Forest Height Estimation Training Dataset for SAR Interferometry: The Sentinel2Height Project

Accurate forest height estimation is essential for monitoring forest health, carbon storage, timber resources, biodiversity, and ecosystem dynamics.It provides critical insights for sustainable forest management and climate change mitigation.While Interferometric Synthetic Aperture Radar (InSAR), particularly single-pass interferometry with a suitable baseline, has demonstrated significant potential in this domain [1, 2], small-baseline repeat-pass interferometry still faces considerable challenges due to temporal decorrelation.One approach to mitigating this issue is the use of machine learning (ML) algorithms, which are increasingly favored for their scalability and ability to process large datasets efficiently.