We present SPAMS: Simple Parameterization for the Motion of Soils, a model to describe the motion of deformable soils in the Vadose zone, mainly peat and clay, herein called shallow soft soils. The SPAMS model estimates the reversible and irreversible vertical component of surface displacement to within sub-centimetre RMSE, using only four parameters: three scaling factors and an integration time. Requiring only meteorological data as an input, its lightweight nature and simple implementation make it a powerful tool when used as a first approximation in inverse problems like those encountered in remote sensing. It has been validated against in-situ data from five test sites in The Netherlands with different Holocene soil strata.

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We introduce the term loss-of-lock to describe a specific form of coherence loss that results in the breakage of a synthetic aperture radar interferometric (InSAR) time series. Loss-of-lock creates a specific pattern in the coherence matrix of a multilooked distributed scatterer (DS) by which it may be detected. Along with identification, we introduce a new DS processing methodology that is designed to mitigate the effects of loss-of-lock by introducing contextual data to assist in the time-series processing. This methodology is of particular relevance to regions that suffer from severe temporal decorrelation, such as northern peatlands. We apply our new method to two subsiding cultivated peatland regions in The Netherlands which previously proved impossible to monitor using DS InSAR techniques. Our results show a very good agreement with in situ validation data as well as spatial correlation between regions and the natural terrain.

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Peat areas in the Netherlands are expected to exhibit extremely dynamic vertical motion, including both reversible and irreversible components. Yet the exact behaviour as a function of time is unknown, and is spatially very variable. This results in a poorly known estimation of greenhouse gas emissions and impact to existing infrastructure, and consequently limited ability to design and deploy mitigating or adaptive measures. In situ measurements are inefficient to capture this dynamic spatio-temporal variability. To monitor wide areas, InSAR has the necessary coverage, resolution, and high temporal sampling, but is very sensitive to noise due to temporal decorrelation of vegetated areas, which in combination with the high temporal dynamics exacerbates the success rate of the phase ambiguity estimation. Moreover, motion measured with InSAR is inherently relative to a reference point which is typically not stable in time. Thus even if InSAR would yield sufficiently coherent results, interpretation is notoriously difficult. To address these problems, an Integrated Geodetic Reference Station (IGRS) was installed in a peat area. It includes SAR reflectors mechanically coupled to a GNSS antenna. Arcs from the IGRS to natural scatterers can therefore be connected to ETRS89. The resulting arcs are thus single-differences of elevation with respect to a reference epoch. In order to reduce noise, and subsequently increase the success rate of ambiguity resolution, we define Elementary Usage Polygons (EUPs), which are expected to move homogeneously. This is achieved by constraining each EUP to have only one subsurface type and one land usage, which allows for the definition of an expected functional model of the vertical movement. Using the expected functional model in combination with the GNSS measurements on the IGRS we can constrain the unwrapping on the arcs between the IGRS and nearby EUPs in order to improve the success rate of the unwrapping. By making use of elevation model data from 2020, the elevation of the reference epoch can be constrained. The single-difference arcs showing elevation change with respect to the reference epoch are transformed into elevations with respect to NAP, allowing for an absolute elevation time series from InSAR observations in the vegetated rapid-moving peat areas of the Netherlands.@en

Phase unwrapping, also known as ambiguity resolution, is an underdetermined problem in which assumptions must be made to obtain a result in SAR interferometry (InSAR) time series analysis. This problem is particularly acute for distributed scatterer InSAR, in which noise levels can be so large that they are comparable in magnitude to the signal of investigation. In addition, deformation rates can be highly nonlinear and orders of magnitude larger than neighboring point scatterers, which may be part of a more stable object. The combination of these factors has often proven too challenging for the conventional InSAR processing methods to successfully monitor these regions. We present a methodology which allows for additional environmental information to be integrated into the phase unwrapping procedure, thereby alleviating the problems described above. We show how problematic epochs that cause errors in the temporal phase unwrapping process can be anticipated by the machine learning algorithms which can create categorical predictions about the relative ambiguity level based on the readily available meteorological data. These predictions significantly assist in the interpretation of large changes in the wrapped interferometric phase and enable the monitoring of environments not previously possible using standard minimum gradient phase unwrapping techniques.

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We present a novel InSAR processing scheme which combines point scatterer (PS) and distributed scatter (DS) approaches in a hybrid framework along with contextual information about the environment under study. Data such as land parcel divisions, precipitation and temperature are integrated into the processing pipeline in order to produce accurate deformation time series estimates of the Dutch peatlands. In addition to these steps, a segmented processing scheme is introduced to manage irreversible losses of coherence in the interferogram stack. Initial results show a promising agreement with in-situ ground truth measurements gathered by extensometer readings of shallow surface deformation.@en