Global demands for food production and increasing environmental concerns have made appropriate soil management an ever-growing priority for land users and policymakers. The implementation of many management techniques, such as precision farming and targeted regulations, relies on detailed and accurate soil information to achieve the intended goals. Classical methods for soil mapping are too costly and labor-intensive to deliver a sufficiently reliable, high-resolution knowledge base for soil management at the local level. 

Our research group therefore seeks to address the growing need for detailed and accurate soil maps by investigating and implementing novel and state-of-the-art technologies. To this end, we combine measured soil properties with satellite imagery, data from ground- and drone-based sensors, and other sources of information, such as legacy data. We utilize statistical methods, including machine learning and geostatistics, to analyze the collected data aiming to deliver new soil maps with higher spatial resolutions and accuracies. We thereby contribute to the successful implementation of sustainable soil management strategies and increase the world’s capacity to meet present and future global challenges.