This article presents the results of a laboratory investigation to explain anomalously high soil moisture estimates observed in retrievals from SAR and scatterometer backscatter, affecting extensive areas of the world associated with arid climates. High-resolution C-band tomographic profiling was applied in experiments to understand the mechanisms underlying these anomalous retrievals. The imagery captured unique high-resolution profiles of the variations in the vertical backscattering patterns through a sandy soil with moisture change. The relative strengths of the surface and subsurface returns were dependent upon both soil moisture and soil structure, incidence-angle, and polarization. Copolarized returns could be dominated by both surface and subsurface returns at times, whereas crosspolarized returns were strongly associated with subsurface features. The work confirms suspicions that anomalous moisture estimates can arise from the presence of subsurface features. Diversity in polarization and incidence angle may provide sufficient diagnostics to flag and correct these erroneous estimates, allowing their incorporation into global soil moisture products.

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.

Spatiotemporal distribution of soil moisture is important for hydrometeorological and agricultural applications. There is growing interest in monitoring soil moisture in relation to soil- and land-based Natural Flood Management (NFM); to understand the soil’s ability, via land-use and management changes, to delay the arrival of flood peaks in nearby watercourses. This paper monitors relative surface soil moisture (rSSM) across the Thames Valley, UK, using Sentinel-1 data, and the TU-Wien Change Detection Algorithm, with novel exploration of monthly and annual normalisation factors and spatial averaging. Two pairs of normalisation factors are introduced to remove impacts from varying local incidence angles: through direct- and multiple regression slopes. The spatiotemporal distribution of rSSM values at various spatial resolutions (1000m, 500m, 250m, and 100m) are assessed. Comparisons with in-situ soil moisture data from the COMSOS-UK network shows that, whilst general temporal trends agree, the difference in effective depth of measurements, coupled with vegetation impacts during the growing season, makes comparison with soil moisture observations difficult. Temporal rSSM trends can be retrieved at spatial resolutions down to 100m, and the rSSM RMSE was found to decrease as the spatial resolution increases. The vegetation effects upon the rSSM are further explored by comparing the two dominant land cover types: Arable and Horticulture, and Improved Grassland. It was found that, whilst the rSSM retrieval for these land covers was possible, and the general soil moisture trend is clear, overlying vegetation during the summer artificially increased the rSSM values.