High-resolution Satellite-derived Changes in Vegetation Phenology and Lake Area in a Central European Peatland

High-resolution Satellite-derived Changes in Vegetation Phenology and Lake Area in a Central European Peatland | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article High-resolution Satellite-derived Changes in Vegetation Phenology and Lake Area in a Central European Peatland Mar Albert-Saiz, Michal Antala, Marcin Stróżecki, Anshu Rastogi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7148738/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Feb, 2026 Read the published version in Remote Sensing → Version 1 posted You are reading this latest preprint version Abstract Current climatic conditions are leading to the drying of peatland ecosystems, compromising their ability to store carbon due to increased decomposition and vegetation shifts. Large-scale monitoring of peatlands is thus essential to quantify the impacts of climate change on their vegetation and hydrology. A central European peatland was studied using PlanetScope high-resolution imagery over seven years as a proof of concept. The results have shown prolonged vegetation season and increased peak value of the Enhanced Vegetation Index due to the changing climate conditions. Higher than average temperatures negatively affected vegetation characterised by higher moss abundance. However, areas dominated by vascular plants have higher greenness and extended vegetation seasons despite elevated temperatures. Moreover, the lake situated in the area has shown a drying pattern, increased intra-annual variations, and a relationship with peatlands’ water table depth dynamics. Hence, the drying reduces the lake area while the peatland part experiences a progressive vegetation shift and phenological changes. Earth and environmental sciences/Climate sciences Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Phenology peatland water table depth (WTD) Enhanced Vegetation Index (EVI) PlanetScope Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Peatlands sustain the global carbon cycle, acting as reservoirs for around 40% of the terrestrial carbon stored in soils 1 . The vegetation is one of the main drivers of this cycle, determining the amount of atmospheric CO 2 sequestered into plant biomass 2 . Over the past few decades, the water table depth (WTD) oscillations have become more extreme due to climate change, leading to an anticipated drying of the peatland’s ecosystem that could result in shifts in vegetation composition 3 , 4 . The new climatic conditions destabilised these ecosystems, potentially transforming them from carbon sinks to carbon sources 5 . Furthermore, their drying makes them more susceptible to fires, which would release more carbon to the atmosphere. Their importance in the global carbon cycle and the need for their conservation/restoration make improving our understanding of peatland adaptation to climatic conditions crucial 6 . Beyond its impact on the carbon cycle, vegetation phenology changes also serve as climate change indicators 3 . For these reasons, monitoring the changes in vegetation phenology and coverage is important to estimate the already present consequences of climate change. Peatlands are considered ecosystems with high heterogeneity of vegetation, particularly in the case of poor fens 7 . Satellites usually provide ≥ 10m spatial resolution, which may be insufficient to detect the heterogeneity of the peatlands’ vegetation. The challenge of analysing vegetation phenology in peatlands stems not only from their heterogeneous vegetation but also from their high water content and periodic waterlogging, which contribute, among others, to more haze episodes than typically observed in other areas. However, high-resolution satellites, such as the PlanetScope ensemble with 3m/pixel, allow the identification of vegetation patches and their growing seasons more properly. The use of PlanetScope across multiple growing seasons, ecosystem types, and climate domains has proven effective in capturing fine-scale spatial variation in vegetation phenology that is not resolved at moderate spatial resolutions, making it a recommended tool for studying canopy-scale phenology 8 . Moreover, PlanetScope revisit time is less than two days, with multiple images available daily, which allows the user to obtain at least two images per month despite the haze. This approach with new high-resolution imagery can help to estimate vegetation phenology and vegetation index oscillations better, increasing our understanding of their dynamics and reactions to climate change. Peatland areas are often associated with water bodies, such as lakes, inside their area or surroundings 9 . Hence, drying caused by drought periods or human activities alters vegetation phenology and negatively impacts water bodies. In the studied area, the lake has suffered several dryings over the years 10 . The lake’s shallowness produces drying in patches; therefore, to properly study the changes in the water body surface, we established the thresholds for the dry areas inside the Land Water Mask (LWM). The changes in lake surface area have often been positively correlated with vegetation phenological changes 11 . Studying the joint dynamics and their coupling with WTD measurements enhances our understanding of seasonal drying patterns and the long-term impacts of climate change, providing valuable insights into the peatland’s hydrological and phenological dynamics. Therefore, in this study, we examined the impact of ongoing climate change on the vegetation phenology and lake area in the Rzecin central European peatland using a time series of high-resolution satellite imagery and on-site WTD and meteorology measurements. The novelty of this work lies in combining PlanetScope imagery with in-situ data to study long-term phenological changes in vegetation, which have never been addressed in this area. This integrated analysis provides new insights into the hydrometeorology influences on vegetation phenology across the entire Rzecin peatland and its different vegetation subareas. The article tests the accuracy of unsupervised methods (segmentation and clustering) to identify dry areas in the lake surface, considering internal drying and not only shrinkage, which has never been done for Rzecin’s lake. The expected results considering the continuous drop in WTD observed in the area are: i) a general enlargement of the length of the growing season and increased greenness with the proliferation of vascular plants; ii) a higher dependence on WTD’s changes in the transitional areas of the peatland with mixed vegetation including mosses; iii) a more direct link with meteorology in the areas with a more stable dominance of shrubs and vascular plants with the vegetation shift not masking the link with vegetation indexes; iv) a continuous drying of the lake showing higher interannual oscillations by time and the summer season showing an increment of dry areas (shrinkage plus inside “dry islands”. Results Changes in vegetation phenology General trends of the vegetation season parameters in the peatland area The robustness of satellite EVI values was higher than satellite NDVI values in the 18 ground points. EVI, when compared with in situ data, the similarity in trends was higher for EVI (NDVI—R = 0.8, p-value = 4.1e-06, RMSE = 0.09; EVI—R = 0.9, p-value = 1.5e-07, RMSE = 0.08). Based on these results, EVI was used to analyse vegetation phenological parameters. The peatland displayed a general rise in the length of the vegetation season from 2018 until 2020. The LOS changes were caused by the delay of the end of the season from late October to early November, while the SOS remained stable around the end of April (± 5 days) until 2021, when it was delayed to 12th May (Table 1 ). The prolongation of the vegetation season during these years was accompanied by the delay of the POS from 1st week of July 2018 to 1st week of August 2020 with a stable peak value of EVI (0.4663 ± 0.0167). Starting from 2021, the POS remained stable around the 3rd week of July, while the EVI value rose by around 0.1. In 2021, the LOS dropped drastically from 192 to 157 days (Table 1 ) due to a cold spring and dry spells during July-August and October. Table 1 The peatland area’s average Length of the Season (LOS) expressed in days, the Start and End of the Season (SOS, EOS), and the maximum value of Enhanced Vegetation Index (EVI max ) with the Peak of the Season (POS) date. LOS SOS EOS EVI max (POS) 1 185 30-04-2017 01-11-2017 0.480 (10-07-2017) 2 175 29-04-2018 21-10-2018 0.477 (04-07-2018) 3 182 05-05-2019 03-11-2019 0.447 (02-08-2019) 4 192 29-04-2020 07-11-2020 0.476 (02-08-2020) 5 157 12-05-2021 16-10-2021 0.574 (22-07-2021) 6 175 07-05-2022 29-10-2022 0.569 (24-07-2022) 7 194 30-04-2023 10-11-2023 0.502 (22-07-2023) The pattern of prolonging the season length broken during 2021 was recovered in 2022–2023. There was an increase in the LOS, with the SOS returning to late April and the EOS progressively returning to early November (Table 1 ). Moreover, the maximum value of EVI (EVI max ) started to decrease in 2021, and the peak date remained around 25th July ± 5 days (Table 1 ). The phenology of the vegetation in the peatland changed over the years studied, and the differences are visible when comparing the parameters of 2017 and 2023 (Fig. 1 ). The vegetation season was generally delayed, as is visible in SoS, POS and EOS data (Table 1 , Fig. 1 ). The most visible increment on the day of SOS and POS is shown in Fig. 1 with red colours during 2023 at the west-northwest corner of the peatland where Phragmites australis (Cav.) Trin. ex Steud dominates. Conversely, some areas showed earlier SoS during 2023. The exceptions of the general trend can be observed, for example, in the area with SOS at DOY 100–120 in 2023 and DOY 120–140 in 2017 at the southwest of the lake (Fig. 1 ). The changes for the EOS showed fewer differences in the peatland. There was a general delay during 2023 for almost all areas with EOS located in DOY 330–350 (Fig. 1 ). Phenology of vegetation patches The changes in vegetation phenology did not follow the same trend in all the peatland areas, as shown in Fig. 1 . These differences were analysed further with the selected vegetation patches (Fig. 6 ). The differences among patches/vegetation were observed through the start and end of the season. The general pattern of P1 showed the season’s earliest start and the latest end. P3 and P4 followed the P1 SOS; the latest was during the whole period for P2 ( Phragmites -dominated). Generally, the season ended the soonest for P4, followed by P2 and P3, with their trends converging in 2023. Furthermore, the earliest POS occurred in P3, followed by P4 and P1, with P2 as last. In the case of LOS, P2 presented the shortest season, followed by P4, P3 and P1 due to the combination of EOS and SOS trends (Table 2 ). Table 2 The Length of the Season (LOS) in days for each vegetation patch (P1-P4). Patches are described in the Methodology. P1 P2 P3 P4 2017 209 182 188 183 2018 222 161 193 161 2019 220 132 187 168 2020 221 160 199 176 2021 186 136 167 153 2022 205 146 183 164 2023 210 159 194 184 The changes in the LOS for each patch were quite similar, with certain stability in P1, P3, and P4 from 2017 to 2020, while P2 LOS became shorter (Table 2 ). During the second half of the period, a V-shaped trend was observed for all patches, with the shortest LOS in 2021. The POS tended to shift forward during the studied period in all patches except the tall shrub-dominated P3, where from 2019 to 2022, the POS occurred sooner each year (Supplementary Table 3). The shifts for the season end were shared for P2-P4, with a slightly sooner EOS in 2018 followed by a continuous delay until 2020 and again from 2021 until 2023. The trend was more continuous for P1, with an advancement of the EOS from 2017 to 2023. Lastly, the oscillations of SOS in the 7 years of analysis were lower than ± 10 days in P3 and P4. In the case of SOS for P1, it occurred earlier each year during 2017–2020 and again from 2021 to 2023. Moreover, P2 showed two different patterns of SOS, shifting later in the year from 2017 to 2019 and the inverse from 2019 until 2023. The role of hydrometeorological variables EVI changes The shared trends of hydrometeorological variables and the EVI averaged per patch, or the total study area, are visible in Fig. 2 with the time series of EVI value (Fig. 2 a) and its linking with air temperature and WTD (Fig. 2 b). EVI trends and air temperature show a Gaussian shape peaking during the summer (Fig. 2 ). At the same time, in the case of the WTD interannual trend, the link with EVI is observed in a reversed way (the lowest WTD is linked to the Tair and EVI peaks) and with a time lag (Fig. 2 ). Analysing further the role of each variable in the EVI trends, the lag in EVI’s response to changes in the hydrometeorological variables was established for the different areas. The cross-correlation analysis in weekly and monthly timesteps revealed a delay in the response of EVI to WTD changes but not to air temperature and precipitation oscillations (Supplementary Tables 5–6). The delay or lag was different for each vegetation patch. The maximum lag in the EVI response was detected in P2, followed by P1, the general area, P4, and P3 (Table 3 ). The differences in the lag in the EVI response decreased as the timestep of the data increased, smoothing the distinction between vegetation areas. Weekly averages of WTD showed the same lag in P1 and the general peatland area, and monthly averages showed the same lag in all areas (Table 3 ). Table 3 EVI delay in response to changes in hydrometeorological variables based on the maximum cross-correlation. The delay is expressed in days for daily data, weeks for weekly data and months for monthly data. Note that 2017 was excluded from analyses due to missing reliable WTD data. P1 P2 P3 P4 General Daily 34 40 20 29 36 Weekly 5 6 3 4 5 Monthly 1 1 1 1 1 The lags were applied to displace the WTD time-series in order to better analyse the role of each hydrometeorological variable in the PLSR. The results pointed out that air temperature governs the oscillations of EVI, followed by WTD with a minimum effect of precipitation both for weekly and monthly time-series (Fig. 3 ). The role of precipitation increased from weekly to monthly values. However, it was still not significant, explaining less than 10% of the EVI changes and with a VIP score below 0.15 (Fig. 3 , Supplementary Table 7). The area where the air temperature was the least important, for a percentage of the variance of EVI explained and VIP score was P4 (Fig. 3 , Supplementary Table 7). At the same time, P4 was also the area where WTD importance was the highest (Fig. 3 , Supplementary Table 7). The similarity in P1 and general area lag was observed again with the monthly and weekly VIP scores of Tair and WTD (Supplementary Table 7). However, the role of precipitation was higher in P1. Moreover, the percentage of EVI oscillations explained by individual variables or their combination was lower in P1 than in the general area (Fig. 3 ). In the case of P3, this was in both timesteps, the area where the least oscillations of EVI could be explained by hydrometeorology (Fig. 3 ). P2 was the area where the EVI changes could be explained the most by hydrometeorology (Fig. 3 ). Changes in Vegetation Phenology In the general peatland area, regression analysis results showed that the changes in LOS can be explained by temperature and WTD oscillations over the years. The LOS and maximum EVI (EVI during the POS, EVI max ) prediction by air temperature and WTD produced a good estimation with relatively low RMSE and high R 2 (Supplementary Fig. 1a-b). However, the fit of the EVI max was more significant (p-value < 0.05) than the LOS fit (p-value < 0.1; Supplementary Fig. 1). The changes in phenology trends described with the break in 2020–2021 were also observed in air temperature. While the air temperature at the beginning of 2019 and 2020 was generally above 0ºC, it was below 0ºC in 2021, reaching a freezing temperature more frequently (Fig. 2 b). In addition, the oscillation of WTD also showed a similar pattern to the changes in vegetation season. While WTD in 2019–2020 remained in a yearly average similar (-13.1cm, -13.9cm), it increased during 2021 by ~ 3 cm (annual average), showing levels that did not reach the surface as in the previous years (Fig. 2 b, Supplementary Table 1). From 2021, WTD showed a stronger increasing trend, reaching its deepest point during summer-autumn 2022 and recovering the levels of 2021 during 2023 due to increased precipitation (Fig. 2 b, Supplementary Table 1). The higher air temperatures and lower WTD were aligned with the increase in the greenness of the patches during 2021 and the general peatland area (Fig. 2 a-b), while the length of the season was drastically reduced, affected mainly by the changes at the start of the season due to colder conditions (Supplementary Table 1, Fig. 2 b). More concretely, in the patches scale, the regression analysis results of WTD, air temperature, and LOS showed again the importance of the hydrometeorological variables for different vegetation canopies (Table 4 , Fig. 4 ). The strongest relationship of these parameters was found in P1 in both cases (LOS and EVI max regressions), displaying the most similar modelled and measured results with low RMSE, high R 2 and high significance (Table 4 , Fig. 4 ). In the case of LOS, the Phragmites spp.-dominated patch (P2) showed the weakest results with no significance of WTD and air temperature as estimators, high RMSE and low R 2 (Table 4 , Fig. 4 ). P2 also presented a non-significant result in the case of EVI max regression but with a relatively high R 2 and low RMSE (Table 4 ). The area with the second highest R 2 and significance in the LOS regression was the Salix spp.-dominated patch (P3, Table 4 ), which also had a high similarity in modelled and measured LOS (Fig. 4 ). However, it was in the same patch (P3) where the least linked EVI max and WTD + air temperature relationship was found (Table 4 ), corresponding to the most stable peak value through the time series with a range of change in EVI max of 0.11 in front of 0.13–0.18 for the rest of the cases. Lastly, the P4 presented a good result for the peak EVI estimation, the second best, while the LOS result showed no significance and relatively high errors, the second worst result (Table 4 ). Table 4 Coefficient of determination (R 2 ), p-value (p-val), Root Mean Square Error (RMSE) and equation of the linear regression between A ) the Length of the Season (LOS; or B ) the maximum value of the Enhanced Vegetation Index (EVI max ; with the annual average of water table depth (WTD) and air temperature (Tair) based prediction for the vegetation patches (P1 – P4). Patches are described in the Methodology. A R 2 p-val RMSE Equation P1 0.95 0.012 4.12 LOS = 27.945 + 0.8364WTD + 20.41T air P2 0.16 0.772 15.28 LOS = 70.031 + 0.1161WTD + 8.43T air P3 0.81 0.080 6.32 LOS = 19.730–0.0077WTD + 17.46T air P4 0.62 0.238 8.83 LOS = − 3.333–1.1575WTD + 15.98T air B P1 0.87 0.045 0.022 EVI max = 1.057–0.0040WTD − 0.06T air P2 0.73 0.141 0.028 EVI max = 1.114–0.0003WTD − 0.06T air P3 0.70 0.163 0.032 EVI max = 0.539–0.0086WTD − 0.02T air P4 0.85 0.060 0.036 EVI max = 0.926–0.0116WTD − 0.06T air Lake surface area oscillations The image analysis of the lake area showed a continuous drying tendency, as shown in the reduction of the area filled with water and the rise of dry spots (Fig. 5 ). The most significant decrease in the water area was observed during the summers of 2022 and 2023 when a larger lake bottom area was exposed, creating temporal “islands”. In addition to drying, the within-year amplitude of the water area increased (Fig. 5 ). The validation showed high accuracy of the lake pixels classified from RGB scenes and an acceptable accuracy for dry pixels (higher than 0.6, Supplementary Table 8). These changes in lake and dry pixels were related to the area’s WTD dynamics. The regressions have proven the relationship between these variables. The WTDs around 0 and − 35 cm presented the most significant outliers of the trends (Supplementary Fig. 2). The dry and water areas’ relationships with WTD were characterised by R 2 of 0.5, with both relationships being statistically significant (p-value < 0.05, Supplementary Table 8). Discussion Changes in vegetation phenology and the influence of hydrometeorology General trends of the vegetation season parameters in the peatland area The peatland area analysis showed changes in vegetation phenology during the study period, with a continuous rise in the LOS and greenness (revealed with EVI maximum values), with the trend only breaking during 2021. The changes in WTD and air temperature explain the break in the phenological changes during 2021 and their continuous trend (Fig. 1 ). The lower annual average temperatures and higher annual average WTD increase the EVI max . Deeper water tables are closely related to the proliferation of vascular plants such as Ericaceous shrubs and Pinus spp ., L and the loss of mosses, with clear relationships reported for peatland areas 3 , 24 . This succession can explain the increase in the average EVI and LOS. Moreover, the lowering of the temperature positively affected the vegetation because 2018–2020 were extremely even anomalously warm 25 , with average annual Tair from 9.75 to 10.08ºC, while the “coldest” years (2021 − 8.48ºC, 2022–9.2ºC) were just normal, and plants did not suffer the stress related to higher temperatures. During 2023, the warmer-than-usual temperatures returned with an annual average similar to 2020. The proliferation of vascular plants due to a deeper water table and higher precipitation (774mm in 2023 vs. 540mm in 2020) lowered the vegetation stress. The reduction of moss coverage and the negative impact of the water table drawdown on them is often counteracted by high precipitation. Increases in precipitation have even compromised studies on the effect of water table drawdowns on peatland vegetation shifts 26 . Our results can be explained by a lower negative impact of increased temperature on vascular plants in comparison to mosses (which desiccate each summer) reported in previous studies 3 , 27 . The change in the temperature pattern in 2021 also justifies the continuous increase of LOS until this year and the recovery of the trend in 2023. Therefore, the results show how unusually warm years negatively affect vegetation, shortening the season and lowering its peak when mosses are more abundant (2017–2020), while deeper water tables induce vegetation succession, increasing the greenness and the LOS (2017–2022). Lastly, the vegetation composition shift to a lower abundance of mosses allows the avoidance of the negative effect of temperature, with 2023 remaining with prolonged LOS and high EVI values 24 . Phenology of vegetation patches The hydrometeorology can strongly influence the seasonality of vegetation patches. A shift in conditions induces growth and possible changes in vegetation dominance when multiple species compete 3 , 24 . This could explain the changes in the vegetation season shape, thus changing the phenological parameters in each patch through the study period. As discussed for the general trends of the peatland, a potential shift to a higher presence of vascular plants in the detriment of mosses can explain the increase in the average greenness of the patches from 2017 until 2022. However, the area where the peak of the season remains the most stable in value and time is dominated by Salix spp. (P3), and the interchange between mosses and vascular plants does not play a role. Thus, there is still the same vegetation, and the dominance of this vegetation does not change due to meteorology. The results presented show that there was a change to more favourable conditions for vascular plants from 2017–2020 to 2021–2023 and a rise in their abundance 28 , which caused an increase in greenness and season length in the second period (Fig. 2 , Table 3 ). However, there is a clear difference in the phenology changes in this period between the areas with a higher mix of vegetation (P1 and P4) and the areas with a clear dominance established (P2 and P3). P2 and P3 are dominated by grasses and shrubs, respectively, and both of these vegetation types usually respond positively to a decline in the water table 29 , 30 , increasing their growth, while species competition in P1 and P4 makes the dynamics more complex. The shared trend shows the increases in LOS progressively, with a delay in the EOS and sooner SOS (2017–2022). The earliest start and latest end of the season were observed for P1 due to the presence of evergreen vegetation ( Sphagnum spp. and V. oxycoccus ; Supplementary Table 2) 31 . This shared trend is disrupted in 2023 when the difference in P1-P4 and P2-P3 appear together with wetter conditions. The increase in precipitation during 2023 changed the SOS tendency, which did not start as soon as the trend forecasted in the case of P2 and P3, and the POS was delayed more than 20 days (Supplementary Table 2–3). While precipitation benefits vascular plant and moss growth (and, in some occasions, shrubs) 32 , the increment of precipitation events also significantly reduced the average photosynthetically active radiation (PAR, data not shown). P2 and P3 vegetation was more affected by this fact, as they are more used to high-light conditions 33 though Phragmites can adapt to low-light on some occasions 34 . This does not happen in the other patches, where the POS changed in less than 5 days, and SOS continued its trend with sooner starts (Supplementary Table 2–3). The increment of precipitation benefited the mosses present in P1 with a rise in the ambient humidity, reducing stress 35 , 36 . Plus, sedges in P1 and P4, larger precipitation events have been marked as inducers of vascular plant growth 32 . The benefits of precipitation and early warmer conditions in spring each year produced the continuation of earlier SOS and similar POS (Fig. 2 b). The stability of the SOS, especially in P3, can be related to a non-apparent change in vegetation composition and a possible stronger role of photoperiod in this vegetation patch 37 . This stability cannot be observed in P2, which presents a reverse trend of the SOS in 2022–2023. This could be the effect of the interference of water reflection in the results as it is the most waterlogged part and remained flooded during the part of the growing period. Another indication of the probable vegetation change is P1 and P4 response to wetter conditions in 2023. Due to the presence of mosses in the mixed vegetation patches (P1 and P4), a positive effect should have been seen following wetter conditions during summer, when mosses are usually dry 28 , 38 . However, this is not observed (Fig. 3 , Supplementary Table 2–3), indicating a low contribution of mosses to the patches’ average EVI max and EOS. Therefore, the individual trends of the patches indicate a similar story to the general trends of the peatland, with an increasing abundance of vascular plants and vegetation seasons ending later, enlarging the season. EVI linkage with hydrometeorological variables The study of WTD, air temperature, and precipitation’s role in the EVI oscillations revealed a difference in the behaviour of vegetation patches. The delay in the response of WTD changes corresponds to the differences in the area base level and regime of WTD plus the vegetation requirements of water. P3 and P4 showed the quickest response of WTD changes, which are the areas on the edge of the peatland with the deepest WTD and no peatland surface oscillations. Additionally, peatlands’ margin/edge areas are usually transitional between peatlands and the adjacent ecosystems 39 , 40 . Therefore, due to greater hydrological variability and the transitional nature of these edge areas, the sensitivity to WTD fluctuations is usually more pronounced than in other peatland parts 41 . In the case of the other two patches, P2 and P1, the difference in their response to WTD changes resides in the speed of the adaptations by the vegetation. P. australis quickly adapts to changing WTDs by extending its roots and rhizomes to access water or quickly proliferating in flooded rewetted sites with aggressive growth 42 , 43 . Meanwhile, the P1 area had a greater delay in the response for WTD changes because the dominant vegetation relies on near-surface soil moisture as the water source, and none has the high plasticity of P. australis to adapt. V. oxycoccus Carex spp. rely on this soil moisture due to a shallower root system 44 , 45 , while Sphagnum spp. with no root system relies on this moisture as they access water by capillarity 46 . The soil moisture reacts slower to WTD changes as ambient humidity acts as a buffer 47 . Hence, the vegetation response also slows down. The study of the role of hydrometeorological variables showed the worst result – the lowest percentage of variance explained – in the area dominated by Salix spp. (P3) in weekly and monthly analysis. Salix spp. showed the most stable trend of EVI, as already observed for EVI max , and therefore the highest resistance to hydrometeorological changes also because it is the area with the least possibility of vegetation succession. This woody species has already been defined as resistant and stable in greenness 48 , 49 , hence supporting the results. In the other edge area, P4, results showed WTD’s highest importance in EVI changes among all vegetation types and the lowest for air temperature. This can be explained by the transitional nature of the vegetation in P3, with WTD apparently showing a bigger role in the EVI changes. The air temperature was the most crucial for the P2 area. With the already explained plasticity of P. australis to WTD, PLSR established again the importance of temperature and the lower impact of WTD when compared to other vegetation 42 , 43 . In the case of P1, the lower capability of temperature and WTD to explain changes in EVI could be led by changes in the vegetation cover, which has already been pointed out as a possible area of decreased moss cover in favour of other vascular plants. The reduction of moss cover in peatlands following deeper water tables is often observed 50 . In general, a rapid response to WTD oscillations in the edges area of the peatland was observed. Still, it showed less impact when dominated by woody vegetation, as there is no possible succession in the conditions studied (not severe WTD changes). Additionally, vegetation has a delayed but more severe response to WTD in more central parts of the peatland. The air temperature plays the main role in vegetation changes for all areas, with woody vegetation showing the highest resistance as it is not affected in P3 by a possible mosses’ desiccation or vegetation succession. Future reactions, thus, are expected to induce major changes in central areas with probable Sphagnum spp. loss, and in the transitional edge area – P4 – the proliferation of P. asutralis to the detriment of the rest of the species could be expected. Lake surface area oscillations The continuous trend within the lake area has shown an increase in the summer drying and more pronounced intra-annual oscillations. The appearance of dry patches and shrinking of the lake is demonstrated with the continuous linear trend of the time series, and though during July-August 2023 we can observe similar extents of the water areas than in 2018, it only occurs for shorter periods (2–3 months), and it is not enough to compensate the high drops of the water area during the year. The lake drying relates to the WTD movement in the peatland; higher WTDs are coupled with a higher amount of dry area, demonstrating the hydrological linkage of the waterbody with the peatland’s groundwater source. The remanent lake in the peatland can be considered small; thus, the linked watershed is less complex and not able to support the lake and palliate its drying during droughts, showing less resilience than larger lakes 11 . The drying tendency of Rzecin peatland’s lake observed in previous studies 10 not only continues but also increases in severity and length, including, apart from the lake shrinkage, the appearance of dry spots inside, creating temporal “islands” during the driest months. The partial drying observed in this study induces hydrological and biological transformation of the peatland area. The partial lake drying impacts the submerged vegetation and the vegetation near it 51 . The changes in surrounding vegetation can also be observed in this study with the phenological changes from Fig. 1 . Moreover, the wildlife may also suffer from the lake drying, modifying the number of birds and fish with a decrease in potential food sources and availability of water 51 , 52 , a succession of chain effects that can lead to a full drying of the area and irreversible changes if no measures are taken. Implications of the expected future changes in vegetation and lake area The results indicated a probable vegetation shift with vascular plants’ proliferation. Particularly with elevated temperatures, vascular plants can induce the acceleration of carbon loss from peat soils with higher litter deposition 53 . Additionally, the encroachment of vascular plants, particularly sedges, can influence methane emissions by acting as conduits for methane transport from peat to the atmosphere, thereby affecting greenhouse gas fluxes 54 . On the bright side, in this article, the presence of vascular plants increased the vegetation cover resilience to phenology changes with fewer changes in LOS and greenness thanks to their proliferation. The increment of LOS may shift the CO 2 patterns, with larger periods in which carbon uptake is higher. However, warmer conditions can cancel this positive effect with rises in vegetation respiration, as has been observed in other peatland areas 55 . The loss of water within the lake and the increased drying severity during the warmer months also potentially increase C losses. The exposure of organic-rich sediments to oxic conditions results in CH 4 and CO 2 emissions 56 . Therefore, future conditions would probably establish the area (peatland and lake) as an important source of C losing their climate change mitigation service. The rest of the ecosystem services provided by the peatland and lake area will also be compromised. The shifts in plant community composition can alter nutrient cycling and water regulation functions. The higher presence of vascular plants will increase the evapotranspiration rates, and together with higher temperatures and WTD, the exposure of peat to oxic conditions will induce its degradation 5 , 53 . Peat decomposition will compromise the peatlands’ water purification, biodiversity and flood palliation ability 6 . Additionally, the loss of mosses and the encroachment of vascular plants can affect the quality of dissolved organic matter, influencing microbial activity and nutrient availability, further compromising water purification and habitat provision 57 , 58 . As previously mentioned, the lake area will lose biodiversity and the potential for climate regulation. Its drying will compromise the water supply and, from an economic perspective, affect, for example, local agriculture 59 . Conclusions The data analysed in this article for the Rzecin peatland have shown the changing behaviour of the area’s vegetation phenology and the differences depending on the type of vegetation cover. Moreover, the lake located in the area has shown a consistent drying pattern with an increment in the severity of interannual oscillations. Summarizing, the key points extracted were: A probable proliferation of vascular plants over mosses produced an increase in greenness (EVImax) and growing season length (LOS). Warmer years stressed mosses, but the expansion of vascular plants (Ericaceous shrubs, Pinus spp.) helped sustain greenness and growing season length despite temperature fluctuations. The mosses showed a low contribution to the patches’ average EVImax and EOS. The increase in precipitation of 2023 should have produced a delay in mosses’ desiccation, due to their abundance drop or proliferation of vascular plants, this “positive” effect was not observed. The lag in vegetation response to WTD was the quickest in peatland edge areas (P3, P4) due to their transitional nature and greater hydrological variability. While in the central part the lag response depended on the vegetation. P. australis adapted rapidly to WTD shifts, possibly through root extension and aggressive growth, while P1 vegetation (e.g., V. oxycoccus, Carex spp., Sphagnum spp.) relied on near-surface soil moisture, delaying its response. Temperature was the most influential factor in EVI changes across most areas, except in the transitional peatland area (P4), where WTD had the strongest impact. Woody vegetation (Salix spp. in P3) showed the highest stability and resistance to temperature and hydrometeorological fluctuations. The lake shows a continuous drying trend with more pronounced intra-annual fluctuations linked to peatland water table depth (WTD) changes showing the lake’s dependence on peatland groundwater. In near future perspectives the results highlight how prolonged deeper water tables may reduce Sphagnum spp. in central peatland areas, while transitional areas (P4) may see an expansion of P. australis, potentially outcompeting other species. Furthermore, the shrinking lake and emerging dry patches will impact the submerged vegetation, surrounding plant communities, and wildlife, potentially leading to long-term ecosystem transformations and biodiversity loss if no intervention occurs. Materials and Methods Study area The Rzecin area is crucial for regional nature conservation, supporting diverse vegetation, including brown mosses, Sphagnum, and various vascular plants. In the area, 26 rare and endangered species and 20 locally threatened species can be found 12 . The peatland provides varied ecological conditions with differences in fertility and water availability, fostering a rich biodiversity of 127 vascular plant species from 84 genera and 43 families, 17 of which are on the Regional Red List. Additionally, 34 moss taxa from 10 families, including endangered species like Sphagnum fuscum and Paludella squarrosa , contribute to the site’s ecological significance 12 , 13 . Human activity has significantly altered the development of the Rzecin peatland over the past two centuries. Between 1880 and 1890, deforestation and the construction of the Rzecin drainage canal initiated major alterations in the wetland ecosystem, leading to habitat acidification and changes in hydrology 12 , 13 . The Rzecin peatland is a large (87 ha) mesotrophic, geogenous wetland, a poor-rich fen (area code Natura 2000: PLH300019) located in Western Poland (52°45′ N latitude, 16°18′ E longitude, 54ma.s.l.) 7 , 14 . In the centre of the peatland, an approximately 70-cm- thick-floating mat of peat substrate is present. In contrast, the peat surface is more anchored to the bedrock through the periphery, producing differences in vegetation and deeper WTD 14 . Due to the presence of a floating mat, the WTD seasonal dynamics are much less in the middle of the peatland with a higher WTD position than at its edge, determining the dominance of Sphagnum spp., Carex rostrata, C. limosa, Eriophorum angustifolium and Oxycoccus pallustris in the middle of the peatland. While at its edge, the dominant plant communities are represented by taller vascular plants like Phragmites australis, Typha latifolia, Carex elata and Salix spp. 14 . For the purpose of this study, four homogenous subareas are selected to represent the different vegetation communities present, dominated by P1 (floating mat) – Sphagnum spp., Carex spp., and Vaccinum oxycoccos L.; P2 (north-west non-floating mat) —Phragmites australis (Cav.) Trin. ex Steud; P3 (south-east non-floating mat) – Salix spp.; and P4 (transitional) – Carex spp. transitioning to Carex spp., Menyanthes trifoliata L., Equisetum fluviatile L. and Comarum palustre L. 15 (Fig. 6 ). The dimension of the patches is 93m x 93m, which corresponds to 31 x 31 pixels of PlanetScope images, and it would be ~ 3 x 3 pixels if using Landsat 8 (30m/pix), highly reducing the detection of the spatial variation of the canopy. The lake studied is located at the eastern part of the peatland (Fig. 6 ), occupying ~ 16ha and overgrown by Typha latifolia L. and Phragmites australis (Cav.) Trin. ex Steud 14 . Satellite imagery The satellite dataset source used in this study was the PlanetScope high-resolution (3m/pix) satellite ensemble, including 144 images of 4 reflectance bands from 2017 to 2023. The type of product used was the PlanetScope Ortho Scene, the analytic surface reflectance. The Ortho Scenes are radiometrically and geometrically-corrected products projected to a WGS84 Web Mercator cartographic map projection with a positional accuracy of less than 10 m Root Mean Square Error (RMSE) at the 90th percentile. The 4-bands (B1 – Blue, B2 – Green, B3 – Red, B4 – NIR) were harmonised to Sentinel 2 with the PlanetScope Application Programming Interface (API) Orders and Tools (Planet Labs PBC). Only the images with the highest publishing stage (finalised) and quality category (standard) were included, ensuring optimal radiometric consistency and minimal processing artefacts. Furthermore, the images were anchored to the same scene through the API’s correction tools to ensure the geometrical corrections. From the available ~ 3200 images of the Dove Classic (PS2) and Dove R (PS2.SD) instruments, the used imagery was selected based on cloud cover below 10%; haze was also a reason for exclusion. Further technical details, including radiometric calibration methods and spectral band specifications, are available in the PlanetScope Product (Specification Planet Labs PBC 2023. Combined Imagery Product Spec FINAL | December 2023 ). In-situ data In-situ data measured in the study area included WTD measurements obtained using TD-divers (Eijkelkamp Soil & Water, the Netherlands) installed in polyvinyl chloride (PVC) tubes permanently mounted to wooden platforms stabilised on piles above the peatland surface. The WTD position was calculated as the difference between the distance from the top edge of the piezometer to the TD-Diver installation depth and the distance from the top edge of the piezometer to the peatland surface 7 . Due to seasonal oscillations of the peatland surface, its position was calculated based on manual measurements of the distance between a fixed point on the platform and the peatland surface, taken every 2–3 weeks. Changes in the peatland surface were then linearly interpolated between measurements and incorporated into WTD calculations. The air temperature (Tair) was measured by radiation-sheltered thermohygrometers HygroVue5 (Campbell Sci., USA). The measurements provide a continuous dataset with half-hourly timesteps throughout the study period 7 . Vegetation and water indexes calculation and selection plus RGB scenes generation The satellite data was used to generate the Enhanced Vegetation Index (EVI) 16 , the Normalised Difference Vegetation Index (NDVI) 17 and the LWM 18 . $$\:NDVI\:=\frac{NIR-RED}{NIR+RED}$$ 1 $$\:EVI\:=2.5\:\bullet\:\:\frac{NIR-RED}{\left(NIR+6\bullet\:RED\:-7.5\bullet\:BLUE\right)+1}$$ 2 $$\:LWM\:=\frac{NIR}{GREEN\:+\:0.0001}\:\bullet\:100$$ 3 Apart from the indexes calculated, the red, green, and blue (RGB) imagery was generated and corrected by intensity, transforming it into greyscale images for Otsu and clustering thresholding. The intensity correction was performed by rescaling the pixel values to the range [0, 1], adjusting the contrast by mapping values between the input and output contrast limits, and, lastly, returning it to the original data type. The limits used the bottom 1% and the top 1% of all pixel values. The selection of the vegetation indices (VIs) for phenological calculations was based on the agreement between the ground VIs values calculated based on the periodic top of canopy reflectance measurements (FieldSpec HandHeld 2, ASD Inc., Boulder, USA) and the extrapolated VIs values from the satellite data for each ground point location. The 18 ground points used were located in the Rzecin experimental station described in Juszczak et al. 19 during 42 field campaigns in 2018–2021. At the same time, the selection between the RGB or LWM scenes for the lake analysis was made based on the accuracy of the ground truth described in section 4.3. Vegetation phenological parameters The identification of vegetation phenological variables (SOS – Start of the Season, EOS – End of the Season, LOS – Length of the Season, and the Peak of the Season – POS) was generated with Decomposition and Analysis of Time Series (DATimeS) software 20 using the VI selected. Whittaker interpolation was applied to the EVI snowless images time series to obtain the index’s daily values. The interpolated time series was then used to retrieve the phenological parameters for each year individually and analyse the changes and trends of the overall area. DATimeS identifies the growing seasons from the time series, detecting three consecutive local minimum, maximum and minimum points with some constraints to avoid noise interferences 20 . The constraints used were the growing season prominence (set to 20%) and the minimum separation between seasons (set to 100). Once the growing season is identified, DATimeS estimates the phenological parameters based on the seasonal, relative or absolute amplitude 20 . In this case, the seasonal amplitude was used and set to 25%. The EOS and SOS were selected “where the left/right part of the curve reaches a fraction of the seasonal amplitude along the rising/decaying part of the curve”. The overall calculations produced images of each year’s phenological parameters generated together with the average area values. Parallelly, phenological variables were generated for the selected vegetation patches (P1 – P4, Fig. 6 ). Image thresholding and lake area estimation The analysis of the lake drying was assessed relative to its coverage in 2018. The March image of 2018, showing the highest coverage of water in the lake, was used to manually delimit the lake area in the Quantum Geographic Information System (QGIS) and create a mask. Further analysis was performed in MATLAB (The MathWorks, Inc., USA), where the mask was used to crop the lake area. The greyscale (RGB) and LWM images were used to estimate the dried and waterlogged areas with k-means clustering and Otsu thresholds. The Otsu thresholding analyses the grayscale image’s histogram to find a threshold that separates the pixel intensities into two classes: foreground (object) and background 21 . This method computes the weighted variance of pixel intensities for all possible threshold values. Then, it establishes a threshold value for the lowest intra-class variance (or highest inter-class variance) 21 . The method was applied in MATLAB using the “imbinarized” function. Meanwhile, the k-means clusters group pixels into K clusters based on their similarity 22 . It randomly selects K initial centroids (representative pixel values), and then each pixel is assigned to the nearest centroid based on the Euclidean distance. Lastly, the centroids’ value is updated by averaging the pixel values of each cluster 22 . This process is repeated through the image iteratively until convergence (i.e., when centroids no longer change significantly). The k-means clustering was applied using the “imsegkmeans” MATLAB function for 3 clusters, with k = 1 corresponding to the area outside the lake masked (NaN values). The respective areas were calculated for each image from the classified pixels of dry and waterlogged lake parts, creating the time series of lake area oscillations. Additionally, one image per month (from March to November) was manually classified to use as validation of the clustering and Otsu methods. The validation was performed by calculating the accuracy as the number of pixels classified as water/dry divided by the total area in the ground truth of water/dry pixels. The best result by method (Otsu or clustering) and scene (RGB or LWM) with higher accuracy is selected and the only one presented in the results. Statistical analyses of time series relationship (vegetation phenological parameters, lake surface area, WTD, precipitation and Tair) All statistical analyses were performed in MATLAB (The MathWorks, Inc., USA). The regression method was used to analyse the relationship between WTD, Tair, lake surface changes, and vegetation phenological parameters. This regression analysis included the development of a multivariate (Tair and WTD) linear model, obtaining its associated statistics, the equation of the linear relationship (y = n + a·WTD + b·Tair) and the estimates. From the model estimates, a model versus measured values comparison was performed to show the model’s associated errors visually. The multivariate regression was used with the phenological parameters and the yearly WTD and Tair average, first for the general area and second for patches. Furthermore, the area-averaged EVI from the vegetation patches was also compared with Tair, WTD, and precipitation using daily data observing the time series trends. Additionally, in order to study this effect further, a cross-correlation analysis was performed pairing EVI with WTD, Tair and precipitation in daily, weekly and monthly timesteps (averages except for precipitation with sums). Once the cross-correlation was performed and the lag between time-series was obtained, the time-series were displaced, and a Partial Least Squares Regression (PLSR) was performed using the “plsregress” MATLAB function. The PLSR models relationships between predictor variables (X – hydrometeorology) and response variables (Y – EVI) while handling multicollinearity and high-dimensional data 23 . Firstly, it extracts latent components (PLS components) that maximise the covariance between X (predictors) and Y (response). The extracted components are used to build a predictive model for Y, and by analysing the Variable Importance in Projection (VIP) scores, PLSR determines the contribution of each predictor to the response variable 23 . PLSR (adjusted for three PLS components) was applied for each vegetation patch and the general area, using all the hydrometeorological variables to obtain the VIP score and the total percentage of EVI variance they explained. Moreover, in order to obtain the individual percentages explained by each hydrometeorological variable, PLSR was also applied individually for WTD, Tair and precipitation, in this case, adjusted for 1 PLS component. The lake area oscillations were analysed by studying the trend of the lake drying using a simple regression of the changes in the values of dry and water areas over time. Furthermore, the analysis also included the study of the link between WTD and lake area oscillations in weekly timesteps (generated with a linear interpolation of the original data). This link was examined with the time series comparison of both parameters and the linear regression (y = n + a·WTD). The abovementioned statistics for all cases refer to the coefficient of determination (R 2 ), Root Mean Square Error (RMSE), and p-values derived from the linear regressions between parameters performed in MATLAB. Declarations Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This work was supported by the discipline of science, environmental engineering, mining, and energy at Poznan University of Life Sciences (Poland) with the Innowator programme under project No. 02/2024/INN and the National Science Centre of Poland (NCN) under projects No. 2016/21/B/ST10/02271 and 2020/37/B/ST10/01213. Author Contribution M.A-S: Conceptualization, data curation, formal analysis, investigation, methodology, visualization, writing–original draft. M A.: Formal analysis, investigation, writing-review and editing. M.S.: Data curation, investigation, supervision. A.R.: Funding acquisition, supervision, writing-review and editing. R.J.: Resources, funding acquisition, supervision, writing–review and editing. All authors have read and agreed to the published version of the manuscript. Data Availability Data is provided within the manuscript or supplementary information files further information is available per request to the corresponding author. References Jungkunst, H. F. et al. Springer Netherlands,. Accounting More Precisely for Peat and Other Soil Carbon Resources. in Recarbonization of the Biosphere vol. 4 127–157 (2012). Peichl, M. et al. Peatland vegetation composition and phenology drive the seasonal trajectory of maximum gross primary production. Sci. Rep. 8 , 8012 (2018). Antala, M., Juszczak, R., van der Tol, C. & Rastogi, A. Impact of climate change-induced alterations in peatland vegetation phenology and composition on carbon balance. Sci. Total Environ. 827 , 154294 (2022). Mbow, H. O. P., Reisinger, A., Canadell, J. & O’Brien, P. Special Report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems (SR2). Ginevra, IPCC 650, (2017). Loisel, J. et al. Expert assessment of future vulnerability of the global peatland carbon sink. Nat. Clim. Chang. 11 , 70–77 (2021). Kopansky, D., Mark, R., Matt, K. & Jonny, H. Global Peatlands Assessment – The State of the World’s Peatlands: Evidence for action toward the conservation, restoration, and sustainable management of peatlands. Main Report. doi:20.500.11822/41222. (2022). Górecki, K. et al. Water table depth, experimental warming, and reduced precipitation impact on litter decomposition in a temperate Sphagnum-peatland. Sci Total Environ 771 , (2021). Moon, M., Richardson, A. D. & Friedl, M. A. Multiscale assessment of land surface phenology from harmonized Landsat 8 and Sentinel-2, PlanetScope, and PhenoCam imagery. Remote Sens. Environ. 266 , 112716 (2021). Zak, D., Maagaard, A. L. & Liu, H. Restoring Riparian Peatlands for Inland Waters: A European Perspective. in Encyclopedia of Inland Waters vol. 3 276–287 (Elsevier, (2022). Barabach, J. The history of Lake Rzecin and its surroundings drawn on maps as a background to palaeoecological reconstruction. Limnol. Rev. 12 , 103–114 (2012). Casagranda, E., Navarro, C., Grau, H. R. & Izquierdo, A. E. Interannual lake fluctuations in the Argentine Puna: relationships with its associated peatlands and climate change. Reg. Environ. Chang. 19 , 1737–1750 (2019). Lamentowicz, M. et al. Reconstructing human impact on peatland development during the past 200 years in CE Europe through biotic proxies and X-ray tomography. Quat Int. 357 , 282–294 (2015). Milecka, K. et al. Hydrological changes in the Rzecin peatland (Puszcza Notecka, Poland) induced by anthropogenic factors: Implications for mire development and carbon sequestration. Holocene 27 , 651–664 (2017). Juszczak, R. & Augustin, J. Exchange of the Greenhouse Gases Methane and Nitrous Oxide Between the Atmosphere and a Temperate Peatland in Central Europe. Wetlands 33 , 895–907 (2013). Bandopadhyay, S. et al. Hyplant-Derived Sun-Induced Fluorescence—A New Opportunity to Disentangle Complex Vegetation Signals from Diverse Vegetation Types. Remote Sens. 11 , 1691 (2019). Hui Qing, L. & Huete, A. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. IEEE Trans. Geosci. Remote Sens. 33 , 457–465 (1995). Rouse, J. W., Haas, R. H., Schell, J. A. & Deering, D. W. Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ . 351 , 309 (1974). Uddin, K., Khanal, N., Chaudhary, S., Maharjan, S. & Thapa, R. B. Coastal morphological changes: Assessing long-term ecological transformations across the northern Bay of Bengal. Environ. Challenges . 1 , 100001 (2020). Juszczak, R. et al. Ecosystem respiration in a heterogeneous temperate peatland and its sensitivity to peat temperature and water table depth. Plant. Soil. 366 , 505–520 (2013). Belda, S. et al. DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection. Environ. Model. Softw. 127 , 104666 (2020). Otsu, N. A. Tlreshold Selection Method from Gray-Level Histograms. IEEE Trans. Syst. Man. Cybern . 9 , 62–66 (1979). Arthur, D. & Sergei, V. k-means ++: The Advantages of Careful Seeding. in Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms 1027–35 (SODA ’07. USA: Society for Industrial and Applied Mathematics, (2007). Rosipal, R. & Krämer, N. Overview and Recent Advances in Partial Least Squares BT - Subspace, Latent Structure and Feature Selection. in (eds. Saunders, C., Grobelnik, M., Gunn, S. & Shawe-Taylor, J.) 34–51Springer Berlin Heidelberg, (2006). Lamentowicz, M. et al. Unveiling tipping points in long-term ecological records from Sphagnum -dominated peatlands. Biol. Lett. 15 , 20190043 (2019). Miętus, M. Climate of Poland 2021 . Polish climate monitoring bulletin (2021). https://www.imgw.pl/sites/default/files/2021-04/imgw-pib-klimat-polski-2020-opracowanie-final-eng-rozkladowki-min.pdf Breeuwer, A. et al. Decreased summer water table depth affects peatland vegetation. Basic. Appl. Ecol. 10 , 330–339 (2009). Norby, R. J., Childs, J., Hanson, P. J. & Warren, J. M. Rapid loss of an ecosystem engineer: Sphagnum decline in an experimentally warmed bog. Ecol. Evol. 9 , 12571–12585 (2019). Bragazza, L. et al. Persistent high temperature and low precipitation reduce peat carbon accumulation. Glob Chang. Biol. 22 , 4114–4123 (2016). Cao, R., Wei, X., Yang, Y., Xi, X. & Wu, X. The effect of water table decline on plant biomass and species composition in the Zoige peatland: A four-year in situ field experiment. Agric. Ecosyst. Environ. 247 , 389–395 (2017). Hanson, P. J. et al. Peatland Plant Community Changes in Annual Production and Composition Through 8 Years of Warming Manipulations Under Ambient and Elevated CO2 Atmospheres. J. Geophys. Res. Biogeosciences . 130 , 1–19 (2025). Korrensalo, A. et al. Species-specific temporal variation in photosynthesis as a moderator of peatland carbon sequestration. Biogeosciences 14 , 257–269 (2017). Radu, D. D. & Duval, T. P. Precipitation frequency alters peatland ecosystem structure and CO2 exchange: Contrasting effects on moss, sedge, and shrub communities. Glob Chang. Biol. 24 , 2051–2065 (2018). Arciszewski, M., Pogorzelec, M., Nowak, B. H., Parzymies, M. & Piejak, M. Towards successful reintroduction of Salix myrtilloides: the importance of monitoring plant physiological indicators during acclimatization. Dendrobiology 92 , 100–111 (2024). An, S. et al. Comparison of the photosynthetic capacity of phragmites Australis in five habitats in Saline-Alkaline wetlands. Plants 9 , 1–17 (2020). Newman, T. R., Wright, N., Wright, B. & Sjögersten, S. Interacting effects of elevated atmospheric CO2 and hydrology on the growth and carbon sequestration of Sphagnum moss. Wetl Ecol. Manag . 26 , 763–774 (2018). Bengtsson, F. et al. Environmental drivers of Sphagnum growth in peatlands across the Holarctic region. J. Ecol. 109 , 417–431 (2021). Öquist, G. & Huner, N. P. A. Photosynthesis of Overwintering Evergreen Plants. Annu. Rev. Plant. Biol. 54 , 329–355 (2003). Salimi, S., Berggren, M. & Scholz, M. Response of the peatland carbon dioxide sink function to future climate change scenarios and water level management. Glob Chang. Biol. 27 , 5154–5168 (2021). Zaret, K. & Holz, A. Exploration of large-scale vegetation transition in wet ecosystems: a comparison of conifer seedling abundance across burned vs. unburned forest-peatland ecotones in Western Patagonia. Front. Glob Chang. 7 , 1–24 (2024). Ratcliffe, J. L. et al. Ecological and environmental transition across the forested-to-open bog ecotone in a west Siberian peatland. Sci. Total Environ. 607–608 , 816–828 (2017). Goud, E. M., Watt, C. & Moore, T. R. Plant community composition along a peatland margin follows alternate successional pathways after hydrologic disturbance. Acta Oecol. 91 , 65–72 (2018). Jatin, S., Swinder, J. S. K. & Naraian, R. Environmental perspectives of Phragmites australis (Cav.) Trin. Ex. Steudel. Appl. Water Sci. 4 , 193–202 (2014). Frei, S., Holderegger, R. & Bergamini, A. Thirty years later: How successful was the restoration of a raised bog in the Swiss Plateau ? Mires Peat 27 , (2021). Jacquemart, A. L. Vaccinium oxycoccos L.(Oxycoccus palustris Pers.) and Vaccinium microcarpum (Turcz. ex Rupr.) Schmalh.(Oxycoccus microcarpus Turcz. ex Rupr). J. Ecol. 85 , 381–396 (1997). Bhuiyan, R. et al. Fine-root biomass production and its contribution to organic matter accumulation in sedge fens under changing climate. Sci Total Environ 858 , (2023). Neill, A. O., Tucker, C. & Kane, E. S. Fresh Air for the Mire-Breathing Hypothesis: Sphagnum Moss and Peat Structure Regulate the Response of CO 2 Exchange to Altered Hydrology in a Northern Peatland Ecosystem. Water 14, (2022). Mezbahuddin, M., Grant, R. F. & Flanagan, L. B. Modeling hydrological controls on variations in peat water content, water table depth, and surface energy exchange of a boreal western Canadian fen peatland. J. Geophys. Res. Biogeosciences . 121 , 2216–2242 (2016). Paajanen, R. et al. Dark-leaved willow (Salix myrsinifolia) is resistant to three-factor (elevated CO 2, temperature and UV-B-radiation) climate change. New. Phytol . 190 , 161–168 (2011). Linkosalmi, M., Tuovinen, J., Nevalainen, O., Peltoniemi, M. & Tani, C. M. Tracking vegetation phenology of pristine northern boreal peatlands by combining digital photography with CO 2 flux and remote sensing data. Biogeosciences 19 , 4747–4765 (2022). Kokkonen, N. et al. A deepened water table increases the vulnerability of peat mosses to periodic drought. J. Ecol. 112 , 1210–1224 (2024). Jitariu, V., Dorosencu, A., Ichim, P. & Ion, C. Severe drought monitoring by remote sensing methods and its impact on wetlands birds assemblages in Nuntași and Tuzla Lakes (Danube Delta Biosphere Reserve). Land 11 , 672 (2022). Sebasti, E. & Green, A. J. Habitat Use by Waterbirds in Relation to Pond Size, Water Depth, and Isolation : Lessons from a Restoration in Southern Spain. 22 , 311–318 (2014). Zeh, L. et al. Vascular plants affect properties and decomposition of moss-dominated peat, particularly at elevated temperatures. Biogeosciences 17 , 4797–4813 (2020). Potvin, L. R., Kane, E. S., Chimner, R. A., Kolka, R. K. & Lilleskov, E. A. Effects of water table position and plant functional group on plant community, aboveground production, and peat properties in a peatland mesocosm experiment (PEATcosm). Plant. Soil. 387 , 277–294 (2015). Walker, T. N. et al. Vascular plants promote ancient peatland carbon loss with climate warming. Glob Chang. Biol. 22 , 1880–1889 (2016). Cobo, M., Goldhammer, T. & Brothers, S. A desiccating saline lake bed is a significant source of anthropogenic greenhouse gas emissions. One Earth . 7 , 1414–1423 (2024). Robroek, B. J. M. et al. Peatland vascular plant functional types affect dissolved organic matter chemistry. Plant. Soil. 407 , 135–143 (2016). Xu, Z. et al. Effect of drainage on microbial enzyme activities and communities dependent on depth in peatland soil. Biogeochemistry 155 , 323–341 (2021). Woolway, R. I. et al. Global lake responses to climate change. Nat. Rev. Earth Environ. 1 , 388–403 (2020). Additional Declarations No competing interests reported. Supplementary Files Supplementaryinformation.docx Cite Share Download PDF Status: Published Journal Publication published 13 Feb, 2026 Read the published version in Remote Sensing → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7148738","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":494370654,"identity":"4e157bed-c2df-4280-98f8-1766016d8b0c","order_by":0,"name":"Mar Albert-Saiz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYPACCQZ+FP4DYrRINiDzE4ixx+AAsVp0288+YPxRY2FvfCP3AHNBRV0e/+wGtgf4tJidSTdg5jkmwWx2Iy+BecYZtmKJOwfYDfBqOZDGwMzAJsFmdiPHgJm3jSex4UYCmwReLeefMTD++CfBYzwDrEUicT5BLTfSGBiAKiUMJMBaDBI3ENbyjOEwbx9Qx5l3CYdnnElI3HgjsR2/X86nMT788a3Onr899+BjYIglzruRfOzBBzxaQOAAhOJhOAxhMLYR0AAHPMCggwA2YrWMglEwCkbByAAAdSlIWYJqSasAAAAASUVORK5CYII=","orcid":"","institution":"Poznań University of Life Sciences","correspondingAuthor":true,"prefix":"","firstName":"Mar","middleName":"","lastName":"Albert-Saiz","suffix":""},{"id":494370655,"identity":"7e61882c-c37d-4fcc-8608-73708927462d","order_by":1,"name":"Michal Antala","email":"","orcid":"","institution":"Poznań University of Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Michal","middleName":"","lastName":"Antala","suffix":""},{"id":494370656,"identity":"ec8eb616-0a71-4f65-b810-558407f244b4","order_by":2,"name":"Marcin Stróżecki","email":"","orcid":"","institution":"Poznań University of Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Marcin","middleName":"","lastName":"Stróżecki","suffix":""},{"id":494370657,"identity":"1049e72d-b4cb-453a-bab1-9b498af2fd15","order_by":3,"name":"Anshu Rastogi","email":"","orcid":"","institution":"Poznań University of Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Anshu","middleName":"","lastName":"Rastogi","suffix":""},{"id":494370658,"identity":"74225c81-2349-4947-955e-2f1b8eb51fd6","order_by":4,"name":"Radoslaw Juszczak","email":"","orcid":"","institution":"Poznań University of Life Sciences","correspondingAuthor":false,"prefix":"","firstName":"Radoslaw","middleName":"","lastName":"Juszczak","suffix":""}],"badges":[],"createdAt":"2025-07-17 11:53:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7148738/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7148738/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.3390/rs18040593","type":"published","date":"2026-02-14T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88258838,"identity":"7f5ee50b-92ae-41c7-96b1-93acd56a9c7c","added_by":"auto","created_at":"2025-08-04 14:59:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":293070,"visible":true,"origin":"","legend":"\u003cp\u003eStart of the Season (SOS), Peak of the Season (POS) and End of the Season (EOS) for 2017 and 2023 with lake area masked. Both values are expressed as Day of the Year (DOY).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7148738/v1/0f5c5149b272cee9955b4a38.png"},{"id":88259309,"identity":"033c24bf-7386-46ee-94f1-096a3b44ab14","added_by":"auto","created_at":"2025-08-04 15:07:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":164700,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of daily Enhanced Vegetation Index (EVI) for patches of different vegetation (P1 – P4; a) and time series of daily air temperature (Tair, dark red), daily average water table depth (WTD, black) and weekly cumulative precipitation (blue columns; b). Note that the \u003cem\u003ein-situ\u003c/em\u003emeasurements of precipitation and WTD started at the end of 2017. Patches are described in the Methodology.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7148738/v1/0f8a502afcd75d50a89b4b9d.png"},{"id":88258840,"identity":"257e15fe-b7c3-4717-a977-fa4889d83d52","added_by":"auto","created_at":"2025-08-04 14:59:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47643,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of the Enhanced Vegetation Index (EVI) explained by air temperature (Tair), water table depth (WTD) and precipitation from the Partial Least Square Regression of these variables individually and together (All), from monthly and weekly values.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7148738/v1/c08a59fff8d5dd9e25093d44.png"},{"id":88258841,"identity":"6f1653c9-fa28-4196-9f2f-5dde79003b49","added_by":"auto","created_at":"2025-08-04 14:59:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":49926,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression of the Length of the Season (LOS, a) and maximum Enhanced Vegetation Index (EVI) value (b) from 2018-2023, with the modelled parameter estimated from the linear regression using the annual average water table depth\u0026nbsp;and\u0026nbsp;air temperature as estimators. This relationship is analysed per vegetation patch (P1 – black, P2 – blue, P3 – orange, P4 – green); the value is shown with dots and the trend as a solid line. The equation of the relationship between LOS/EVI\u003csub\u003emax\u003c/sub\u003e with the WTD and air temperature and the statistics are shown in Table 4.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7148738/v1/3ee864ce528baad3c0a07016.png"},{"id":88258845,"identity":"fa3ab07e-c681-4eb3-bab2-1651fcc86f02","added_by":"auto","created_at":"2025-08-04 14:59:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":82551,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of lake area (water) and exposed lake bottom (dry) changes relative to the beginning of 2018. The straight lines represent the linear trends of the changes.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7148738/v1/38c83ec6ad37350ac599040b.png"},{"id":88258846,"identity":"cc87027d-9ab9-45af-b5ab-7b7565c44a33","added_by":"auto","created_at":"2025-08-04 14:59:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":146479,"visible":true,"origin":"","legend":"\u003cp\u003eRzecin peatland RGB scene generated from PlanetScope 07/2023 image. Black rectangles locate vegetation patches (P1–P4).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7148738/v1/ba9548826d0333c530e4f4b6.png"},{"id":102873105,"identity":"8a63240d-8436-442b-9880-4cf4c69d1e30","added_by":"auto","created_at":"2026-02-17 18:50:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1948922,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7148738/v1/592499c8-5412-4f28-a63c-a0466563d64b.pdf"},{"id":88259310,"identity":"b1617105-71fd-4e54-b729-38818fc28593","added_by":"auto","created_at":"2025-08-04 15:07:32","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":119592,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7148738/v1/be5697f2b2be374afe304484.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eHigh-resolution Satellite-derived Changes in Vegetation Phenology and Lake Area in a Central European Peatland\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePeatlands sustain the global carbon cycle, acting as reservoirs for around 40% of the terrestrial carbon stored in soils \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The vegetation is one of the main drivers of this cycle, determining the amount of atmospheric CO\u003csub\u003e2\u003c/sub\u003e sequestered into plant biomass \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Over the past few decades, the water table depth (WTD) oscillations have become more extreme due to climate change, leading to an anticipated drying of the peatland\u0026rsquo;s ecosystem that could result in shifts in vegetation composition \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The new climatic conditions destabilised these ecosystems, potentially transforming them from carbon sinks to carbon sources \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Furthermore, their drying makes them more susceptible to fires, which would release more carbon to the atmosphere. Their importance in the global carbon cycle and the need for their conservation/restoration make improving our understanding of peatland adaptation to climatic conditions crucial \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Beyond its impact on the carbon cycle, vegetation phenology changes also serve as climate change indicators \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. For these reasons, monitoring the changes in vegetation phenology and coverage is important to estimate the already present consequences of climate change.\u003c/p\u003e\u003cp\u003ePeatlands are considered ecosystems with high heterogeneity of vegetation, particularly in the case of poor fens \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Satellites usually provide\u0026thinsp;\u0026ge;\u0026thinsp;10m spatial resolution, which may be insufficient to detect the heterogeneity of the peatlands\u0026rsquo; vegetation. The challenge of analysing vegetation phenology in peatlands stems not only from their heterogeneous vegetation but also from their high water content and periodic waterlogging, which contribute, among others, to more haze episodes than typically observed in other areas. However, high-resolution satellites, such as the PlanetScope ensemble with 3m/pixel, allow the identification of vegetation patches and their growing seasons more properly. The use of PlanetScope across multiple growing seasons, ecosystem types, and climate domains has proven effective in capturing fine-scale spatial variation in vegetation phenology that is not resolved at moderate spatial resolutions, making it a recommended tool for studying canopy-scale phenology \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Moreover, PlanetScope revisit time is less than two days, with multiple images available daily, which allows the user to obtain at least two images per month despite the haze. This approach with new high-resolution imagery can help to estimate vegetation phenology and vegetation index oscillations better, increasing our understanding of their dynamics and reactions to climate change.\u003c/p\u003e\u003cp\u003ePeatland areas are often associated with water bodies, such as lakes, inside their area or surroundings \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Hence, drying caused by drought periods or human activities alters vegetation phenology and negatively impacts water bodies. In the studied area, the lake has suffered several dryings over the years \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The lake\u0026rsquo;s shallowness produces drying in patches; therefore, to properly study the changes in the water body surface, we established the thresholds for the dry areas inside the Land Water Mask (LWM). The changes in lake surface area have often been positively correlated with vegetation phenological changes \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Studying the joint dynamics and their coupling with WTD measurements enhances our understanding of seasonal drying patterns and the long-term impacts of climate change, providing valuable insights into the peatland\u0026rsquo;s hydrological and phenological dynamics.\u003c/p\u003e\u003cp\u003eTherefore, in this study, we examined the impact of ongoing climate change on the vegetation phenology and lake area in the Rzecin central European peatland using a time series of high-resolution satellite imagery and on-site WTD and meteorology measurements. The novelty of this work lies in combining PlanetScope imagery with in-situ data to study long-term phenological changes in vegetation, which have never been addressed in this area. This integrated analysis provides new insights into the hydrometeorology influences on vegetation phenology across the entire Rzecin peatland and its different vegetation subareas. The article tests the accuracy of unsupervised methods (segmentation and clustering) to identify dry areas in the lake surface, considering internal drying and not only shrinkage, which has never been done for Rzecin\u0026rsquo;s lake. The expected results considering the continuous drop in WTD observed in the area are: i) a general enlargement of the length of the growing season and increased greenness with the proliferation of vascular plants; ii) a higher dependence on WTD\u0026rsquo;s changes in the transitional areas of the peatland with mixed vegetation including mosses; iii) a more direct link with meteorology in the areas with a more stable dominance of shrubs and vascular plants with the vegetation shift not masking the link with vegetation indexes; iv) a continuous drying of the lake showing higher interannual oscillations by time and the summer season showing an increment of dry areas (shrinkage plus inside \u0026ldquo;dry islands\u0026rdquo;.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eChanges in vegetation phenology\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGeneral trends of the vegetation season parameters in the peatland area\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe robustness of satellite EVI values was higher than satellite NDVI values in the 18 ground points. EVI, when compared with in situ data, the similarity in trends was higher for EVI (NDVI\u0026mdash;R\u0026thinsp;=\u0026thinsp;0.8, p-value\u0026thinsp;=\u0026thinsp;4.1e-06, RMSE\u0026thinsp;=\u0026thinsp;0.09; EVI\u0026mdash;R\u0026thinsp;=\u0026thinsp;0.9, p-value\u0026thinsp;=\u0026thinsp;1.5e-07, RMSE\u0026thinsp;=\u0026thinsp;0.08). Based on these results, EVI was used to analyse vegetation phenological parameters.\u003c/p\u003e\u003cp\u003eThe peatland displayed a general rise in the length of the vegetation season from 2018 until 2020. The LOS changes were caused by the delay of the end of the season from late October to early November, while the SOS remained stable around the end of April (\u0026plusmn;\u0026thinsp;5 days) until 2021, when it was delayed to 12th May (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The prolongation of the vegetation season during these years was accompanied by the delay of the POS from 1st week of July 2018 to 1st week of August 2020 with a stable peak value of EVI (0.4663\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0167). Starting from 2021, the POS remained stable around the 3rd week of July, while the EVI value rose by around 0.1. In 2021, the LOS dropped drastically from 192 to 157 days (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) due to a cold spring and dry spells during July-August and October.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe peatland area\u0026rsquo;s average Length of the Season (LOS) expressed in days, the Start and End of the Season (SOS, EOS), and the maximum value of Enhanced Vegetation Index (EVI\u003csub\u003emax\u003c/sub\u003e) with the Peak of the Season (POS) date.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLOS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSOS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEOS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEVI\u003csub\u003emax\u003c/sub\u003e (POS)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e30-04-2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e01-11-2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e0.480 (10-07-2017)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e29-04-2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e21-10-2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e0.477 (04-07-2018)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e05-05-2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e03-11-2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e0.447 (02-08-2019)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e29-04-2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e07-11-2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e0.476 (02-08-2020)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e12-05-2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e16-10-2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e0.574 (22-07-2021)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e07-05-2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e29-10-2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e0.569 (24-07-2022)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e\u003cp\u003e30-04-2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e\u003cp\u003e10-11-2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e\u003cp\u003e0.502 (22-07-2023)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe pattern of prolonging the season length broken during 2021 was recovered in 2022\u0026ndash;2023. There was an increase in the LOS, with the SOS returning to late April and the EOS progressively returning to early November (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Moreover, the maximum value of EVI (EVI\u003csub\u003emax\u003c/sub\u003e) started to decrease in 2021, and the peak date remained around 25th July\u0026thinsp;\u0026plusmn;\u0026thinsp;5 days (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe phenology of the vegetation in the peatland changed over the years studied, and the differences are visible when comparing the parameters of 2017 and 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The vegetation season was generally delayed, as is visible in SoS, POS and EOS data (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The most visible increment on the day of SOS and POS is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e with red colours during 2023 at the west-northwest corner of the peatland where \u003cem\u003ePhragmites australis\u003c/em\u003e (Cav.) Trin. ex Steud dominates. Conversely, some areas showed earlier SoS during 2023. The exceptions of the general trend can be observed, for example, in the area with SOS at DOY 100\u0026ndash;120 in 2023 and DOY 120\u0026ndash;140 in 2017 at the southwest of the lake (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe changes for the EOS showed fewer differences in the peatland. There was a general delay during 2023 for almost all areas with EOS located in DOY 330\u0026ndash;350 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhenology of vegetation patches\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe changes in vegetation phenology did not follow the same trend in all the peatland areas, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These differences were analysed further with the selected vegetation patches (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The differences among patches/vegetation were observed through the start and end of the season. The general pattern of P1 showed the season\u0026rsquo;s earliest start and the latest end. P3 and P4 followed the P1 SOS; the latest was during the whole period for P2 (\u003cem\u003ePhragmites\u003c/em\u003e-dominated). Generally, the season ended the soonest for P4, followed by P2 and P3, with their trends converging in 2023. Furthermore, the earliest POS occurred in P3, followed by P4 and P1, with P2 as last. In the case of LOS, P2 presented the shortest season, followed by P4, P3 and P1 due to the combination of EOS and SOS trends (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe Length of the Season (LOS) in days for each vegetation patch (P1-P4). Patches are described in the Methodology.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2017\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e183\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2018\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e161\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e168\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2020\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e176\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2021\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e153\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2022\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e164\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2023\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e184\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe changes in the LOS for each patch were quite similar, with certain stability in P1, P3, and P4 from 2017 to 2020, while P2 LOS became shorter (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). During the second half of the period, a V-shaped trend was observed for all patches, with the shortest LOS in 2021. The POS tended to shift forward during the studied period in all patches except the tall shrub-dominated P3, where from 2019 to 2022, the POS occurred sooner each year (Supplementary Table\u0026nbsp;3). The shifts for the season end were shared for P2-P4, with a slightly sooner EOS in 2018 followed by a continuous delay until 2020 and again from 2021 until 2023. The trend was more continuous for P1, with an advancement of the EOS from 2017 to 2023. Lastly, the oscillations of SOS in the 7 years of analysis were lower than \u0026plusmn;\u0026thinsp;10 days in P3 and P4. In the case of SOS for P1, it occurred earlier each year during 2017\u0026ndash;2020 and again from 2021 to 2023. Moreover, P2 showed two different patterns of SOS, shifting later in the year from 2017 to 2019 and the inverse from 2019 until 2023.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe role of hydrometeorological variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eEVI changes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe shared trends of hydrometeorological variables and the EVI averaged per patch, or the total study area, are visible in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e with the time series of EVI value (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) and its linking with air temperature and WTD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). EVI trends and air temperature show a Gaussian shape peaking during the summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). At the same time, in the case of the WTD interannual trend, the link with EVI is observed in a reversed way (the lowest WTD is linked to the Tair and EVI peaks) and with a time lag (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAnalysing further the role of each variable in the EVI trends, the lag in EVI\u0026rsquo;s response to changes in the hydrometeorological variables was established for the different areas. The cross-correlation analysis in weekly and monthly timesteps revealed a delay in the response of EVI to WTD changes but not to air temperature and precipitation oscillations (Supplementary Tables\u0026nbsp;5\u0026ndash;6). The delay or lag was different for each vegetation patch. The maximum lag in the EVI response was detected in P2, followed by P1, the general area, P4, and P3 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The differences in the lag in the EVI response decreased as the timestep of the data increased, smoothing the distinction between vegetation areas. Weekly averages of WTD showed the same lag in P1 and the general peatland area, and monthly averages showed the same lag in all areas (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEVI delay in response to changes in hydrometeorological variables based on the maximum cross-correlation. The delay is expressed in days for daily data, weeks for weekly data and months for monthly data. Note that 2017 was excluded from analyses due to missing reliable WTD data.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGeneral\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDaily\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWeekly\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMonthly\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe lags were applied to displace the WTD time-series in order to better analyse the role of each hydrometeorological variable in the PLSR.\u003c/p\u003e\u003cp\u003eThe results pointed out that air temperature governs the oscillations of EVI, followed by WTD with a minimum effect of precipitation both for weekly and monthly time-series (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The role of precipitation increased from weekly to monthly values. However, it was still not significant, explaining less than 10% of the EVI changes and with a VIP score below 0.15 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Table\u0026nbsp;7). The area where the air temperature was the least important, for a percentage of the variance of EVI explained and VIP score was P4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Table\u0026nbsp;7). At the same time, P4 was also the area where WTD importance was the highest (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Table\u0026nbsp;7).\u003c/p\u003e\u003cp\u003eThe similarity in P1 and general area lag was observed again with the monthly and weekly VIP scores of Tair and WTD (Supplementary Table\u0026nbsp;7). However, the role of precipitation was higher in P1. Moreover, the percentage of EVI oscillations explained by individual variables or their combination was lower in P1 than in the general area (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the case of P3, this was in both timesteps, the area where the least oscillations of EVI could be explained by hydrometeorology (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). P2 was the area where the EVI changes could be explained the most by hydrometeorology (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eChanges in Vegetation Phenology\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the general peatland area, regression analysis results showed that the changes in LOS can be explained by temperature and WTD oscillations over the years. The LOS and maximum EVI (EVI during the POS, EVI\u003csub\u003emax\u003c/sub\u003e) prediction by air temperature and WTD produced a good estimation with relatively low RMSE and high R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e (Supplementary Fig.\u0026nbsp;1a-b). However, the fit of the EVI\u003csub\u003emax\u003c/sub\u003e was more significant (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) than the LOS fit (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1; Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e\u003cp\u003eThe changes in phenology trends described with the break in 2020\u0026ndash;2021 were also observed in air temperature. While the air temperature at the beginning of 2019 and 2020 was generally above 0\u0026ordm;C, it was below 0\u0026ordm;C in 2021, reaching a freezing temperature more frequently (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). In addition, the oscillation of WTD also showed a similar pattern to the changes in vegetation season. While WTD in 2019\u0026ndash;2020 remained in a yearly average similar (-13.1cm, -13.9cm), it increased during 2021 by ~\u0026thinsp;3 cm (annual average), showing levels that did not reach the surface as in the previous years (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Supplementary Table\u0026nbsp;1). From 2021, WTD showed a stronger increasing trend, reaching its deepest point during summer-autumn 2022 and recovering the levels of 2021 during 2023 due to increased precipitation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, Supplementary Table\u0026nbsp;1). The higher air temperatures and lower WTD were aligned with the increase in the greenness of the patches during 2021 and the general peatland area (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-b), while the length of the season was drastically reduced, affected mainly by the changes at the start of the season due to colder conditions (Supplementary Table\u0026nbsp;1, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eMore concretely, in the patches scale, the regression analysis results of WTD, air temperature, and LOS showed again the importance of the hydrometeorological variables for different vegetation canopies (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The strongest relationship of these parameters was found in P1 in both cases (LOS and EVI\u003csub\u003emax\u003c/sub\u003e regressions), displaying the most similar modelled and measured results with low RMSE, high R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and high significance (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the case of LOS, the \u003cem\u003ePhragmites\u003c/em\u003e spp.-dominated patch (P2) showed the weakest results with no significance of WTD and air temperature as estimators, high RMSE and low R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). P2 also presented a non-significant result in the case of EVI\u003csub\u003emax\u003c/sub\u003e regression but with a relatively high R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and low RMSE (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The area with the second highest R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e and significance in the LOS regression was the \u003cem\u003eSalix\u003c/em\u003e spp.-dominated patch (P3, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), which also had a high similarity in modelled and measured LOS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, it was in the same patch (P3) where the least linked EVI\u003csub\u003emax\u003c/sub\u003e and WTD\u0026thinsp;+\u0026thinsp;air temperature relationship was found (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), corresponding to the most stable peak value through the time series with a range of change in EVI\u003csub\u003emax\u003c/sub\u003e of 0.11 in front of 0.13\u0026ndash;0.18 for the rest of the cases. Lastly, the P4 presented a good result for the peak EVI estimation, the second best, while the LOS result showed no significance and relatively high errors, the second worst result (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCoefficient of determination (R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e), p-value (p-val), Root Mean Square Error (RMSE) and equation of the linear regression between \u003cb\u003eA\u003c/b\u003e) the Length of the Season (LOS; or \u003cb\u003eB\u003c/b\u003e) the maximum value of the Enhanced Vegetation Index (EVI\u003csub\u003emax\u003c/sub\u003e; with the annual average of water table depth (WTD) and air temperature (Tair) based prediction for the vegetation patches (P1 \u0026ndash; P4). Patches are described in the Methodology.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-val\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eRMSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eEquation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eP1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eLOS\u0026thinsp;=\u0026thinsp;27.945\u0026thinsp;+\u0026thinsp;0.8364WTD\u0026thinsp;+\u0026thinsp;20.41T\u003csub\u003eair\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eP2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e15.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eLOS\u0026thinsp;=\u0026thinsp;70.031\u0026thinsp;+\u0026thinsp;0.1161WTD\u0026thinsp;+\u0026thinsp;8.43T\u003csub\u003eair\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eP3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e6.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eLOS\u0026thinsp;=\u0026thinsp;19.730\u0026ndash;0.0077WTD\u0026thinsp;+\u0026thinsp;17.46T\u003csub\u003eair\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eP4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e8.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eLOS\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.333\u0026ndash;1.1575WTD\u0026thinsp;+\u0026thinsp;15.98T\u003csub\u003eair\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eB\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eP1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eEVI\u003csub\u003emax\u003c/sub\u003e = 1.057\u0026ndash;0.0040WTD \u0026minus;\u0026thinsp;0.06T\u003csub\u003eair\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eP2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eEVI\u003csub\u003emax\u003c/sub\u003e = 1.114\u0026ndash;0.0003WTD \u0026minus;\u0026thinsp;0.06T\u003csub\u003eair\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eP3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eEVI\u003csub\u003emax\u003c/sub\u003e = 0.539\u0026ndash;0.0086WTD \u0026minus;\u0026thinsp;0.02T\u003csub\u003eair\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eP4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eEVI\u003csub\u003emax\u003c/sub\u003e = 0.926\u0026ndash;0.0116WTD \u0026minus;\u0026thinsp;0.06T\u003csub\u003eair\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eLake surface area oscillations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe image analysis of the lake area showed a continuous drying tendency, as shown in the reduction of the area filled with water and the rise of dry spots (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The most significant decrease in the water area was observed during the summers of 2022 and 2023 when a larger lake bottom area was exposed, creating temporal \u0026ldquo;islands\u0026rdquo;. In addition to drying, the within-year amplitude of the water area increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe validation showed high accuracy of the lake pixels classified from RGB scenes and an acceptable accuracy for dry pixels (higher than 0.6, Supplementary Table\u0026nbsp;8). These changes in lake and dry pixels were related to the area\u0026rsquo;s WTD dynamics. The regressions have proven the relationship between these variables. The WTDs around 0 and \u0026minus;\u0026thinsp;35 cm presented the most significant outliers of the trends (Supplementary Fig.\u0026nbsp;2). The dry and water areas\u0026rsquo; relationships with WTD were characterised by R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e of 0.5, with both relationships being statistically significant (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Supplementary Table\u0026nbsp;8).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cb\u003eChanges in vegetation phenology and the influence of hydrometeorology\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGeneral trends of the vegetation season parameters in the peatland area\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe peatland area analysis showed changes in vegetation phenology during the study period, with a continuous rise in the LOS and greenness (revealed with EVI maximum values), with the trend only breaking during 2021. The changes in WTD and air temperature explain the break in the phenological changes during 2021 and their continuous trend (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The lower annual average temperatures and higher annual average WTD increase the EVI\u003csub\u003emax\u003c/sub\u003e. Deeper water tables are closely related to the proliferation of vascular plants such as Ericaceous shrubs and \u003cem\u003ePinus spp\u003c/em\u003e., L and the loss of mosses, with clear relationships reported for peatland areas \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. This succession can explain the increase in the average EVI and LOS.\u003c/p\u003e\u003cp\u003eMoreover, the lowering of the temperature positively affected the vegetation because 2018\u0026ndash;2020 were extremely even anomalously warm \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, with average annual Tair from 9.75 to 10.08\u0026ordm;C, while the \u0026ldquo;coldest\u0026rdquo; years (2021\u0026thinsp;\u0026minus;\u0026thinsp;8.48\u0026ordm;C, 2022\u0026ndash;9.2\u0026ordm;C) were just normal, and plants did not suffer the stress related to higher temperatures. During 2023, the warmer-than-usual temperatures returned with an annual average similar to 2020. The proliferation of vascular plants due to a deeper water table and higher precipitation (774mm in 2023 vs. 540mm in 2020) lowered the vegetation stress. The reduction of moss coverage and the negative impact of the water table drawdown on them is often counteracted by high precipitation. Increases in precipitation have even compromised studies on the effect of water table drawdowns on peatland vegetation shifts \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Our results can be explained by a lower negative impact of increased temperature on vascular plants in comparison to mosses (which desiccate each summer) reported in previous studies \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The change in the temperature pattern in 2021 also justifies the continuous increase of LOS until this year and the recovery of the trend in 2023.\u003c/p\u003e\u003cp\u003eTherefore, the results show how unusually warm years negatively affect vegetation, shortening the season and lowering its peak when mosses are more abundant (2017\u0026ndash;2020), while deeper water tables induce vegetation succession, increasing the greenness and the LOS (2017\u0026ndash;2022). Lastly, the vegetation composition shift to a lower abundance of mosses allows the avoidance of the negative effect of temperature, with 2023 remaining with prolonged LOS and high EVI values \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhenology of vegetation patches\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe hydrometeorology can strongly influence the seasonality of vegetation patches. A shift in conditions induces growth and possible changes in vegetation dominance when multiple species compete \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. This could explain the changes in the vegetation season shape, thus changing the phenological parameters in each patch through the study period. As discussed for the general trends of the peatland, a potential shift to a higher presence of vascular plants in the detriment of mosses can explain the increase in the average greenness of the patches from 2017 until 2022. However, the area where the peak of the season remains the most stable in value and time is dominated by \u003cem\u003eSalix\u003c/em\u003e spp. (P3), and the interchange between mosses and vascular plants does not play a role. Thus, there is still the same vegetation, and the dominance of this vegetation does not change due to meteorology.\u003c/p\u003e\u003cp\u003eThe results presented show that there was a change to more favourable conditions for vascular plants from 2017\u0026ndash;2020 to 2021\u0026ndash;2023 and a rise in their abundance \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, which caused an increase in greenness and season length in the second period (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, there is a clear difference in the phenology changes in this period between the areas with a higher mix of vegetation (P1 and P4) and the areas with a clear dominance established (P2 and P3). P2 and P3 are dominated by grasses and shrubs, respectively, and both of these vegetation types usually respond positively to a decline in the water table \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, increasing their growth, while species competition in P1 and P4 makes the dynamics more complex.\u003c/p\u003e\u003cp\u003eThe shared trend shows the increases in LOS progressively, with a delay in the EOS and sooner SOS (2017\u0026ndash;2022). The earliest start and latest end of the season were observed for P1 due to the presence of evergreen vegetation (\u003cem\u003eSphagnum\u003c/em\u003e spp. and \u003cem\u003eV. oxycoccus\u003c/em\u003e; Supplementary Table\u0026nbsp;2) \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. This shared trend is disrupted in 2023 when the difference in P1-P4 and P2-P3 appear together with wetter conditions. The increase in precipitation during 2023 changed the SOS tendency, which did not start as soon as the trend forecasted in the case of P2 and P3, and the POS was delayed more than 20 days (Supplementary Table\u0026nbsp;2\u0026ndash;3). While precipitation benefits vascular plant and moss growth (and, in some occasions, shrubs)\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, the increment of precipitation events also significantly reduced the average photosynthetically active radiation (PAR, data not shown). P2 and P3 vegetation was more affected by this fact, as they are more used to high-light conditions \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e though Phragmites can adapt to low-light on some occasions \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. This does not happen in the other patches, where the POS changed in less than 5 days, and SOS continued its trend with sooner starts (Supplementary Table\u0026nbsp;2\u0026ndash;3). The increment of precipitation benefited the mosses present in P1 with a rise in the ambient humidity, reducing stress \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Plus, sedges in P1 and P4, larger precipitation events have been marked as inducers of vascular plant growth \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The benefits of precipitation and early warmer conditions in spring each year produced the continuation of earlier SOS and similar POS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The stability of the SOS, especially in P3, can be related to a non-apparent change in vegetation composition and a possible stronger role of photoperiod in this vegetation patch \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. This stability cannot be observed in P2, which presents a reverse trend of the SOS in 2022\u0026ndash;2023. This could be the effect of the interference of water reflection in the results as it is the most waterlogged part and remained flooded during the part of the growing period.\u003c/p\u003e\u003cp\u003eAnother indication of the probable vegetation change is P1 and P4 response to wetter conditions in 2023. Due to the presence of mosses in the mixed vegetation patches (P1 and P4), a positive effect should have been seen following wetter conditions during summer, when mosses are usually dry \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. However, this is not observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Table\u0026nbsp;2\u0026ndash;3), indicating a low contribution of mosses to the patches\u0026rsquo; average EVI\u003csub\u003emax\u003c/sub\u003e and EOS. Therefore, the individual trends of the patches indicate a similar story to the general trends of the peatland, with an increasing abundance of vascular plants and vegetation seasons ending later, enlarging the season.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEVI linkage with hydrometeorological variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study of WTD, air temperature, and precipitation\u0026rsquo;s role in the EVI oscillations revealed a difference in the behaviour of vegetation patches.\u003c/p\u003e\u003cp\u003eThe delay in the response of WTD changes corresponds to the differences in the area base level and regime of WTD plus the vegetation requirements of water. P3 and P4 showed the quickest response of WTD changes, which are the areas on the edge of the peatland with the deepest WTD and no peatland surface oscillations. Additionally, peatlands\u0026rsquo; margin/edge areas are usually transitional between peatlands and the adjacent ecosystems \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Therefore, due to greater hydrological variability and the transitional nature of these edge areas, the sensitivity to WTD fluctuations is usually more pronounced than in other peatland parts \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. In the case of the other two patches, P2 and P1, the difference in their response to WTD changes resides in the speed of the adaptations by the vegetation. \u003cem\u003eP. australis\u003c/em\u003e quickly adapts to changing WTDs by extending its roots and rhizomes to access water or quickly proliferating in flooded rewetted sites with aggressive growth \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Meanwhile, the P1 area had a greater delay in the response for WTD changes because the dominant vegetation relies on near-surface soil moisture as the water source, and none has the high plasticity of \u003cem\u003eP. australis\u003c/em\u003e to adapt. \u003cem\u003eV. oxycoccus Carex\u003c/em\u003e spp. rely on this soil moisture due to a shallower root system \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, while \u003cem\u003eSphagnum\u003c/em\u003e spp. with no root system relies on this moisture as they access water by capillarity \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. The soil moisture reacts slower to WTD changes as ambient humidity acts as a buffer \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Hence, the vegetation response also slows down.\u003c/p\u003e\u003cp\u003eThe study of the role of hydrometeorological variables showed the worst result \u0026ndash; the lowest percentage of variance explained \u0026ndash; in the area dominated by \u003cem\u003eSalix\u003c/em\u003e spp. (P3) in weekly and monthly analysis. \u003cem\u003eSalix\u003c/em\u003e spp. showed the most stable trend of EVI, as already observed for EVI\u003csub\u003emax\u003c/sub\u003e, and therefore the highest resistance to hydrometeorological changes also because it is the area with the least possibility of vegetation succession. This woody species has already been defined as resistant and stable in greenness \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, hence supporting the results. In the other edge area, P4, results showed WTD\u0026rsquo;s highest importance in EVI changes among all vegetation types and the lowest for air temperature. This can be explained by the transitional nature of the vegetation in P3, with WTD apparently showing a bigger role in the EVI changes. The air temperature was the most crucial for the P2 area. With the already explained plasticity of \u003cem\u003eP. australis\u003c/em\u003e to WTD, PLSR established again the importance of temperature and the lower impact of WTD when compared to other vegetation \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. In the case of P1, the lower capability of temperature and WTD to explain changes in EVI could be led by changes in the vegetation cover, which has already been pointed out as a possible area of decreased moss cover in favour of other vascular plants. The reduction of moss cover in peatlands following deeper water tables is often observed \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn general, a rapid response to WTD oscillations in the edges area of the peatland was observed. Still, it showed less impact when dominated by woody vegetation, as there is no possible succession in the conditions studied (not severe WTD changes). Additionally, vegetation has a delayed but more severe response to WTD in more central parts of the peatland. The air temperature plays the main role in vegetation changes for all areas, with woody vegetation showing the highest resistance as it is not affected in P3 by a possible mosses\u0026rsquo; desiccation or vegetation succession. Future reactions, thus, are expected to induce major changes in central areas with probable \u003cem\u003eSphagnum\u003c/em\u003e spp. loss, and in the transitional edge area \u0026ndash; P4 \u0026ndash; the proliferation of \u003cem\u003eP. asutralis\u003c/em\u003e to the detriment of the rest of the species could be expected.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLake surface area oscillations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe continuous trend within the lake area has shown an increase in the summer drying and more pronounced intra-annual oscillations. The appearance of dry patches and shrinking of the lake is demonstrated with the continuous linear trend of the time series, and though during July-August 2023 we can observe similar extents of the water areas than in 2018, it only occurs for shorter periods (2\u0026ndash;3 months), and it is not enough to compensate the high drops of the water area during the year. The lake drying relates to the WTD movement in the peatland; higher WTDs are coupled with a higher amount of dry area, demonstrating the hydrological linkage of the waterbody with the peatland\u0026rsquo;s groundwater source. The remanent lake in the peatland can be considered small; thus, the linked watershed is less complex and not able to support the lake and palliate its drying during droughts, showing less resilience than larger lakes \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe drying tendency of Rzecin peatland\u0026rsquo;s lake observed in previous studies \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e not only continues but also increases in severity and length, including, apart from the lake shrinkage, the appearance of dry spots inside, creating temporal \u0026ldquo;islands\u0026rdquo; during the driest months. The partial drying observed in this study induces hydrological and biological transformation of the peatland area. The partial lake drying impacts the submerged vegetation and the vegetation near it \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. The changes in surrounding vegetation can also be observed in this study with the phenological changes from Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Moreover, the wildlife may also suffer from the lake drying, modifying the number of birds and fish with a decrease in potential food sources and availability of water \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, a succession of chain effects that can lead to a full drying of the area and irreversible changes if no measures are taken.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImplications of the expected future changes in vegetation and lake area\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe results indicated a probable vegetation shift with vascular plants\u0026rsquo; proliferation. Particularly with elevated temperatures, vascular plants can induce the acceleration of carbon loss from peat soils with higher litter deposition \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Additionally, the encroachment of vascular plants, particularly sedges, can influence methane emissions by acting as conduits for methane transport from peat to the atmosphere, thereby affecting greenhouse gas fluxes \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. On the bright side, in this article, the presence of vascular plants increased the vegetation cover resilience to phenology changes with fewer changes in LOS and greenness thanks to their proliferation. The increment of LOS may shift the CO\u003csub\u003e2\u003c/sub\u003e patterns, with larger periods in which carbon uptake is higher. However, warmer conditions can cancel this positive effect with rises in vegetation respiration, as has been observed in other peatland areas \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. The loss of water within the lake and the increased drying severity during the warmer months also potentially increase C losses. The exposure of organic-rich sediments to oxic conditions results in CH\u003csub\u003e4\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e emissions \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Therefore, future conditions would probably establish the area (peatland and lake) as an important source of C losing their climate change mitigation service.\u003c/p\u003e\u003cp\u003eThe rest of the ecosystem services provided by the peatland and lake area will also be compromised. The shifts in plant community composition can alter nutrient cycling and water regulation functions. The higher presence of vascular plants will increase the evapotranspiration rates, and together with higher temperatures and WTD, the exposure of peat to oxic conditions will induce its degradation \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Peat decomposition will compromise the peatlands\u0026rsquo; water purification, biodiversity and flood palliation ability \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Additionally, the loss of mosses and the encroachment of vascular plants can affect the quality of dissolved organic matter, influencing microbial activity and nutrient availability, further compromising water purification and habitat provision \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. As previously mentioned, the lake area will lose biodiversity and the potential for climate regulation. Its drying will compromise the water supply and, from an economic perspective, affect, for example, local agriculture \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe data analysed in this article for the Rzecin peatland have shown the changing behaviour of the area\u0026rsquo;s vegetation phenology and the differences depending on the type of vegetation cover. Moreover, the lake located in the area has shown a consistent drying pattern with an increment in the severity of interannual oscillations. Summarizing, the key points extracted were:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eA probable proliferation of vascular plants over mosses produced an increase in greenness (EVImax) and growing season length (LOS).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eWarmer years stressed mosses, but the expansion of vascular plants (Ericaceous shrubs, Pinus spp.) helped sustain greenness and growing season length despite temperature fluctuations.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe mosses showed a low contribution to the patches\u0026rsquo; average EVImax and EOS. The increase in precipitation of 2023 should have produced a delay in mosses\u0026rsquo; desiccation, due to their abundance drop or proliferation of vascular plants, this \u0026ldquo;positive\u0026rdquo; effect was not observed.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe lag in vegetation response to WTD was the quickest in peatland edge areas (P3, P4) due to their transitional nature and greater hydrological variability. While in the central part the lag response depended on the vegetation. P. australis adapted rapidly to WTD shifts, possibly through root extension and aggressive growth, while P1 vegetation (e.g., V. oxycoccus, Carex spp., Sphagnum spp.) relied on near-surface soil moisture, delaying its response.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTemperature was the most influential factor in EVI changes across most areas, except in the transitional peatland area (P4), where WTD had the strongest impact. Woody vegetation (Salix spp. in P3) showed the highest stability and resistance to temperature and hydrometeorological fluctuations.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe lake shows a continuous drying trend with more pronounced intra-annual fluctuations linked to peatland water table depth (WTD) changes showing the lake\u0026rsquo;s dependence on peatland groundwater.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eIn near future perspectives the results highlight how prolonged deeper water tables may reduce Sphagnum spp. in central peatland areas, while transitional areas (P4) may see an expansion of P. australis, potentially outcompeting other species. Furthermore, the shrinking lake and emerging dry patches will impact the submerged vegetation, surrounding plant communities, and wildlife, potentially leading to long-term ecosystem transformations and biodiversity loss if no intervention occurs.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003eStudy area\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Rzecin area is crucial for regional nature conservation, supporting diverse vegetation, including brown mosses, Sphagnum, and various vascular plants. In the area, 26 rare and endangered species and 20 locally threatened species can be found \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. The peatland provides varied ecological conditions with differences in fertility and water availability, fostering a rich biodiversity of 127 vascular plant species from 84 genera and 43 families, 17 of which are on the Regional Red List. Additionally, 34 moss taxa from 10 families, including endangered species like \u003cem\u003eSphagnum fuscum\u003c/em\u003e and \u003cem\u003ePaludella squarrosa\u003c/em\u003e, contribute to the site\u0026rsquo;s ecological significance \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHuman activity has significantly altered the development of the Rzecin peatland over the past two centuries. Between 1880 and 1890, deforestation and the construction of the Rzecin drainage canal initiated major alterations in the wetland ecosystem, leading to habitat acidification and changes in hydrology \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The Rzecin peatland is a large (87 ha) mesotrophic, geogenous wetland, a poor-rich fen (area code Natura 2000: PLH300019) located in Western Poland (52\u0026deg;45\u0026prime; N latitude, 16\u0026deg;18\u0026prime; E longitude, 54ma.s.l.) \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In the centre of the peatland, an approximately 70-cm- thick-floating mat of peat substrate is present. In contrast, the peat surface is more anchored to the bedrock through the periphery, producing differences in vegetation and deeper WTD \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Due to the presence of a floating mat, the WTD seasonal dynamics are much less in the middle of the peatland with a higher WTD position than at its edge, determining the dominance of \u003cem\u003eSphagnum\u003c/em\u003e spp., \u003cem\u003eCarex rostrata, C. limosa, Eriophorum angustifolium\u003c/em\u003e and \u003cem\u003eOxycoccus pallustris\u003c/em\u003e in the middle of the peatland. While at its edge, the dominant plant communities are represented by taller vascular plants like \u003cem\u003ePhragmites australis, Typha latifolia, Carex elata\u003c/em\u003e and \u003cem\u003eSalix\u003c/em\u003e spp. \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFor the purpose of this study, four homogenous subareas are selected to represent the different vegetation communities present, dominated by P1 (floating mat) \u0026ndash; Sphagnum spp., Carex spp., and \u003cem\u003eVaccinum oxycoccos\u003c/em\u003e L.; P2 (north-west non-floating mat) \u0026mdash;Phragmites australis (Cav.) Trin. ex Steud; P3 (south-east non-floating mat) \u0026ndash; Salix spp.; and P4 (transitional) \u0026ndash; \u003cem\u003eCarex\u003c/em\u003e spp. transitioning to \u003cem\u003eCarex\u003c/em\u003e spp., \u003cem\u003eMenyanthes trifoliata\u003c/em\u003e L., \u003cem\u003eEquisetum fluviatile\u003c/em\u003e L. and \u003cem\u003eComarum palustre\u003c/em\u003e L. \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The dimension of the patches is 93m x 93m, which corresponds to 31 x 31 pixels of PlanetScope images, and it would be ~\u0026thinsp;3 x 3 pixels if using Landsat 8 (30m/pix), highly reducing the detection of the spatial variation of the canopy.\u003c/p\u003e\u003cp\u003eThe lake studied is located at the eastern part of the peatland (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), occupying\u0026thinsp;~\u0026thinsp;16ha and overgrown by \u003cem\u003eTypha latifolia\u003c/em\u003e L. and \u003cem\u003ePhragmites australis\u003c/em\u003e (Cav.) Trin. ex Steud \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSatellite imagery\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe satellite dataset source used in this study was the PlanetScope high-resolution (3m/pix) satellite ensemble, including 144 images of 4 reflectance bands from 2017 to 2023. The type of product used was the PlanetScope Ortho Scene, the analytic surface reflectance. The Ortho Scenes are radiometrically and geometrically-corrected products projected to a WGS84 Web Mercator cartographic map projection with a positional accuracy of less than 10 m Root Mean Square Error (RMSE) at the 90th percentile. The 4-bands (B1 \u0026ndash; Blue, B2 \u0026ndash; Green, B3 \u0026ndash; Red, B4 \u0026ndash; NIR) were harmonised to Sentinel 2 with the PlanetScope Application Programming Interface (API) Orders and Tools (Planet Labs PBC). Only the images with the highest publishing stage (finalised) and quality category (standard) were included, ensuring optimal radiometric consistency and minimal processing artefacts. Furthermore, the images were anchored to the same scene through the API\u0026rsquo;s correction tools to ensure the geometrical corrections. From the available\u0026thinsp;~\u0026thinsp;3200 images of the Dove Classic (PS2) and Dove R (PS2.SD) instruments, the used imagery was selected based on cloud cover below 10%; haze was also a reason for exclusion. Further technical details, including radiometric calibration methods and spectral band specifications, are available in the PlanetScope Product (Specification Planet Labs PBC 2023. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCombined Imagery Product Spec FINAL | December 2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eIn-situ data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn-situ data measured in the study area included WTD measurements obtained using TD-divers (Eijkelkamp Soil \u0026amp; Water, the Netherlands) installed in polyvinyl chloride (PVC) tubes permanently mounted to wooden platforms stabilised on piles above the peatland surface. The WTD position was calculated as the difference between the distance from the top edge of the piezometer to the TD-Diver installation depth and the distance from the top edge of the piezometer to the peatland surface \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Due to seasonal oscillations of the peatland surface, its position was calculated based on manual measurements of the distance between a fixed point on the platform and the peatland surface, taken every 2\u0026ndash;3 weeks. Changes in the peatland surface were then linearly interpolated between measurements and incorporated into WTD calculations.\u003c/p\u003e\u003cp\u003eThe air temperature (Tair) was measured by radiation-sheltered thermohygrometers HygroVue5 (Campbell Sci., USA). The measurements provide a continuous dataset with half-hourly timesteps throughout the study period \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eVegetation and water indexes calculation and selection plus RGB scenes generation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe satellite data was used to generate the Enhanced Vegetation Index (EVI) \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, the Normalised Difference Vegetation Index (NDVI) \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and the LWM \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:NDVI\\:=\\frac{NIR-RED}{NIR+RED}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:EVI\\:=2.5\\:\\bullet\\:\\:\\frac{NIR-RED}{\\left(NIR+6\\bullet\\:RED\\:-7.5\\bullet\\:BLUE\\right)+1}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:LWM\\:=\\frac{NIR}{GREEN\\:+\\:0.0001}\\:\\bullet\\:100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eApart from the indexes calculated, the red, green, and blue (RGB) imagery was generated and corrected by intensity, transforming it into greyscale images for Otsu and clustering thresholding. The intensity correction was performed by rescaling the pixel values to the range [0, 1], adjusting the contrast by mapping values between the input and output contrast limits, and, lastly, returning it to the original data type. The limits used the bottom 1% and the top 1% of all pixel values.\u003c/p\u003e\u003cp\u003eThe selection of the vegetation indices (VIs) for phenological calculations was based on the agreement between the ground VIs values calculated based on the periodic top of canopy reflectance measurements (FieldSpec HandHeld 2, ASD Inc., Boulder, USA) and the extrapolated VIs values from the satellite data for each ground point location. The 18 ground points used were located in the Rzecin experimental station described in Juszczak et al. \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e during 42 field campaigns in 2018\u0026ndash;2021. At the same time, the selection between the RGB or LWM scenes for the lake analysis was made based on the accuracy of the ground truth described in section 4.3.\u003c/p\u003e\u003cp\u003e\u003cb\u003eVegetation phenological parameters\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe identification of vegetation phenological variables (SOS \u0026ndash; Start of the Season, EOS \u0026ndash; End of the Season, LOS \u0026ndash; Length of the Season, and the Peak of the Season \u0026ndash; POS) was generated with Decomposition and Analysis of Time Series (DATimeS) software \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e using the VI selected. Whittaker interpolation was applied to the EVI snowless images time series to obtain the index\u0026rsquo;s daily values. The interpolated time series was then used to retrieve the phenological parameters for each year individually and analyse the changes and trends of the overall area. DATimeS identifies the growing seasons from the time series, detecting three consecutive local minimum, maximum and minimum points with some constraints to avoid noise interferences \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The constraints used were the growing season prominence (set to 20%) and the minimum separation between seasons (set to 100). Once the growing season is identified, DATimeS estimates the phenological parameters based on the seasonal, relative or absolute amplitude \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In this case, the seasonal amplitude was used and set to 25%. The EOS and SOS were selected \u0026ldquo;where the left/right part of the curve reaches a fraction of the seasonal amplitude along the rising/decaying part of the curve\u0026rdquo;. The overall calculations produced images of each year\u0026rsquo;s phenological parameters generated together with the average area values. Parallelly, phenological variables were generated for the selected vegetation patches (P1 \u0026ndash; P4, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eImage thresholding and lake area estimation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe analysis of the lake drying was assessed relative to its coverage in 2018. The March image of 2018, showing the highest coverage of water in the lake, was used to manually delimit the lake area in the Quantum Geographic Information System (QGIS) and create a mask. Further analysis was performed in MATLAB (The MathWorks, Inc., USA), where the mask was used to crop the lake area.\u003c/p\u003e\u003cp\u003eThe greyscale (RGB) and LWM images were used to estimate the dried and waterlogged areas with k-means clustering and Otsu thresholds. The Otsu thresholding analyses the grayscale image\u0026rsquo;s histogram to find a threshold that separates the pixel intensities into two classes: foreground (object) and background \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. This method computes the weighted variance of pixel intensities for all possible threshold values. Then, it establishes a threshold value for the lowest intra-class variance (or highest inter-class variance) \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The method was applied in MATLAB using the \u0026ldquo;imbinarized\u0026rdquo; function. Meanwhile, the k-means clusters group pixels into K clusters based on their similarity \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. It randomly selects K initial centroids (representative pixel values), and then each pixel is assigned to the nearest centroid based on the Euclidean distance. Lastly, the centroids\u0026rsquo; value is updated by averaging the pixel values of each cluster \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. This process is repeated through the image iteratively until convergence (i.e., when centroids no longer change significantly). The k-means clustering was applied using the \u0026ldquo;imsegkmeans\u0026rdquo; MATLAB function for 3 clusters, with k\u0026thinsp;=\u0026thinsp;1 corresponding to the area outside the lake masked (NaN values). The respective areas were calculated for each image from the classified pixels of dry and waterlogged lake parts, creating the time series of lake area oscillations. Additionally, one image per month (from March to November) was manually classified to use as validation of the clustering and Otsu methods. The validation was performed by calculating the accuracy as the number of pixels classified as water/dry divided by the total area in the ground truth of water/dry pixels. The best result by method (Otsu or clustering) and scene (RGB or LWM) with higher accuracy is selected and the only one presented in the results.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical analyses of time series relationship (vegetation phenological parameters, lake surface area, WTD, precipitation and Tair)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll statistical analyses were performed in MATLAB (The MathWorks, Inc., USA). The regression method was used to analyse the relationship between WTD, Tair, lake surface changes, and vegetation phenological parameters. This regression analysis included the development of a multivariate (Tair and WTD) linear model, obtaining its associated statistics, the equation of the linear relationship (y\u0026thinsp;=\u0026thinsp;n\u0026thinsp;+\u0026thinsp;a\u0026middot;WTD\u0026thinsp;+\u0026thinsp;b\u0026middot;Tair) and the estimates. From the model estimates, a model versus measured values comparison was performed to show the model\u0026rsquo;s associated errors visually.\u003c/p\u003e\u003cp\u003eThe multivariate regression was used with the phenological parameters and the yearly WTD and Tair average, first for the general area and second for patches. Furthermore, the area-averaged EVI from the vegetation patches was also compared with Tair, WTD, and precipitation using daily data observing the time series trends. Additionally, in order to study this effect further, a cross-correlation analysis was performed pairing EVI with WTD, Tair and precipitation in daily, weekly and monthly timesteps (averages except for precipitation with sums). Once the cross-correlation was performed and the lag between time-series was obtained, the time-series were displaced, and a Partial Least Squares Regression (PLSR) was performed using the \u0026ldquo;plsregress\u0026rdquo; MATLAB function. The PLSR models relationships between predictor variables (X \u0026ndash; hydrometeorology) and response variables (Y \u0026ndash; EVI) while handling multicollinearity and high-dimensional data \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Firstly, it extracts latent components (PLS components) that maximise the covariance between X (predictors) and Y (response). The extracted components are used to build a predictive model for Y, and by analysing the Variable Importance in Projection (VIP) scores, PLSR determines the contribution of each predictor to the response variable \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. PLSR (adjusted for three PLS components) was applied for each vegetation patch and the general area, using all the hydrometeorological variables to obtain the VIP score and the total percentage of EVI variance they explained. Moreover, in order to obtain the individual percentages explained by each hydrometeorological variable, PLSR was also applied individually for WTD, Tair and precipitation, in this case, adjusted for 1 PLS component.\u003c/p\u003e\u003cp\u003eThe lake area oscillations were analysed by studying the trend of the lake drying using a simple regression of the changes in the values of dry and water areas over time. Furthermore, the analysis also included the study of the link between WTD and lake area oscillations in weekly timesteps (generated with a linear interpolation of the original data). This link was examined with the time series comparison of both parameters and the linear regression (y\u0026thinsp;=\u0026thinsp;n\u0026thinsp;+\u0026thinsp;a\u0026middot;WTD).\u003c/p\u003e\u003cp\u003eThe abovementioned statistics for all cases refer to the coefficient of determination (R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e), Root Mean Square Error (RMSE), and p-values derived from the linear regressions between parameters performed in MATLAB.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the discipline of science, environmental engineering, mining, and energy at Poznan University of Life Sciences (Poland) with the Innowator programme under project No. 02/2024/INN and the National Science Centre of Poland (NCN) under projects No. 2016/21/B/ST10/02271 and 2020/37/B/ST10/01213.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.A-S: Conceptualization, data curation, formal analysis, investigation, methodology, visualization, writing\u0026ndash;original draft. M A.: Formal analysis, investigation, writing-review and editing. M.S.: Data curation, investigation, supervision. A.R.: Funding acquisition, supervision, writing-review and editing. R.J.: Resources, funding acquisition, supervision, writing\u0026ndash;review and editing. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files further information is available per request to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJungkunst, H. F. et al. Springer Netherlands,. Accounting More Precisely for Peat and Other Soil Carbon Resources. in \u003cem\u003eRecarbonization of the Biosphere\u003c/em\u003e vol. 4 127\u0026ndash;157 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeichl, M. et al. Peatland vegetation composition and phenology drive the seasonal trajectory of maximum gross primary production. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 8012 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAntala, M., Juszczak, R., van der Tol, C. \u0026amp; Rastogi, A. Impact of climate change-induced alterations in peatland vegetation phenology and composition on carbon balance. \u003cem\u003eSci. Total Environ.\u003c/em\u003e \u003cb\u003e827\u003c/b\u003e, 154294 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMbow, H. O. P., Reisinger, A., Canadell, J. \u0026amp; O\u0026rsquo;Brien, P. Special Report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems (SR2). \u003cem\u003eGinevra, IPCC\u003c/em\u003e 650, (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLoisel, J. et al. Expert assessment of future vulnerability of the global peatland carbon sink. \u003cem\u003eNat. Clim. Chang.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 70\u0026ndash;77 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKopansky, D., Mark, R., Matt, K. \u0026amp; Jonny, H. \u003cem\u003eGlobal Peatlands Assessment \u0026ndash; The State of the World\u0026rsquo;s Peatlands: Evidence for action toward the conservation, restoration, and sustainable management of peatlands. Main Report.\u003c/em\u003e doi:20.500.11822/41222. (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eG\u0026oacute;recki, K. et al. Water table depth, experimental warming, and reduced precipitation impact on litter decomposition in a temperate Sphagnum-peatland. \u003cem\u003eSci Total Environ\u003c/em\u003e \u003cb\u003e771\u003c/b\u003e, (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoon, M., Richardson, A. D. \u0026amp; Friedl, M. A. Multiscale assessment of land surface phenology from harmonized Landsat 8 and Sentinel-2, PlanetScope, and PhenoCam imagery. \u003cem\u003eRemote Sens. Environ.\u003c/em\u003e \u003cb\u003e266\u003c/b\u003e, 112716 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZak, D., Maagaard, A. L. \u0026amp; Liu, H. Restoring Riparian Peatlands for Inland Waters: A European Perspective. in Encyclopedia of Inland Waters vol. 3 276\u0026ndash;287 (Elsevier, (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarabach, J. The history of Lake Rzecin and its surroundings drawn on maps as a background to palaeoecological reconstruction. \u003cem\u003eLimnol. Rev.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 103\u0026ndash;114 (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCasagranda, E., Navarro, C., Grau, H. R. \u0026amp; Izquierdo, A. E. Interannual lake fluctuations in the Argentine Puna: relationships with its associated peatlands and climate change. \u003cem\u003eReg. Environ. Chang.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 1737\u0026ndash;1750 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLamentowicz, M. et al. Reconstructing human impact on peatland development during the past 200 years in CE Europe through biotic proxies and X-ray tomography. \u003cem\u003eQuat Int.\u003c/em\u003e \u003cb\u003e357\u003c/b\u003e, 282\u0026ndash;294 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMilecka, K. et al. Hydrological changes in the Rzecin peatland (Puszcza Notecka, Poland) induced by anthropogenic factors: Implications for mire development and carbon sequestration. \u003cem\u003eHolocene\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 651\u0026ndash;664 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJuszczak, R. \u0026amp; Augustin, J. Exchange of the Greenhouse Gases Methane and Nitrous Oxide Between the Atmosphere and a Temperate Peatland in Central Europe. \u003cem\u003eWetlands\u003c/em\u003e \u003cb\u003e33\u003c/b\u003e, 895\u0026ndash;907 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBandopadhyay, S. et al. Hyplant-Derived Sun-Induced Fluorescence\u0026mdash;A New Opportunity to Disentangle Complex Vegetation Signals from Diverse Vegetation Types. \u003cem\u003eRemote Sens.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 1691 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHui Qing, L. \u0026amp; Huete, A. A feedback based modification of the NDVI to minimize canopy background and atmospheric noise. \u003cem\u003eIEEE Trans. Geosci. Remote Sens.\u003c/em\u003e \u003cb\u003e33\u003c/b\u003e, 457\u0026ndash;465 (1995).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRouse, J. W., Haas, R. H., Schell, J. A. \u0026amp; Deering, D. W. Monitoring vegetation systems in the Great Plains with ERTS. \u003cem\u003eNASA Spec. Publ\u003c/em\u003e. \u003cb\u003e351\u003c/b\u003e, 309 (1974).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUddin, K., Khanal, N., Chaudhary, S., Maharjan, S. \u0026amp; Thapa, R. B. Coastal morphological changes: Assessing long-term ecological transformations across the northern Bay of Bengal. \u003cem\u003eEnviron. Challenges\u003c/em\u003e. \u003cb\u003e1\u003c/b\u003e, 100001 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJuszczak, R. et al. Ecosystem respiration in a heterogeneous temperate peatland and its sensitivity to peat temperature and water table depth. \u003cem\u003ePlant. Soil.\u003c/em\u003e \u003cb\u003e366\u003c/b\u003e, 505\u0026ndash;520 (2013).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBelda, S. et al. DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection. \u003cem\u003eEnviron. Model. Softw.\u003c/em\u003e \u003cb\u003e127\u003c/b\u003e, 104666 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOtsu, N. A. Tlreshold Selection Method from Gray-Level Histograms. \u003cem\u003eIEEE Trans. Syst. Man. Cybern\u003c/em\u003e. \u003cb\u003e9\u003c/b\u003e, 62\u0026ndash;66 (1979).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArthur, D. \u0026amp; Sergei, V. k-means ++: The Advantages of Careful Seeding. in \u003cem\u003eProceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms\u003c/em\u003e 1027\u0026ndash;35 (SODA \u0026rsquo;07. USA: Society for Industrial and Applied Mathematics, (2007).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosipal, R. \u0026amp; Kr\u0026auml;mer, N. Overview and Recent Advances in Partial Least Squares BT - Subspace, Latent Structure and Feature Selection. in (eds. Saunders, C., Grobelnik, M., Gunn, S. \u0026amp; Shawe-Taylor, J.) 34\u0026ndash;51Springer Berlin Heidelberg, (2006).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLamentowicz, M. et al. Unveiling tipping points in long-term ecological records from Sphagnum -dominated peatlands. \u003cem\u003eBiol. Lett.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 20190043 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMiętus, M. \u003cem\u003eClimate of Poland 2021\u003c/em\u003e. \u003cem\u003ePolish climate monitoring bulletin\u003c/em\u003e (2021). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.imgw.pl/sites/default/files/2021-04/imgw-pib-klimat-polski-2020-opracowanie-final-eng-rozkladowki-min.pdf\u003c/span\u003e\u003cspan address=\"https://www.imgw.pl/sites/default/files/2021-04/imgw-pib-klimat-polski-2020-opracowanie-final-eng-rozkladowki-min.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBreeuwer, A. et al. Decreased summer water table depth affects peatland vegetation. \u003cem\u003eBasic. Appl. Ecol.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 330\u0026ndash;339 (2009).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNorby, R. J., Childs, J., Hanson, P. J. \u0026amp; Warren, J. M. Rapid loss of an ecosystem engineer: Sphagnum decline in an experimentally warmed bog. \u003cem\u003eEcol. Evol.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 12571\u0026ndash;12585 (2019).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBragazza, L. et al. Persistent high temperature and low precipitation reduce peat carbon accumulation. \u003cem\u003eGlob Chang. Biol.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, 4114\u0026ndash;4123 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCao, R., Wei, X., Yang, Y., Xi, X. \u0026amp; Wu, X. The effect of water table decline on plant biomass and species composition in the Zoige peatland: A four-year in situ field experiment. \u003cem\u003eAgric. Ecosyst. Environ.\u003c/em\u003e \u003cb\u003e247\u003c/b\u003e, 389\u0026ndash;395 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHanson, P. J. et al. Peatland Plant Community Changes in Annual Production and Composition Through 8 Years of Warming Manipulations Under Ambient and Elevated CO2 Atmospheres. \u003cem\u003eJ. Geophys. Res. Biogeosciences\u003c/em\u003e. \u003cb\u003e130\u003c/b\u003e, 1\u0026ndash;19 (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKorrensalo, A. et al. Species-specific temporal variation in photosynthesis as a moderator of peatland carbon sequestration. \u003cem\u003eBiogeosciences\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 257\u0026ndash;269 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRadu, D. D. \u0026amp; Duval, T. P. Precipitation frequency alters peatland ecosystem structure and CO2 exchange: Contrasting effects on moss, sedge, and shrub communities. \u003cem\u003eGlob Chang. Biol.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 2051\u0026ndash;2065 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArciszewski, M., Pogorzelec, M., Nowak, B. H., Parzymies, M. \u0026amp; Piejak, M. Towards successful reintroduction of Salix myrtilloides: the importance of monitoring plant physiological indicators during acclimatization. \u003cem\u003eDendrobiology\u003c/em\u003e \u003cb\u003e92\u003c/b\u003e, 100\u0026ndash;111 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAn, S. et al. Comparison of the photosynthetic capacity of phragmites Australis in five habitats in Saline-Alkaline wetlands. \u003cem\u003ePlants\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 1\u0026ndash;17 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNewman, T. R., Wright, N., Wright, B. \u0026amp; Sj\u0026ouml;gersten, S. Interacting effects of elevated atmospheric CO2 and hydrology on the growth and carbon sequestration of Sphagnum moss. \u003cem\u003eWetl Ecol. Manag\u003c/em\u003e. \u003cb\u003e26\u003c/b\u003e, 763\u0026ndash;774 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBengtsson, F. et al. Environmental drivers of Sphagnum growth in peatlands across the Holarctic region. \u003cem\u003eJ. Ecol.\u003c/em\u003e \u003cb\u003e109\u003c/b\u003e, 417\u0026ndash;431 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u0026Ouml;quist, G. \u0026amp; Huner, N. P. A. Photosynthesis of Overwintering Evergreen Plants. \u003cem\u003eAnnu. Rev. Plant. Biol.\u003c/em\u003e \u003cb\u003e54\u003c/b\u003e, 329\u0026ndash;355 (2003).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSalimi, S., Berggren, M. \u0026amp; Scholz, M. Response of the peatland carbon dioxide sink function to future climate change scenarios and water level management. \u003cem\u003eGlob Chang. Biol.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 5154\u0026ndash;5168 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZaret, K. \u0026amp; Holz, A. Exploration of large-scale vegetation transition in wet ecosystems: a comparison of conifer seedling abundance across burned vs. unburned forest-peatland ecotones in Western Patagonia. \u003cem\u003eFront. Glob Chang.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 1\u0026ndash;24 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRatcliffe, J. L. et al. Ecological and environmental transition across the forested-to-open bog ecotone in a west Siberian peatland. \u003cem\u003eSci. Total Environ.\u003c/em\u003e \u003cb\u003e607\u0026ndash;608\u003c/b\u003e, 816\u0026ndash;828 (2017).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoud, E. M., Watt, C. \u0026amp; Moore, T. R. Plant community composition along a peatland margin follows alternate successional pathways after hydrologic disturbance. \u003cem\u003eActa Oecol.\u003c/em\u003e \u003cb\u003e91\u003c/b\u003e, 65\u0026ndash;72 (2018).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJatin, S., Swinder, J. S. K. \u0026amp; Naraian, R. Environmental perspectives of Phragmites australis (Cav.) Trin. Ex. Steudel. \u003cem\u003eAppl. Water Sci.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e, 193\u0026ndash;202 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrei, S., Holderegger, R. \u0026amp; Bergamini, A. Thirty years later: How successful was the restoration of a raised bog in the Swiss Plateau ? \u003cem\u003eMires Peat\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJacquemart, A. L. Vaccinium oxycoccos L.(Oxycoccus palustris Pers.) and Vaccinium microcarpum (Turcz. ex Rupr.) Schmalh.(Oxycoccus microcarpus Turcz. ex Rupr). \u003cem\u003eJ. Ecol.\u003c/em\u003e \u003cb\u003e85\u003c/b\u003e, 381\u0026ndash;396 (1997).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBhuiyan, R. et al. Fine-root biomass production and its contribution to organic matter accumulation in sedge fens under changing climate. \u003cem\u003eSci Total Environ\u003c/em\u003e \u003cb\u003e858\u003c/b\u003e, (2023).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNeill, A. O., Tucker, C. \u0026amp; Kane, E. S. Fresh Air for the Mire-Breathing Hypothesis: Sphagnum Moss and Peat Structure Regulate the Response of CO 2 Exchange to Altered Hydrology in a Northern Peatland Ecosystem. \u003cem\u003eWater\u003c/em\u003e 14, (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMezbahuddin, M., Grant, R. F. \u0026amp; Flanagan, L. B. Modeling hydrological controls on variations in peat water content, water table depth, and surface energy exchange of a boreal western Canadian fen peatland. \u003cem\u003eJ. Geophys. Res. Biogeosciences\u003c/em\u003e. \u003cb\u003e121\u003c/b\u003e, 2216\u0026ndash;2242 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaajanen, R. et al. Dark-leaved willow (Salix myrsinifolia) is resistant to three-factor (elevated CO 2, temperature and UV-B-radiation) climate change. \u003cem\u003eNew. Phytol\u003c/em\u003e. \u003cb\u003e190\u003c/b\u003e, 161\u0026ndash;168 (2011).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLinkosalmi, M., Tuovinen, J., Nevalainen, O., Peltoniemi, M. \u0026amp; Tani, C. M. Tracking vegetation phenology of pristine northern boreal peatlands by combining digital photography with CO 2 flux and remote sensing data. \u003cem\u003eBiogeosciences\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 4747\u0026ndash;4765 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKokkonen, N. et al. A deepened water table increases the vulnerability of peat mosses to periodic drought. \u003cem\u003eJ. Ecol.\u003c/em\u003e \u003cb\u003e112\u003c/b\u003e, 1210\u0026ndash;1224 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJitariu, V., Dorosencu, A., Ichim, P. \u0026amp; Ion, C. Severe drought monitoring by remote sensing methods and its impact on wetlands birds assemblages in Nuntași and Tuzla Lakes (Danube Delta Biosphere Reserve). \u003cem\u003eLand\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 672 (2022).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSebasti, E. \u0026amp; Green, A. J. Habitat Use by Waterbirds in Relation to Pond Size, Water Depth, and Isolation : Lessons from a Restoration in Southern Spain. \u003cb\u003e22\u003c/b\u003e, 311\u0026ndash;318 (2014).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZeh, L. et al. Vascular plants affect properties and decomposition of moss-dominated peat, particularly at elevated temperatures. \u003cem\u003eBiogeosciences\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 4797\u0026ndash;4813 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePotvin, L. R., Kane, E. S., Chimner, R. A., Kolka, R. K. \u0026amp; Lilleskov, E. A. Effects of water table position and plant functional group on plant community, aboveground production, and peat properties in a peatland mesocosm experiment (PEATcosm). \u003cem\u003ePlant. Soil.\u003c/em\u003e \u003cb\u003e387\u003c/b\u003e, 277\u0026ndash;294 (2015).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWalker, T. N. et al. Vascular plants promote ancient peatland carbon loss with climate warming. \u003cem\u003eGlob Chang. Biol.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, 1880\u0026ndash;1889 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCobo, M., Goldhammer, T. \u0026amp; Brothers, S. A desiccating saline lake bed is a significant source of anthropogenic greenhouse gas emissions. \u003cem\u003eOne Earth\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e, 1414\u0026ndash;1423 (2024).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRobroek, B. J. M. et al. Peatland vascular plant functional types affect dissolved organic matter chemistry. \u003cem\u003ePlant. Soil.\u003c/em\u003e \u003cb\u003e407\u003c/b\u003e, 135\u0026ndash;143 (2016).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu, Z. et al. Effect of drainage on microbial enzyme activities and communities dependent on depth in peatland soil. \u003cem\u003eBiogeochemistry\u003c/em\u003e \u003cb\u003e155\u003c/b\u003e, 323\u0026ndash;341 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWoolway, R. I. et al. Global lake responses to climate change. \u003cem\u003eNat. Rev. Earth Environ.\u003c/em\u003e \u003cb\u003e1\u003c/b\u003e, 388\u0026ndash;403 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Phenology, peatland, water table depth (WTD), Enhanced Vegetation Index (EVI), PlanetScope","lastPublishedDoi":"10.21203/rs.3.rs-7148738/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7148738/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCurrent climatic conditions are leading to the drying of peatland ecosystems, compromising their ability to store carbon due to increased decomposition and vegetation shifts. Large-scale monitoring of peatlands is thus essential to quantify the impacts of climate change on their vegetation and hydrology. A central European peatland was studied using PlanetScope high-resolution imagery over seven years as a proof of concept. The results have shown prolonged vegetation season and increased peak value of the Enhanced Vegetation Index due to the changing climate conditions. Higher than average temperatures negatively affected vegetation characterised by higher moss abundance. However, areas dominated by vascular plants have higher greenness and extended vegetation seasons despite elevated temperatures. Moreover, the lake situated in the area has shown a drying pattern, increased intra-annual variations, and a relationship with peatlands\u0026rsquo; water table depth dynamics. Hence, the drying reduces the lake area while the peatland part experiences a progressive vegetation shift and phenological changes.\u003c/p\u003e","manuscriptTitle":"High-resolution Satellite-derived Changes in Vegetation Phenology and Lake Area in a Central European Peatland","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-04 14:59:27","doi":"10.21203/rs.3.rs-7148738/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b7558006-e700-4ee2-9871-c2b4f1718e25","owner":[],"postedDate":"August 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":52620194,"name":"Earth and environmental sciences/Climate sciences"},{"id":52620195,"name":"Biological sciences/Ecology"},{"id":52620196,"name":"Earth and environmental sciences/Ecology"},{"id":52620197,"name":"Earth and environmental sciences/Environmental sciences"},{"id":52620198,"name":"Earth and environmental sciences/Hydrology"}],"tags":[],"updatedAt":"2026-02-17T18:50:21+00:00","versionOfRecord":{"articleIdentity":"rs-7148738","link":"https://doi.org/10.3390/rs18040593","journal":{"identity":"remote-sensing","isVorOnly":true,"title":"Remote Sensing"},"publishedOn":"2026-02-14 00:00:00","publishedOnDateReadable":"February 14th, 2026"},"versionCreatedAt":"2025-08-04 14:59:27","video":"","vorDoi":"10.3390/rs18040593","vorDoiUrl":"https://doi.org/10.3390/rs18040593","workflowStages":[]},"version":"v1","identity":"rs-7148738","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7148738","identity":"rs-7148738","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}