Sjulgård H., Graf L.V., Colombi T., Hirte J., Keller T., Aasen H.
Earth observation reveals reduced winter wheat growth and the importance of plant available water during drought.
Biogeosciences, 22, (5), 2025, 1341-1354.

Oriani F., Aasen H., Schneider M.
Different growth response of mountain rangeland habitats to inter-annual weather fluctuations.
Alpine Botany, In Press, 2025.

Treier S., Lukas R., Hund A., Kirchgessner N., Aasen H., Walter A., Herrera Mourente J. M.
Digital lean phenotyping methods in the context of wheat variety testing.
In: North American Plant Phenotyping Network (NAPPN) Annual Conference. 14 February, Hrsg. NAPPN, West Lafayette, Indiana (USA). 2024.

Treier S., Herrera Mourente J. M., Hund A., Kirchgessner N., Aasen H., Walter A., Roth L.
Improving drone-based uncalibrated estimates of wheat canopy temperature in plot experiments by accounting for confounding factors in a multi-view analysis.
ISPRS Journal of Photogrammetry and Remote Sensing, 218, Part A, 2024, 721-741.

Treier S., Roth L., Hund A., Kirchgessner N., Aasen H., Walter A., Herrera Mourente J. M.
Digital lean phenotyping methods in the context of wheat variety testing: The cases of canopy temperature and phenology.
In: 18th Congress of the European Society for Agronomy. 27 August, Hrsg. ESA, Rennes (FR). 2024.

Treier S., Roth L., Hund A., Kirchgessner N., Aasen H., Walter A., Herrera Mourente J. M.
Digital lean phenotyping methods in the context of wheat variety testing: The cases of canopy temperature and phenology.
In: North American Plant Phenotyping Network (NAPPN) Annual Conference. 14 February, Hrsg. North American Plant Phenotyping Network (NAPPN), Lincoln. 2024.

Anderegg J., Kirchgessner N., Aasen H., Zumsteg O., Keller B., Zenkl R., Walter A., Hund A.
Thermal imaging can reveal variation in stay-green functionality of wheat canopies under temperate conditions.
Frontiers in Plant Science, 15, 2024, Artikel 1335037.

Aasen H., Gilgen A., Ledain S.
Improving large-scale hybrid LAI retrieval with local soil data and noise.
In: PANGEOS COST Action. 4. July, Sofia (BG). 2024, 1-15.

Türkoglu M. Ö., Aasen H., Schindler K., Wegner J.
Country-wide cross-year crop mapping from optical satellite image time series.
In: EGU24. 14 April, Vienna. 2024, 1.

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, Hrsg. 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., Berger K., Brede B., Graf L. V., Aasen H., Roujean J.-L., Machwitz M., Schlerf M., Atzberger C., Prikaziuk E., Ganeva D., Tomelleri E., Croft H., Reyes Muñoz P., Garcia Millan V. und weitere
Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity.
Biogeosciences, 21, (2), 2024, 473-511.

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, Hrsg. 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.