33 1-Jan-38 1-Jan-38 1-Jan-36 1-Dec-39
Page 47 of 77
Table 12. Land for Mapping: Risk Assessment user case table
Use case Land for Mapping: Risk Assessment
Need and Copernicus Service Related
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
Soil moisture at the surface
Land surface temperature
Leaf Area Index (LAI)
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
50 km horizontal resolution, 24 h revisit time, 0.01
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
State of the art (EU) Spatial resolution [m]
0.41 horizontal 0.01 vertical
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
Page 48 of 77
Table 13. Sea Ice Melting Emissions user case table
Use case Sea Ice Melting Emissions
Need and Copernicus Service Related
Copernicus marine service, Copernicus climate service & Polar regions
Use case description
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.
List of related measurements (CEOS)
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
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
5-10 km horizontal resolution, 5
km vertical resolution, 4 h revisit
time, 10 ppb accuracy
N/A requirement not
available
State of the art (EU) Spatial resolution [m]
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
No missions available after
1-Jan-27 1-Dec-39 1-Jan-33 1-Jan-33 1-Dec-37 N/A 1-Jan-27
List of related measurements (CEOS)
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
resolution, 24 h revisit
time, 5 % accuracy
12 km horizontal resolution, 24 h revisit
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
resolution, 3 h revisit
time, 0.25/classes
accuracy
0.25 km horizontal resolution, 24 h revisit time, 5
accuracy
State of the art (EU) Spatial resolution [m]
15 N/A
0.41 15 3 horizontal
1 vertical 0.8 horizontal 3
State of the art revisit time [min]
38.9 N/A
0.0 14.9 0.0 0.0 0
Continuity Gaps None N/A
None None None None None
No missions available after
1-Jan-33 N/A
1-Dec-39 1-Dec-39 1-Jan-33 1-Jan-38 1-Jan-33
Page 49 of 77
Table 14. Climate for Ozone Layer and UV Assessment use case table
Use case Climate for Ozone Layer and UV Assessment
Need and Copernicus Service Related
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
List of related measurements (CEOS)
Ozone profile
Downwelling (Incoming) short-wave radiation at the Earth surface
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
10 km horizontal resolution, 1 h revisit time, 1 % accuracy
25 km horizontal resolution, 3 h revisit time, 5 % accuracy
N/A N/A
100 km horizontal resolution, 3 h revisit
time, 1 W/m2 accuracy
Requirement not available
State of the art (EU) Spatial resolution [m]
250 horizontal 1000 vertical
275 275 10 N/A N/A
275 10
State of the art revisit time [min]
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
No missions available after
1-Dec-39 1-Oct-38 1-Oct-19 1-Dec-39 N/A N/A
1-Jan-38 01-Jan-27
Page 50 of 77
Table 15. Natural Habitat and Protected Species Monitoring use case table
Use case Natural Habitat and Protected Species Monitoring
Need and Copernicus Service Related
Biodiversity Assessment, Copernicus climate service
Use case description
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.
List of related users
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
List of related measurements (CEOS)
Downwelling (Incoming) short-wave radiation at the Earth surface
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
100 km horizontal resolution, 3 h revisit time, 1
W/m2 accuracy
100 km horizontal resolution, 3 h revisit time, 1
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
State of the art (EU) Spatial resolution [m]
275 500 horizontal
250 vertical 2300 horitzonal
100 vertical 250 horizontal 1000 vertical
5.3 5.3 0.25 0.45 0.41
State of the art revisit time [min]
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
No missions available after
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
List of related measurements (CEOS)
Surface Coherent Change Detection
Soil moisture at the surface
Soil moisture in the roots
region
Land surface temperature
Permafrost
Leaf Area Index (LAI)
Normalized Differential Vegetation Index (NDVI)
Vegetation Canopy (cover)
Vegetation Canopy (height)
Elicited needs N/A
50 km horizontal resolution, 24 h revisit time, 0.01
m3/m3
N/A
50 km horizontal resolution, 24 h revisit time, 0.01
m3/m3
0.25 km horizontal resolution, 24 h revisit time, 5
accuracy
0.25 km horizontal resolution, 24 h
revisit time, 20 % accuracy
2 km horizontal resolution, 24 h revisit time, 5 %
accuracy
0.07/classes accuracy
requirement not available
State of the art (EU) Spatial resolution [m]
3 0.8 N/A
0.8 3 5.3 0.41 1 1
State of the art revisit time [min]
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
No missions available after
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
Page 51 of 77
Table 16. Atmosphere for Weather Forecast use case table
Use case Atmosphere for Weather Forecast
Need and Copernicus Service Related
Weather forecast, Atmosphere service for Air quality and atmospheric monitoring
Use case description
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
List of related users
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
List of related measurements (CEOS)
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
1 km horizontal resolution, 24 h revisit time, 0.01
accuracy
N/A
100-200 km horizontal (1 km vertical
resolution) resolution, 1 week revisit time, 10%
accuracy
1 km horizontal resolution, 24 h revisit time, 0.01
accuracy
25 km horizontal resolution, 4 h
revisit time, 2 % accuracy
State of the art (EU) Spatial resolution [m]
3 horizontal 0.3 vertical
N/A 1 30 N/A 30 horizontal
30 vertical N/A 66 horizontal
30 vertical 30 horizontal
30 vertical 250 horizontal
0.3 vertical
State of the art revisit time [min]
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
No missions available after
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
Page 52 of 77
Table 17 Atmosphere for Weather Forecast use case table (continued)
List of related measurements (CEOS)
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)
Land surface temperature
Elicited needs
100 km horizontal resolution, 3 h
revisit time, 1 W/m2 accuracy
100 km horizontal resolution, 3 h
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
25 km horizontal resolution, 3 h
revisit time, 0.1 mm accuracy
50 km horizontal resolution, 3 h
revisit time, 10 % accuracy
50 km horizontal resolution (0.3 km
vertical
resolution), 1 h revisit time, 10 %
accuracy
1 km horizontal resolution, 1 h
revisit time, 1 K accuracy
State of the art (EU) Spatial resolution [m]
275 500 horizontal
250 vertical 300 horizontal
250 vertical 250 horizontal
0.3 vertical 40 horizontal
300 horizontal 125 vertical
66 horizontal 30 vertical
300 horizontal 250 vertical
10
State of the art revisit time [min]
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
No missions available after
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
Page 56 of 77
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.
Page 57 of 77
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.
Page 58 of 77
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.
Page 59 of 77
9 BIBLIOGRAPHY
[1] Commitee on Earth Observation Satellites, “CEOS MISSION, INSTRUMENTS AND MEASUREMENTS DATABASE ONLINE.” .
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[5] Copernicus Space Component Data Access, “Copernicus Contributing Missions.” .
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[7] R. Ramakrishnan and J. Gehrke, Database Management Systems, 2nd ed. Berkeley, CA, USA: Osborne/McGraw-Hill, 2000.
[8] C. J. Date, A guide to the SQL standard: a user’s guide to the standard relational language SQL. Reading, Mass: Addison-Wesley Pub. Co, 1987.
[9] E. Zell, A. K. Huff, A. T. Carpenter, and L. A. Friedl, “A user-driven approach to determining critical earth observation priorities for societal benefit,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 5, no. 6, pp. 1594–1602, 2012.
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[11] “Sentinel Convoy: Synergetic Observation by Missions Flying in Formation with European Operational Missions,” SpaceTec partners, Mar. 2016.
[12] Space-Tec partners, “Assessing the economic Value of Copernicus: The potential of Earth Observation and Copernicus Downstream Services for the Agriculture Sector,” 2012.
[13] ESA-GMES, “PROMOTE final report, Atmosphere,” 2010.
[14] R. Capes and M. Dean, “TERRAFIRMA Final Report,” GMES TERRAFIRMA, 2011.
[15] PHARE, “The European Environment State and outlook,” European Commission Phare Program, 2000.
[16] “GLOB-LAND Service,” GMES Global Land Working Group, 2010.
[17] S. S. T. Ltd, “Applications of Earth Observation,” 2015.
[18] European comission, “Adaptation to climate change,” 2014.
[19] “RESPOND Final Report,” GMES Services Supporting Humanitarian Relief, Disaster Reduction & Reconstruction, 2011.
[20] A. Burzykowska, T. Bondo, and S. Coulson, “Earth Observation for Green Growth,” Frascati, 2013.
[21] Space-Tec partners, “Assessing the economic Value of Copernicus: The potential of Earth Observation and Copernicus Downstream Services for the
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non-Life Insurance Sector,” 2012.
[22] L. Romero, “MyOcean 2: Marine and Coastal Environment User Requirements document,” 2009.
[23] L. Romero, “MyOcean 2: Weather Forecast and Climate impact User Requirements document,” Sep. 2009.
[24] Space-Tec partners, “Assessing the economic Value of Copernicus: The potential of Earth Observation and Copernicus Downstream Services for the Oil and Gas Extraction Sector,” 2012.
[25] Space-Tec partners, “Assessing the economic Value of Copernicus: The potential of Earth Observation and Copernicus Downstream Services for Water Transport Sector,” 2012.
[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.
[44] “EMS Risk & Recovery Mapping Product Portfolio,” Copernicus, 2015.
[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
Inquiring survey (input from IHI)
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
Inquiring survey (input from IHI)
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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Mission Name BOL EOL
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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Mission Name BOL EOL
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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Mission Name BOL EOL
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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Mission Name BOL EOL
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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Mission Name BOL EOL
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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Mission Name BOL EOL
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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Mission Name BOL EOL
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