Soil moisture is a fundamental component of the global hydrological cycle and plays a critical role in various terrestrial ecosystem processes, including global energy, water, and carbon budgets. Spatially explicit assessment of soil moisture, therefore, provides important cues for understanding local, regional and global energy and water budgets at various scales ranging from local to regional to global (Ali et al., 2015). Remote sensing technique that estimates soil moisture using different sensors, e.g. passive, active, and thermal, serves as the primary approach for global soil moisture mapping (Mohanty et al., 2017; Zeng et al., 2019). However, satellite remote sensing derived soil moisture maps often have a lower spatial resolution (25-40 km) (Mohanty et al., 2017).

Here, we investigate LiDAR derived terrain indices using machine learning models to predict soil wet areas and moisture variation across Sweden at 2m spatial resolution.

For more details, check out our recent publications - Ågren et al. (2021), Paul et al. (2022).