The need to develop and provide integrated observation systems to better understand and manage global and regional environmental change is one of the major challenges facing Earth system science today. In 2008, the German Helmholtz Association took up this challenge and launched the German research infrastructure TERrestrial ENvironmental Observatories (TERENO). The aim of TERENO is the establishment and maintenance of a network of observatories as a basis for an interdisciplinary and long-term research programme to investigate the effects of global environmental change on terrestrial ecosystems and their socio-economic consequences. State-of-the-art methods from the field of environmental monitoring, geophysics, remote sensing, and modelling are used to record and analyze states and fluxes in different environmental disciplines from groundwater through the vadose zone, surface water, and biosphere, up to the lower atmosphere. Over the past 15 years we have collectively gained experience in operating a long-term observing network, thereby overcoming unexpected operational and institutional challenges, exceeding expectations, and facilitating new research. Today, the TERENO network is a key pillar for environmental modelling and forecasting in Germany, an information hub for practitioners and policy stakeholders in agriculture, forestry, and water management at regional to national levels, a nucleus for international collaboration, academic training and scientific outreach, an important anchor for large-scale experiments, and a trigger for methodological innovation and technological progress. This article describes TERENO’s key services and functions, presents the main lessons learned from this 15-year effort, and emphasises the need to continue long-term integrated environmental monitoring programmes in the future.

Landslides pose a significant threat to society and infrastructure and their occurrence is projected to increase in many regions under the effect of climate change. There is an urgent demand for reliable monitoring of this natural hazard. The combination of spaceborne remote sensing data with state-of-the-art machine learning algorithms offers valuable tools for landslide detection in remote areas. However, a key limitation for the detection lies in the scale factor, especially for methods relying on pixel neighbourhoods. This study presents an innovative methodology which combines a spatial graph with SAR and multi-spectral change products. The graph integrates the flow direction based on the topography into the neighbourhood determination. This unique neighbourhood allows for the preservation of the unique shape and signature of an individual landslide. This paper compares the proposed graph neighbourhood to a common square window approach. Therefore, a RFC is trained with neighbourhood statistics from both approaches and applied to landslides of varying extent. A research area in New Zealand’s West Coast region is selected due to the continuous evolution of a single landslide over multiple events. The graph approach shows promising results, particularly for small-scale events which are successfully detected while being missed by the common window approach. Using the graph neighbourhood, we can even detect the smallest visible extent of the landslide at 2-3 pixels (30-45m) width. The main limitation of the proposed approach lies in the quality of the input data. Future work will focus on the improvement of the Sentinel-1 and Sentinel-2 pre-processing.