Background and aims Drained peatlands are being rewetted to restore a variety of important ecosystem services and functions. Yet, the effect of realized groundwater level on fine-root traits and plant functioning is inconclusive so far. The aim of this study was to investigate fine-root traits and plant functioning at surface near groundwater levels on peat soil aiming at detecting drivers of intraspecific trait variation and...

We have extended a classical quality control (QC) algorithm by integrating a deep learning neural network, resulting in SalaciaML-2-Arctic , a tool for automated QC of Arctic Ocean temperature and salinity profile data. The neural network component was trained on the Unified Database for Arctic and Subarctic Hydrography (UDASH), which has been quality-controlled and labeled by expert oceanographers. SalaciaML-2-Arctic successf...

Accurate estimation of biomass in grasslands is essential for understanding ecosystem health and productivity. Unmanned Aerial Vehicles (UAVs) have emerged as valuable tools for biomass estimation using canopy height models derived from high-resolution imagery. However, the impact of field disturbances, such as lodging and molehills, on the accuracy of biomass estimation using UAV-derived canopy height models remains underexpl...

Any use of observational data for data assimilation requires adequate information of their representativeness in space and time. This is particularly important for sparse, non-synoptic data, which comprise the bulk of oceanic in situ observations in the Arctic. To quantify spatial and temporal scales of temperature and salinity variations, we estimate the autocorrelation function and associated decorrelation scales for the Ame...