Speaker
Description
See attached pdf for version with figs and equations
Introduction and Motivation
Land subsidence in the coastal peatlands of The Netherlands is becoming an increasingly critical issue as it is closely linked with sea level rise, flooding risks and greenhouse gas emissions due to soil oxidation [1,2]. Despite the importance of this problem, it is very difficult to accurately assess subsidence levels across the country. While InSAR techniques employing stable point scatterers (PS) have been successfully used to monitor subsidence in the Netherlands [3,4,5], these PS points are usually founded at greater depths, and are not representative of the motion of the neighbouring terrain. Several papers presented at Fringe 2023 [6,7] advocated for the complete ignoration of DS information and to simply interpolate PS data over a given AOI. While this approach may work to monitor tectonic or other deep-seated deformation phenomena, it is not appropriate for measuring and characterizing shallow-based soil-related processes such as land-groundwater interactions and soil oxidation.
Attempts to directly monitor the dynamics of peatland soils surface with distributed scatterer (DS) techniques have encountered significant challenges, the most significant of which is the seasonal loss of interferometric coherence every spring, which results in a discontinuous phase time series. Fig. 1 illustrates the problem of seasonal coherence loss. Sufficiently coherent interferometric combinations can be made between epochs in the autumn and winter seasons (indicated by the dashed red boxes), allowing time series analysis to be carried out. However, for several months every spring and summer, the observed interferometric coherence is so low that no useful information is likely to be present in any interferogram made with an acquisition during this period. A further complication is the fact that there are no coherent combinations which can be made between the two individual coherent periods (the NE upper-right and lower-leftSW regions of the matrix in Fig. 1), which implies that the two coherent periods are disconnected. We denote this phenomenon with the term loss-of-lock [8]. The disconnect between the two coherent periods means that the gap between them is no longer constrained by integer ambiguities; there exists an unknown real-valued shift between the periods which must be resolved in order to obtain a single, consistent time series. This shift represents the unknown and unmeasurable displacement history that occurred during the incoherent periods.
Methodology
We postulate that neighbouring parcels with matching land use, land cover, soil type, and groundwater management can be expected to behave in a similar manner, such that we can bridge the incoherent data gap described in Section 1 by combining the coherent observations of several similarly behaving regions to estimate a single set of common displacement model parameters. This model can then be used to estimate the vertical shifts between the time series segments according to the relation
ϕ(t)=(-4π cosθ)/λ∙[M(x,t)+∆z]+ϵ.
By taking the difference in time between phases, Δϕ(t), the shift term ∆z drops out and the model parameters of x can be estimated directly. The shift for a given coherent observation can subsequently be estimated by taking the average difference between the model and the phase time series over the coherent period. In this abstract, we show the results of an empirical hydrological model based on precipitation and evapotranspiration [9].
Results
The methodology is tested in an area of interest around Zegveld, NL, shown in Fig. 2. This area is chosen due to the large peat deposits in the area, and the availability of in-situ validation data. Validation data is provided by extensometer measurements which provide a continuous time series of soil height measurements at one location [10]. The root mean squared error (RMSE) is evaluated between the group median result for the period of overlap (May 2020 - Jan. 2022), giving an RMSE of 6.7 mm. It should be noted that we do not expect these two measurements to match exactly, as the InSAR result is the median of a large spatial extent, while the extensometer data is from a single point.
Conclusion
We demonstrate a new methodology for estimating the ground motion of cultivated peatlands using DS time series InSAR. We show how discontinuities in a decorrelated time series can be bridged by considering the measurements of nearby similarly behaving regions. Our initial results show that the approach is promising, and we have been able to successfully validate our result against the ground truth data we have available with a low degree of error. To our knowledge, this is first accurate multi-year InSAR measurement of peatland surface motion in the Netherlands.
Acknowledgement
This research is part of the Living on Soft Soils (LOSS): Subsidence and Society project, and is supported by the Dutch Research Council (NWO-NWA-ORC), grant no.: NWA.1160.18.259,
URL: nwa-loss.nl.
References
[1] G. Erkens, M. J. van der Meulen, and H. Middelkoop, “Double trouble: Subsidence and CO2 respiration due to 1,000 years of Dutch coastal peatlands cultivation,” Hydrogeology Journal, vol. 24, no. 3, pp. 551–568, 2016.
[2] G. Erkens, T. Bucx, Dam, R. D. Lange, and J. G. Lambert, Sinking Cities: An Integrated Approach to Solutions, In: The Making of a Riskier Future: How Our Decisions Are Shaping Future Disaster Risk. World Bank, 2016.
[3] M. Caro Cuenca and R. F. Hanssen, “Subsidence due to peat decomposition in the Netherlands, kinematic observations from radar interferometry,” in Proc. ESA Fringe Workshop, (Frascati, Italy), pp. 1–6, 2008.
[4] M. Caro Cuenca, R. F. Hanssen, A. Hooper, and M. Arikan, “Surface deformation of the whole Netherlands after PSI analysis,” in Proc. ESA Fringe Workshop, (Frascati, Italy), pp. 19–23, 2011.
[5] R. F. Hanssen, F. J. van Leijen, G. Erkens, E. Stouthamer, K. Cohen, and Others, “Land motion service of the Netherlands.” https://bodemdalingskaart.nl/en-us/, 2018.
[6] H. Zebker, “Fine-Scale Measurement Of Deformation From Removal Of Decorrelated Pixels In InSAR Time Series – A Proposed Data Flow For High-Volume InSAR Systems”, in Proc. ESA Fringe Workshop, Leeds, United Kingdom, 2023.
[7] M. Zebker and J. Chen, “Land Subsidence Over Densely Vegetated Aquifers in Texas and the Central Valley, CA Derived from Spaceborne Radar Data”, in Proc. ESA Fringe Workshop, Leeds, United Kingdom, 2023.
[8] P. Conroy, S.A.N. van Diepen, F, J. van Leijen, and R. F. Hanssen, “Bridging Loss-of-Lock in InSAR Time Series of Distributed Scatterers,” IEEE Transactions on Geoscience and Remote Sensing (In Review), 2023.
[9] P. Conroy, S. A. N. Van Diepen, and R. F. Hanssen, “SPAMS: A New Empirical Model for Soft Soil Surface Displacement Based on Meteorological Input Data,” Geoderma (In Review), 2023.
[10] S. van Asselen, G. Erkens, and F. de Graaf, “Monitoring shallow subsidence in cultivated peatlands,” Proceedings of the International Association of Hydrological Sciences, vol. 382, pp. 189–194, 2020.
