Challenges facing the agricultural sector, such as climate change, shrinking land and water resources and land degradation, are a major threat to food security. Key in facing these challenges is better management of the existing land and water resources. Accurate and detailed data on crop and water conditions is essential to make this possible. Farmers need reliable data for efficient resource management, monitoring crop developments and understanding market conditions for planting decisions. Existing data solutions do not meet their needs. In developed countries, Smart Farming requires better information than currently available. In many less developed countries even the most basic information is not available, leading to poor agricultural practices and low productivity. eLEAF aims to deliver data that does meet farmer’s needs through its Sensor Integration for Agricultural Management (SIAM) innovation. It is a complete redesign of the way this data is generated, applying a ‘big data’ approach to information collection and processing. The core data stream for SIAM is satellite based optical earth observation data. SIAM will enable information driven innovation, supporting highly needed productivity increases and improvements in resource use efficiency. This will enhance the resilience of Europe’s agricultural sector against climate change and improve its competitiveness on the global market. Ultimately, it is eLEAF’s ambition to increase food security by support agricultural development worldwide. This feasibility study is the first step towards commercialization of SIAM. It covers the technical, commercial and financial viability, resulting in an elaborate business & financial plan.

Droughts are worsening due to changing climate, affecting all reliant on water resources. Since agriculture is by far the largest consumer of freshwater, sustainable production practices are the only way forward to mitigate the most severe impacts on the local environment, which is also acknowledged by the Australian agricultural sector. Scalable and transparent methods to report on the water productivity and sustainability are needed to allow agriculture industries to continuously improve their productive and sustainable water use. However, local reporting initiatives using in-situ data are labour-intensive, costly, and not scalable. Therefore, we aim to inform the Australian agricultural sector on the advances in EO-based water productivity and sustainability reporting. We will accomplish this by integrating state-of-the-art EO-based estimates on irrigation and precipitation and alternatives to locally sourced inputs with advanced hydrological models. Contrasting to most existing EO-based water productivity and sustainability indicators, we aim to include most of the intricacies of irrigation systems and the consequent losses that occur during the application of irrigation, enhancing the reliability of the indicators. The proposed system will not only deliver these indicators for the entire Australian continent but also provide transparency on each of the components used to calculate the indicators. Although we will focus on the entire Australian irrigated agricultural sector, we have a particular interest in the Namoi and Murrumbidgee River valleys where we have already established lasting relationships with local stakeholders. The REINFORM consortium is well experienced in leading and implementing EU funded projects. With 4 research focussed SMEs, a private sector company and a university, we have the scientific depth needed for research and development, as well as the commercial insight and flexibility needed to meet market demand beyond REINFORM.

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.

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.