Mitigating Atmospheric Effects in InSAR Stacking Based on Ensemble Forecasting with a Numerical Weather Prediction Model

The interferometric synthetic aperture radar (InSAR) technique is widely utilized to measure ground-surface displacement. One of the main limitations of the measurements is the atmospheric phase delay effects. For satellites with shorter wavelengths, the atmospheric delay mainly consists of the tropospheric delay influenced by temperature, pressure, and water vapor. Tropospheric delay can be calculated using numerical weather prediction (NWP) model at the same moment as synthetic aperture radar (SAR) acquisition. Scientific researchers mainly use ensemble forecasting to produce better forecasts and analyze the uncertainties caused by physic parameterizations. In this study, we simulated the relevant meteorological parameters using the ensemble scheme of the stochastic physic perturbation tendency (SPPT) based on the weather research forecasting (WRF) model, which is one of the most broadly used NWP models. We selected an area in Foshan, Guangdong Province, in the southeast of China, and calculated the corresponding atmospheric delay. InSAR images were computed through data from the Sentinel-1A satellite and mitigated by the ensemble mean of the WRF-SPPT results. The WRF-SPPT method improves the mitigating effect more than WRF simulation without ensemble forecasting. The atmospherically corrected InSAR phases were used in the stacking process to estimate the linear deformation rate in the experimental area. The root mean square errors (RMSE) of the deformation rate without correction, with WRF-only correction, and with WRF-SPPT correction were calculated, indicating that ensemble forecasting can significantly reduce the atmospheric delay in stacking. In addition, the ensemble forecasting based on a combination of initial uncertainties and stochastic physic perturbation tendencies showed better correction performance compared with the ensemble forecasting generated by a set of perturbed initial conditions without considering the model’s uncertainties.

Modified Interpolation Method of Multi-Reference Station Tropospheric Delay Considering the Influence of Height Difference

In network real-time kinematic (NRTK) positioning, atmospheric delay information is critical for generating virtual observations at a virtual reference station (VRS). The traditional linear interpolation method (LIM) is widely used to obtain the atmospheric delay correction. However, even though the conventional LIM is robust in the horizontal direction of the atmospheric error, it ignores the influence of the vertical direction, especially for the tropospheric error. If the height difference between the reference stations and the rover is large and, subsequently, tropospheric error and height are strongly correlated, the performance of the traditional method is degraded for tropospheric delay interpolation at the VRS. Therefore, considering the height difference between the reference stations and the rover, a modified linear interpolation method (MLIM) is proposed to be applied to a conventional single Delaunay triangulated network (DTN). The systematic error of the double-differenced (DD) tropospheric delay in the vertical direction is corrected first. The LIM method is then applied to interpolate the DD tropospheric delay at the VRS. In order to verify the performance of the proposed method, we used two datasets from the American NOAA continuously operating reference stations (CORS) network with significant height differences for experiments and analysis. Results show that the DD tropospheric delay interpolation accuracy obtained by the modified method is improved by 84.1% and 69.6% on average in the two experiments compared to the conventional method. This improvement is significant, especially for low elevation satellites. In rover positioning analysis, the traditional LIM has a noticeable systematic deviation in the up component. Compared to the conventional method, the positioning accuracy of the MLIM method is improved in the horizontal and vertical directions, especially in the up component. The accuracy of the up component is reduced from tens of centimeters to a few centimeters and demonstrates better positioning stability.

Validation of tropospheric ties at the test setup GNSS co-location site Potsdam

Atmospheric ties are theoretically affected by the height differences between antennas at the same site and the meteorological conditions. However, there is often a discrepancy between the expected zenith delay differences and those estimated from geodetic analysis, potentially degrading a combined solution employing atmospheric ties. In order to investigate the possible effects on GNSS atmospheric delay, this study set up an experiment of four co-located GNSS stations of the same type, both antenna and receiver. Specific height differences for each antenna w.r.t the reference antenna are given. One antenna was equipped with a radome at the same height and type as a antenna close to the ground. In addition, a meteorological sensor was used for meteorological data recording. The results show that tropospheric ties from the analytical equation based on meteorological data from GPT3, Numerical Weather Model, and in-situ measurements, and ray-traced tropospheric ties, reduced the bias of zenith delay roughly by 72 %. However, the in-situ tropospheric ties yield the best precision in this study. These results demonstrate, that the instrument effects on GNSS zenith delays were mitigated by using the same instrument. In contrast, the radome causes unexpected bias of GNSS zenith delays in this study. Additionally, multipath effects at low-elevation observations degraded the tropospheric east gradients.

Mitigation of Atmospheric Artefacts in Multi Temporal InSAR: A Review

The complexity of the atmosphere renders the modelling of the atmospheric delay in multi temporal InSAR difficult. This limits the potential of achieving millimetre accuracy of InSAR-derived deformation measurements. In this paper we review advances in tropospheric delay modelling in InSAR, tropospheric correction methods and integration of the correction methods within existing multi temporal algorithms. Furthermore, we investigate ingestion of the correction techniques by different InSAR applications, accuracy performance metrics and uncertainties of InSAR derived measurements attributed to tropospheric delay. Spatiotemporal modelling of atmospheric delay has evolved and can now be regarded as a spatially correlated turbulent delay with varying degree of anisotropy random in time and topographically correlated seasonal stratified delay. Tropospheric corrections methods performance is restricted to a case by case basis and ingestion of these methods by different applications remains limited due to lack of their integration into existing algorithms. Accuracy and uncertainty assessments remain challenging with most studies adopting simple statistical metrics. While advances have been made in tropospheric modelling, challenges remain for the calibration of atmospheric delay due to lack of data or limited resolution and fusion of multiple techniques for optimal performance.

Refined InSAR landslides deformation monitoring with tropospheric delay correction using multi-temporal moving-window linear model

<p>SAR Interferometry (InSAR) has been proven to be effective for measuring landslides deformation. However, the accuracy of InSAR application of landslide mapping and monitoring is limited by the complex atmospheric distortion in alpine valley areas. The sparse external atmospheric data cannot accurately reflect the complex heterogeneous atmosphere in alpine valley areas. The conventional atmospheric delay corrections based on InSAR phase are weakened by the presence of confounding signals (e.g., tropospheric delays, deformation signals, and topographic errors) and spatial heterogeneity of the troposphere.<br>In this study, we propose the multi-temporal moving-window linear model to estimate the stratified tropospheric delay. The linear relationship between the multi-temporal interferometric phases and the local terrain is estimated using moving windows and then we retrieve the vertical stratified atmospheric phase over the whole scene. Taking into account the deformation information and phase unwrapping errors, the model is solved by an iterative robust estimation algorithm weighted by both deformation rates and residual unwrapping phases.<br> We first compared our model with four other InSAR atmospheric delay correction methods: traditional empirical linear model, REA5 numerical atmospheric model, GACOS, and temporal/spatial filtering method. The results demonstrated that our proposed model has the best performance on atmospheric delay correction over the reservoir of the Lianghekou hydropower station using Sentinel-1 datasets. Meanwhile, our model was less affected by randomly turbulent phase and phase unwrapping errors, which significantly improves the accuracy of landslide deformation detection and monitoring.<br>Then, we integrated the multi-temporal moving window atmospheric delay correction model into the STAMPS-SBAS program. The high-precision wide-area time series deformation over the reservoir area can be obtained through iterating phase unwrapping and atmospheric delay correction. In particular, the phase unwrapping errors were gradually corrected during the iteration process. The improvement of the proposed model on the landslide investigations was validated through using UAV images and field surveys. Some landslides that have not been identified in the traditional time-series InSAR results can be identified after atmospheric delay correction by the proposed model.</p>

Developing InSAR atmospheric delay correction model based on GEONET ZTD and its gradient

<p> In InSAR analysis, the effect of microwave propagation delay in the Earth's atmosphere such as the nuetral atmospheric delay and the ionospheric delay is recognized as the primary noise for surface deformation researchs like Earthquake source modeling, tectonic fault motion, and volcanic activity monitoring. Although, for the ionospheric delay, we can now apply the range split spectrum method (SSM) to effectively mitigate it, the mitigation of the neutral atmospheric delay noise remains difficult and is the research problem to be solved. Recently, Arief and Heki (2020) developed a new method to retrieve two-dimensional Zenith Wet Delay (ZWD) distribution at sea level based on the GNSS ZWD and delay gradient derived from the Japanese GNSS network named GEONET. Here we proposed a new InSAR delay correction method based on modifying the Arief and Heki's method and applied it to the ALOS-2 ScanSAR interferograms to evaluate its effectiveness.<br>  In our study, we used 5-minute interval GNSS PPP data provided by the Nevada Geodetic Laboratory in Nevada University, Reno. Since InSAR atmospheric delay contains both hydrostatic and wet components, we estimated two-dimensional Zenith Total Delay (ZTD) distribution at sea level instead of ZWD, and we simaltaneously estimated height dependence of ZTD as a linear function. The model cosists of the regularly distributed grids with 5 km interval and the height dependence. The retrieval of ZTD distribution is performed by the least squares inversion with the smoothing constraint. The retrieved ZTD is then projected onto the InSAR line-of-sight direction and calculated a difference of two epochs to generate an InSAR delay model. Interferograms were generated by RINC ver.0.41r using 16 ALOS-2 ScanSAR level 1.1 full-aperture data covering Kanto Plain in Japan. We applied the SSM to all of interferograms to correct the ionospheric delay noise before applying the proposed tropospheric delay correction.<br>  The result of applying proposed correction method showed that the correction effectively reduced the phase variance, especially in the long-wavelength phase variation. The phase standard deviation (STD) in the whole scene decreased from 35.95 mm to 25.84 mm by applying the proposed GNSS-based correction method. For comparing effectiveness of the proposed method with existing methods, we also calculated the phase STD derived by applying the GACOS model and the numerical weather model-based correction using the Japan Meteorological Agency's Meso-scale model data. The result of comparison showed that the proposed GNSS-based method most reduced the whole-scene phase STD. The GACOS model decreased the STD to 30.96 mm, and the JMA MSM decrease to 27.71 mm, respectively. We then calculate the distance-dependence of the phase STD based on the variogram model. The variogram derived from all the interferograms showed the speriority of the GNSS-based correction, although the STD in distance shorter than 20 km seemed no differences between correction methods.</p>

High-Precision CO2 Column Length Analysis on the Basis of a 1.57-μm Dual-Wavelength IPDA Lidar

For high-precision measurements of the CO2 column concentration in the atmosphere with airborne integrated path differential absorption (IPDA) Lidar, the exact distance of the Lidar beam to the scattering surface, that is, the length of the column, must be measured accurately. For the high-precision inversion of the column length, we propose a set of methods on the basis of the actual conditions, including autocorrelation detection, adaptive filtering, Gaussian decomposition, and optimized Levenberg–Marquardt fitting based on the generalized Gaussian distribution. Then, based on the information of a pair of laser pulses, we use the direct adjustment method of unequal precision to eliminate the error in the distance measurement. Further, the effect of atmospheric delay on distance measurements is considered, leading to further correction of the inversion results. At last, an airborne experiment was carried out in a sea area near Qinhuangdao, China on 14 March 2019. The results showed that the ranging accuracy can reach 0.9066 m, which achieved an excellent ranging accuracy on 1.57-μm IPDA Lidar and met the requirement for high-precision CO2 column length inversion.

E-GVAP Status and future

<p>E-GVAP (the EIG EUMETNET GNSS Water Vapour Programme) is an operational service providing atmospheric delay estimates for use in operational meteorology in near real-time. This is done in a close collaboration between geodetic and meteorological institutions. The use of the GNSS delay estimates is found to increase the skill of weather forecasts. By the start of 2019 E-GVAP did, along with EUMETNET itself, entered a new phase. In E-GVAP 4 the main product will still be zenith total delays (ZTD), with a focus on improving timeliness, in support of the high resolution, local weather models with frequent updates being set up these years. But in addition there will be focus on GNSS derived slant total delay (STD) estimates. Several of the weather models used in Europe are being prepared for STD assimilation. The STDs provide additional information, on atmospheric asymmetries, on top of the information contained in a single ZTD estimate</p>