IEEE Geoscience and Remote Sensing Magazine - September 2017 - 31

SS
0.0

0.2

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FPL

FPU

1.0
FU

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(b)
EW

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GCW

FIGURE 7. A breakdown of the OUCIS score into its main components using radar diagrams. (a) Agriculture and Forestry: Hydric Stress, (b)

Marine for Weather Forecast, and (c) Sea Ice Monitoring: Extent, Thickness.

and Forestry: Hydric Stress in Figure 7(a), the main variable contributing to the result is the NS. Moreover, the figure indicates that this score remains unchanged across all
weighting schemes, because its user base is very large (the
fraction is 0.45, or 45%) and the score is insensitive to the
priorities of users.
DISCUSSION
As shown in Figure 5, use cases connected with the Copernicus marine service, such as Marine for Weather Forecast
and Sea Ice Monitoring: Extent, Thickness, could benefit
significantly from improvement of the products' update
frequency. Currently, more than 50% of the products in
the marine portfolio do not meet user needs. Even though
products in the Copernicus atmosphere service are in
preoperational mode and already deliver value to end
users, approximately 50-60% of the products are underperforming in terms of update frequency. Higher update
frequencies will present new opportunities for new applications to emerge.
Contrary to the use cases related to the marine and atmosphere service, those related to the Copernicus land service currently meet most user needs, except for the access
time attribute. Use cases such as Agriculture and Forestry:
Hydric Stress and Land for Basic Maps: Risk Assessment
would benefit greatly from improvement in the attributed access time. Considering that the update frequency
for land-service-related use cases is adequate and the fact
that the access time is composed of the update frequency
and the latency of the on-ground processing, the high score
is due to rather slow data processing on the ground. This
means that the problem is not connected with the space
component; hence, distributed space architectures cannot
solve this issue.
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As mentioned previously, the Copernicus climate service is not fully operational. However, it is mostly a combination of atmosphere and marine services; hence, the
preceding discussions concerning those cases also apply
here. Apart from this, use cases such as the anthropogenic
effects of air pollution and biodiversity assessment will
benefit greatly from improvements in the update frequency dimension, which can successfully be achieved by the
introduction of DSS.
LIMITATIONS AND FUTURE WORK
The methodology described here has many limitations,
some of which have already been mentioned. First, there
is no measure of product quality due to the nonsimilarities of the data for this attribute. This issue must be addressed in depth in future work. In the "Copernicus User
Value Delivery Chain" section, we defined the elements
of the value chain to be considered when scoring use
cases. As mentioned previously, this analysis considers
only the user layer, as shown in Figure 1. In the "Results" section, we show the product maturity across the
horizontal coverage, update frequency, and access time
parameters. These parameters heavily depend on and
are connected to the space component and, more precisely, to the measurements component (as seen in Figure 1). Hence, future work can tackle the deeper reasons
behind product performance and suggest more precise
solutions for the space component to meet user needs in
a more efficient way.
In the "Dependence of Product Requirement Coverage on Product-Performance Improvement" section,
we showed how product requirements coverage depends on improving the infrastructure for different attributes. We assumed the cost of the improvement to
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Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - September 2017