Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1016/j.ecoinf.2026.104032 |
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| Title (Primary) | Evaluating 3D-CNN-based sharpening of EnMAP imagery for plant trait retrieval in a riparian forest |
| Author | Mederer, D.; Müller, M.; Cherif, E.; Kattenborn, T.; Feilhauer, H. |
| Source Titel | Ecological Informatics |
| Year | 2026 |
| Department | RS |
| Volume | 99 |
| Page From | art. 104032 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Data and Software links | https://doi.org/10.5281/zenodo.https://doi.org/10.5281/zenodo.1728028717280287 |
| Supplements | Supplement 1 |
| Keywords | Deep learning; Vegetation remote sensing; Foliar leaf traits; Biophysical plant properties; Imaging spectroscopy |
| Abstract | Plant functional traits are important indicators of ecosystem functioning. Recent hyperspectral satellite missions such as EnMAP provide detailed spectral information that enables the monitoring of plant traits across large spatial scales. However, their coarse spatial resolution (30 m) affects validation efforts and applications in fragmented landscapes. Satellite scenes can be sharpened with image fusion techniques by combining hyperspectral data with finer resolution multispectral data. Yet, it remains unclear whether these fused images provide benefits for retrieving plant functional traits. In this study, we evaluated whether 3D-CNN-based sharpening of EnMAP with Sentinel-2 data preserves spectral fidelity, how it affects trait retrieval performance relative to original EnMAP, and whether it provides spatially more detailed plant trait maps in a riparian forest. The method was validated with field data collected in a riparian forest in Leipzig (Germany), including five key plant traits and fine-resolution airborne HySpex imagery. Trait retrieval was performed with a state-of-the-art deep learning model applied to original EnMAP data, sharpened EnMAP products, and EnMAP-like HySpex datasets at multiple resolutions. Results show that sharpening preserved spectral fidelity and increased the spatial detail of trait maps. Retrieval accuracy did not improve significantly, but paired error comparisons indicated no systematic degradation for chlorophyll, carotenoids, LMA, and EWT, while LAI showed increased error. Our findings indicate that 3D-CNN-based image fusion can support spatially more detailed EnMAP trait maps, but the products here should be interpreted primarily as representations of relative spatial trait variation rather than as accurate pixel-wise trait estimates. By preserving spectral fidelity while increasing spatial detail, the approach provides a pathway for future studies to extend the use of spaceborne hyperspectral data in monitoring ecosystem structure and function. |
| Mederer, D., Müller, M., Cherif, E., Kattenborn, T., Feilhauer, H. (2026): Evaluating 3D-CNN-based sharpening of EnMAP imagery for plant trait retrieval in a riparian forest Ecol. Inform. 99 , art. 104032 10.1016/j.ecoinf.2026.104032 |
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