Airborne Interferometric Synthetic Aperture Radar Research Articles

Airborne Short-Baseline Millimeter Wave InSAR System Analysis and Experimental Results

For the challenges of high-precision mapping in complex terrain, a novel airborne Interferometric Synthetic Aperture Radar (InSAR) system is designed. This system, named ASMIS (Airborne Short-Baseline Millimeter-Wave InSAR System), adopts the coplanar antenna and a pod-type structure. This design makes the system lightweight and highly integrated. It can be compatible with small general aviation flight platforms. The baseline is millimeters in size, which greatly simplifies the unwrapping process. The coplanar antennas have two advantages: they maximize the baseline utilization and minimize the Doppler decorrelation and the motion error inconsistency. Acquisition campaigns of the system have been carried out in Boao, Bayannur, and Chengde, China. In the Chengde experimental area, we designed an antiparallel flight experiment to account for the topographic relief. High-precision Digital Orthophoto Maps (DOMs) and Digital Surface Models (DSMs) at a scale of 1:5000 were obtained. The coordinate Root Mean Square Error (RMSE) of the checkpoints within the obtained DSM is less than 0.82 m in altitude and 3 m horizontally. The RMSE of the Sparse Ground Control Points (GCPs) within the obtained DSM is less than 0.3 m in altitude. Experimental results from different areas, including plains, mountains, and coastlines, demonstrate the system’s performance.

National-scale geodatabase of catchment characteristics in the Philippines for river management applications.

Quantitative descriptions of stream network and river catchment characteristics provide valuable context for enabling geomorphologically-informed sustainable river management. For countries where high-quality topographic data are available, there are opportunities to enable open access availability of baseline products from systematic assessment of morphometric and topographic characteristics. In this study, we present a national-scale assessment of fundamental topographic characteristics of Philippine river systems. We applied a consistent workflow using TopoToolbox V2 to delineate stream networks and river catchments using a nationwide digital elevation model (DEM) acquired in 2013 and generated through airborne Interferometric Synthetic Aperture Radar (IfSAR). We assessed morphometric and topographic characteristics for 128 medium- to large-sized catchments (catchment area > 250 km2) and organised the results in a national-scale geodatabase. The dataset realises the potential of topographic data as part of river management applications, by enabling variations in hydromorphology to be characterised and contextualised. The dataset is used to reveal the diversity of stream networks and river catchments in the Philippines. Catchments have a continuum of shapes (Gravelius compactness coefficient ranges from 1.05 to 3.29) with drainage densities that range from 0.65 to 1.23 km/km2. Average catchment slope ranges from 3.1 to 28.1° and average stream slope varies by more than an order of magnitude from 0.004 to 0.107 m/m. Inter-catchment analyses show the distinctive topographic signatures of adjacent river catchments; examples from NW Luzon highlight topographic similarity between catchments whereas examples from Panay Island shown marked topographic differences. These contrasts underline the importance of using place-based analyses for sustainable river management applications. By designing an interactive ArcGIS web-application to display the national-scale geodatabase, we improve data accessibility and enable users to freely access, explore and download the data (https://glasgow-uni.maps.arcgis.com/apps/webappviewer/index.html?id=a88b9ca0919f4400881eab4a26370cee). The national-scale geodatabase provides a baseline understanding of fundamental topographic characteristics in support of varied geomorphological, hydrological and geohazard susceptibility applications.

Experimental Results of Three-Dimensional Modeling and Mapping with Airborne Ka-Band Fixed-Baseline InSAR in Typical Topographies of China

Interferometric synthetic aperture radar (InSAR) has become a key technology for producing high-precision digital surface models (DSMs) and digital orthophoto maps (DOMs) in full time and all weathers. Airborne millimeter-wave InSAR, with large-scale and high-resolution imaging, is characterized by high spatial resolution, flexibility, and immunity to loss-of-correlation. This paper introduces our modeling experiments with airborne dual-antenna, Ka-band InSAR regarding typical topographies of China. Ka-band SAR data were acquired in designated experimental areas in flat (Heyang area in Shaanxi) and mountainous areas (Shibing area in Guizhou and Qionglai area in Sichuan). The key processing of the experimental data for DSMs and DOMs is demonstrated in the paper, especially the proposed robust and efficient phase unwrapping (PU) method for the interferometric data and block adjustment method of strip calibration. The results show that the proposed unwrapping method can provide reliable unwrapped phase results in undulating areas, and the block adjustment can carry out consistent calibration for strips with sparse ground control points (GCPs). The accuracy assessment of the DSM shows that the coordinate root mean square error (RSME) of the obtained DSM is less than 2 m in height, and 2.5 m horizontally, which meets the 1:5000 requirement for topographic mapping in difficult areas.

Human Activity Detection Based on Multipass Airborne InSAR Coherence Matrix

This letter focuses on the detection of subtle human activity, from multipass airborne interferometric synthetic aperture radar (InSAR) images, of such activities causing absolute decorrelation between two acquisitions. These activities can be vehicle track, footprint, grazing, and human construction. The millimetric sensitivity of the interferometric phase makes it valuable for detecting such activities. It can bring a large value to civil and military applications. However, there are always high false alarms in the conventional detection method. To solve this problem, a human activity detection method based on the coherence matrix is approached in this letter. In the final part of this letter, a preliminary validation of the method is provided by processing actual multipass airborne InSAR data stacks acquired by the Aerospace Information Research Institute, Chinese Academy of Sciences. It is noted that the proposed method reaches reliable results in comparison with the ground truth.

Enhancing Coherence Images for Coherent Change Detection: An Example on Vehicle Tracks in Airborne SAR Images

In Synthetic Aperture Radar (SAR) interferometry, one of the most widely used measures for the quality of the interferometric phase is coherence. However, in favorable conditions coherence can also be used to detect subtle changes on the ground, which are not visible in the amplitude images. For such applications, i.e., coherent change detection, it is important to have a good contrast between the unchanged (high-coherence) parts of the scene and the changed (low-coherence) parts. In this paper, an algorithm is introduced that aims at enhancing this contrast. The enhancement is achieved by a combination of careful filtering of the amplitude images and the interferometric phase image. The algorithm is applied to an airborne interferometric SAR image pair recorded by the SmartRadar experimental sensor of Hensoldt Sensors GmbH. The data were recorded during a measurement campaign over the Bann B installations of POLYGONE Range in southern Rhineland-Palatinate (Germany), with a time gap of approximately four hours between the overflights. In-between the overflights, several vehicles were moved on the site and the goal of this work is to enhance the coherence image such that the tracks of these vehicles can be detected as completely as possible in an automated way. Several coherence estimation schemes found in the literature are explored for the enhancement, as well as several commonly used speckle filters. The results of these filtering steps are evaluated visually and quantitatively, showing that the mean gray-level difference between the low-coherence tracks and their high-coherence surroundings could be enhanced by at least 28%. Line extraction is then applied to the best enhancement. The results show that the tracks can be detected much more completely using the coherence contrast enhancement scheme proposed in this paper.

The Detection of Active Sinkholes by Airborne Differential LiDAR DEMs and InSAR Cloud Computing Tools

InSAR (Interferometric Synthetic Aperture Radar) cloud computing and the subtraction of LiDAR (Light Detection and Ranging) DEMs (Digital Elevation Models) are innovative approaches to detect subsidence in karst areas. InSAR cloud computing allows for analyzing C-band Envisat and Sentinel S1 SAR images through web platforms to produce displacement maps of the Earth’s surface in an easy manner. The subtraction of serial LiDAR DEMs results in the same product but with a different level of accuracy and precision than InSAR maps. Here, we analyze the capability of these products to detect active sinkholes in the mantled evaporite karst of the Ebro Valley (NE Spain). We found that the capability of the displacement maps produced with open access, high-resolution airborne LiDAR DEMs was up to four times higher than InSAR displacement maps generated by the Geohazard Exploitation Platform (GEP). Differential LiDAR maps provide accurate information about the location, active sectors, maximum subsidence rate and growing trend of the most rapid and damaging sinkholes. Unfortunately, artifacts and the subsidence detection limit established at −4 cm/yr entailed important limitations in the precise mapping of the sinkhole edges and the detection of slow-moving sinkholes and small collapses. Although InSAR maps provided by GEP show a worse performance when identifying active sinkholes, in some cases they can serve as a complementary technique to overcome LiDAR limitations in urban areas.

Refining the conversion between phase and height in airborne and UAV-borne SAR interferometry

ABSTRACT Airborne or unmanned aerial vehicle (UAV)-borne interferometric synthetic aperture radar (InSAR) can offer topography and forest measurement with high accuracy and high resolution. In these measurements, the conversion between height and interferometric phase is frequently involved. The existing conversion approximations may be more suitable for the space-borne InSAR. However, for the airborne or UAV-borne InSAR in a low flight altitude, these conversion formulas cannot work well for topography and forest height measurement. To achieve high-quality InSAR measurements, we derive a refined conversion formula based on the Taylor expansion, i.e. Firsec-order conversion formula. Differing from existing studies contributed by first-order derivative, the Firsec-order conversion formula expands a second-order term. During refined phase-to-height (PHAtoH) conversion, an external low-precision digital elevation model (DEM) is introduced to calculate and then remove the second-order phase from the original interferometric phase. Thus, the high-precision DEM is reconstructed based on the refined phase rather than the differential phase. In addition, this article investigates the sensitivity of the second-order term to different baseline lengths and flight altitudes with respect to two common baseline configurations in airborne or UAV-borne InSAR. The results show that for the horizontal baseline configuration, the second-order term must be considered. In addition, the longer the baseline and the lower the flight altitude, the larger the second-order phase, especially for the UAV-borne InSAR geometries with altitudes of under 2 km. However, for the vertical baseline configuration, the adoption of the refined formula depends on the actual situation. The performance test shows that the Firsec-order conversion formula has much higher conversion accuracies than traditional ones.

Permafrost Dynamics Observatory-Part I: Postprocessing and Calibration Methods of UAVSAR L-Band InSAR Data for Seasonal Subsidence Estimation.

Interferometric synthetic aperture radar (InSAR) has been used to quantify a range of surface and near surface physical properties in permafrost landscapes. Most previous InSAR studies have utilized spaceborne InSAR platforms, but InSAR datasets over permafrost landscapes collected from airborne platforms have been steadily growing in recent years. Most existing algorithms dedicated toward retrieval of permafrost physical properties were originally developed for spaceborne InSAR platforms. In this study, which is the first in a two part series, we introduce a series of calibration techniques developed to apply a novel joint retrieval algorithm for permafrost active layer thickness retrieval to an airborne InSAR dataset acquired in 2017 by NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar over Alaska and Western Canada. We demonstrate how InSAR measurement uncertainties are mitigated by these calibration methods and quantify remaining measurement uncertainties with a novel method of modeling interferometric phase uncertainty using a Gaussian mixture model. Finally, we discuss the impact of native SAR resolution on InSAR measurements, the limitation of using few interferograms per retrieval, and the implications of our findings for cross‐comparison of airborne and spaceborne InSAR datasets acquired over Arctic regions underlain by permafrost.

Extensibility of U-Net Neural Network Model for Hydrographic Feature Extraction and Implications for Hydrologic Modeling

Accurate maps of regional surface water features are integral for advancing ecologic, atmospheric and land development studies. The only comprehensive surface water feature map of Alaska is the National Hydrography Dataset (NHD). NHD features are often digitized representations of historic topographic map blue lines and may be outdated. Here we test deep learning methods to automatically extract surface water features from airborne interferometric synthetic aperture radar (IfSAR) data to update and validate Alaska hydrographic databases. U-net artificial neural networks (ANN) and high-performance computing (HPC) are used for supervised hydrographic feature extraction within a study area comprised of 50 contiguous watersheds in Alaska. Surface water features derived from elevation through automated flow-routing and manual editing are used as training data. Model extensibility is tested with a series of 16 U-net models trained with increasing percentages of the study area, from about 3 to 35 percent. Hydrography is predicted by each of the models for all watersheds not used in training. Input raster layers are derived from digital terrain models, digital surface models, and intensity images from the IfSAR data. Results indicate about 15 percent of the study area is required to optimally train the ANN to extract hydrography when F1-scores for tested watersheds average between 66 and 68. Little benefit is gained by training beyond 15 percent of the study area. Fully connected hydrographic networks are generated for the U-net predictions using a novel approach that constrains a D-8 flow-routing approach to follow U-net predictions. This work demonstrates the ability of deep learning to derive surface water feature maps from complex terrain over a broad area.

Comparison of Surface Subsidence Measured by Airborne and Satellite InSAR Over Permafrost Areas Near Yellowknife Canada.

In addition to spaceborne Interferometric Synthetic Aperture Radar (InSAR), airborne data such as those obtained by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) have also been utilized to measure surface subsidence in permafrost areas in recent years. Motivated by the integration of multiplatform InSAR data, we generated two UAVSAR interferograms and one Advanced Land Observing Satellite (ALOS)‐2 L‐band interferogram over a permafrost area near Yellowknife, Canada, then compared the surface subsidence in the thaw seasons of 2017. The correlation coefficient and the root mean square error (RMSE) of subsidence difference are calculated to compare the airborne and spaceborne InSAR measurements. The results demonstrate that the two UAVSAR measurements are self‐consistent, with the correlation coefficient between independent airborne measurements ∼0.7. While the RMSE of the difference between surface subsidence measured by UAVSAR and ALOS2 is ∼2.0 cm, and the correlation coefficients are less than 0.41, that is, a noticeable deviation exists between the UAVSAR and ALOS2 results possibly due to different spatial resolution and the calibration processing of airborne and spaceborne InSAR data. In addition, both UAVSAR and ALOS2 interferograms show larger surface subsidence within taiga needleleaf forest regions than in regions of other biome types (including needleleaf forest, shrubland, and grassland). The results demonstrate that a scheme for the elimination of systematic differences needs to be developed before merging multisource InSAR results. This intercomparison will provide valuable insights for narrowing the gap between radar‐based measurements and planning the integration of airborne and satellite InSAR measurements in permafrost environments.

A New Approach for Forest Height Inversion Using X-Band Single-Pass InSAR Coherence Data

In this article, a multilevel model (MLM) for forest height inversion is introduced and investigated using X-band single-pass interferometric synthetic aperture radar (InSAR) coherence data. Compared with the two-level model (TLM), as a generalized model, the MLM is more in line with the characteristics of forest structure and the scattering mechanism for X-band data. Based on the MLM model, three simplified MLM models were derived: TLMm, SINC, and MTND. An improved method of calculating coherence considering the assumptions of the MLM model is proposed. At two test sites, airborne (CASMSAR) and spaceborne (TanDEM-X) X-band single-pass InSAR data and LiDAR H100 data were used to verify the proposed new approach. The results showed that the MTND model can obtain more reliable and accurate inversion results compared to the SINC model and the TLMm. With airborne InSAR data, the MTND model’s highest accuracy was 82.56%, and the root mean square error (RMSE) was 2.4 m. With spaceborne InSAR data, the highest inversion accuracy of the MTND model was 73.55%, with an RMSE of 4.92 m. The inversion accuracy of forest height can be effectively improved by ensuring that the theoretical models and calculations of coherence obey the same assumptions.

CVCMFF Net: Complex-Valued Convolutional and Multifeature Fusion Network for Building Semantic Segmentation of InSAR Images

Building segmentation of synthetic aperture radar (SAR) images is a challenging task that has not been solved well. High-resolution interferometric SAR (InSAR) images can provide delicate textures and interferometric phase images useful for building segmentation. However, current semantic segmentation networks in computer vision cannot be directly applied in InSAR building segmentation tasks to get good results because of the InSAR images’ particularity. In this article, we present a novel complex-valued convolutional and multifeature fusion network (CVCMFF Net) specifically for building semantic segmentation of InSAR images. This CVCMFF Net not only learns from the complex-valued SAR images but also considers multiscale and multichannel feature fusion. It can effectively segment the layover, shadow, and background on both the simulated InSAR building images and the real airborne InSAR images. The segmentation performance of CVCMFF Net is significantly improved compared with those of other state-of-the-art networks. By feature visualization, the feature extraction rule and feature fusion mechanism of the network are explored. We hope that the proposed network can be beneficial to InSAR phase filtering, phase unwrapping, and information extraction in urban areas.

Improving access to geodetic imaging crustal deformation data using GeoGateway

GeoGateway (http://geo-gateway.org) is a web-based interface for analysis and modeling of geodetic imaging data and to support response to related disasters. Geodetic imaging data product currently supported by GeoGateway include Global Navigation Satellite System (GNSS) daily position time series and derived velocities and displacements and airborne Interferometric Synthetic Aperture Radar (InSAR) from NASA’s UAVSAR platform. GeoGateway allows users to layer data products in a web map interface and extract information from various tools. Extracted products can be downloaded for further analysis. GeoGateway includes overlays of California fault traces, seismicity from user selected search parameters, and user supplied map files. GeoGateway also provides earthquake nowcasts and hazard maps as well as products created for related response to natural disasters. A user guide is present in the GeoGateway interface. The GeoGateway development team is also growing the user base through workshops, webinars, and video tutorials. GeoGateway is used in the classroom and for research by experts and non-experts including by students.

Correction of Time-Varying Baseline Errors Based on Multibaseline Airborne Interferometric Data Without High-Precision DEMs

The time-varying baseline error (TVBE) caused by the residual motion errors is one of the most significant error sources in the airborne interferometric synthetic aperture radar (InSAR). Although global digital elevation model (DEM) products can be used to extract the TVBE for areas without high-precision DEMs, they cause remarkable height differences (HDs) relative to the actual InSAR-measured topography. In addition, due to the low flight height of airborne SAR platforms, the range-dependent phases resulted from the HDs have significant impacts on the TVBE estimation. Through the mutual restraint of multibaseline observations, this research establishes a multibaseline parameterized model jointly considering HDs and TVBEs in each range direction for the absolute phase calibration. A weighted truncated singular value decomposition (TSVD) algorithm is used to solve the ill-posed problem in joint parameter estimation. Note that, to improve the TVBE extraction, a two-step strategy is adopted. In step one, TVBEs and HDs are preliminarily estimated by the multibaseline parameterized model. After that, bare areas with flat topography, where the truths of HDs can be assumed to be 0, are selected to correct the bias of the estimated HDs. If the flat and bare areas are absent, global ecosystem dynamics investigation (GEDI) or Ice, Cloud, and land Elevation Satellite (ICESAT) points can be used instead. In step two, the multibaseline parameterized model is updated by the calibrated DEM and then used to reestimate the TVBEs. The proposed method was validated by using a simulated data set and two real data sets (i.e., the P- and L-band data sets acquired by the airborne E-SAR system during the BioSAR2008 campaign). The results show that, with respect to the reference values, the root-mean-square error (RMSE) of the calibrated DEMs is 0.48 m for the simulated data set, 2.6 m for the P-band data set, and 3.6 m for the L-band data set, suggesting that the high-precision DEMs are well reconstructed and the TVBE-induced phases can be precisely extracted. After correcting the TVBE-induced phases, the accuracy of the DEMs generated by every linked phase (LP) is improved by at least 38.2% for P-band and by at least 51.0% for L-band. To assess the effectiveness and practicability of the proposed model, we further investigated the influence of the limited multibaseline number, and the baseline combination, and the precision of external DEMs on the TVBE correction. In conclusion, without high-precision DEMs, the multibaseline parameterized method can work well in accurately extracting the TVBE, even for the limited airborne InSAR data set.

Precursory ground deformation of the 2018 phreatic eruption on Iwo-Yama volcano, revealed by four-dimensional joint analysis of airborne and spaceborne InSAR

We present detailed maps of local-scale 3D deformation preceding the 2018 phreatic eruption at Iwo-yama volcano (south of Kyushu Island, Japan), using a combination of airborne and spaceborne Interferometric Synthetic Aperture Radar (InSAR) data. The 3D and 2.5D deformation maps obtained at different periods allow us to successfully track their spatiotemporal evolution and to infer the transition of subsurface conditions responsible for the precursory deformation observed from 2014 to 2018. From 2014 to 2016, ground inflation depicted an axisymmetric pattern with the maximum displacement at the center of the deformed area. However, from 2016 to 2018, an inflation peak moved to the southern edge of the area deformed during 2014–2016 and became more localized, which was close to the newly generated vents in the 2018 eruption. Modeling of the inflations suggests that pressurization within a crack at a depth of 150 m beneath the Iwo-yama geothermal area caused the 2014–2016 deformation and had continued until the 2018 eruption. Modeling results highlight the persistence of the local ground inflation pattern just above the southern edge of the crack, which suggests the presence of a shallower inflation source contributing to the local inflation. Consequently, we interpret the sequence of these deformations as follows: from 2014, deeper-rooted fluid started to inject into a fluid-saturated crack at 150-m depth, which caused the 2014–2016 deformation. Then, after 2016, the crack inflation continued because of the continuous fluid injection and formed another pressurized part directly above the southern tip of the crack. Additionally, the results of the time-series analysis of the satellite InSAR data revealed that the local inflation started around April 2017 for which thermal activity including a mud emission became pronounced around the location of the local inflation. As a result of an episodic increase in supply rate of magmatic fluids from a deep magma reservoir from early 2018, a phreatic eruption finally occurred in the vicinity of the most deformed point, providing a clue for predicting future eruption sites, as was also observed in the Hakone 2015 eruption.

Coherent Markov Random Field-Based Unreliable DSM Areas Segmentation and Hierarchical Adaptive Surface Fitting for InSAR DEM Reconstruction.

A digital elevation model (DEM) can be obtained by removing ground objects, such as buildings, in a digital surface model (DSM) generated by the interferometric synthetic aperture radar (InSAR) system. However, the imaging mechanism will cause unreliable DSM areas such as layover and shadow in the building areas, which seriously affect the elevation accuracy of the DEM generated from the DSM. Driven by above problem, this paper proposed a novel DEM reconstruction method. Coherent Markov random field (CMRF) was first used to segment unreliable DSM areas. With the help of coherence coefficients and residue information provided by the InSAR system, CMRF has shown better segmentation results than traditional traditional Markov random field (MRF) which only use fixed parameters to determine the neighborhood energy. Based on segmentation results, the hierarchical adaptive surface fitting (with gradually changing the grid size and adaptive threshold) was set up to locate the non-ground points. The adaptive surface fitting was superior to the surface fitting-based method with fixed grid size and threshold of height differences. Finally, interpolation based on an inverse distance weighted (IDW) algorithm combining coherence coefficient was performed to reconstruct a DEM. The airborne InSAR data from the Institute of Electronics, Chinese Academy of Sciences has been researched, and the experimental results show that our method can filter out buildings and identify natural terrain effectively while retaining most of the terrain features.

Airborne InSAR Interferometric Phase Analysis, Unwrapping Method, and Fast Implementation in Low Coherence Areas

Due to synthetic aperture radar (SAR) imaging geometry and scattering characteristics, there are some low coherence areas in interferometric SAR (InSAR) data, such as shadow and water, where the interferometric phase is discontinuous. Therefore, it is difficult to prevent the phase error spreading in the process of phase unwrapping. In this article, the characteristics of the interferometric phase in the shadow and water areas are theoretically analyzed, respectively. According to the analysis, instead of the usual integral path setting strategy, a novel unwrapping method based on the phase compensation is proposed to avoid the unwrapping error. Besides, a fast implementation method of the quality-guided unwrapping algorithm based on the heap sort is proposed to improve the efficiency. The experiment by real airborne InSAR data validates the effectiveness of the proposed method.

Phase unwrapping method based on parallel local minimum reliability dual expanding for large-scale data

Considering data reliability, the optimized connections of dual residues for phase discontinuity reconstruction provide a more reliable scheme and produce more robust unwrapped results. However, their practical implementation usually involves a time-consuming iterative global operation, which is not suitable for application to the phase unwrapping (PU) of big blocks of interferometric synthetic aperture radar (InSAR) phase data. A parallel PU method based on local minimum reliability dual expanding is proposed. With given quality weight maps, the dual reliability is defined based on residues, and the minimum reliability residue pairs are introduced to represent possible discontinuous boundaries. We provide a dynamic expanding method for the pairs with local minimum reliability searching and dual merging. The final minimum balanced trees obtained are used for path integrating of PU with the aid of reliability maps. The calculation of reliability maps, the residue pair searching, and the dynamic expanding are designed to be carried out in parallel. We employ the interface propagation scheme based on the eikonal equation and flood-filling for the parallel implement. Two big blocks of airborne InSAR data were processed by the proposed method, the experimental results and analysis verified its robustness and efficiency for the large-scale PU problem.

Topographic Changes During the 2018 Kīlauea Eruption From Single‐Pass Airborne InSAR

AbstractThe 2018 eruption of Kīlauea volcano, Hawai‘i, was its most effusive in over 200 years. We apply the airborne Glacier and Ice Surface Topography Interferometer (GLISTIN‐A) interferometric synthetic aperture radar (InSAR) instrument to measure topographic change associated with the eruption. The GLISTIN‐A radar flew in response to the eruption, acquiring observations of Kīlauea on 7 days between 18 May and 15 September 2018. Topography differences were computed relative to GLISTIN‐A observations in 2017. Bare‐Earth topography and offshore bathymetry were used to correct for vegetation and creation of new coastal land within the lower East Rift Zone (LERZ) lava flow field. We estimate that the LERZ subaerial flows total bulk volume is 0.593 ± 0.011 km3 and that the summit collapse volume is −0.836 ± 0.002 km3. Within the temporal sampling and uncertainty from submarine flow volumes, we find that both the LERZ and caldera volume changes were approximately linear.

Adaptive two-filter smoothing based on second-order divided difference filter for distributed position and orientation system

The distributed position and orientation system (POS) uses transfer alignment to accurately measure multi-node time-spatial reference information, which is necessary for compensating the motion error of imaging sensors used in aerial surveys. Lever-arm deformation in transfer alignment will result in time-varying covariance of measurement noise, which decreases the estimation accuracy. To address this problem, a second-order adaptive divided difference two-filter smoother (ADDTFS) is proposed for transfer alignment. The second-order adaptive divided difference filter (ADDF2) severs as the forward filter to address nonlinearity in the system. The ADDF2 can estimate the measurement noise covariance in real time based on adaptive estimation using innovation information. The weighted statistical linear regression formulation is used in the backward filter to independently estimate the states and error covariance matrices. Then the forward and backward results are combined, to obtain high-precision estimation results in post-processing. Finally, the vehicle experiment results show that the proposed method can improve the attitude precision of transfer alignment in a distributed POS by 27.84%, which has been successfully applied to motion compensation of airborne interferometric synthetic aperture radar (InSAR).