Aasen H.
10 years at the intersection of plant phenotyping and remote sensing.
In: Optimal and cost-effective UAV sensor synergies for trait-based field phenotyping and precision agriculture. 8 May, Publ. PANGEOS, Poznan (PL). 2024.
Ledain S., Stumpf F., Gilgen A., Aasen H.
Radiative transfer model-based LAI retrieval from Sentinel-2 data through machine learning, adding phenological constraints and soil information.
In: EGU General Assembly. 14 - 19 April, Vienna. 2024, 1.
Larcher D., Ledain S., Aasen H.
Comparing radiative transfer model-based LAI retrieval with in-situ observations and mechanistic modelling for grassland growth assessment.
In: EGU General Assembly. 14 - 19 April, Vienna. 2024, 1.
Kramer K., Oriani F., Schneider M. K., Aasen H., Calanca P.
Integrating Sentinel-2 information into a growth model for assessing Alpine grassland dynamics under climate change.
In: European Geoscience Union Annual Meeting 2024. 15 April, Vienna. 2024, 1-37.
Kooistra L., Aasen H., et al., Berger K.
Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity.
Biogeosciences Discussions, In Press, 2023, 1-67.
Graf L., Merz Q., Walter A., Aasen H.
Insights from field phenotyping improve satellite remote sensing based in-season estimation of winter wheat growth and phenology.
Remote Sensing of Environment, 299, 2023, 1-16.
Oriani F., Aasen H., Schneider M. K.
Monitoring pasture vegetation using satellite remote sensing: which images and workflow?
In: Pastoralp Final Conference. 15. März, Bard (Italy). 2023.
Perich G., Turkoglu M. O., Graf L. V., Wegner J. D., Aasen H., Walter A., Liebisch F.
Pixel-based yield mapping and prediction from Sentinel-2 using spectral indices and neural networks.
Field Crops Research, 292, 2023, 1-13.
Merz Q., Walter A., Aasen H.
Using high-resolution drone data to assess apparent agricultural field heterogeneity at different spatial resolutions.
In: 42. GIL-Jahrestagung, Künstliche Intelligenz in der Agrar- und Ernährungswirtschaft. 22. Februar, Publ. Gesellschaft für Informatik e.V., 2022, 195-200.
Graf L., Aasen H., Perich G.
EOdal: An open-source Python package for large-scale agroecological research using Earth Observation and gridded environmental data.
Computers and Electronics in Agriculture, 203, 2022, 1-5.
Treier S., Roth L., Herrera J. M., Hund A., Aasen H., Pellet D., Walter A.
Enhancing canopy temperature measurement precision with an uncalibrated thermal drone camera.
In: XVIIIth Eucarpia Biometrics in Plant Breeding Conference. 21. September, Paris Saclay - Eucarpia. 2022.
Aasen H., Roth L.
Advances in high-throughput crop phenotyping using unmanned aerial vehicles (UAVs).
In: Advances in plant phenotyping for more sustainable crop production. Publ. Walter, Achim, Burleigh Dodds. 2022, 179-200.
Merz Q., Walter A., Maier, R., Hörtnagl L., Buchmann N., Kirchgessner N., Aasen H.
Relationship of leaf elongation rate of young wheat leaves, gross primary productivity and environmental variables in the field with hourly and daily temporal resolution.
Agricultural and Forest Meteorology, 320, 2022, 1-10.
Graf L. V., Bernardino P., Steinhauser S., Un Nisa Z., Berger K., Ganeva D., Palazón S. B., Verrelst J., Aasen H.
Impacts of radiometric uncertainty on the estimation of land surface phenology metrics.
In: ESA Living Planet Symposium. 23. Mai, Publ. European Space Agency, Bonn. 2022, 1.
Merz Q. N., Walter A., Maier R., Hörtnagl L., Buchmann N., Kirchgessner N., Aasen H.
Relationship of leaf elongation rate of young wheat leaves, gross primary productivity and environmental variables in the field with hourly and daily temporal resolution.
Agricultural and Forest Meteorology, 320, 2022, 1-10.