Since decades, even centuries, blanket and raised bogs in the central-northern United Kingdom have been exposed to climatic and anthropogenic stressors like drainage, land management, evapotranspiration, peat extraction and/or atmospheric pollution. As a result, those landscapes undergo substantial mass changes and soil compaction, but the quantification of these processes remains a challenge: In-situ observations are cost-extensive and/or often biased due to an unstable local measurement reference.

To overcome this, we analyze remotely-sensed surface uplift rates from the European Ground Motion Service (EGMS) that were extracted from four years (2019-2023) of radar-interferometric (InSAR) time-series. We also consulted point-wise uplift rates from two decades of positioning (GNSS) measurements to reference ongoing bedrock uplift. We validate these observations with analytical models that mimic a the load response while also accounting for a remaining glacial isostatic adjustment.

The surface rate maps show ~50 km-wide uplift bulges that correlate with land classified as heathlands and bogs. Maximum uplift surrounding the heather/bogs reaches 2-7 mm/yr. The bogs/heathlands themselves, however, exhibit distinct subsidence due to mass loss (carbon, water) of up to 10 mm/yr, which is already corrected for the simultaneous bedrock uplift due to unloading. Based on these observations, we can reproduce the spatial bedrock uplift pattern with our load model. The explanation of the signal amplitudes requires further fine-tuning of the model parameters and a better understanding of the in-situ bio-chemical processes. This approach will enable us to quantify the amount of water-vs.-carbon loss in this particular landscape.

How to cite: Metzger, S., Dill, R., Klemann, V., Sachs, T., and Zahra, A.: Quantifying mass loss in raised bogs and heathlands in the central-northern United Kingdom from satellite-geodetic uplift rates, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14452, https://doi.org/10.5194/egusphere-egu26-14452, 2026.

Monitoring of peatland groundwater levels is crucial for effective bog rewetting. Traditional in-situ measurement methods are often costly and impractical in countries with expansive peatland areas. Synthetic Aperture Radar (SAR) may be an approach for remotely estimating groundwater levels over large areas due to its sensitivity to soil properties, such as soil moisture.  In this study, we investigated the relationships between terrain-corrected Sentinel-1 C-band SAR backscatter intensity (γ₀) and groundwater level (GWL) at two temperate raised bogs: (1) a near-intact site (Cors Fochno bog, Wales, United Kingdom; 11 dip wells; Area = ~ 6.3 km²); and (2) an industrially-extracted, ‘bare-peat’ site (Castlegar Bog, Co. Galway, Ireland; 34 dip wells; Area = ~ 3.2 km²). Both sites have recently undergone rewetting measures, primarily bunding and drain blockage. For the industrially extracted Castlegar Bog, initial linear regression analysis between γ₀ and GWL yielded average correlation coefficients (r) of 0.33 and 0.47 for VV and VH polarization, respectively. However, average correlation values increased when the dataset was separated into pre- and post-rewetting periods. Values of 0.55 and 0.64 for VV and VH, respectively, were found before restoration, and 0.43 and 0.54 for VV and VH, respectively, were found after restoration. For the near-intact Cors Fochno bog, SAR intensity exhibited very weak correlation with GWL, with average r values of 0.34 and 0.16 for VV and VH polarizations, respectively. Average correlation values changed to 0.41 and 0.14 for VV and VH after accounting for and filtering out rainfall events preceding each acquisition.  Consequently, our results indicate a limited capability of SAR backscatter intensity to serve as a reliable proxy for GWL in near-intact temperate raised peatlands. We hypothesize that the limited correlation is attributable to two main factors. Firstly, GWL in near-intact sites typically remains approximately 10 cm below the surface with minimal fluctuation, thereby maintaining a consistently saturated peat layer and limiting variance in dielectric properties beyond background noise levels. Secondly, vegetation acts as a buffer, temporarily retaining rainfall in above-ground and near-surface layers, which increases local volumetric water content, leading to surface saturation that affects the SAR backscattering mechanism. On the other hand, these findings indicate that SAR-based monitoring of GWL using C-band data is effective in highly degraded or extracted temperate peatlands, where water table fluctuations are pronounced and where vegetation impacts on the SAR signal are reduced due to extensive bare peat exposure.

How to cite: Khoshlahjeh Azar, M., Hrysiewicz, A., Donohue, S., Regan, S., Renou-Wilson, F., Reddin, E., Williamson, J., and P. Holohan, E.: Assessing the Potential of Sentinel-1 SAR Backscatter intensity for Monitoring Groundwater Levels in Temperate Raised Bogs, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20531, https://doi.org/10.5194/egusphere-egu26-20531, 2026.

Raised bogs are ombrotrophic peatland ecosystems whose long-term functioning depends on the stability of a vertically growing peat surface sustained by tightly coupled ecohydrological feedbacks. In intact systems, the peat surface undergoes reversible seasonal shrink–swell behaviour (“bog breathing”), allowing bogs to self-regulate in response to water-table fluctuations. Prolonged hydroclimatic stress or artificial drainage for land use activities can disrupt this oscillatory regime, leading to peat consolidation, loss of water-storage capacity, and progressive surface subsidence. Despite extensive national and EU-level protection, the stability of near-intact raised bogs has rarely been assessed at national scales.

Here, we use vertical ground-motion time series from the European Ground Motion Service (EGMS) data to quantify seasonal and interannual peat surface dynamics across raised bog Special Areas of Conservation (SACs) in Ireland between 2019 and 2023. Surface elevation changes were analysed for 52 paired raised bog sites representing both active raised bogs (habitat type 7110) and degraded raised bogs capable of natural regeneration (7120), using over 39,000 EGMS measurement points located within bog SAC boundaries. Long-term elevation trends were quantified using linear regression, alongside analysis of seasonal surface oscillations associated with bog breathing.

Across the study sites, almost all raised bogs exhibit clear seasonal surface oscillations alongside a persistent decline in mean surface elevation over the five-year observation period. Across the protected sites at the national scale, this corresponds to a median surface lowering of approximately 3 mm per year, with similar magnitudes observed in both active and degraded raised bogs. Mean subsidence rates are slightly more negative but remain within a narrow range, and variability across sites is moderate. These preliminary results indicate that long-term surface lowering represents a shift away from stable peat surface equilibrium in raised bogs designated for protection in Ireland, affecting not only degraded sites but also bogs classified as “active”.

Our findings indicate that water-level monitoring alone may not be sufficient to assess raised bog condition. Declining surface elevation reduces peat specific yield, meaning that apparently stable or high water levels can mask a loss of hydrological storage capacity and self-regulation. Consequently, raised bogs may appear hydrologically “healthy” while undergoing structural degradation and progressive subsidence. The surface elevation decline observed across almost all protected raised bogs highlights the need to integrate surface motion metrics into peatland monitoring, conservation assessment, and restoration planning to avoid irreversible ecohydrological degradation.

How to cite: Ahmad, S., Habib, W., and Liu, H.: Satellite-derived evidence of recent peat surface elevation decline across the protected raised bogs of Ireland , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21222, https://doi.org/10.5194/egusphere-egu26-21222, 2026.

Peatland degradation is a critical global climate issue, releasing millions of tonnes of CO₂ annually due to drainage and changes in land use. As countries strive to meet net-zero targets, restoring degraded peatlands has become a priority for carbon sequestration and biodiversity conservation. However, monitoring peatland recovery remains a challenge, especially for large-scale restoration projects. This research is driven by the need for low-cost validation of peatland re-wetting schemes, enabling robust monitoring of peat physical condition and hydrological recovery, with implications for carbon accounting and agriculture’s contribution to net-zero targets. This study addresses this gap by applying remote sensing data from Interferometric Synthetic Aperture Radar (InSAR) to track peat surface motion in Cambridgeshire's Great Fen, one of the UK’s largest lowland peatland restoration initiatives. Sentinel-1 InSAR data (2015–2025) were used to quantify ground motion and derive deformation-based proxies for peat carbon flux. Our analysis revealed distinct subsidence patterns for undrained, early-restored, and later-restored farms, enabling first-order, deformation-based carbon flux estimation under common parameter assumptions. Early-restored farms experienced subsidence rates of up to 1.17 cm/year and deformation-associated carbon flux proxies of 14.50 tons CO₂/ha/year, compared to 1.40 cm/year and 17.37 tons CO₂/ha/year in later-restored sites. National Nature Reserves (Holme Fen and Woodwalton), which remained undrained, recorded the lowest subsidence (~0.48 cm/year) and lowest deformation-associated carbon loss proxy (5.98 tons CO₂/ha/year), linked to restoration timelines and peat moisture regimes. These estimates, interpreted as relative indicators rather than direct measurements of net ecosystem carbon balance, demonstrate InSAR’s utility for tracking peatland condition and relative peat carbon vulnerability across restoration timelines. Seasonal fluctuations aligned with soil moisture and precipitation anomalies, indicating a strong hydrological control on peat surface motion. Together, these findings show that InSAR provides a high-resolution, cost-effective tool for continuous monitoring of peatland physical dynamics, supporting comparative assessment of restoration outcomes and climate-relevant land management decisions.

How to cite: Khouakhi, A., Patil, A., Girkin, N., and Holman, I.: Assessing Peat Surface Motion using Interferometric Synthetic Aperture Radar (InSAR) in The Great Fen Area of Cambridgeshire, UK , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10244, https://doi.org/10.5194/egusphere-egu26-10244, 2026.

Tropical wetlands are among the Earth’s most critical ecosystems, playing a vital role in regulating global water and carbon cycles, buffering extreme weather events, and supporting biodiversity that sustains millions of people. Despite their importance, these ecosystems are highly vulnerable to climate change, and our understanding of their seasonal extent, their role in climate mitigation, and their response to changing climatic conditions remains limited. This lack of knowledge hinders the development of effective climate adaptation strategies and constrains projections of future carbon emissions. To address these gaps, this study and related ongoing work aim to develop an integrated framework that combines multiple methodologies, data sources, and analytical tools to improve the monitoring of surface water inundation in major floodplain systems.

A key component of this framework is understanding the availability, characteristics, and interpretative value of different remote sensing datasets. As a case study, this work focuses on two major wetland systems: the Sudd in South Sudan and the Pantanal in South America. Satellite observation datasets are compiled and categorized into static and dynamic products. The static datasets include GlobCover, GLWDv2 and SWAMP, while the dynamic datasets comprise WAD2M, JRC Global Surface Water (GSW), CYGNSS water mask, and GRACE Total Water Storage. Static datasets are used to assess long-term changes in wetland extent and classification, whereas dynamic datasets capture seasonal variability in wetland extent. Together, these datasets enable comparison between seasonal dynamics and long-term trends, providing improved insight into future projections of wetland extent. The dynamic datasets are projected onto a common grid to assess their consistency and agreement with one another. These datasets are also used to develop a Long Short Term Memory (LSTM) network, capable of capturing both seasonal variability and long-term trends, which is then applied to project future wetland dynamics.

This work represents an important first step towards reducing uncertainty in global wetland mapping. Building on this foundation, the study aims to use the Joint UK Land Environment Simulator (JULES) to simulate wetland extent and hydrological dynamics across selected tropical wetland regions (Sudd, Pantanal). Model simulations are driven by newly developed ancillary inputs, including land cover parameters, soil properties, and topographic information, to assess their influence on simulated wetland extent and seasonal flooding patterns. The resulting JULES outputs are systematically evaluated against EO-based wetland datasets such as GLWDv2, GlobCover, and WAD2M to identify areas of agreement, model sensitivities, and potential sources of bias. Through this comparative analysis, the study benchmarks the capability of the JULES land surface model to represent tropical wetland dynamics and provides insights into optimal data configurations for large-scale wetland modelling.

As the project develops, machine learning approaches are further applied to forecast wetland dynamics and to inform improvements in the representation of wetlands within climate models. Ultimately, this integrated modelling and data-driven framework aims to contribute to more reliable climate predictions and to provide decision-makers with clearer, evidence-based information for climate adaptation and mitigation planning.

How to cite: Pantula, C., J Parker, R., Balzter, H., Ruiz Villena, C., R Marthews, T., and Fatima, K.: Understanding changes in tropical wetlands with remote sensing, machine learning and land surface modleing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3075, https://doi.org/10.5194/egusphere-egu26-3075, 2026.

Wetlands contribute to human well-being in multiple ways. These contributions take various forms, including goods such as food and other raw materials, but also through services like carbon sequestration, flood and climate regulation. Despite their importance, wetlands are being lost at annual rates exceeding 0.5% of their global extent over the past sixty years, mainly due to conversion to other land uses. Several multilateral agendas emphasize the importance of wetland monitoring and protection; however, progress toward established targets remains limited by the lack of consistent national datasets and operational monitoring tools. Satellite observations provide globally consistent data that enable systematic wetland monitoring. In particular, synthetic aperture radar (SAR) has been successfully used for mapping wetland flooding dynamics due to their all-weather acquisition capability and the ability to identify below canopy inundation processes. The increasing availability from current and planned SAR missions poses an opportunity to advance space-based wetland monitoring, but their operational implementation requires the development of flexible and scalable frameworks.

This study aims to develop WetSAT-ML, a satellite-based machine learning (ML) framework for mapping wetland flooding dynamics and trends using Sentinel-1 SAR data. The approach combines radar features with supervised and unsupervised ML classification algorithms, to distinguish different inundation categories, including open water, vegetated water, and land. Random Forest and K-means models were trained using two training and validation areas in the South Florida Everglades (USA) and the lower Atrato River Basin (Colombia). These sites were selected considering their wetland variability and the availability of reference data. The first version of the models was trained using all available Sentinel-1 observations from 2024 over the selected regions, capturing the full hydrological seasonality. Reference training and validation datasets included gauge measurements and manually annotated data for both regions. The trained WetSAT-ML models are being evaluated through a proof of concept across five wetland systems in South Asia and South America, including the Meghna River wetlands in Bangladesh; the Atrato River, Ayapel, and Barbacoa wetlands in Colombia; and the Pantanal wetlands spanning the Brazil–Bolivia border. The test sites represent a wide range of hydroclimatic conditions, geomorphological settings, and vegetation cover.

Preliminary results indicate that WetSAT-ML consistently captures spatial patterns and intra-annual inundation dynamics that are coherent with the known hydrological regimes of the test regions. Cross-site comparisons reveal clear differences in key hydroperiod parameters related to inundation persistence, seasonal amplitude, and ecosystem connectivity. Overall, the results provide a foundation for operational wetland monitoring applications. WetSAT-ML is open access, and the first public version is available in a GitHub repository: https://github.com/sei-latam/WETSAT_v2. The next steps of the research will focus on cross-validation using independent datasets and expanding the training database across additional wetland areas.

How to cite: Palomino-Ángel, S., Méndez, C., Zamora, D., Santos, T., Prasad, S., and Piman, T.: WetSAT-ML: A Machine Learning Framework for Mapping Wetland Flooding Dynamics using Sentinel-1 Observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16380, https://doi.org/10.5194/egusphere-egu26-16380, 2026.

Wetlands are among the most hydrologically dynamic ecosystems, particularly across northern boreal and subarctic regions of Canada, where seasonal freeze-thaw cycles and precipitation variability drive pronounced fluctuations in surface water and soil moisture. These landscapes play a critical role in carbon storage, ecosystem functioning, and regional hydrology, yet their remoteness severely limits the availability of long-term in situ soil moisture observations. Consequently, satellite-based microwave remote sensing has become an essential tool for monitoring wetland hydrological dynamics at large spatial scales.

NASA’s Soil Moisture Active Passive (SMAP) mission provides global soil moisture estimates at a coarse spatial resolution (~36 km). However, retrieval performance declines significantly in wetland-dominated regions due to the mixed influence of open water, saturated soils, and vegetation within a single footprint. This mixture complicates the interpretation of passive microwave brightness temperatures and increases uncertainties in soil moisture products. Improving SMAP performance in these environments requires a better understanding of how water dynamics influence the satellite signal.

In northern Canadian wetlands, small surface water bodies such as shallow ponds, ephemeral pools, and saturated depressions exhibit substantial seasonal variability, especially during snowmelt and early summer. Although individually below SMAP’s resolution, their aggregated extent may substantially affect observed brightness temperatures and mimic soil moisture variability. This study investigates whether temporal changes in small surface water extent can serve as a proxy for soil moisture variations within SMAP footprints.

We analyzed the relationship between in situ soil moisture, SMAP brightness temperatures, and surface water extent across two wetland regions: the Attawapiskat River (CA-ARB and C-ARF) and the Kinosheo Lakes (CA-KLP). Soil moisture data from eddy covariance flux towers (2017-2021) were used for snow- and ice-free periods (June-October). Small surface water bodies were mapped using Sentinel-1 SAR imagery and the Canadian Digital Elevation Model (CDEM) data through a random forest classification approach, then aggregated to the SMAP footprint scale for analysis.

Results show strong correlations between in situ soil moisture and surface water extent (r > 0.58), as well as between surface water extent and SMAP brightness temperatures (r > 0.77). These findings indicate that surface water dynamics capture spatially representative hydrological variability within SMAP pixels and help address scale-mismatch issues between point in situ measurements and coarse satellite products. The study demonstrates the potential of surface water extent as a proxy variable to support calibration, validation, and future improvement of SMAP soil moisture retrievals in wetland-dominated regions.

How to cite: Amini, Y., Millard, K., Berg, A., Humphreys, E., and Richardson, M.: Soil Moisture Correlation with Small Water Body Extents in Canadian Wetlands: Application to SMAP Soil Moisture Improvement, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16084, https://doi.org/10.5194/egusphere-egu26-16084, 2026.

Emergent wetlands occupy the transitional zone between permanent wetlands and upland environments and provide a range of ecosystem services. These services strongly depend on inundation patterns and vegetation dynamics, which are often difficult to monitor using in-situ approaches due to limited accessibility, dense vegetation, and the need to minimise disturbance, particularly in protected areas. Remote sensing offers an effective means to overcome these constraints by providing consistent information over large areas in a timely and cost-efficient manner. Synthetic aperture radar (SAR), in particular, is well suited due to its sensitivity to water occurrence beneath vegetation canopies. In emergent wetlands, double-bounce scattering of the radar waves between standing water and vertical vegetation components typically results in elevated backscatter and coherence compared to other land-surface types. However, this response is influenced by several factors, including radar wavelength, vegetation structure, and water level. We analyse time series of Sentinel-1 backscatter intensity and coherence with the goal of characterising inter and intra-annual variations in surface water extent between 2015 and 2025. We interpreted the SAR-derived metrics using a comprehensive reference dataset including water level, high-resolution imagery from unmanned aerial vehicles, meteorological data and vegetation indices. The study was conducted at a long-term ecosystem research site at the shallow, subsaline Lake Neusiedl, located in the Pannonian lowlands of Eastern Austria. The lake is a Ramsar site of international importance due to its ecological significance, particularly for breeding and migratory birds. More than half its surface is covered by one of the largest continuous reed belts in Europe, dominated by Phragmites australis. During spring, the reed belt shows a clear double-bounce signature whereas in summer, high vegetation and declining water levels lead to a decrease in backscatter and coherence. During a prolonged drought period, which lasted from 2019 to 2022, water extent at Lake Neusiedl decreased significantly followed by a marked recovery starting in 2023. Our results showcase both potential and limitations of water extent retrieval in emergent wetlands based on C-band SAR data and hold important lessons for future wetland monitoring using data acquired at longer wavelengths by SAR missions, such as NISAR and ROSE-L. In addition, the observed coherence patterns offer initial indications of the potential for retrieving wetland water level changes using SAR interferometry.

How to cite: Schlaffer, S., Dorninger, P., and Qiu, S.: Sentinel-1 SAR backscatter and coherence patterns during dry and wet periods at a large emergent wetland in the Pannonian lowlands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17935, https://doi.org/10.5194/egusphere-egu26-17935, 2026.

Inland freshwater aquaculture which includes a new crop-aquaculture system accounts for 77 % of aquaculture production worldwide and contributed significantly to the global demand for fish products. Previous aquaculture monitoring efforts, however, mainly focused on coastal aquaculture ponds and hardly covered inland freshwater aquaculture areas. Here, based on the time-series Sentinel-1 and 2 data and the Google Earth Engine (GEE) platform, we developed a hierarchical framework for mapping inland freshwater aquaculture with different aquaculture types (both aquaculture ponds and rice-crayfish fields) in the Jianghan Plain (JHP), one of the most important inland freshwater aquaculture regions in China. First, we constructed two phenological temporary windows (T1 and T2) and used an automatic threshold extraction method (OTSU approach) to generate potential aquaculture layers in both two temporary windows. Second, based on the potential aquaculture layer in T1, the actual aquaculture was further distinguished from other water bodies (e.g., lakes, rivers, and ditches) by combining spectral and texture features and utilizing a random forest classifier from 2016 to 2022. Finally, by the differences in variations in aquaculture ponds and rice-crayfish fields in T1 and T2, we generated annual 10m maps of fine aquaculture area in the Jianghan Plain from 2017 to 2022, with overall accuracies (OA) of 87.5 %–98.7 % and Kappa coefficients of 0.81–0.98. We found a significant increase in the total aquaculture area in  the JHP, from 2266 ± 66 km² in 2016 to 4766 ± 111 km² in 2022, with rice-crayfish fields contributing the most, mainly related to a series of stimulus policies. This study proposed a novel framework for monitoring complex inland freshwater aquacultures over large areas and revealed the rapid expansion of inland aquaculture in South China.

How to cite: Wang, C., Dong, J., Zhou, Y., Cui, Y., Chen, X., Di, Y., Xiao, X., and Zhang, G.: Satellite observed rapid inland aquaculture expansion in Jianghan Plain, China from 2016 to 2022, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11704, https://doi.org/10.5194/egusphere-egu26-11704, 2026.