Satellite InSAR time series are used to estimate the displacements of radar scatterers. This estimation problem includes the estimation of integer phase ambiguities, which is an ill-posed problem. Consequently, InSAR displacement estimation cannot yield unique solutions and may therefore be significantly biased. Here we show that phase reduction, using a priori information and the remove-compute-restore (RCR) methodology, is a viable way to solve this problem, as it reduces the likelihood of ambiguity errors. We found that application of this methodology to pastures on peat soils leads to a significant improvement in the estimated displacements. We assert that InSAR displacement estimation should always include an explicit statement on the first-order approximations and included assumptions on expected signal smoothness. We anticipate that a more systematic inclusion of the RCR method in standard processing algorithms will lead to more reliable and repeatable results of InSAR analyses.

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The Rhine-Meuse-Scheldt delta is shaped by natural and manmade landscapes. Over many polder areas, soils are drained to be used as pastures. Around 30% of the pastures are situated on peat soils, of which many are located in the western part of the Netherlands, known as the ‘Green Heart’. Peat is composed of organic materials that oxidize and emit greenhouse gases when exposed to air as a consequence of the draining. Oxidation of peat soils results in volume reduction and subsequent subsidence. As a result, the groundwater level rises relative to the surface. Consequently, the soil needs to be dewatered to keep it sufficiently dry for farming, resulting in more oxidation, and therefore more subsidence. This process is bound to continue until the peat soils have disappeared completely. The societal cost of land subsidence due to peat soils are estimated to be 5200 million euro for urban areas and 200 million euro for peatland pastures, for a period until 2050.@en