## 1 Introduction
Another high priority use case with observation gaps (Table 1) is the Agriculture and Forestry: Hydric Stress. The key measurements to cover for this use case are important to study the hydrological cycles, agriculture production, climatology, and meteorology. With the objective to cover these 20 measurements with gaps, we designed a methodology that focuses on the critical technologies to complement Copernicus observation gaps. The methodology applied to select the appropriate sensors and platforms is sketched in Figure 1. First, a survey of the commercial small platform capabilities is presented in terms of mass, payload power, communications, pointing knowledge, and control. Second, the state-of-the-art sensors in terms of mass, power consumption, swath, and data rate is presented. Each sensor or technology is then studied to cover the observation gaps. Based on the survey of the instrument capabilities and data quality, a summary of the existing, and emerging in EO sensors is given, including the scientific and technological limitations in terms of spatial resolution, accuracy, and swath. Within these bounds, the potential instruments are selected according to the available commercial small platforms. The reference instruments are evaluated based on the variables with gaps that can be measured using a scoring method. This scoring method assigns a high score to the sensors that present lower power consumption, lower mass, and high data quality (better accuracy, smaller spatial resolution, and/or wider coverage). Finally, the most relevant instrument technologies compatible with small platforms are identified to complement the existing Copernicus Services for the selected use cases. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Use Case [5]**} & **Copernicus** & **2020-2030** & & \\  & **Services** & **Copernicus** & **Contributing** & **Measurements** \\  & **Related** & **Instrument/Mission [7]** & **Instrument/Mission [8]** & **with Gaps Detected [6]** \\ \hline \hline \multirow{4}{*}{\(1\)} & & & PALSAR-3/ALOS-4 & \\  & & & SAR-2000 S.G/CSG & Wind speed over sea surface (horizontal), \\  & & & SAR-C/Sentinel-1 & SAR/HRWS & Ocean surface currents, \\  & & & SRAL/Sentinel-3 & SAR-X/TSN-NG & Dominant wave direction, \\  & Forecast & & OLCI/Sentinel-3 & SARX/TAZ & Dominant wave period, \\  & & Posidon-4/Sentinel-6 & SWIM/CrosSAT & Significant wave height, \\  & & & ASCAT/MetOp & Atmospheric pressure over sea surface. \\ \hline \hline \multirow{4}{*}{\(2\)} & & & PALSAR-3/ALOS-4 & Sea surface temperature, \\  & & & SAR-2000 S.G/CSG & Sea ice cover, \\  & & & SAR/HRWS & Sea ice type, \\  & & & SAR/HRWS & Sea ice thickness, \\  & & & SAR-X/TSN-NG & Iceberg tracking, \\  & & & SAR-C/Sentinel-1 & SARX/PAZ & Sea ice drift, \\  & & & SLTR, OLCI, SRAL & SWIM/CrosSAT & Sea ice extent, \\  & & & /Sentinel-3 & ASCAT/MetOp & Wind speed over sea surface horizontal, \\  & & & & SCA/MetOp-SG & Ocean surface currents, \\  & & & MSI/Earth-CAE & \\  & & & IASI and AVHRR-3/MetOp & Dominant wave direction, \\  & & & & METimage,IASI-NG/MetOp+SG & Dominant wave period, \\  & & & & Significant wave height. \\ \hline \hline \multirow{4}{*}{\(3\)} & & & SEVERI/MSG & \\  & & & MSI/Earth-CAE & \\  & & & IASI and AVHRR-3/MetOp & Color dissolved organic matter, \\  & & & & METimage,IASI-NG/MetOp & Ocean imagery and water leaving radiance, \\  & & & SAR-C/Sentinel-1 & FCI/MTG-1 & Ocean chlorophyll concentration, \\  & & & & IRS/MTG-S & Monitoring system- vessels.