The continuous monitoring of ground deformations can be provided by various methods, such as leveling, photogrammetry, laser scanning, satellite navigation systems, Synthetic Aperture Radar (SAR), and many others. However, ensuring sufficient spatiotemporal resolution of high-accuracy measurements can be challenging using only one of the mentioned methods. The main goal of this research is to develop an integration methodology, sensitive to the capabilities and limitations of Differential Interferometry SAR (DInSAR) and Global Navigation Satellite Systems (GNSS) monitoring techniques. The fusion procedure is optimized for local nonlinear strong deformations using the forward Kalman filter algorithm. Due to the impact of unexpected observations discontinuity, a backward Kalman filter was also introduced to refine estimates of the previous system’s states. The current work conducted experiments in the Upper Silesian coal mining region (southern Poland), with strong vertical deformations of up to 1 m over 2 years and relatively small and horizontally moving subsidence bowls (200 m). The overall root-mean-square (RMS) errors reached 13, 17, and 35 mm for Kalman forward and 13, 17, and 34 mm for Kalman backward in North, East, and Up directions, respectively, in combination with an external data source - GNSS campaign measurements. The Kalman filter integration outperformed standard approaches of 3-D GNSS estimation and 2-D InSAR decomposition.

Compact and low-cost radar transponders are an attractive alternative to corner reflectors (CRs) for interferometric synthetic aperture radar (InSAR) deformation monitoring, datum connection, and geodetic data integration. Recently, such transponders have become commercially available for C-band sensors, which poses relevant questions on their characteristics in terms of radiometric, geometric, and phase stability. Especially for extended time series and for high-precision geodetic applications, the impact of secular or seasonal effects, such as variations in temperature and humidity, has yet to be proven. In this article, we address these challenges using a multitude of short baseline experiments with four transponders and six CRs deployed at test sites in The Netherlands and Slovakia. Combined together, we analyzed 980 transponder measurements in Sentinel-1 time series to a maximum extent of 21 months. We find an average radar cross section (RCS) of over 42 dBm2 within a range of up to 15° of elevation misalignment, which is comparable to a triangular trihedral CR with a leg length of 2.0 m. Its RCS shows the temporal variations of 0.3-0.7 dBm2 (standard deviation), which is partially correlated with surface temperature changes. The precision of the InSAR phase double differences over short baselines between a transponder and a stable reference CRs is found to be 0.5-1.2 mm (one sigma). We observe a correlation with surface temperature, leading to seasonal variations of up to ±3 mm, which should be modeled and corrected for in high-precision InSAR applications. For precise SAR positioning, we observe antenna-specific constant internal electronic delays of 1.2-2.1 m in slant range, i.e., within the range resolution of the Sentinel-1 interferometric wide (IW) product, with a temporal variability of less than 20 cm. Comparing similar transponders from the same series, we observe distinct differences in performance. Our main conclusion is that these characteristics are favorable for a wide range of geodetic applications. For particular demanding applications, individual calibration of single devices is strongly recommended.

The estimation of Signal-to-Clutter Ratio (SCR) of a radar point target, such as a corner reflector, is an essential step for synthetic aperture radar (SAR) interferometry and positioning, as it influences the phase measurement variance as well as the absolute positioning precision. The standard method to estimate the SCR of a point target relies on the debatable assumption of spatial ergodicity, using the clutter of the surrounding as representative of the clutter at the point target. Here, we estimate the SCR of a corner reflector using a time series of SAR measurements, i.e.,\ assuming temporal ergodicity. This assumption is often more realistic, particularly in a complex environment, in the presence of other point scatterers, and for small-sized reflectors. Empirical results on a corner reflector network, using Sentinel-1 SAR measurements, show that the temporal method yields a less biased and more precise estimate of the average SCR. A second experiment shows that the InSAR phase variance as well as positioning precision, predicted using SCR estimated by the temporal estimation method, is closer to the truth.

Artificial radar reflectors, such as corner reflectors or transponders, are commonly used for radiometric and geometric Synthetic Aperture Radar (SAR) sensor calibration, SAR interferometry (InSAR) applications over areas with few natural coherent scatterers, and InSAR datum connection and geodetic integration. Despite the current abundance of regular SAR time series, no free and open-source software (FOSS) dedicated to analyzing SAR time series of artificial radar reflectors exists. In this paper, we present a FOSS Python toolbox for efficient and automatic estimation of: (i) the clutter level of a particular site before a corner reflector installation, (ii) the Radar Cross Section (RCS) to track a corner reflector’s performance and detect outliers, for example, due to damage or debris accumulation, (iii) the Signal-to-Clutter Ratio (SCR) to predict the positioning precision and the InSAR phase variance, (iv) the InSAR displacement time series of a corner reflector network. We use the toolbox to analyze Sentinel-1 SAR time series of the network of 23 corner reflectors for InSAR monitoring of landslides in Slovakia.

The recent release of consumer-grade dual-frequency receivers sparked scientific interest into use of these cost-efficient devices for high precision positioning and tropospheric delay estimations. Previous analyses with low-cost single-frequency receivers showed promising results for the estimation of Zenith Tropospheric Delays (ZTDs). However, their application is limited by the need to account for the ionospheric delay. In this paper we investigate the potential of a low-cost dual-frequency receiver (U-blox ZED-F9P) in combination with a range of different quality antennas. We show that the receiver itself is very well capable of achieving high-quality ZTD estimations. The limiting factor is the quality of the receiving antenna. To improve the applicability of mass-market antennas, a relative antenna calibration is performed, and new absolute Antenna Exchange Format (ANTEX) entries are created using a geodetic antenna as base. The performance of ZTD estimation with the tested antennas is evaluated, with and without antenna Phase Center Variation (PCV) corrections, using Precise Point Positioning (PPP). Without applying PCVs for the low-cost antennas, the Root Mean Square Errors (RMSE) of the estimated ZTDs are between 15 mm and 24 mm. Using the newly generated PCVs, the RMSE is reduced significantly to about 4 mm, a level that is excellent for meteorological applications. The standard U-blox ANN-MB-00 patch antenna, with a circular ground plane, after correcting the phase pattern yields comparable results (0.47 mm bias and 4.02 mm RMSE) to those from geodetic quality antennas, providing an all-round low-cost solution. The relative antenna calibration method presented in this paper opens the way for wide-spread application of low-cost receiver and antennas.

Dual-frequency Global Navigation Satellite Systems (GNSSs) enable the estimation of Zenith Tropospheric Delay (ZTD) which can be converted to PrecipitableWater Vapor (PWV). The density of existing GNSS monitoring networks is insufficient to capture small-scale water vapor variations that are especially important for extreme weather forecasting. A densification with geodetic-grade dual-frequency receivers is not economically feasible. Cost-efficient single-frequency receivers offer a possible alternative. This paper studies the feasibility of using low-cost receivers to increase the density of GNSS networks for retrieval of PWV. We processed one year of GNSS data from an IGS station and two co-located single-frequency stations. Additionally, in another experiment, the Radio Frequency (RF) signal from a geodetic-grade dual-frequency antenna was split to a geodetic receiver and two low-cost receivers. To process the single-frequency observations in Precise Point Positioning (PPP) mode, we apply the Satellite-specific Epoch-differenced IonosphericDelay (SEID)model using two different reference network configurations of 50-80 km and 200-300 km mean station distances, respectively. Our research setup can distinguish between the antenna, ionospheric interpolation, and software-related impacts on the quality of PWV retrievals. The study shows that single-frequency GNSS receivers can achieve a quality similar to that of geodetic receivers in terms of RMSE for ZTD estimations. We demonstrate that modeling of the ionosphere and the antenna type are the main sources influencing the ZTD precision.

This contribution proposes a new approach for the analysis and preparation of geodetic data for the use in geophysical modeling. The approach resolves the problem of non-uniformity in the datasets obtained by different measurement techniques. The approach is based on two main steps: uniformization of the data using a standardized data format, and the application of the CUPiDO conversion tool to construct double-difference observations. Both steps are described in detail. By using double-difference observations, the effect of different reference points and geodetic datums is eliminated, thereby making the outcomes of the CUPiDO tool well suited for an integrated inversion to estimate a model. The CUPiDO tool will be made publicly available.

InSAR deformation estimates form a 'free network' referred to an arbitrary datum, e.g. by assuming a reference point in the image to be stable. Consequently, the estimates of any measurement point in the image are dependent of these postulations on reference point stability, and the estimates cannot be compared with datasets of other types of measurement (e.g. historical levelling data or sea-level changes). Yet, some applications require 'absolute' InSAR estimates, i.e. expressed in a well-defined terrestrial reference frame (TRF). We achieve this using collocated InSAR and GNSS measurements, achieved by rigidly attaching phase-stable millimetre-precision compact active transponders to permanent GNSS antennas. The InSAR deformation estimates at these transponders are then estimated in a TRF using the GNSS measurements. Consequently, deformation estimates at all other scatterers are now also defined in the same TRF.