The dataset contains information on plant-bee interactions in an agricultural landscape with diverse intensities of land use management, in Germany and Belgium. It was collected during spring and early summer in 2020 and 2021 using two complementary types of sampling: standardized transects (5 transects of 50 m long in 1 h of netting) and targeted sampling in which flowers were observed for diverse periods of times, anywhere in an area of 50 to 150 m². The species identity was obtained with field keys and DNA barcoding. The dataset is of use for building pollinator networks and in combination with other datasets on environmental characteristics of the area to better understand species distributions and interactions. Indeed, we include in the dataset information on environmental parameters from the plots of sampling (spatial coordinates, land use intensity index, landscape heterogeneity index, plant diversity), which can support further correlational analyses.

Rivers have shaped humanity since its beginning; most societies have formed on their banks benefitting from water resources or transportation pathways. Today, rivers stand at the centre of contrasting visions. For some, they are a resource to be harnessed; for others, a living landscape to restore and protect. They are at the forefront of the climate struggle, capable of both safeguarding our communities and challenging them. So, what should our relationship with rivers be?We asked artists to reflect on it through the project Flow. This project invited 50 artists to explore the river Lech, from its source in the Austrian Alps to its meeting with the Danube in Germany. The Lech was chosen because its story reflects that of many rivers: it has long been central to the growth of cities, agriculture, and industry. Over time, the river was modified and confined to serve human needs. Today, as climate change and biodiversity crisis urge us to rethink how we manage water and landscapes, the Lech is becoming central once again. Restoration efforts are helping it thrive as a natural ecosystem and a place where communities reconnect with nature.The Lech River was divided into individual segments, each based on morphological and historical uniform characteristics and represented by time-lapse videos created from Sentinel-2 satellite images, along with a field recording of ambient sound captured on that stretch of water. Musicians were invited to interpret this material and compose original pieces. From headwaters to the confluence with the Danube, artists reimagined different stretches of the river in sound, narrating its landscapes and changes through music. The project fostered an ongoing dialogue between scientists and musicians through a series of collaborative meetings held over a four-month period.Through this meeting of art and science, the Lech became more than a line on the map. It became a flowing story — one that we can listen to, reflect on, and imagine in new ways. It calls on all of us to ponder: What is a river? And, how far can we go in giving it back its freedom?

Earth Observation archives now encompass petabytes of multispectral imagery, yet transforming these heterogeneous collections into analysis-ready data (ARD) cubes remains a critical bottleneck. We present an open-source Python package that unifies cloud masking, co-registration, and super-resolution into a seamless Xarray-based workflow, tailored specifically to close practical gaps in ARD cube generation. Leveraging scalable high-performance computing (HPC) infrastructure, our framework delivers rapid, reproducible cube construction and incremental updates, enabling users to build or extend large time-series data cubes without reprocessing historical scenes. Besides HPCs, our package is also suitable for local processing of Sentinel-2 data. Our approach integrates (1) s2cloudless, a probabilistic cloud-masking algorithm offering user-defined thresholds to overcome the rigid limitations of the Sentinel-2 Scene Classification Layer (SCL) and STAC metadata; (2) AROSICS, a sliding-window co-registration routine that ensures sub-pixel alignment over complex, dynamic landscapes to produce smoother temporal metrics and more consistent change detection; and (3) SEN2SR, a deep-learning super-resolution model that refines all bands to 2.5 m, revealing fine geomorphic and ecological features previously obscured at native resolutions. Together, these components address three recurring ARD cube gaps in existing Xarray-based toolkits: adaptive cloud filtering, robust time-series alignment, and integrated spatial enhancement within a single, reproducible pipeline. To maximize accessibility and reuse, the package is accompanied by well documented, interactive Python notebooks that guide users through configuration, and end-to-end cube generation. Validated on the German Aerospace Center’s terrabyte HPDA clusters, the pipeline runs equally well on local workstation and can be accessed at https://github.com/BaturalpArisoy/stac2cube.

Earth Observation archives now encompass petabytes of multispectral imagery, yet transforming these heterogeneous collections into analysis-ready data (ARD) cubes remains a critical bottleneck. We present an open-source Python package that unifies cloud masking, co-registration, and super-resolution into a seamless Xarray-based workflow, tailored specifically to close practical gaps in ARD cube generation. Leveraging scalable high-performance computing (HPC) infrastructure, our framework delivers rapid, reproducible cube construction and incremental updates, enabling users to build or extend large time-series data cubes without reprocessing historical scenes. Besides HPCs, our package is also suitable for local processing of Sentinel-2 data. Our approach integrates (1) s2cloudless, a probabilistic cloud-masking algorithm offering user-defined thresholds to overcome the rigid limitations of the Sentinel-2 Scene Classification Layer (SCL) and STAC metadata; (2) AROSICS, a sliding-window co-registration routine that ensures sub-pixel alignment over complex, dynamic landscapes to produce smoother temporal metrics and more consistent change detection; and (3) SEN2SR, a deep-learning super-resolution model that refines all bands to 2.5 m, revealing fine geomorphic and ecological features previously obscured at native resolutions. Together, these components address three recurring ARD cube gaps in existing Xarray-based toolkits: adaptive cloud filtering, robust time-series alignment, and integrated spatial enhancement within a single, reproducible pipeline. To maximize accessibility and reuse, the package is accompanied by well documented, interactive Python notebooks that guide users through configuration, and end-to-end cube generation. Validated on the German Aerospace Center’s terrabyte HPDA clusters, the pipeline runs equally well on local workstation and can be accessed at https://github.com/BaturalpArisoy/stac2cube.

Climate change continues to impact diverse ecosystems. Drylands stand out as particularly vulnerable environments, as they are highly responsive to key indicators of change. The sensitivity and response time of these regions remain largely unknown, underscoring the need for a deeper understanding of their systems.Arid regions are considered optimal for Earth Observation based research, primarily due to factors such as minimal anthropogenic disturbance, sparse vegetation cover, and low cloud coverage. These attributes make drylands advantageous for studying and monitoring the impact of climate change, providing valuable insights into these vulnerable ecosystems.Southern Mongolia stands out as an especially well-suited study area to test novel approaches and to detect land surface changes over both space and time. The basin of Orog Nuur was selected in this study to observe long-term environmental changes, building on significant prior studies conducted around the drainage basin.Our approach emphasizes the utilization of state-of-the-art earth observation technology to unveil the dynamics of desert ecosystems. This involves cloud-based processing, such as Google Earth Engine and the German High Performance Data Analytics (HPDA) platform “terrabyte”. Throughout the project, we will apply various multispectral and active SAR techniques spanning 50 years to monitor geomorphological processes, ecosystem changes and ongoing surface dynamics linked to climate change indicators. Some of important pillars of the long-term time series analysis can be listed as greening and precipitation events, lake level dynamics, dune movement rates, mapping of sedimentological, geomorphological provinces and aeolian coverage, in order to understand frequency-magnitude relationships.The findings will be supported by a series of fieldworks covered by UAS campaigns and auxiliary ground-truth sensors, ensuring the accuracy of our estimations by in-situ measurements. Based on the derived surface characteristics, various ecosystems will be defined, and a high-level ecosystem integrity model will be developed. Ultimately, our model aims to represent the intactness, functioning and structure of the different ecosystems within arid regions. Additionally, due to our high temporal study concept, the model will serve as the base for quantifiable measurements of the responsiveness and adaptiveness of the ecosystems.Having a model for ecosystem intactness not only help to preserve fragile ecosystems but also strengthens the resilience and adaptive capacity of communities. Furthermore, the transferability of our framework to other drylands may also lead to a comprehensive understanding of the arid characteristics.Keywords: Earth Observation, arid regions, dryland, remote sensing, climate change, impact, geomorphological process, ecological modelling, land surface dynamics

Riverine landscapes are shaped by the feedbacks between hydrological, geomorphological and ecological processes. These feedbacks occur across multiple scales, from the scale of single plants modifying the hydraulic forces around it to the formation of landforms like islands which in turn lead to the emergence of specific river types such as braided or anastomising. Over the past years, the field of biogeomorphology has significantly improved the understanding of the interaction of vegetation and hydro-morphological processes. Despite recent scientific progress, research gaps remain. In particular, it is still poorly understood, how processes happening on small scales, such as sedimentation in the lee of an individual plant or a piece of large wood, lead to the emergence of landforms and reach scale river types and how – vice versa – the specific landform pattern within river types foster small scale processes. The concept of Panarchy considering a number of adaptive cycles linking the different scales of the fluvial biogeomorphic system is a promising candidate for analyzing cross-scale vegetation-hydromorphology feedbacks. However, developing methods for quantitative studies is still an ongoing challenge in biogeomorphological research.We introduce an empirical approach for filling this research gap driven by a combination of field mapping and state-of-the-art remote sensing taking the Naryn River, a large free flowing river in Kyrgyzstan, as a case study. In the field, we map vegetation traits and geomorphic characteristics and link them to the stages of the biogeomorphic succession concept. Then, we utilize the computational potential of the “Terrabyte” cloud computing platform of the German Aerospace Center (DLR) to analyze temporally dense time series from the Sentinel-1 and -2 archives. To map vegetation and hydro-geomorphic characteristics (vegetation height, density, biomass, share of bare sediment, grainsize, duration of inundation) and to assess how these biogeomorphic traits change over time, we make use of the capabilities of the recently available foundational deep learning model “Clay” as state-of-the-art artificial intelligence method in earth observation. This enables river corridor scale analysis of the spatial-temporal dynamics of hydro-geomorphic disturbance, rejuvenation potential (windows of opportunity), vegetation growth as well as the emergence of biogeomorphic feedback windows and therefore tracking the biogeomorphic succession. This gives us the possibility to study adaptive cycles on different scales and construct Panarchies for different river types occurring along the Naryn River. Our approach is a significant step towards the quantification of biogeomorphic feedbacks across multiple scales and advances the empirical understanding of the role of scale dependence of biogeomorphic feedbacks which lead to the emergence of riverine landscape pattern.