Spatially continuous data in biogeosciences are urgently requested to assess patterns and trends in ecosystem dynamics. Remote sensing is a powerful tool to provide such data and methods for its application to estimate ecosystem variables evolved rapidly during the recent years. New sensors deliver large hyperspectral, LiDAR and Radar datasets requesting new approaches to dealing with Big Data. In this context, machine learning algorithms are frequently used to link large remote sensing data to ecosystem variables. In this session, we welcome contributions which present novel approaches of mapping, monitoring and modelling ecosystem characteristics combining machine learning with remotely sensed data with a special focus on products to estimate ecosystem processes, functions and services.