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[ID: 435] Developing soil moisture maps at high spatiotemporal resolution
PI: Francesco Zignol
Digital soil moisture maps are valuable tools to practitioners, stakeholders and researchers for planning forest management and promoting nature conservation. In this project, we evaluate a machine-learning procedure that combines LIDAR-derived terrain and vegetation indices, reanalysis climatological data and in-situ field measurements to develop daily soil moisture maps at 2-meter resolution for 3 study sites in Sweden (Krycklan, Asa, and Grimsö).