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.

Vegetation phenology is an important indicator of climate change and ecosystem productivity. However, the monitoring of vegetation generative phenology through remote sensing techniques does not allow for species-specific retrieval in mixed ecosystems; hence, land surface phenology (LSP) is used instead of traditional plant phenology based on plant organ emergence and development observations. Despite the estimated timing of the LSP parameters being dependent on the vegetation index (VI) used, inadequate attention was paid to the evaluation of the commonly used VIs for LSP of different vegetation covers. We used two years of data from the experimental site in central European peatland, where plots of two peatland vegetation communities are under a climate manipulation experiment. We assessed the accuracy of LSP retrieval by simple remote sensing metrics against LSP derived from gross primary production and canopy chlorophyll content time series. The product of Near-Infrared Reflectance of Vegetation and Photosynthetically Active Radiation (NIRvP) and Green Chromatic Coordinates (GCC) was identified as the best-performing remote sensing metrics for peatland physiological and structural phenology, respectively. Our results suggest that the changes in the physiological phenology due to increased temperature are more prominent than the changes in the structural phenology. This may mean that despite a rather accurate assessment of the structural LSP of peatland by remote sensing, the changes in the functioning of the ecosystem can be underestimated by simple VIs. This ground-based phenological study on peatlands provides the base for more accurate monitoring of interannual variation of carbon sink strength through remote sensing.

Gross primary productivity (GPP), the primary driver of carbon accumulation, governs the sequestration of atmospheric CO₂ into biomass. However, GPP cannot be measured directly, as photosynthesis and respiration are simultaneous. At canopy level in plot-scale studies, GPP can be estimated through the closed chamber-based measurements of net ecosystem exchange (NEE) and ecosystem respiration (Reco). This technique is cost-effective and widely used in small-scale studies with short vegetation, but measurements are periodic-based and require temporal interpolations. The rectangular hyperbolic model (RH) was the basis of this study, developing two temperature-dependent factors following a linear and exponential shift in GPP when the temperature oscillates from the optimum for vegetation performance. Additionally, a water table depth (WTD)-dependent model and an exponential model were tested. In the peak season, modified RH models showed the best performance, while for the rest of the year, the best model varied for each subplot. The statistical results demonstrate the limitations of assuming the light-use efficiency as a fixed shape mechanism (using only one model). Therefore, a multi-model approach with the best performance model selected for each period is proposed to improve GPP estimations for peatlands.

This study explored the mitigating effect of silicon (Si) on drought- and waterlogging-induced stresses in common buckwheat. Twenty-day-old plants were subjected to 15 days of drought or waterlogging followed by a 15-day recovery. Silicon was applied twice via foliar spray. Both stresses significantly reduced plant growth: height, fresh weight, and dry weight decreased by 54-62% under drought and 46-74% under waterlogging. Silicon supplementation significantly mitigated these reductions. During recovery, Si-treated plants exhibited further improvements: with height increasing by 5-7%, fresh weight by 18-34%, and dry weight by 30-33% under drought and waterlogging, respectively. Stress also impaired physiological status. Relative water content (RWC) decreased by 33% (drought) and 37% (waterlogging), while electrolyte leakage (EL) increased by 44% and 39%, respectively. Silicon maintained higher RWC (by 23-25%) and reduced EL (by 23-36%), indicating improved cellular integrity. Gas exchange parameters declined sharply with photosynthetic rate (P_n) decreasing by 84-98%, stomatal conductance (g_s) by 66-75%, and transpiration rate (T_r) by 65-86% under both stresses but improved with Si application during recovery (P_n by 4-65%, g_s by 60-65%, T_r by 96-245%). Fluorescence transients revealed higher PSII disruption under waterlogging (evidenced by positive K-band) than drought. Silicon application suppressed K-band formation and restored PSII efficiency. Overall, Si alleviated drought- and waterlogging-induced stress, accelerated recovery, and enhanced photosynthetic performance in buckwheat. These findings indicate that Si supplementation could serve as a valuable approach for strengthening plant resilience in environments prone to extreme moisture fluctuations.

Current climatic conditions are drying peatland ecosystems, compromising carbon storage through increased decomposition and vegetation shifts. Large-scale monitoring is essential to quantify climate change impacts on vegetation and hydrology. PlanetScope high-resolution imagery (3 m pixel) over seven years (2017–2023) served as proof-of-concept for a central European peatland (Rzecin, Poland). The enhanced vegetation index (EVI) was selected based on ground validation (R = 0.9 vs. 0.8 for NDVI-normalised vegetation index). Phenological metrics (SOS—start of the season; EOS—end of the season; LOS—length of the season; POS—peak of the season; EVImax; amplitude; area) were derived via DATimeS from snow-free EVI time series. Trends were analysed using pixel-wise slopes, change-point detection (break ~2020–2021), paired correlations, subarea (P1–P4) behaviour, and PCA, alongside air temperature (Tair), precipitation, and water table depth (WTD). Results revealed LOS and peak EVI increased until 2020, a 2021 break, and a 2022–2023 recovery, signalling nonlinear vegetation reorganisation. Transitional mire floating mats (Sphagnum spp.–Carex spp.–Vaccinium oxycoccus) showed the longest seasons/highest greenness but weakest hydrometeorological links, implying rising internal dynamics. Phragmites mats, fern–sedge edges, and riparian willow differed in tolerance or sensitivity to WTD and precipitation oscillations. Tair dominated EVI seasonality across types, while WTD and precipitation controlled phenology and greenness in edges, showing better results with phase-aligned means. Vascular plants outpaced mosses in peak EVI and persistence, with patch-specific shifts.