The Remote Sensing in Geo- and Ecosystem Reserach (RSGE) group uses Earth observation data and in-situ measurements to analyze spatio-temporal pattens in the biosphere. We model, map and monitor functional ecosystem properties, species composition and diversity to assess effects of human impacts, climatic extremes and other drivers of ecosystem changes. In these analyses, we put an emphasis on the fuzziness of spatial patterns in ecosystems and develop methods to map them as continuous fields using remote sensing data. We aim to address (geo-)ecological problems in a spatial context to gain insights in the composition and the functioning of the terrestrial biosphere.

The Earth System Data Science (ESDS) group embraces a wide array of highly relevant research questions via data-driven research. Example questions from our researchers are:

• How do ecosystem functions respond to climate extremes?
• Which changes in societal and environmental pre-conditions trigger societal vulnerability to environmental hazards and what are the feedbacks?
• How can we quantify nonlinear interactions in coupled Earth system dynamics?
• What do novel citizen science data tell us about macroecological processes?

We assume that long-term observations encode the necessary knowledge to answer these questions in the different domains of the coupled Earth system and human activity. Hence, we mainly focus on developing methods to extract the valuable information in these data in order to confront them with models, and gain new insights.

The Remote sensing department in UFZ aims to develop novel remote sensing products to capture land surface water and vegetation dynamics. It will fully leverage multi-monitoring techniques that allow observing vegetation characteristics, soil moisture and evaporation dynamics at different spatiotemporal scales. The retrieved products and established processing framework will be applied to evaluate and integrate with the hydrological model system mHM, and the forest model FORMIND and to bridge the gap to biodiversity ground assessments. It is envisaged to provide a better prediction of the impacts of climate and land use change on the environment.

The focus of the Geoinformatics and Remote Sensing Group (GeoRS) lies in the evaluation of multispectral and hyperspectral remote sensing data, the application of imaging and non-imaging laboratory and field spectroscopy as well as spatial modeling with geographic information systems, numerical simulation models and geostatistical methods. We use optical remote sensing data in different spatial and spectral resolutions to inventory the state of diverse eco- and geosystems, to record their dynamics and to analyze systemic functions. We combine this information with pedological, vegetation-related, hydrological or sedimentological terrain data in order to analyze landscape conditions and dynamics at various temporal and spatial scales. In addition to laboratory spectroscopic methods, another focus of the working group is the acquisition and evaluation of field spectroscopic data. On the one hand, these provide spatially high-resolution, hyperspectral inventory data (e.g. in the area of "digital soil mapping" or the sociological characterization of vegetation), on the other hand they allow the spectral connection with multispectral and hyperspectral image data recorded from different remote sensing platforms. The integration of satellite image data (reflective, thermal) in spatial modeling approaches, e.g. for the analysis of spatial distribution patterns of remote sensing sensitive model variables and simulation results is an important issue in our group. Current work relates in particular to habitat modeling and the linking of remote sensing data with mesoscale hydrological process models, the latter for example by retrieved LST and ETa patterns.