**6. Quantitative Method to Identify the Potential Technologies to Cover the Future Copernicus Gaps**

In order to identify the potential technologies to cover future gaps over Copernicus infrastructure, a quantitative method has been defined starting from the perspective of the instrument technologies and the variables with gaps. The analysis is centered on the list of the top 10 use cases and 20 variables detected with gaps, and the potential instruments which have been proposed in Table 8. A quantitative method has been applied to rank the technologies suitable to measure the variables with gaps, and identify which technologies cover most of the requirements. The rank order weights used is based on the user requirements, and measurements priorities.

A weighting system for the instrument performance parameter has been implemented. First, it defined the numerical score for each instrument capability based on user requirements (Table 9). Then, these numerical scores are evaluated for each measurement with gaps and each factor. In this way, the numerical score for latency is assigned for measurement that required latency time <1 h; for spatial resolution, a high score is assigned for measurement that required spatial resolution <1 km; for the revisit time, a high score for geophysical variables that required <3 h is assigned; for accuracy, a high score for measurements that require accuracy better than the state of the art is assigned. For payload mass and power consumption, the corresponding score for mini and micro platform is assigned; the measurement relevance was assigned taking the following:

• High relevance measurements: ocean surface currents, wind speed over sea surface, dominant wave direction, and significant wave height measurements.


Then, the weights for each factor (latency, revisit time, spatial resolution, accuracy, payload mass, payload power, and measurement relevance) are derived by the normalization of the average of the numerical score assigned for each measurement:

$$\mathcal{W}\_{\dot{j}} = \frac{\frac{1}{n} \sum\_{i}^{n} \text{Numerical}\_{score\_{i}}}{\sum\_{j}^{m} \frac{1}{n} \sum\_{i}^{n} \text{Numerical}\_{score\_{i}}},\tag{1}$$

where *i* represents each measurement, and *j* represents each factor. In order to identify the potential technologies, new numerical scores are assigned based on the instrument capabilities to measure the variables with gaps and how those meet the user requirements. Instrument attributes are defined in Table 10. The requirements for the geophysical variables are evaluated in terms of seven criteria (factors) or instrument capabilities:


This scoring method assigns a lower score to the technologies that require a large instrument (large mass and high power consumption), and the technologies that present low data quality (low coverage, low spatial resolution, high latency, low accuracy, and low relevance for specific measurement). The score for each instrument is expressed in the following equation:

$$\text{Instrument}\_{\text{score}-by-memcurrent} = \sum\_{j}^{m} (\frac{Numerical\_{\text{score}}}{3} \* \mathcal{W}\_{j}),\tag{2}$$

where *j* represents each technology performances' parameters such as latency, spatial resolution, swath, accuracy, payload mass, payload power consumption, and data relevance for each potential instrument; *Numericalscore* is assigned to each instrument by measurement (0, 1, 2 or 3); and *Wk*, is the weight assigned for each factor obtained of Equation (1) (Table 9, second column).

Four critical use cases were evaluated, such as Marine for Weather forecast, Sea Ice Monitoring, Agriculture and Forestry: Hydric Stress, and Fishing Pressure (Table 11). Subsequently, high, medium, and low priority measurements were defined and its weights were assigned according to the use case to evaluate:

$$\mathcal{W}\_{i} = \frac{\textit{Numerical}\_{score\_{i}}}{\sum\_{i}^{n} \textit{Numerical}\_{score\_{i}}}.\tag{3}$$


**Table 9.** Definition of the numerical score for the criteria and result of the weights.

**Table 10.** Instrument technologies' attributes and related numerical scores.



**Table 11.** The priority level of the measurement according to the use case priority.

Priority level and numerical score: L: Low = 1; M: Medium = 2; H: High = 3.

When the instrument score by measurement is defined, the ranking of the instruments is obtained. The instrument ranking (Table 12) is computed as:

$$\text{Ranking}\_{\text{instrument}} = \sum\_{i}^{n} (\text{instrument}\_{\text{score}-by-\text{measurement}} \* \mathcal{W}\_{i}).\tag{4}$$


**Table 12.** Ranking results for each technology for each use case.

In order to evaluate the robustness of the methodology implemented, a sensitivity analysis at 25% has been performed to estimate the impact of the weights over the ranking of the technologies. Figure 3 shows the same trend in the rank of the technologies by varying randomly 100 times all weights at the same time for each use case prioritized. In this model, the priority level of the measurements and the number of measurements that can measure the sensors are the critical parameters to rank the technologies.

When the priority use case is Marine for Weather Forecast, the key technologies in ranked order are GNSS-R, X- band SAR imager, and Radar Altimeter with SAR processing (Table 12, columns 1 and 2). The sensitivity analysis is summarized in Figure 3a. The simultaneously random weights defined a clear trend in each technology. Columns 1 and 3 of the Table 12 shows the relevant technologies when selecting the Sea Ice Monitoring use case as the priority. They are X-band SAR, GNSS-R, X-, K-, Ka-, W-band MWIm, and Radar Altimeter (SAR). Figure 3b presents a similar tendency in the results when the weights are varying randomly.

The valuable technologies for the Agriculture- Hydric stress use case in ranked order are Multispectral sensors, GNSS-R, Hyperspectral, and L-band MW; the same distribution has been found in the sensitivity analysis (Figure 3c). Figure 3d shows the sensitivity analysis of the technology rank when the Fishing Pressure use case is the priority. The most important technology also is the Multispectral sensor.

In general, the prioritized list of the main technologies to ensure that the gaps are covered taking into account the priority level of different use cases in the time frame 2020–2030 are GNSS-R, imaging X-band SAR, with 1 km of spatial resolution, and Multispectral sensor. GNSS-R provides support to marine and land services of Copernicus and can collaborate with other technologies to improve the measurements. SAR can provide several data from the ocean and can collaborate with the land data. The best ranked optical payload to support multiple services of Copernicus program is a Multispectral sensor with bands in the VIS (442.5, 485, 490, 510, 560, 640, 660, 665 nm), NIR (1610 nm), MWIR (3.7, and 4.05 μm) and TIR (8.55, 11, and 12 μm).
