Forest monitoring is critical for climate change mitigation. However, existing global tree height maps provide only static snapshots and do not capture temporal forest dynamics, which are essential for accurate carbon accounting. We introduce ECHOSAT, a global and temporally consistent tree height map at 10 m resolution spanning multiple years. To this end, we resort to multi-sensor satellite data to train a specialized vision transformer model, which performs pixel-level temporal regression. A self-supervised growth loss regularizes the predictions to follow growth curves that are in line with natural tree development, including gradual height increases over time, but also abrupt declines due to forest loss events such as fires. Our experimental evaluation shows that our model improves state-of-the-art accuracies in the context of single-year predictions. We also provide the first global-scale height map that accurately quantifies tree growth and disturbances over time. We expect ECHOSAT to advance global efforts in carbon monitoring and disturbance assessment. The maps can be accessed at https://github.com/ai4forest/echosat.
@article{arxiv.2602.21421,
title = {ECHOSAT: Estimating Canopy Height Over Space And Time},
author = {Jan Pauls and Karsten Schrödter and Sven Ligensa and Martin Schwartz and Berkant Turan and Max Zimmer and Sassan Saatchi and Sebastian Pokutta and Philippe Ciais and Fabian Gieseke},
journal= {arXiv preprint arXiv:2602.21421},
year = {2026}
}