ECHOSAT
Estimating Canopy Height Over Space And Time
🌍 Under Review
1University of Münster 2LSCE, CEA-CNRS-UVSQ 3Zuse Institute Berlin 4NASA Jet Propulsion Laboratory
* Corresponding author
ECHOSAT provides the first global, temporally consistent tree height map at 10m resolution, enabling accurate monitoring of forest growth and disturbances from 2018 to 2024.
Abstract
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 10m 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.
Key Contributions
Why does this matter?
Forests absorb ~3.5 Pg of carbon per year — almost half of anthropogenic fossil fuel emissions. Accurate monitoring of forest carbon dynamics requires understanding not just where trees are, but how they change over time. ECHOSAT enables this for the first time at global scale.
- First Global Spatio-Temporal Tree Height Map: We provide the first high-resolution (10m) tree height map covering the entire globe across seven years (2018–2024), enabling reliable monitoring of forest dynamics and disturbances at unprecedented scale.
- Novel Growth Loss Framework: We introduce a self-supervised loss specifically designed for training temporal regression models with sparsely distributed and temporally irregular ground truth labels, enforcing physically realistic forest growth patterns.
- Inherent Temporal Consistency: Our model learns realistic temporal forest height dynamics without post-processing, accurately capturing both natural growth and abrupt disturbances like fires or logging.
Method Overview
Model architecture: ECHOSAT uses a Swin Transformer-based encoder-decoder architecture that processes multi-temporal Sentinel-1 (SAR) and Sentinel-2 (optical) satellite imagery to predict pixel-level tree heights across time. The growth loss enforces temporal consistency by regularizing predictions to follow realistic growth curves.
Technical Details:
- Architecture: Hierarchical Swin Transformer with spatio-temporal attention
- Input: Monthly composites of Sentinel-1 (SAR) and Sentinel-2 (optical) data
- Labels: GEDI LiDAR measurements for supervision
- Training: Two-stage approach with Huber loss pretraining followed by Growth Loss fine-tuning
- Output: Per-pixel tree height predictions at 10m resolution for each month from 2018-2024
Results
We evaluate ECHOSAT against existing global tree height maps on single-year predictions using GEDI LiDAR measurements as ground truth. Our model not only achieves state-of-the-art accuracy for static height estimation, but uniquely provides temporally consistent predictions that capture realistic forest dynamics — something no existing global product can offer.
Comparison with existing global tree height maps (Lang et al., Potapov et al., Tolan et al.) on single-year predictions.
ECHOSAT captures realistic temporal dynamics: gradual tree growth and abrupt height decreases.
Quantitative Comparison (2020)
Evaluation on global GEDI test set for tree heights ≥ 5m. Lower MAE/RMSE and higher R² are better.
Visualizations
Example tree height predictions across diverse forest types: tropical rainforests (Amazon, Congo Basin), boreal forests, temperate forests, and managed plantations.
Temporal Dynamics: Growth & Disturbances
Left: Gradual tree growth captured over multiple years. Right: Abrupt height decrease detected following a fire event, demonstrating ECHOSAT's ability to monitor forest disturbances.
BibTeX
@inproceedings{pauls2026echosat,
title = {ECHOSAT: Estimating Canopy Height Over Space And Time},
author = {Pauls, Jan and Schr{\"o}dter, Karsten and Ligensa, Sven and
Schwartz, Martin and Turan, Berkant and Zimmer, Max and
Saatchi, Sassan and Pokutta, Sebastian and Ciais, Philippe and
Gieseke, Fabian},
booktitle = {International Conference on Learning Representations (ICLR)},
year = {2026},
note = {Under Review}
}Acknowledgements
We thank the European Space Agency (ESA) for providing Sentinel-1 and Sentinel-2 data, and NASA for the GEDI mission data. This work was supported by [funding information to be added upon acceptance].