## 6 Quantitative Method to Identify the Potential Technologies to Cover the Future Copernicus Gaps
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:  
\[W_{j}=\frac{\frac{1}{n}\sum_{i}^{n}\text{{Numerical}}_{score_{i}}}{\sum_{j}^{m} \frac{1}{n}\sum_{i}^{n}\text{{Numerical}}_{score_{i}}}, \tag{1}\]  
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:  
* Latency is referred the time to be processed the data to obtain the product. * Swath is related to the ability of the instrument in order to cover an area, a wide swath indicates minor revisit time. * Spatial resolution is evaluated for the reference instruments according to the user requirements for each measurement. * Accuracy is a component of the data quality; it is evaluated according to it being closed to the user requirements for each reference instrument. * Payload mass is evaluated for each reference instrument, giving priority to the instruments that are best suited to smaller platforms. * Payload power is related to the power consumption of the payload; it also brings priority to the instruments that are best suited to smaller platforms. * Data relevance is the potential of the sensor to provide the measure based on sensing constraints (e.g., long time to analyze the data, data limited by cloud cover, and daylight only)  
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:  
\[Instrument_{score-by-measurement}=\sum_{j}^{m}(\frac{Numerical_{score}}{3}*W_{j}), \tag{2}\]  
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; \(Numerical_{score}\) is assigned to each instrument by measurement (0, 1, 2 or 3); and \(W_{k}\), 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:\[W_{i}=\frac{Numerical_{score_{i}}}{\sum_{i}^{n}Numerical_{score_{i}}}. \tag{3}\] \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Instrument Capabilities**} & \multicolumn{4}{c}{**Numerical Score**} \\ \cline{2-5}  & **0** & **1** & **2** & **3** \\ \hline Latency & N/A & high & medium & low \\ \hline Spatial Resolution & N/A & worse than & minimum & requirement meet \\  & & required & requirement met & or better \\ \hline Swath & N/A & Narrow & Moderate & Wide \\  & & swath \textless{}400 km & swath \textless{}1000 km & swath \textgreater{}1000 \\ \hline Accuracy & N/A & Worse than & Equal to & Requirement meet \\  & & required & requirement & or better \\ \hline Payload mass & N/A & large & mini & nano-micro \\ \hline Payload power & & & & \\ Consumption & N/A & \textgreater{}150 W & 25–150 W & \textless{}25 W \\ \hline Data relevance & N/A & Marginal & High & Primary \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Instrument technologies’ attributes and related numerical scores.