## 3 Results on EO Policies and Decision-Making Structures
### Essential Climate Variables
Sea Surface Temperature is studied at resolutions between 1 and 100 km [37]. Ocean colour is the radiance from the ocean normalised by the illuminating irradiance. It provides information on ocean albedo, seawater constituents like Chlorophyll-a, and phytoplankton pigments. Ocean colour remote sensing (OCRS) products assess ecosystem health, productivity, marine resource management, the global carbon cycle, and quantify climate impacts. Currently, the resolutions for Ocean colour have been defined at 4 km for both water leaving radiance and Chlorophyll-a concentrations [38; 39]. However, ocean colour provision as an ECV is provided under ESA's Climate Change Initiative through the Sentinel-3 OCLI sensor at a resolution of 300 m. As discussed in the literature reviewed, there is a consistent gap in resolutions < 10 m, which is useful for inland water detection of Chlorophyll-a and other water quality parameters [2; 3; 15; 24; 25; 40; 41]. This need gives rise to the term \"lake colour\", which is not consistently used in the literature and as seen below could provide more maturity (in terms of definition of measurable parameters) to the \"Lakes\" ECV. Sea ice is a key climate indicator in polar regions, impacting albedo, energy budget, and air-sea exchanges. Parameters like concentration, area, extent, motion, and thickness define its state, influencing water masses and freezing/melting processes. The decreasing sea ice surface extension in the Arctic has a significant impact on the Earth's albedo and contributes to the Arctic amplification of temperature [42]. Furthermore, the weaker surface temperature gradient between the Arctic and regions to the south affect the polar jet stream patterns and the weather in mid-latitudes [43]. Lakes variable provides information on changes at the lake level and area, required monthly for climate assessment purposes. Approximately 95% of the volume of water held globally in approximately 4,000,000 lakes is contained in the world's 80 largest lakes. Lakes ECV includes the following: Lake Water Level, Lake Water Extent, Lake Surface Water temperature, Lake Ice Cover and Thickness, and Lake Surface Reflectance. Although lake colour, i.e., the estimation of Chlorophyll-a in lakes, is foreseen as part of this ECV there is currently no operational worldwide product providing these data. Nevertheless, there have been efforts to provide such estimates in local scenarios based on high-resolution data, such as from the Landsat family [25]. We make the case in this paper that privately ownedsatellite data could be used to feed this ECV, providing a clearer definition of variables such as Chlorophyll-a estimations and other water quality parameters monitoring, which is not currently done. EO data, when combined with the definition of these ECV variables, are extremely valuable to support the sustainable development goals (SDGs). For lakes, SDGs 3, 6, 11, 14 and 16 are applicable and dependent on EO data [44]. The production of satellite-based timeseries of some of these ECVs has been crucial in many environmental studies and the discussion of higher temporal and spatial resolution, a constant subject in specialised scientific meeting [45]. In the following pages, we will analyse the resolution bottlenecks that, if surmounted, could provide higher resolution data to finally implement operational products such as the lake colour.