Leander participated in the #ML4Earth workshop, featuring insightful talks on EO foundation models, explainable AI and an excellent panel discussion. Many thanks to @DLR_en, Xiaoxiang Zhu and her team for organizing this outstanding event.

Nizom Farmonov has published in Science of Remote Sensing presenting a hybrid model to map forest traits from EnMAP data with limited field samples using GPR & Active Learning, improving trait retrieval accuracy. https://doi.org/10.1016/j.srs.2025.100253
#RemoteSensing #ML #EnMAP #ActiveLearning

Nizom is presenting his poster on the topic of "Mapping Forest Canopy Nitrogen Content From #EnMAP Imaging Spectroscopy by Coupled Leaf-Canopy Radiative Transfer Models and Gaussian Process Regression" at the #LPS25 in Vienna.
#Hyperspectral #RemoteSensing #ForestEcology

🎓 Congratulations to Marius on successfully defending his PhD thesis:
“South American Tropical Lowland Cloud Forest (TLCF) under Climate Extremes: Fog Dynamics, Refugial Areas and Microclimate Modelling.”

Last week, MW3 project participants met at LCRS in Marburg. They visited the new #sapflow #sensorboxes in the Marburg Open Forest & discussed first results of the monitoring system analyzing #treegrowth responses to #climateextremes. More info: https://mw3-project.com/ #MOF

Last week we were finally able to lift and install our new HUND BAA500e automatic #pollenmonitor at the #Lahnberge. We are looking forward to the first data for the #LOEWE HABITAT project.

Congratulations to Michaela for successfully defending her #PhD thesis on "Forecasting radiation fog by combining station and
satellite data using Machine Learning"

Congratulations to our colleague Christine Kolbe on the successful defence of her #PhD thesis on "Measuring Precipitation from Space - a Satellite View on the Tibetan Plateau".

We are excited to share some of our first crop-type maps for the smallholder regions of Siaya, #Kenya, at @DLR 's interactive touch table during the World Space Forum in Bonn. The maps are based on #EnMAP hyperspectral imagery. We work with @AgriBORA, which is in direct contact with Kenyan smallholder farmers, to gather the necessary training data for our machine-learning models. We look forward to expanding our maps to broader regions of western Kenya soon.