The Remote Sensing Centre for Earth System Research (RSC4Earth) is a joint institution of the Leipzig University and the Helmholtz Centre for Environmental Research (UFZ). The RSC4Earth bundles Leipzig's research activities in the field of remote sensing in earth system sciences and biodiversity research. In particular, we investigate the dynamics of ecosystems and water resources in global change across spatial and temporal scales.

Textwolke zum Zentrum für Fernerkundung in der Erdsystemforschung und dessen Forschung, Grafik: RSC4Earth
Text cloud on the Remote Sensing Centre for Earth System Research and its research. Graphic: RSC4Earth

Research Foci

In its research activities, RSC4Earth focuses on answering the following questions:

  • How do geosystems and ecosystems respond to fundamental environmental changes and social transformation processes on multiple spatio-temporal scales?
  • How can we quantify relationships between biodiversity and ecosystem functions on local to global scales?
  • What methodological developments enable an improved understanding of spatio-temporal change in the water, energy and carbon cycles?
  • How can we use remote sensing and modelling approaches to deepen our understanding of the Earth system?

This involves the use of remote sensing techniques ranging from field spectroscopy and UAV-based imaging to the analysis of data from global satellite systems. The analysed data streams require methods of "big data science" to enable efficient processing and analysis.

Working Groups

Remote Sensing of the Water Cycle (at the UFZ)

Luftaufnahme eines Flusses in Argentinien, Foto: André Künzelmann/UFZ
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Remote Sensing in Geo- and Ecosystem Research

Aus Fernerkundungsdaten abgeleitete Diversität von Bestäubungsstrategien der Vegetation, Abbildung: Hannes Feilhauer
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Geoinformatics and Remote Sensing (Institute for Geography)

Verschieden eingefärbte Satellitenbildproduktreihe am Beispiel Leipzig und südliches Umland zeigt verschiedene Fernerkundungsmethoden zur Darstellung verschiedener Oberflächeneigenschaften
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Modelling Approaches in Remote Sensing

Earth System Data Cube, Abbildung: Mahecha et al., 2020
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