Atmospheric Delay Research Articles

Integrated Water Vapor during Rain and Rain-Free Conditions above the Swiss Plateau

Water vapor column density, or vertically-integrated water vapor (IWV), is monitored by ground-based microwave radiometers (MWR) and ground-based receivers of the Global Navigation Satellite System (GNSS). For rain periods, the retrieval of IWV from GNSS Zenith Wet Delay (ZWD) neglects the atmospheric propagation delay of the GNSS signal by rain droplets. Similarly, it is difficult for ground-based dual-frequency single-polarisation microwave radiometers to separate the microwave emission of water vapor and cloud droplets from the rather strong microwave emission of rain. For ground-based microwave radiometry at Bern (Switzerland), we take the approach that IWV during rain is derived from linearly interpolated opacities before and after the rain period. The intermittent rain periods often appear as spikes in the time series of integrated liquid water (ILW) and are indicated by ILW ≥ 0.4 mm. In the present study, we assume that IWV measurements from radiosondes are not affected by rain. We intercompare the climatologies of IWV(rain), IWV(no rain), and IWV(all) obtained by radiosonde, ground-based GNSS atmosphere sounding, ground-based MWR, and ECMWF reanalysis (ERA5) at Payerne and Bern in Switzerland. In all seasons, IWV(rain) is 3.75 to 5.94 mm greater than IWV(no rain). The mean IWV differences between GNSS and radiosonde at Payerne are less than 0.26 mm. The datasets at Payerne show a better agreement than the datasets at Bern. However, the MWR at Bern agrees with the radiosonde at Payerne within 0.41 mm for IWV(rain) and 0.02 mm for IWV(no rain). Using the GNSS and rain gauge measurements at Payerne, we find that IWV(rain) increases with increase of the precipitation rate during summer as well as during winter. IWV(rain) above the Swiss Plateau is quite well estimated by GNSS and MWR though the standard retrievals are limited or hampered during rain periods.

HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

Use of GNSS Tropospheric Delay Measurements for the Parameterization and Validation of WRF High-Resolution Re-Analysis over the Western Gulf of Corinth, Greece: The PaTrop Experiment

In the last thirty years, Synthetic Aperture Radar interferometry (InSAR) and the Global Navigation Satellite System (GNSS) have become fundamental space geodetic techniques for mapping surface deformations due to tectonic movements. One major limiting factor to those techniques is the effect of the troposphere, as surface velocities are of the order of a few mm yr−1, and high accuracy (to mm level) is required. The troposphere introduces a path delay in the microwave signal, which, in the case of GNSS Precise Point Positioning (PPP), can nowadays be partly removed with the use of specialized mapping functions. Moreover, tropospheric stratification and short wavelength spatial turbulences produce an additive noise to the low amplitude ground deformations calculated by the (multitemporal) InSAR methodology. InSAR atmospheric phase delay corrections are much more challenging, as opposed to GNSS PPP, due to the single pass geometry and the gridded nature of the acquired data. Thus, the precise knowledge of the tropospheric parameters along the propagation medium is extremely useful for the estimation and correction of the atmospheric phase delay. In this context, the PaTrop experiment aims to maximize the potential of using a high-resolution Limited-Area Model for the calculation and removal of the tropospheric noise from InSAR data, by following a synergistic approach and integrating all the latest advances in the fields of remote sensing meteorology (GNSS and InSAR) and Numerical Weather Forecasting (WRF). In the first phase of the experiment, presented in the current paper, we investigate the extent to which a high-resolution 1 km WRF weather re-analysis can produce detailed tropospheric delay maps of the required accuracy, by coupling its output (in terms of Zenith Total Delay or ZTD) with the vertical delay component in GNSS measurements. The model is initially operated with varying parameterization, with GNSS measurements providing a benchmark of real atmospheric conditions. Subsequently, the final WRF daily re-analysis run covers an extended period of one year, based on the optimum model parameterization scheme demonstrated by the parametric analysis. The two datasets (predicted and observed) are compared and statistically evaluated, in order to investigate the extent to which meteorological parameters that affect ZTD can be simulated accurately by the model under different weather conditions. Results demonstrate a strong correlation between predicted and observed ZTDs at the 19 GNSS stations throughout the year (R ranges from 0.91 to 0.93), with an average mean bias (MB) of –19.2 mm, indicating that the model tends to slightly underestimate the tropospheric ZTD as compared to the GNSS derived values. With respect to the seasonal component, model performance is better during the autumn period (October–December), followed by spring (April–June). Setting the acceptable bias range at ±23 mm (equal to the amplitude of one Sentinel-1 C-band phase cycle when projected to the zenithal distance), it is demonstrated that the model produces satisfactory results, with a percentage of ZTD values within the bias margin ranging from 57% in summer to 63% in autumn.

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.

Detecting Lake Level Change From 1992 to 2019 of Zhari Namco in Tibet Using Altimetry Data of TOPEX/Poseidon and Jason-1/2/3 Missions

Zhari Namco, a large lake in the Tibetan Plateau (TP), is sensitive to climate and environmental change. However, it is difficult to retrieve accurate and continuous lake levels for Zhari Namco. A robust strategy, including atmospheric delay correction, waveform retracking, outlier deletion, and inter-satellite adjustment, is proposed to generate a long-term series of lake levels for Zhari Namco through multi-altimeter data. Apparent biases are found in troposphere delay correction from different altimeter products and adjusted using an identical model. The threshold (20%) algorithm is employed for waveform retracking. The two-step method combining a sliding median filter and 2σ criterion is used to eliminate outliers. Tandem mission data of altimeters are used to estimate inter-satellite bias. Finally, a 27-year-long lake level time series of Zhari Namco are constructed using the TOPEX/Poseidon-Jason1/2/3 (T/P-Jason1/2/3) altimeter data from 1992 to 2019, resulting in an accuracy of 10.1 cm for T/P-Jason1/2/3. Temperature, precipitation, lake area, equivalent water height, and in situ gauge data are used for validation. The correlation coefficient more than 0.90 can be observed between this result and in situ gauge data. Compared with previous studies and existing database products, our method yields sequences with the best observational quality and the longest continuous monitoring in Zhari Namco. The time series indicates that the lake level in Zhari Namco has increased by ∼ 5.7 m, with an overall trend of 0.14 ± 0.01 m/yr, showing a fluctuating rate (1992–1999: −0.25 ± 0.05 m/yr, 2000–2008: 0.26 ± 0.04 m/yr, 2009–2016: −0.05 ± 0.03 m/yr, 2017–2019: 1.34 ± 0.34 m/yr). These findings will enhance the understanding of water budget and the effect of climate change in the TP.

Comparative analysis of four different single-frequency PPP models on positioning performance and atmosphere delay retrieval

Single-frequency precise point positioning (SF-PPP) has attracted increasing attention due to its high precision and cost effectiveness. With various strategies to handle the dominant error, i.e., ionosphere delay, the ionosphere-float (IF), ionosphere-free-half (IFH), ionosphere-corrected (IC), and ionosphere-weighted (IW) SF-PPP models are certain to possess different characteristics and performance levels. This study is dedicated to assessing and comparing the four models from model characteristics, positioning performance, and atmosphere delay retrieval. The model comparison shows that IC and IW models are full-rank while IF and IFH models have a rank deficiency of size one that will result in biased estimations, which means the better solvability of IC and IW models. The experiments are carried out based on the 7-day Global Positioning System (GPS) observations collected at 57 global Multi-GNSS Experiment (MGEX) stations and Global Ionosphere Map (GIM) products. The results indicate that the IW model can accelerate SF-PPP convergence and achieve higher positioning accuracy compared to the other three SF-PPP models, especially in kinematic mode. With convergence criteria of 0.25 m in horizontal and 0.5 m in vertical, the east/north/up convergence times of IW model are 0.5/15.0/25.0 min and 0.5/16.0/36.5 min for static and kinematic modes, respectively. The IW model is able to achieve an instantaneous positioning accuracy of 0.28/0.35/0.75 m. In addition, a real kinematic test also demonstrates the best positioning solutions of IW model. Regarding troposphere delay retrieval, the IF, IFH, and IW models obtain a comparable daily accuracy of 3.0 cm on average, while the IC model achieves the worst accuracy of 8.0 cm. For precise ionosphere delay estimation, IW model only needs an average initialization time of 34.3 min, but a longer initialization time of 51.6 min is required for IF model. The daily precision of ionosphere delay estimation for IW model can reach up to 10.8 cm. At the present accuracy of GIM products, it is suggested that the IW model should be adopted for SF-PPP first due to its superior performance in positioning and atmosphere delay retrieval.

Deriving Centimeter-Level Coseismic Deformation and Fault Geometries of Small-To-Moderate Earthquakes From Time-Series Sentinel-1 SAR Images

Small-to-moderate earthquakes (e.g. ≤Mw5.5) occur much more frequently than large ones (e.g. >Mw6.0), yet are difficult to study with InSAR due to their weak surface deformation that are severely contaminated by atmospheric delays. Here we propose a stacking method using time-series SAR images that can effectively suppress atmospheric phase screens and extract weak coseismic deformation in centimeter to sub-centimeter level. Using this method, we successfully derive coseismic surface deformations for three small-to-moderate (Mw∼5) earthquakes in Tibet Plateau and Tienshan region from time-series Sentinel-1 SAR images, with peak line-of-sight deformation ranging from 5–6 mm to 13 mm. We also propose a strategy to downsample interferograms with weak deformation signal based on quadtree mesh obtained from preliminary slip model. With the downsampled datasets, we invert for the centroid locations, fault geometries and slips of these events. Our results demonstrate the potential of using time-series InSAR images to enrich earthquake catalog with geodetic observations for further study of earthquake cycle and active tectonics.

Evaluation of Weather Research and Forecast (WRF) microphysics schemes in simulating zenith total delay for InSAR atmospheric correction

ABSTRACT Many researches have demonstrated the potential of Interferometric Synthetic Aperture Radar (InSAR) atmospheric correction method based on the Weather Research and Forecast (WRF) model. However, there remain some doubts about its robustness and accuracy over complex topographical areas. For meteorology, microphysics schemes play a significant role on the simulation of atmospheric parameters. But no studies have focused specifically on the impact of the microphysics schemes (MPs) on the atmospheric zenith total delay (ZTD) simulation for InSAR atmospheric correction. Therefore, we test four sophisticated MPs in WRF version 3.9.1 to simulate ZTD during the SAR signal propagation over Haiyuan in China in summer. And the ZTD values from WRF are validated by comparing to those obtained from four global position system (GPS) stations. The results show that all of the MPs can effectively reconstruct the major absolute ZTD, but regarding differential ZTD values, the Morrison double moment (M2M) and WRF Double moment 6 classes Model (WDM6) predict more accurate differential ZTD data. Moreover, WRF-based InSAR atmospheric correction utilizing the M2M obtains the most accurate deformation map results. A proper choice of MP schemes can enhance the WRF model to simulate more precise ZTD and contribute to reach a conclusion on the robustness of WRF-based InSAR atmospheric correction, especially in the complex mountain area in summer.

Utilization of WRF 3D Meteorological Data to Calculate Slant Total Delay for InSAR Atmospheric Correction

The atmosphere introduces excess delays into the synthetic aperture radar (SAR) signal trajectory, especially in the troposphere. InSAR atmospheric correction methods include the use of SAR data and external water vapor products. The latter is more effective. However, since the removal of atmospheric effects should use atmospheric delay products in the direction of the line of sight (LOS), it is necessary to convert the zenith total delay to slant delay in the LOS direction. Conventionally, the zenith delay is divided by the cosine of the average incident angles to obtain slant phase delays. But this method could cause large errors because it ignores the atmospheric horizontal gradient change and the small-scale vertical structure. These problems can be solved by using three-dimensional atmospheric data simulated by numerical models, especially in the case of intense weather changes or complex terrain. However, few scholars paid attention to the application into InSAR atmospheric correction, because of the computation complexity and low efficiency. As the requirement for higher accuracy and the introduction of large errors caused by increasing incidence angles, it is significantly imperative to make the utmost of this method. Weather Research Forecast (WRF) model can provide the precipitate water vapor (PWV) and refraction index at different levels in the three dimensions, and then the slant total delay can be obtained for removing the atmospheric effect on the InSAR process. The results demonstrate that using 3D data can obtain more accurate slant total delay and improve the accuracy of surface deformation from InSAR technology.

Performance Analysis of BDS-2, BDS-3 and GPS in Relative Positioning through Short, Medium and Long Baselines

BDS-3 (Beidou System 3) reached global coverage in June 2020. In comparison with BDS-2(Beidou system 2), BDS-3 has greatly improved in terms of satellite orbits, atomic clocks and signal transmissions. BDS-3 is now being used widely in practice. This paper is focused on performance analysis of BDS-3 vs.BDS-2 and GPS. Six baselines of different lengths were selected to result in single-system multi-frequency relative positioning solutions from BDS-2, BDS-3 and GPS, respectively. The long baselines were resolved by using the random walk constraints on the residual atmospheric delay. The positioning performance of each system was analysed against the atmospheric delay error and the convergence property of the floated ambiguity resolution. The experiments showed that the convergence speed of BDS-3 was twice as fast as BDS-2, and the average convergence speed was also faster than GPS, which was increased by about 2.7%. In comparison with BDS-2, the accuracy of BDS-3 was improved by about 40%, but the accuracy was 7% lower than the GPS solution and the positioning stability of BDS-3 was also not as good as GPS.

An Improved Method for InSAR Atmospheric Phase Correction in Mountainous Areas

Time series interferometric synthetic aperture radar (TS-InSAR) has been a powerful tool for monitoring land surface deformation over the last two decades. Atmospheric effects cause large-scale delays in InSAR observations, which is one of the difficulties facing deformation calculations from differential InSAR and time-series InSAR. Currently, the atmospheric delay is derived mainly from auxiliary data from sources such as the global navigation satellite system (GNSS) and moderate-resolution imaging spectroradiometry (MODIS), but GNSS data are limited by the sparse distribution of observation stations. MODIS data may also not temporally match SAR image acquisition, which leads to low accuracy in atmospheric phase correction. This article presents a decomposition method to remove the atmospheric delay. We consider the atmospheric phase to be caused by the combined changes in spatial position and elevation. Therefore, quadtree segmentation is applied to divide the topographic units, and we improve the drift function of universal kriging by adding an elevation component. We then interpolate the whole atmospheric phase space from reliable sampling points estimated by the coherence coefficient. Using Sentinel-1 data, we test the proposed method in discriminating and monitoring a mining subsidence area in Shanxi Province and compare the results with the results from interferometric point target analysis and the network-based variance-covariance estimation method. The results demonstrate that the proposed method is superior to existing methods for the detection of deformation inverted from TS-InSAR.

Mapping Surface Deformation Over Tatun Volcano Group, Northern Taiwan Using Multitemporal InSAR

The Tatun Volcano Group (TVG) is located at the northern coast of Taiwan Island with only 15 km to the Taipei metropolis. Recent geothermal and geochemical studies suggest that the TVG is dormant-active rather than extinct, implying the eruption potential to devastate the nearby area of 7 million inhabitants. Although some geodetic tools (e.g., global positioning system and precise leveling) have been utilized to analyze the activity associated with volcanism, it is still challenging for conventional interferometric synthetic aperture radar (InSAR) technique to obtain clear ground surface movement at the TVG due to decorrelation and complex atmospheric delay. In this study, we present an improved multi-temporal InSAR (MTInSAR) approach to explore the temporal evolution and spatial extent of displacement at the TVG from 19 L-band ALOS/PALSAR images acquired between 2007 and 2011. Stratified atmospheric delays and orbit error are corrected with a patch-based joint model as different phase components can be separated according to their distinct spatiotemporal characteristics. The spatial deformation patterns indicate an uplift of 10 mm/year at the SW-NE ridge and subsidence with a rate of 10 mm/year at the E-W ridge. The temporal deformation variations at Mt. Chihshin and Huangzuei have high similarity with correlation coefficients of 0.9 above. This indicates that the subsidence at Mts. Chihshin and Huangzuei might be caused by released hydrothermal fluids related to tectono-magmatic activity within the hydrothermal system beneath the TVG.

Monitoring Bridge Vibrations Based on GBSAR and Validation by High-Rate GPS Measurements

Ground-Based synthetic aperture radar (GBSAR) is a novel technique for monitoring surface deformation and structural vibrations due to its unique advantages: GBSAR can continually and simultaneously observe a large range of landscapes with a high spatiotemporal resolution. Using GBSAR under the fixed azimuth scanning mode to monitor vibrations is a promising way to monitor a bridge's structural health. Several GBSAR-based bridge vibration experiments have been conducted and verified; however, observation precision tests using high-rate global positioning system (GPS) data are rare, especially for the GAMMA portable radar interferometer II (GPRI-II). In addition, it is complicated to split high-frequency signals into valuable information and undesired noise. Considering these limitations, an on-site experiment using the GPRI-II and high-rate GPS receivers was conducted to evaluate the utility of the GPRI-II. Moreover, a denoising method based on the least-squares adjustment and a triangular chain is proposed under the assumption that the atmospheric phase delay can be eliminated between minuscule time intervals. In addition, an external comparative experiment between the GPRI-II and GPS receivers and an internal comparative experiment was performed to evaluate the effectiveness and reliability, respectively, of the proposed method. The results show that the GPRI-II observation accuracy for permanent scatterers reaches 5-6 millimeters. For the effectiveness of the proposed method, the precision improvement for distributed scatterers is more significant than that for permanent scatterers, and both the internal precision and the external precision have more conspicuous relationships with the deviation of the amplitude than with the coherence.

Modeling and Assessment of Atmospheric Delay for Gps/Galileo/Bds Ppp-Rtk in Regional-Scale

Modeling and Assessment of Atmospheric Delay for Gps/Galileo/Bds Ppp-Rtk in Regional-Scale

Statistical assessment metrics for InSAR atmospheric correction: Applications to generic atmospheric correction online service for InSAR (GACOS) in Eastern China

The atmospheric effect represents one of the major error sources in Interferometric Synthetic Aperture Radar (InSAR), and its mitigation is found crucial for high-precision InSAR applications. Numerous studies on InSAR atmospheric correction methods and applications covering a wide range of regions worldwide have been reported with varying degrees of success. However, more efforts on the performance assessment are needed, and the conclusions may lack statistical significance due to the limited interferograms involved in most of these analyses. To optimally utilise different InSAR atmospheric correction methods and avoid potential uncertainty caused, appropriate statistical metrics to assess the correction performance must be set up. In this work, we provide a general guideline for statistical assessment of InSAR atmospheric correction. Based on the physical properties of the atmosphere, three metrics are applied: (i) the phase standard deviation which assesses the overall performance of the correction; (ii) the spatial structure function which evaluates the reduction of the long-wavelength atmospheric effect; and (iii) the phase-elevation correlation coefficient which measures the reduction of the stratified component of the atmospheric delay. The performance of the Generic Atmospheric Correction Online Service for InSAR (GACOS) products for two typical terrains in Eastern China is evaluated. Statistical results of the 1250 Sentinel-1 interferograms covering the Yellow River Delta and Shandong hilly region show that (i) GACOS reduces the interferometric phase standard deviation in 84.6% of the interferograms by an average of 36.4%; (ii) the phase decorrelation distance decreases from 321 km to 225 km on average after correction; and (iii) the mean phase-elevation correlation declines by 33.3% for the areas with considerable topographic variations. The results verify the effectiveness of GACOS products in Eastern China for the first time, and the three proposed metrics further characterise the sources of improvement after correction.

Stability Analysis of Geometric Positioning Accuracy of YG-13 Satellite

High-resolution synthetic aperture radar (SAR) satellites have become an important way to observe the earth. However, the geometric positioning accuracy of SAR satellite images across different times and spaces is an important factor affecting the realization of global remote sensing applications. In this study, a multimode hybrid geometric calibration method that incorporates an atmospheric propagation delay correction and that can be used to detect systematic errors affecting the geometric positioning accuracy of SAR satellites is described. The spatiotemporal variation reasons of geometric positioning error sources for spaceborne SAR are then analyzed. Finally, the stability of geometric positioning accuracy is evaluated using the described method on data extracted from Yaogan-13 (YG-13) SAR satellite images with long time series and multiple test areas in China. The results reveal that during the study period (2015-2017), the geometric positioning accuracy of the YG-13 SAR system was relatively stable and is better than 3 m regardless of the spatial distribution, after removal of systematic pulse-dependent slant range errors and atmospheric correction. Furthermore, the validation results provide a reference for the design of SAR satellite systems, the establishment of calibration periods, and quantitative remote sensing application.

Initial evaluation and analysis of NRTK positioning performance with new BDS-3 signals

In 2018, China established the primary constellation of the BeiDou-3 global navigation satellite system (BDS-3), and the construction of this satellite system is due to be completed in 2020. The signal emitted by BDS-3 will provide global positioning, navigation and timing services. This study assessed the performance of network real-time kinematic (NRTK) in reference to the new BDS-3 signals. The ambiguity resolution, retrieval, and interpolation of the double-differenced (DD) observation corrections and positioning precision are assessed based on datasets collected using four SR480 receivers that track the open signals of BDS-3. Subsequently, the NRTK, using the combined BDS-2 and BDS-3 measurements, is compared with the NRTK using only BDS-2 measurements. In addition, the positioning results of NRTK are compared with those of RTK. The results show that the DD atmospheric delay corrections with centimeter-level accuracy can be derived from the network of regional reference stations, and the positioning accuracy of NRTK is improved by incorporating the BDS-3 measurements.

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.

Investigation on Mining Subsidence Based on Sentinel-1A Data by SBAS-InSAR technology—Case Study of Ningdong Coalfield, China

Ground deformation characterization was difficult to obtain over large spatial areas before the invention of the Satellite radar interferometry (InSAR) technique. Especially underground mining in the Loess Plateau of China, it causes large-scale ground damage within a short period of time. A small baseline subset (SBAS) algorithm can overcome some limitations of InSAR technology, such as temporal decorrelation, spatial decorrelation, and atmospheric delay. In this study, SBAS-InSAR technology was applied to process 19 scenes of Sentinel-1A data in Ningdong Coalfield, China. We investigated and analyzed the mining subsidence status from March 2015 to June 2016. There are 6 ground deformation areas in the cumulative subsidence maps, and the maximum cumulative subsidence value is -178cm distributed in the Renjiazhuang mining area during this period. The deformation rate map shows that the maximum deformation rate was -117cm/year. GPS data above the working tunnel was collected in six mining areas in Shigouyi. The subsidence value of SBAS data is consistent with GPS observation station data. The results reveal the evolution process of subsidence in mining subsidence and are helpful to the early warning of the mine disaster.

ARU-Net: Reduction of Atmospheric Phase Screen in SAR Interferometry Using Attention-Based Deep Residual U-Net

Atmospheric phase screen (APS) is a very critical issue for the application of interferometric synthetic aperture radar (InSAR) techniques. The spatial-temporal variations of APS are the dominant error source in interferograms and may completely mask displacement signals. Many external meteorological data-based methods and phase-based methods have been developed in the past decades, but all have their inherent limitations. In this article, we propose a deep learning-based method, which is based on an attention-based deep residual U-shaped network (ARU-Net), to mitigate atmospheric artifacts. With this approach, APS patches and clean interferogram patches are sampled from InSAR interferograms to train the network. After training, the network can be used to mitigate the APS for individual interferograms. Compared with the generic atmospheric correction model (GACOS) and the advanced time-series InSAR method distributed scatterer interferometry (DSI), the key advantage of our method is that atmospheric delay can be effectively learned and removed from individual high-resolution interferometric phase itself without external data. Accuracy was validated by using individual and stacked interferograms from TerraSAR-X data over the Hong Kong International Airport (HKIA) and Hong Kong Science Park (HKSP) sites. The results showed that our method consistently delivered greater standard deviation (SD) reduction after APS correction than the GACOS method. Moreover, the time-series results were in agreement with the DSI and leveling measurements. The effectiveness of the proposed ARU-Net to remove APS effects from interferograms shows great potential for the development of a new set of deep learning-based APS reduction methods.