This systematic literature review (SLR) provides a comprehensive overview of remote sensing (RS) applications in northern peatlands from 2017 to 2022, utilising various platforms, including in situ, UAV, airborne, and satellite technologies. It addresses the challenges and limitations presented by the sophisticated nature of northern peatland ecosystems. This SLR reveals an in-creased focus on mapping, monitoring, and hydrology but identifies noticeable gaps in peatland degradation research. Despite the benefits of remote sensing, such as extensive spatial coverage and consistent monitoring, challenges persist, including high costs, underexplored areas, and limitations in hyperspectral data application. Fusing remote sensing data with on-site research offers new insights for regional peatland studies. However, challenges arise from issues like the cost of high-resolution data, coverage limitations, and inadequate field validation data in remote areas. This review suggests refining methodologies, validating with high-resolution data, and addressing these limitations for future research.

The rapid expansion of remote sensing provides recent and developed advances in monitoring wetlands. Integrating cloud computing with these techniques has been identified as an effective tool, especially for dealing with heterogeneous datasets. In this study, we conducted a systematic literature review (SLR) to determine the current state-of-the-art knowledge for integrating remote sensing and cloud computing in the monitoring of wetlands. The results of this SLR revealed that platform-as-a-service was the only cloud computing service model implemented in practice for wetland monitoring. Remote sensing applications for wetland monitoring included prediction, time series analysis, mapping, classification, and change detection. Only 51% of the reviewed literature, focused on the regional scale, used satellite data. Additionally, the SLR found that current cloud computing and remote sensing technologies are not integrated enough to benefit from their potential in wetland monitoring. Despite these gaps, the analysis revealed that economic benefits could be achieved by implementing cloud computing and remote sensing for wetland monitoring. To address these gaps and pave the way for further research, we propose integrating cloud computing and remote sensing technologies with the Internet of Things (IoT) to monitor wetlands effectively.

Endocrine-disrupting compounds (EDCs) are emerging environmental pollutants that are known to the disrsupt hormonal system of many vertebrates. Amphibians, with their aquatic larval stages and high sensitivity to waterborne contaminants, are especially vulnerable to EDC exposure. Despite increasing concerns over EDC pollution, systematic monitoring of these compounds in surface waters remains limited in many regions, including the European Union. This study investigates the effects of water from the Warta River, one of the largest rivers in Central Europe, an urban waterway subjected to significant anthropogenic pressure and known to contain EDCs on body condition, digit ratio, and gonadal development in two brown frog species: the common frog Rana temporaria and the moor frog Rana arvalis. We propose DR as a potential biomarker of endocrine disruption, as it is linked to hormonal impact in the early development of vertebrates. In this study, tadpoles were reared in the semi-open experimental setup with tanks containing river or potable tap water as a control. Contrary to expectations, no significant differences were observed in body condition, digit ratio, or gonadal structure, suggesting that EDC concentrations in the river water may not have been high enough to induce detectable effects. However, a consistent relation between DR and sex was observed in both species, underscoring its potential as a biologically meaningful trait. Notably, the potable tap water used as a control exhibited contamination levels comparable to the river water, raising concerns about the efficacy of current water treatment methods and highlighting the challenges of establishing true reference conditions in environmental studies.

The rising global temperature makes understanding the impact of warming on plant physiology in critical ecosystems essential, as changes in plant physiology can either help mitigate or intensify climate change. The northern peatlands belong to the most important parts of the global carbon cycle. Therefore, knowledge of the ongoing and future climate change impacts on peatland vegetation photosynthesis is crucial for further refinement of peatland or global carbon cycle and vegetation models. As peat moss (Sphagnum spp.) and bog cranberry (Vaccinium oxycoccos L.) represent some of the most common plant functional groups of peatland vegetation, we examined the impact of experimental warming on the status of their photosynthetic apparatus during the early vegetation season. We also studied the differences in the winter to early spring transition of peat moss and bog cranberry photosynthetic activity. We have shown that peat moss starts photosynthetic activity earlier because it relies on light-dependent energy dissipation through the winter. However, bog cranberry needs a period of warmer temperature to reach full activity due to the sustained, non-regulated, heat dissipation during winter, as suggested by the doubling of photosystem II efficiency and 36% decrease in sustained heat dissipation between the mid-March and beginning of May. The experimental warming further enhanced the performance of photosystem II, indicated by a significant increase in the photosystem II performance index on an absorption basis due to warming. Therefore, our results suggest that bog cranberry can benefit more from early spring warming, as its activity is sped up more compared to peat moss. This will probably result in faster shrub encroachment of the peatlands in the warmer future. The vegetation and carbon models should take into account the results of this research to predict the peatland functions under changing climate conditions.

The Fluorescence Explorer (FLEX) mission aims to monitor vegetation sun-induced chlorophyll fluorescence (SIF), a proxy for ecosystem health. Studying photosynthesis and its relationship with SIF provides valuable insights into the physiological responses of ecosystems to environmental stress. Peatlands are among the most valuable ecosystems in the carbon cycle, acting as carbon storage and sinks in normal conditions but becoming carbon sources in drought conditions.This study investigates the correlation between SIF and Gross Primary Productivity (GPP) in the temperate peatland in Poland. Measurements by the FloX system with a temporal resolution of ~30 seconds between June and October 2019 and CO2 fluxes measured by manual chambers were used for this purpose. GPP was calculated from the subsequent net ecosystem exchange and respiration measurements, and the gaps between measurements were modelled with a Michaelis-Menten rectangular hyperbola. SIF was retrieved using the Improved Fraunhofer Line Depth (iFLD) Spectral Fit Method (SFM) and Spectrum Fitting (SpecFit) algorithms in the O2-A and O2-B bands. The data were analysed at different time intervals (30 min, 1 h, 3 h, whole day, and the entire dataset).Our results show that the strength of the SIF-GPP relationships changes significantly with time interval. Correlations tend to weaken or break (r2

Peatlands, critical global carbon sinks, are facing increasing threats from climate change-driven heatwaves and droughts. These threats can cause a midday depression in carbon uptake through photosynthetic inhibition. Using high-temporal-resolution solar-induced chlorophyll fluorescence (SIF; ~30 s) and chamber-based CO₂ flux measurements, we investigated the coupling between SIF and gross primary production (GPP) during extreme events (air temperature > 25 °C and vapour pressure deficit > 15 hPa) in a northern peatland. Our results show that SIF tracks GPP closely under non-stress conditions (daily R² = 0.86–0.96). However, during combined heat and drought stress, midday correlations collapsed (Case A: R² = 0.04 on 27 June; Case B: R² = 0.15 and 0.01 on 29 and 30 June, respectively), indicating severe decoupling. Importantly, we discovered legacy effects from multi-day heat exposure: on 26 June, vegetation with prior cumulative stress (Case A) showed weak morning coupling (R² = 0.07), while vegetation without prior stress history (Case B) maintained strong coupling (R² = 0.93). This suggests that cumulative stress alters baseline physiology and can exacerbate midday mismatches; therefore, not just current condition controls photosynthetic regulation. These findings highlight limitations of SIF-based GPP estimation at sub-daily timescales during stress, particularly in heterogeneous peatland systems where canopy composition and physiological responses could vary among plant functional types.