%0 Journal Article %1 cherif2023spectra %A Cherif, Eya %A Feilhauer, Hannes %A Berger, Katja %A Ewald, Michael %A Hank, Tobias B %A Kovach, Kyle R %A Townsend, Philip A %A Wang, Zhihui %A Kattenborn, Teja %D 2023 %J EGU General Assembly Conference Abstracts %K xack %P EGU-10901 %T From spectra to functional plant traits: Transferable multi-trait models from heterogeneous and sparse data %X Our understanding of the Earth's functional biodiversity and its imprint on ecosystem functioning, structure and resilience is still incomplete. Large-scale information on vegetation properties ('plant traits') is critical to assess functional diversity and its role in the Earth system. Such parameters are constantly changing due to variations in environmental conditions which makes extensive in-situ measurements in the field not logistically feasible. The advent of the upcoming space-borne hyperspectral missions will facilitate to map these properties. However, we are still lacking efficient and accurate methods to translate hyperspectral reflectance into large scale information on plant traits across biomes, land cover and sensor types. Yet, the absence of globally representative data sets on reflectance data and the corresponding in-situ measurements represents a bottleneck to develop empirical models for estimating plant …

@article{cherif2023spectra, abstract = {Our understanding of the Earth's functional biodiversity and its imprint on ecosystem functioning, structure and resilience is still incomplete. Large-scale information on vegetation properties ('plant traits') is critical to assess functional diversity and its role in the Earth system. Such parameters are constantly changing due to variations in environmental conditions which makes extensive in-situ measurements in the field not logistically feasible. The advent of the upcoming space-borne hyperspectral missions will facilitate to map these properties. However, we are still lacking efficient and accurate methods to translate hyperspectral reflectance into large scale information on plant traits across biomes, land cover and sensor types. Yet, the absence of globally representative data sets on reflectance data and the corresponding in-situ measurements represents a bottleneck to develop empirical models for estimating plant …}, added-at = {2025-02-27T10:56:18.000+0100}, author = {Cherif, Eya and Feilhauer, Hannes and Berger, Katja and Ewald, Michael and Hank, Tobias B and Kovach, Kyle R and Townsend, Philip A and Wang, Zhihui and Kattenborn, Teja}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/261c3fda4ecdbdd549b892825a6658336/scadsfct}, citation = {EGU General Assembly Conference Abstracts, EGU-10901, 2023}, interhash = {902da9c0b0fe45c86b95609f000723cf}, intrahash = {61c3fda4ecdbdd549b892825a6658336}, journal = {EGU General Assembly Conference Abstracts}, keywords = {xack}, pages = {EGU-10901}, timestamp = {2025-02-27T10:56:18.000+0100}, title = {From spectra to functional plant traits: Transferable multi-trait models from heterogeneous and sparse data}, year = 2023 }