Evaluating land and tree cover datasets for the identification of agroforestry in temperate Europe

Agroforestry, the integration of trees on productive agricultural land (Mosquera-Losada et al., 2018), can be identified through remote sensing methods by the combination of land cover and tree cover maps. Previous work has classified agroforestry as agricultural land with greater than 5% tree cover (Lawson et al., 2025; Zomer et al., 2016).

However, there exist several regional, European, and global land cover and tree cover products that could be suitable for agroforestry identification, but these products vary in resolution, data inputs, and methodology of production. Our work benchmarked the performance of four land cover maps(Büttne et al., 2021; Karvatte et al., 2021; Schultz et al., 2025; UKCEH, 2022) and nine tree cover maps(Brandt et al., 2024; Copernicus, 2023, 2025; Hunter et al., 2025; Lang et al., 2023; Tolan et al., 2024; Weinstein et al., 2020) that were capable of mapping trees outside of woodlands.  We evaluated the datasets’ ability to identify agroforestry on 25 agroforestry sites across the United Kingdom, including a mix of silvoarable and silvopastoral systems, as well as planting ages, densities, and species, as well as nearby agricultural (no trees) and woodland (no agriculture) control fields.

We found that a number of datasets used in previous studies underperformed when distinguishing agroforestry from control fields as well as the previously utilised pixel-based approaches being unsuitable to identify agroforestry fields as a whole. Datasets with coarse resolutions (>10m) often confused proximal small woodlands for trees within agricultural fields. Many datasets struggled to map trees in silvoarable systems, likely due to their linear arrangement differing from that of other trees outside of woodlands.

In addition, the majority of datasets were unable to identify agroforestry sites planted since 2000, suggesting a 20-year lag in identification. The only tree identification method capable of identifying young sites was the fine-tuning of the DeepForest tree detection model (Weinstein et al., 2020) with local data, suggesting a need for tree cover datasets that are capable of identifying seedlings and saplings.

We used the best-performing datasets (balanced accuracy 83-87%) to create a map of agroforestry, with quality flags to signify agreement between datasets. We conclude that there is 517,300 ha (3% of UAA) of area under agroforestry in the United Kingdom. Our map could have further use cases for calculating the uptake of agroforestry, as well as its benefits to people and nature, such as carbon storage, biodiversity impacts, farm income, and health of crops and livestock. We also conclude the need for model training on agroforestry trees, to both identify young trees and those with complex planting arrangements.