Faculty of Mathematics and Natural Sciences - Earth Observation Lab

Dr. Akpona Okujeni

Dr. Akpona Okujeni

Postdoctoral researcher

09 Akpona Okujeni
E-Mail akpona.okujeni@geo.hu-berlin.de
Office location Rudower Chaussee 16, Room 2'218b
Phone +49 (0)30 2093-6829
Fax +49 (0)30 2093-6848
Postal address Unter den Linden 6, 10099 Berlin, Germany

About me

My research is motivated by a variety of exciting possibilities Earth observation offers to study our planet’s ecosystems. I am primarily using multiple passive but also active remote sensing systems to quantify biodiversity- and climate-relevant land surface properties as well as land surface change processes. Developing and exploring innovative machine learning methods is central to my research interests. My studies extend across a wide range of application domains and contribute to the development of novel remote sensing toolkits for analyzing ecosystems under global change.


Visit my website for more information.


Curriculum Vitae

Since 2022

Associated postdoctoral researcher, Einstein Research Unit Climate and Water under Change (CliWaC)

Since 2014

Postdoctoral researcher. Earth Observation Lab, HU-Berlin, Germany (Projects: EnMAP Core Science Team, UrbanEARS).

11-12 2012

Visiting researcher (DAAD fellowship), Columbia University, Lamont-Doherty Earth Observatory, New York.


Doctoral researcher, Geomatics Lab, Humboldt-Universität zu Berlin (Project: Berlin II)


Diplom-Student in Geography, Geoinformation Science and Geology, Humboldt-Universität zu Berlin.


Selected Publications

Kowalski, K., Okujeni, A., Brell, M., & Hostert, P. (2022). Quantifying drought effects in Central European grasslands through regression-based unmixing of intra-annual Sentinel-2 time series. Remote Sensing of Environment, 268, 112781. 10.1016/j.rse.2021.112781

Frantz, D., Schug, F., Okujeni, A., Navacchi, C., Wagner, W., van der Linden, S., & Hostert, P. (2021). National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series. Remote Sensing of Environment, 252, 112128. 10.1016/j.rse.2020.112128

Okujeni, A., Jänicke, C., Cooper, S., Frantz, D., Hostert, P., Clark, M., Segl, K., & van der Linden, S. (2021). Multi-season unmixing of vegetation class fractions across diverse Californian ecoregions using simulated spaceborne imaging spectroscopy data. Remote Sensing of Environment. 10.1016/j.rse.2021.112558

Okujeni, A., Canters, F., Cooper, S.D., Degerickx, J., Heiden, U., Hostert, P., Priem, F., Roberts, D.A., Somers, B., & van der Linden, S. (2018). Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities. Remote Sensing of Environment, 216, 482-496. 10.1016/j.rse.2018.07.011

Okujeni, A., van der Linden, S., Suess, S., & Hostert, P. (2017). Ensemble Learning From Synthetically Mixed Training Data for Quantifying Urban Land Cover With Support Vector Regression. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 1640-1650. 10.1109/JSTARS.2016.2634859


My full publication list can be found here.