IEEE Geoscience and Remote Sensing Magazine - September 2017 - 27

TABLE 2. THE COMPLETE LIST OF USERS WITH WEIGHTS. (CONTINUED)
USER

SOURCE DOCUMENT

GCW

UN chapters for food and agriculture issues

[54], [61]

UN Habitat: UNHABITAT

[54]

UN chapters for humanitarian relief and national development

[54], [61]

UN statistical databases/UN Human Settlements Program

[54]

Universities

[46]

Weather prediction centers

[66]

Wildlife preservation organizations

[72]

World meteorological organization

[44]

Logistics management agencies

Inquiring survey

National security organization

Inquiring survey

Ship building companies

Inquiring survey

Shipping operating agencies

Inquiring survey

scores of the ten top-scoring use cases with the TW scheme
are presented in Table 3.
The database contains another 55 scored use cases. Most
of the top ten cases correlate with the results and conclusions from [16]. For example, the use case Agriculture and
Forestry: Hydric Stress correlates with the Soil Moisture and
LAI top-scoring SBAs [16]. The use cases connected with the
emergency service are an exception.
Although in [16] related SBAs are shortlisted, the scoring methodology proposed in this article underrepresents
them. The reason for this is the relative maturity of the
emergency service and the bias in the first three weighting schemes toward big corporations and other powerful
market players with mature market opportunities. Hence,
as shown in the following section, the only top-scoring
use case, from the emergency for thematic mapping service, is also very sensitive to user-weight schemes.
ANALYSIS OF OUCIS SENSITIVITY
TO USER-WEIGHT SCHEMES
This section describes sensitivity plots for all top- scoring
use cases, as presented in Figure 4. As the sensitivity
charts show, the change across different weighting
schemes is not substantial, and all cases maintain
their ordinality. Nevertheless, there is still variation
in some cases, especially among the global challenges
and other schemes.
For example, the case of Climate for Ozone Layer and Ultraviolet (UV) [Figure 4(c)] shows an increase of score when
GCWs are included, as this case is connected to NGOs,
humanitarian relief organizations, and research institutes.
Conversely, the case for Sea Ice Monitoring: Extent, Thickness experiences a drop in importance in terms of GCWs,
as it is closely related to marine operations connected with
state, industry, and security users, who attain top scores for
PWs and TWs.
Also notable are the cases of Fishing Pressure and Fish
Stock Management [Figure 5(d)] and Agriculture and
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IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE

TABLE 3. OUCIS SCORES OF THE TOP-RANKING
USE CASES.
USE CASE NAME

OUCIS (TW)

Marine for Weather Forecast

Sea Ice Monitoring: Extent, Thickness

0.8263

Fishing Pressure, Stock Assessment

0.774

Land for Infrastructure Status Assessment

0.7556

Agriculture and Forestry: Hydric Stress

0.7535

Land for Basic Mapping: Risk Assessment

0.6842

Sea Ice Melting Emissions Assessment

0.6739

Atmosphere for Weather Forecast

0.6667

Climate for Ozone Layer and UV

0.6666

Natural Habitat and Protected Species
Monitoring

0.652

Forestry: Hydric Stress, which always score above 0.7, as well
as the case of Marine for Weather Forecast, which is highest in PWs and TWs. Cases like these are often connected
either with highly relevant users or with underperforming
services. A detailed discussion and breakdown of the OUCIS score are presented in the "OUCIS Breakdown to Main
Elements" section.
SERVICES PERFORMANCE ANALYSIS
Here, we present the results of scoring for the Copernicus service-performance analysis, which is detailed in the "Overall
Use Case Interest Score" section. As the data in the database
were gathered from public reports and documents, the security service was underrepresented and is therefore not considered here.
Figure 5 presents histograms of both the overall service-maturity score and the service-product-maturity
distributions. As can be seen from Figure 5(a), the highest-scoring service is the marine one, meaning it is the
lowest-performing service of them all. The second is the
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Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - September 2017