Of all terrestrial ecosystems, peatlands store carbon most effectively in long-term scales of millennia. However, many peatlands have been drained for peat extraction or agricultural use. This converts peatlands from sinks to sources of carbon, causing approx. 5% of the anthropogenic greenhouse effect and additional negative effects on other ecosystem services. Rewetting peatlands can mitigate climate change and may be combined with management in the form of paludiculture. Rewetted peatlands, however, do not equal their pristine ancestors and their ecological functioning is not understood. This holds true especially for groundwater-fed fens. Their functioning results from manifold interactions and can only be understood following an integrative approach of many relevant fields of science, which we merge in the interdisciplinary project WETSCAPES. Here, we address interactions among water transport and chemistry, primary production, peat formation, matter transformation and transport, microbial community, and greenhouse gas exchange using state of the art methods. We record data on six study sites spread across three common fen types (Alder forest, percolation fen, and coastal fen), each in drained and rewetted states. First results revealed that indicators reflecting more long-term effects like vegetation and soil chemistry showed a stronger differentiation between drained and rewetted states than variables with a more immediate reaction to environmental change, like greenhouse gas (GHG) emissions. Variations in microbial community composition explained differences in soil chemical data as well as vegetation composition and GHG exchange. We show the importance of developing an integrative understanding of managed fen peatlands and their ecosystem functioning.

The restoration of drained peatlands to re-establish biodiverse peat-forming plant communities and typical habitats is a long-term process. To document this process, monitoring concepts must be found that are as operational, cost-effective and non-invasive as possible for the new sensitive ecosystems. The monitoring of the developing plant communities using multisensory Unmanned Aerial Vehicle (UAV) data has great potential. We investigated two fen sites in north-eastern Germany that were rewetted in the late 1990s. The areas were flown with a Fixed-Wing UAV and three sensors (RGB, multispectral, thermal). A multisensor dataset consisting of the sensor data, plant height and five spectral indices was classified with a random forest algorithm. The classification accuracies were 87.1 and 89.0% for 10 and 11 classes for the respective sites. Furthermore, the band importance was analyzed using the Gini index. Plant height in combination with the multispectral information were the most important variables. This study underlines the suitability of UAVs for monitoring spectrally similar vegetation because of their capability to generate 3D surface models using structure-from-motion methods and to carry different sensor systems. In addition, they can be used on a regional scale and mostly independent of cloud cover. Secondary results of the study are that the band importance can be determined by Random Forest algorithms even with few trees (e.g. 10 trees), because the order remains very stable independently of the number of trees.

The rewetting of peatlands is regarded as an important nature-based climate solution and intended to reconcile climate protection with the restoration of self-regulating ecosystems that are resistant to climate impacts. Although the severity and frequency of droughts are predicted to increase as a consequence of climate change, it is not well understood whether such extreme events can jeopardize rewetting measures. The goal of this study was to better understand drought effects on vegetation development and the exchange of the two important greenhouse gases CO₂ and CH₄, especially in rewetted fens. Based on long-term reference records, we investigated anomalies in vegetation dynamics, CH₄ emissions, and net CO₂ exchange, including the component fluxes of ecosystem respiration (R_eco) and gross ecosystem productivity (GEP), in a rewetted fen during the extreme European summer drought in 2018. Drought-induced vegetation dynamics were derived from remotely sensed data. Since flooding in 2010, the fen was characterized by a patchy mosaic of open-water surfaces and vegetated areas. After years of stagnant vegetation development, drought acted as a trigger event for pioneer species such as Tephroseris palustris and Ranunculus sceleratus to rapidly close persistent vegetation gaps. The massive spread of vegetation assimilated substantial amounts of CO₂. In 2018, the annual GEP budget increased by 20 % in comparison to average years (2010–2017). R_eco increased even by 40 %, but enhanced photosynthetic CO₂ sequestration could compensate for half of the drought-induced increase in respiratory CO₂ release. Altogether, the restored fen remained a net CO₂ sink in the year of drought, though net CO₂ sequestration was lower than in other years. CH₄ emissions were 20 % below average on an annual basis, though stronger reduction effects occurred from August onwards, when daily fluxes were 60 % lower than in reference years. Our study reveals an important regulatory mechanism of restored fens to maintain their net CO₂ sink function even in extremely dry years. It appears that, in times of more frequent climate extremes, fen restoration can create ecosystems resilient to drought. However, in order to comprehensively assess the mitigation prospects of peatland rewetting as a nature-based climate solution, further research needs to focus on the long-term effects of such extreme events beyond the actual drought period.

In modern agriculture, the spatially differentiated assessment of the leaf area index (LAI) is of utmost importance to allow an adapted field management. Current hyperspectral satellite systems provide information with a high spectral but only a medium spatial resolution. Due to the limited ground sampling distance (GSD), hyperspectral satellite images are often insufficient for precision agricultural applications. In the presented study, simulated hyperspectral data of the upcoming Environmental Mapping and Analysis Program (EnMAP) mission (30 m GSD) covering an agricultural region were pan-sharpened with higher resolution panchromatic aisaEAGLE (airborne imaging spectrometer for applications EAGLE) (3 m GSD) and simulated Sentinel-2 images (10 m GSD) using the spectral preserving Ehlers Fusion. As fusion evaluation criteria, the spectral angle (α_spec) and the correlation coefficient (R) were calculated to determine the spectral preservation capability of the fusion results. Additionally, partial least squares regression (PLSR) models were built based on the EnMAP images, the fused datasets and the original aisaEAGLE hyperspectral data to spatially predict the LAI of two wheat fields. The aisaEAGLE model provided the best results (R²_cv = 0.87) followed by the models built with the fused datasets (EnMAP–aisaEAGLE and EnMAP–Sentinel-2 fusion each with a R²_cv of 0.75) and the simulated EnMAP data (R²_cv = 0.68). The results showed the suitability of pan-sharpened EnMAP data for a reliable spatial prediction of LAI and underlined the potential of pan-sharpening to enhance spatial resolution as required for precision agriculture applications.