09.07.2025 Paper with broad participation from the Tree-M consortium published

We are excited to announce a scientific publication featuring broad participation from the Tree-M consortium, in collaboration with the DLR EnMAP project: “Optimizing hybrid models for forest leaf and canopy trait mapping from EnMAP hyperspectral data with limited field samples” (Science of Remote Sensing, 2025).

Collecting leaves in the canopy of an oak tree in the Marburg University Forest. This fieldwork provided ground-truth data for validating remote sensing models of leaf and canopy traits

This study, led by Prof. Jörg Bendix’s group at the department of geography, Philipps-Universität Marburg, is part of the DLR EnMAP project and focuses on advancing forest trait mapping using satellite hyperspectral data from EnMAP. The researchers combined advanced computer models and machine learning to estimate important leaf and canopy traits, even when only limited field data are available.
Co-authors from the Tree-M consortium contributed through fieldwork and laboratory analyses, collecting leaves and characterizing key leaf traits from Quercus robur in the Marburg University Forest. These ground-truth data were important for validating the models, showcasing the potential for accurate, large-scale forest monitoring with minimal field effort.

This publication showcases the strength of interdisciplinary collaboration in advancing forest trait mapping using hyperspectral remote sensing. Congratulations to all authors!