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 estimate of 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 two (32) 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.

GEDTM30: global ensemble digital terrain model at 30 m and derived multiscale terrain variables.

Production and validation of an open global ensemble digital terrain model (GEDTM30) and derived terrain variables at 1 arc-s spacing grid ( 30 m spatial resolution) is described. Copernicus DEM, ALOS World3D, and object height models were combined in a data fusion approach to generate a globally consistent digital terrain model (DTM). This DTM was then used to compute 15 standard terrain variables across six scales (30, 60, 120, 240, 480 and 960 m).

Spatiotemporal prediction of soil organic carbon density in Europe (2000-2022) using earth observation and machine learning.

This article describes a comprehensive framework for soil organic carbon density (SOCD, kg/m) modeling and mapping, based on spatiotemporal random forest (RF) and quantile regression forests (QRF). A total of 45,616 SOCD observations and various Earth observation (EO) feature layers were used to produce 30 m SOCD maps for the EU at four-year intervals (2000-2022) and four soil depth intervals (0-20 cm, 20-50 cm, 50-100 cm, and 100-200 cm). Per-pixel 95% probability prediction intervals (PIs) and extrapolation risk probabilities are also provided.

Land potential assessment and trend-analysis using 2000-2021 FAPAR monthly time-series at 250 m spatial resolution.

The article presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refers to the potential vegetation productivity in the hypothetical absence of short-term anthropogenic influence, such as intensive agriculture and urbanization. Knowledge on this ecological land potential could support the assessment of levels of land degradation as well as restoration potentials.