Multisource data analysis at the catchment scale to quantify and map sustainable agricultural management practices

Natural environments face substantial challenges from human activities related to food, feed, and energy production. Unsustainable nutrient management is a key issue, with excess nutrients leaching into the groundwater cycle or escaping intended cropland through other pollution pathways ending up in the atmosphere or in nearby coastal systems. This nutrient loss depletes soil health, contributes to the climate crisis and impacts water quality, especially when combined with intensive farming practices lacking conservation efforts. Innovative mitigation actions, such as the Nature-based Solutions framework, designed to enhance water quality and advance sustainability in agricultural management, require thorough assessment and monitoring to encourage stakeholder participation in these strategies. Conducting research to explore the extent of their effects is thus essential, with a deeper understanding of the nutrient cycle playing a pivotal role in achieving these goals.

With the cumulatively increasing availability of remote sensing data sources and advancements in machine learning technologies, automating monitoring and assessment efforts has become a hot and important topic. The challenge is to construct transparent and transferable models capable of working with real-time data to accurately predict crop types, crop status or other desired features. The primary goal of this study is to investigate how an automated multisource data analysis approach, with a focus on remotely sensed data, can support the quantification and mapping of sustainability efforts in agricultural crop management while enhancing the understanding of nutrient flow within large-scale agricultural catchments. Centered on the Hjarbæk Fjord in Denmark, the study also aims to assess the transferability of its models across different sites in Europe. This research is part of a broader project investigating the potential of integrating permanent grasslands into crop rotations as a Nature-based Solution in the catchments surrounding Hjarbæk Fjord. The project aims to develop a decision support tool to guide the planning and optimization of grassland implementation in terms of extend and location. This tool is designed to maximize benefits across various parameters, including the number of stakeholders impacted, economic considerations, crop yield, biodiversity, and other critical factors. The output of the current study, involving the training of a deep learning model to predict cropland trends related to grassland implementation, can in turn be integrated as input for the described decision support tool.

This is an explorative study that relies on the availability of accurate ground truth data to train and validate a deep learning model, providing insights into trends associated with the implementation of sustainable management strategies. A key challenge lies in acquiring knowledge of and access to comprehensive datasets that capture relevant parameters, such as actual yield values, quantitative values of nutrients in different stages of the growth season and different nutrient pools within the cropland environment, accurate accounts of management actions and other contributors to the nutrient cycle. Additional challenges involve preprocessing satellite data to establish a robust pipeline for the automated collection of satellite imagery, ensuring a coherent time series. This includes addressing temporal and spatial data gaps through extrapolated estimations to create a consistent dataset.