Our Agricultural Landscape Intelligence System (ALIS) integrates multi-source data—including remotely sensed imagery, environmental data (e.g., weather, topography, soil), and in-situ observations—to provide a comprehensive view of agricultural ecosystems. We interpret these data streams using state-of-the-art machine learning, mechanistic models grounded in ecophysiological processes, and expert knowledge from Earth Observation (EO), ecology, and agriculture.
All data are stored in a machine learning–ready format, enabling near real-time analysis, annual reporting for various regulatory frameworks, and long-term trend assessments related to climate change and land management. This approach enables the analysis of ecophysiological dynamics across countries and continents.
For Switzerland, ALIS maintains a digital twin of the nation’s agricultural ecosystems, enabling continuous monitoring, in-depth analysis, and advanced simulation based on harmonized and continuously updated EO data streams.
For validating our data products, we are very thankful for the collaboration with our partners, e.g. the NABO and MAUS team from Agroscope, the SwissFutureFarm, Versuchstation Luzern, ETH Zürich’s Crop Science Group, HAFL and the agricultural schools of Strickhof, Arenenberg and Grangeneuve.