Shortages of freshwater will be one of the most pressing problems in feeding the world this century. To optimize use of available water it is important to distribute it wisely over the various competing interests, in particular agriculture, which is responsible for 70% of all freshwater use. Irrigation is currently often unsustainable, while groundwater reserves are becoming depleted and many places in the world are suffering water shortages. Action is therefore required now to use space and in-situ monitoring systems, to create a better sense of water availability and optimise use across the planet. WaterSENSE will provide water-availability and mapping services for any place in the world at different time and space resolutions, based on integrated Copernicus data, hydrological models and local data. The results of these services will be open access so as to further develop value-adding services. WaterSENSE itself will deliver the essential value-added service of monitoring compliance of local water use against water rights and regulations (‘water auditing’). The first application will be in the multi-climate Murray-Darling Basin in Australia, followed by validation in South Africa and the Netherlands. Consortium partners already provide water-availability and water-auditing services in the latter two countries. Novel research in the project will develop scalable information services, based on advanced big-data processing algorithms, to determine variables such as evapotranspiration, irrigation water use, rainfall and soil moisture, as well as machine learning to allow automatic data processing and reduce uncertainty in the hydrological variables determined. DIAS services for data provision, as well as cloud hosting and processing of computational services, will be developed and implemented. Existing successful partnership models will be refined to ensure service providers in the water value chain achieve healthy business development.

DINOSAR aims to develop Copernicus based algorithms to support smart farming applications that can be used worldwide, clouds, or no clouds. At the moment, most EO based crop monitoring tools are based on optical satellite inputs. In areas with substantial cloud cover the use of these applications is extremely limited. To be able to introduce more sustainable crop management practices, reliable and continuous time series on crop phenology and health throughout the growing season are needed. This will support farmers to match agricultural inputs (fertilisers, pesticides, water) with what the crop actually needs, decreasing their environmental footprint. DINOSAR will do this by integrating the diagnostic power of optical, infrared and Synthetic Aperture Radar (SAR) signals. With the DINOSAR project we intend to kickstart a revolution in EO-based solutions that tackle challenges in agriculture (under clouds) by making full use of the Copernicus infrastructure. We intend to take the existing methodology a step further by designing a multi-sensor operational monitoring method for a single crop (sugarcane) capable of operating on large data volumes, and then extrapolating this approach to practical field cases and to other crops (and geographies) for which the application of EO-based applications has been underexplored. Rather than looking at optical and SAR based data as two parallel signals, we will focus on integrating the two early on in the processing chain. This has not been done before. Sugarcane in Colombia is our initial test-case, but we will not stop there. DINOSAR will also develop a methodology integrating the combined observations from optical, infrared and SAR EO satellites to monitor other crops in other geographies.

Roughly 30% of Nationally Determined Contributions (NDCs) under the Paris Agreement (PA) include land-based mitigation measures, but there are still significant uncertainties in their effectiveness to deliver negative emissions. Aside from the expected shortfall of all current NDCs to deliver on the below 2 oC ambition, this uncertainty adds to the risks to human wellbeing as a result of climate change. Land-use based mitigation technologies (LMTs) can play a crucial role in the global efforts to meet the PA goals and the Sustainable Development Goals (SDGs). Considering the land-climate-development interface, LANDMARC aims to assess the impacts of LMTs as net sinks for greenhouse gas (GHGs) by applying unique mixed-methods approach. LANDMARC assesses the potential and feasibility of LMTs in the AFOLU sector by: a) quantitatively assessing environmental, social-economic, co-benefits and trade-offs identified through a suite of monitoring tools and model system (including land use, climate and economic models) complemented by; b) qualitative assessments guided by stakeholder engagement. This mixed-method approach allows us to provide more detailed insights on the effectiveness and climate resilience of LMTs at different spatial scales (e.g. scaling up from local/national level to the regional/global level). These tools, services and approaches will contribute to land-based LMT decision support in the private sector and by policy makers. LANDMARC is an interdisciplinary consortium with expertise from ecology, engineering, climate sciences, global carbon cycle, soil sciences, satellite earth observation sciences, agronomy, economics, social sciences, and business. There is a balanced representation of partners from academia, SMEs, and NGOs from the EU, Africa, Asia and the Americas, which ensures a wide coverage of LMTs operating in different contexts (e.g. climates, land-use practices, socio-economic etc.) and spatial scales.