Carbon storage and active carbon sequestration within peatlands strongly depend on water table depth and soil moisture availability. With increasing efforts to protect and restore peatland ecosystems, the assessment of their hydrological condition is highly necessary but remains challenging. Synthetic aperture radar (SAR) satellite observations likely offer an efficient way to obtain regular information with complete spatial coverage over northern peatlands. Studies have indicated that both radar backscatter amplitude and phase are sensitive to peatland condition. Very recently, Differential Interferometric Synthetic Aperture Radar (DInSAR) has been reported as being capable of monitoring ground deformation patterns at the millimetre scale, which are a response to peatland hydrological condition. To further investigate the promise of SAR for peatland monitoring, a laboratory-based polarimetric C-band SAR system was used to acquire the dynamic radar behaviour of a 4 m (l) ×1 m (w) × 0.25 m (d) reconstructed peatland. A forced 4-month drought was introduced with very-high-resolution imagery taken every 2 hours, capturing details of the vertical backscatter patterning through the peat at the centimetric scale. The results showed a clear coherent response both in radar backscatter amplitude and phase to change in water table level and soil moisture. Similar responses were seen across all polarizations. Phase demonstrated a coherent and deterministic change across the experiment; the average differential phase increase across all polarizations was 118° for 17 cm of water table drawdown. Interpreted as the physical movement of the surface, this corresponded to 8.3 mm of surface subsidence. Both phase and amplitude changes were near-linear with changes in the water table depth; amplitude showed a correspondingly strong concomitant mean decrease of 7 dB across all polarizations during the experiment. The results demonstrate the close sensitivity of radar backscatter to hydrological patterns in a peatland ecosystem. The phase result, in particular, strongly supports the notion that differential phase from satellites can be utilized to measure ground deformation as a proxy for the hydrological state.

Peatland restoration has become a common land-use management practice in recent years, with the water table depth (WTD) being one of the key monitoring elements, where it is used as a proxy for various ecosystem functions. Regular, uninterrupted, and spatially representative WTD data in situ can be difficult to collect, and therefore, remotely sensed data offer an attractive alternative for landscape-scale monitoring. In this study, we illustrate the application of Sentinel-1 SAR backscatter for water table depth monitoring in near-natural and restored blanket bogs in the Flow Country of northern Scotland. Among the study sites, the near-natural peatlands presented the smallest fluctuations in the WTD (with depths typically between 0 and 15 cm) and had the most stable radar signal throughout the year (~3 to 4 dB amplitude). Previously drained and afforested peatlands undergoing restoration management were found to have higher WTD fluctuations (depths up to 35 cm), which were also reflected in higher shifts in the radar backscatter (up to a ~6 dB difference within a year). Sites where more advanced restoration methods have been applied, however, were associated with shallower water table depths and smoother surfaces. Three models—simple linear regression, multiple linear regression, and the random forest model—were evaluated for their potential to predict water table dynamics in peatlands using Sentinel-1 SAR backscatter. The random forest model was found to be the most suited, with the highest correlation scores, lowest RMSE values, and overall good temporal fit (R² = 0.66, RMSE = 2.1 cm), and multiple linear regression came in a close second (R² = 0.59, RMSE = 4.5 cm). The impact of standing water, terrain ruggedness, and the ridge and furrow aspect on the model correlation scores was tested but found not to have a statistically significant influence. We propose that this approach, using Sentinel-1 and random forest models to predict the WTD, has strong potential and should be tested in a wider range of peatland sites.

High water table depths (WTD) and water-saturated soil are important elements for peatlands to remain healthy and are crucial targets in peatland restoration projects. Recent studies have suggested that Earth Observation data might be applicable for this task, but proposed models often lack either the spatial extension or the temporal dimension. This study has been developing a spatio-temporal model of peatland water table depth by combining time series of satellite data, namely Sentinel-1, Sentinel-2; aerial data, namely Getmapping Digital Surface Model (DSM); and field collected water table depth measurements to provide the ability to evaluate WTD changes both spatially and over time. Water table depth measurements were collected from 59 loggers between February and September of 2018, with loggers covering peatlands in various condition clustered around four research sites in the North of Scotland. NDVI, NDWI and OPTRAM indices were derived from reconstructed cloud-free imagery of Sentinel-2 at 5-day intervals. Similarly, VV, VH, and incidence angle values were obtained from Sentinel-1 imagery at the same time interval. Finally, a Topographic Wetness Index (TWI) was calculated using GetMapping DSM data. A Generalised Additive Model (GAM) was then fitted using all above mentioned inputs with 70% training and 30% testing split method. The model showed a good overall fit (R2=0.59 for training data; R2=0.49 for testing data), with optical covariates outperforming the radar covariates. The model was then applied spatially using the R terra package, providing raster imagery of predicted WTD for 24 unique dates with clear distinction in wetness both over different seasons, and spatially in the landscape.Following this successful application, work is in progress to test the model on additional sites across Scotland and on European level to further test the applicability of the model to a wider range of northern peatlands.

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