2.1 Methodology

Land subsidence was mapped using time series of Sentinel-1 SLC images from descending track 18. Time series covered period from October 2014 to January 2019 or September 2018 in case of EMSN057 respectively. In total more than 180 images were acquired, stacked and analyzed by PS InSAR (Persistent Scatterer Interferometry) technique. Images were first co-registered to optimal master image using TanDEM-X DEM and precise orbital information to achieve sub-pixel accuracy. In the next step atmospheric delay was estimated as the low-pass component of the phase residuals from displacement estimations on set of points with the lowest amplitude dispersion. In case of EMSN057, every city was processed separately and reference points were selected individually for each processed area. Therefore, their stability and comparability of absolute figures of estimated subsidence rates between areas and with EMSN062 could not be guaranteed. In EMSN062, the reference point was selected in the hilly area at the border with Cambodia, whose rock outcrops are supposed to be stable in contrast to the most of the Delta. In this case, each data swath was originally processed separately but due to deteriorating PS's quality at the swath edges each swath was divided into two sub-areas. Therefore, six sub-areas were processed independently and results were merged at the end of processing assuring each sub-area was referenced to the common stable reference point. Due to size of the Delta, density of resulting PS points could not be as high as for 3 cities and their number was reduced by application of thresholding considering amplitude stability and coherence. Standard PS InSAR (Ferretti et al., 2001) algorithm implemented in the SARProZ© was applied on each point in order to estimate the displacement rate (average annual velocity), its standard deviation, displacement time series and other attributes. The rate was estimated using linear displacement model assuming that characters of displacement trends are mostly linear in time and that their magnitude is significantly smaller than 2.8 cm per acquisition frequency, i.e. half of SAR sensor's wavelength per 12 d until September 2016 and per 6 d afterwards. Displacements were measured in satellite's line of sight (LOS), which ranged from 30–45^∘ from the vertical direction. Subsidence rate was recalculated from LOS to vertical plane direction under assumption that most of PS points in the Delta should be subject to subsidence while not possessing significant displacement component in horizontal direction. Each point was accompanied by attributes associated to quality of detected variables and showing the trend of detected ground displacement in time. As there had been many construction activities in the Delta during 2014–2019 period, each point was supplied with additional information about the start and the end date of the reflector estimated using evolution of detected radar intensity signal.

2.2 PSI-estimates of subsidence rates

The detected movement of the persistent scatterers was used to estimate vertical velocity of the objects (Fig. 1). Generally, there is higher PS point density in urbanized area, while considerably lower density in rural or natural landscapes. Initial general findings are in line with patterns and outcomes identified by previous studies (Erban et al., 2014; Minderhoud et al., 2018) though quantitative assessment still needs to be conducted: subsidence hot spots are located mostly in built-up areas with average annual subsidence rates ranging from 20–50 mm yr⁻¹ and agriculture areas are being affected to lesser extent with rates amounting to 0–20 mm yr⁻¹.

Figure 1PSI-estimated average subsidence velocity in the Mekong Delta between 2014 and 2019. Based on Copernicus Emergency Management Service product (© 2019 European Union), [EMSN062] Mekong Delta, Vietnam: Average Subsidence Velocity map.

The results of the two datasets each serve their own purpose in application. The EMSN062 results facilitate comparative assessment of subsidence patterns at regional level with high reliability. They reveal areas more prone to subsidence and may be assessed in conjunction with built-up, land use or groundwater extraction information for the whole Delta. However, point density is insufficient for analysis at local level, e.g. for individual city. As demonstrated for 3 cities this gap is bridged by results from localized and detailed analysis from EMSN057. As shown in Fig. 2, some small and many medium-size buildings were covered by one more multiple detected PS points. High variability of subsidence rates was detected at very local (building-to-building) level in all analyzed areas. Such micro-level variability are likely caused by subsoil compaction due to different load contributions of infrastructure and buildings depending on their size, built-up material, foundations and also the object's age.

Figure 2Estimated average subsidence velocity rate of movement in the area of Long Xuyen hospital. Subsidence rates are assigned to individual buildings. Outcomes of PS InSAR analysis were confirmed by field validation. Based on products of Copernicus Emergency Management Service (© 2018 European Union), [EMSN057] Long Xuyen, Vietnam: Average Subsidence Velocity map. © Google Maps.

2.3 Vertical velocity of individual buildings or infrastructure

The high spatial resolution provided by the Sentinel-1 derived InSAR data allows the identification of vertical motion rates of objects on the ground. Horizontal inaccuracies in the dataset make it difficult to attribute specific PSI points to individual houses in dense urban settlements. It is however possible to correlate specific PSI point to larger buildings or free standing objects, like high-voltage power pylons in a rice paddy. Data from the EMSN057 dataset is especially suitable to identify individual objects because of the higher PSI density and provides, for the first time in the Mekong delta, to compare velocity difference between different, adjacent objects which provides relevant information on depth-dependent subsidence (Fig. 3). The relation between objects acting as PSI points and actual subsidence of the surrounding delta surface does not correlate one-on-one as objects may sink faster or slower, depending on structural weight and foundation depth.

Figure 3Differential subsidence of individual objects and their relation between foundation depth and weight. Combining this relation with the PSI-estimated subsidence rates can be used to investigate and quantify depth-dependent subsidence rates.

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Figure 4PSI-estimated average subsidence velocity of An Giang University buildings in Long Xuyen. Subsidence rates are assigned to individual buildings. The founded buildings (green outline) subside at a speed of ∼0.5 mm yr⁻¹ while the surrounding area subsides at a rate of ∼20 mm yr⁻¹, resulting in about 150–200 mm in ten years since construction (inset photo). Outcomes of PS InSAR analysis were confirmed by field validation. Based on products of Copernicus Emergency Management Service (© 2018 European Union), [EMSN057] Long Xuyen, Vietnam: Average Subsidence Velocity map.

Preliminary, qualitative in-situ field observations show that roads, parking spaces and similar infrastructure have the same subsidence rates as nearby ground. Small buildings with few floors show mostly similar rates of sinking as roads while most bigger, taller buildings tend to have significantly lower subsidence rates, likely related to a deeper foundation depth. The University of An Giang in the city of Long Xuyen, constructed ten years ago, is an example of a building complex with significantly lower subsidence rates compared to nearby roads and open spaces. The university buildings move downward with ∼0.5 mm yr⁻¹ while the roads subside with ∼20 mm yr⁻¹ (Fig. 4).

2.4 Practical application of new PSI data to create elevation projections for urban flood analysis

The PSI data is used within the framework of the ongoing GIZ project: “Mekong Urban Flood Resilience and Drainage Programme” to create projections of elevation in three cities of the Mekong delta (Rach Gia, Long Xuyen, Ca Mau) to investigate the effect of subsidence on future urban flooding and drainage. The PSI-based subsidence estimates were projected, assuming a gradual reduction in rates of 1 % yr⁻¹ (assuming settlement decreases over time with increasing compaction) and subtracting from the Digital Elevation Model (DEM) of the cities. Although interpolation of PSI points estimate leads to uncertainties, as individual points do not necessarily represent subsidence rates of its surrounding area, this approach does provide a reasonably well first estimate of future elevation.

Figure 5Elevation map of Long Xuyen city in An Giang province in 2018, based on leveling survey in 2010, corrected to 2018 using additional survey data and new PSI estimates. And elevation projected for 2050, following substraction of interpolated PSI-estimated subsidence rates. The projected DEM is used to evaluate future urban flood resilience and drainage capacity of the city.

GIZ used projected DEMs for the layout of the drainage systems for Ca Mau, Long Xuyen and Rach Gia with the aim of facilitating smooth operations at least until the middle of the century. For the city of Ca Mau it appears unlikely that purely gravity driven drainage systems will be viable. Within the next two decades relative sea-level rise will necessitate the protection of the city with a ring of dykes and the use of pumps.