Lucas Gomes

Lucas de Carvalho Gomes is a soil scientist with a strong focus on digital soil mapping, soil health and spatial assessment of ecosystem services. He currently holds a postdoctoral position at Aarhus University's Department of Agroecology where he is applying machine learning approaches to map soil properties from field to Pan-Europeans scales.

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Sessions

04-08
18:30
4min
Mapping the potential soil water repellency in Denmark
Lucas Gomes

Soil water repellency (SWR) affects water dynamics from nano to ecosystem scales, and it is driven by intricate interactions between climate, vegetation, soil properties, and microorganisms. However, the spatial distribution of SWR at ecosystem level as well as the underlying drivers across diverse habitats, land uses and soils textures remain underexplored. This study presents a comprehensive survey of SWR in Denmark, with approximately 7,500 samples, and its predicted spatial distribution. We used digital soil mapping methods (Quantile Random Forest) to map and identify the relationship between SWR and various environmental variables, including vegetation (via satellite imagery), soil properties (texture and soil organic carbon), and landforms (slope and wetness index). The predicted maps at 10 m resolution revealed that SWR varies across different land uses and vegetation types, with higher values in natural areas (e.g., heathlands and coniferous forests) compared to grasslands and croplands (mostly hydrophilic). The analysis also identified soil organic carbon, Sentinel band 2 (SB3_spring) and clay content as key drivers of spatial variation in SWR at national level. Within natural habitats and grasslands, we found that soil texture significantly influences SWR intensity, which generally decreases as clay content increases across most habitat types, except for heathlands. While the predicted maps provided valuable insights into SWR distribution and its environmental drivers, further research is needed to explore the spatio-temporal dynamics of SWR within each habitat, particularly in relation to soil moisture changes. This study highlights the potential of combining machine learning and remote sensing to advance knowledge of SWR, and it can provide crucial spatial information for managing water resources and enhancing soil health and ecosystem resilience in the face of climate change.

soil organic carbon
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