Global 30-m annual median vegetation height maps (2000-2022) based on ICESat-2 data and Machine Learning

Accurately measuring vegetation height is essential for understanding ecosystem structure, carbon storage, and biodiversity, yet global height models have overwhelmingly focused on forests, excluding ecosystems with shorter herbaceous vegetation or shrubs. To address this gap in vegetation structure data, we developed the first global model to estimate median vegetation height annually from 2000-2022 at 30 m resolution, using ICESat-2 satellite Lidar, Landsat cloud free composites, and other Earth Observation raster data. Thirty (30) million ICESat-2 20 m segments were used within 10 independent draws to build ensemble Gradient Boosted Tree (GBT) models and estimate 90% prediction intervals. Our model achieves a root mean square error (RMSE) of 2.35 m, R ² values of 0.515 and a D ² regression score of 0.62 estimated via hold-out validation. Comparisons with existing global height products show that our approach increases detail and heterogeneity of height in short vegetation ecosystems. The modeling pipeline is open source and available on Github with reference samples, trained models and output maps publicly available under CC-BY license. Produced maps will be made available via the SpatioTemporal Asset Catalog (http://stac.openlandmap.org) and Google Earth Engine upon publication. In the meantime, beta versions of the product can be accessed through the Global Pasture Watch Early Access data program (https://survey.alchemer.com/s3/7859804/Pasture-Early-Adopters), which provides data in exchange for feedback.