Comprehensive evaluation of geospatial foundation models (Geo-FMs) requires benchmarking across diverse tasks, sensors, and geographic regions. However, most existing benchmark datasets are limited to segmentation or classification tasks, and focus on specific geographic areas. To address this gap, we introduce a globally distributed dataset for forest aboveground biomass (AGB) estimation, a pixelwise regression task. This benchmark dataset combines co-located hyperspectral imagery (HSI) from the Environmental Mapping and Analysis Program (EnMAP) satellite and predictions of AGB density estimates derived from the global ecosystem dynamics investigation (GEDI) lidars, covering seven continental regions. Our experimental results on this dataset demonstrate that the evaluated Geo- FMs can match or, in some cases, surpass the performance of a baseline U-Net, especially when fine-tuning the encoder. By releasing this globally distributed hyperspectral benchmark dataset, we aim to facilitate the development and evaluation of Geo-FMs for HSI applications. Leveraging this dataset additionally enables research into geographic bias and the generalization capacity of Geo-FMs. Published in IEEE Geoscience and Remote Sensing Letters: https://ieeexplore.ieee.org/document/11164504 .