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Estimating horizontal winds in and around typhoons is important for improved monitoring and prediction of typhoons and mitigating their damages. Here, we present a new algorithm for estimating typhoon winds using multiple satellite observations and its application to Typhoon Soulik (2018). Four kinds of satellite remote sensing data, along with their relationship to typhoon intensity, derived statistically from hundreds of historical typhoon cases, were merged into the final product of typhoon wind (MT wind): 1) geostationary-satellite-based infrared images (IR wind), 2) passive microwave sounder (MW wind), 3) feature-tracked atmospheric motion vectors, and 4) scatterometer-based sea surface winds (SSWs). The algorithm was applied to two cases (A and B) of Typhoon Soulik and validated against SSWs independently retrieved from active microwave synthetic aperture radar (SAR) and microwave radiometer (AMSR2) images, and vertical profiles of wind speed derived from reanalyzed data and dropsonde observations. For Case A (open ocean), the algorithm estimated the realistic maximum wind, radius of maximum wind, and radius of 15 m/s, which could not be estimated using the reanalysis data, demonstrating reasonable and practical estimates. However, for Case B (when the typhoon rapidly weakened just before making landfall in the Korean Peninsula), the algorithm significantly overestimated the parameters, primarily due to the overestimation of typhoon intensity. Our study highlights that realistic typhoon winds can be monitored continuously in real-time using multiple satellite observations, particularly when typhoon intensity is reasonably well predicted, providing timely analysis results and products of operational importance.

Accurately tracking marine traffic considering security and commercial activities is still challenging despite its increasing global importance. Recently, space-borne synthetic aperture radar (SAR) is being considered to accurately monitor maritime traffic, and techniques to detect the position of ships and estimate their velocity have become essential. Here, we investigated the potential for automatic estimation of ship velocity using the azimuth offset between ships and wakes detected using convolutional neural network (CNN) coupled with SAR imagery. We found that azimuth offset is proportional to the Doppler shift effect of the back-scattered signal in SAR, thus, it relates to the radial velocity of a moving target. Consequently, we propose a method whereby a CNN is applied to automatically detect ship wakes from TanDEM-X data. In this method, ship velocity is calculated using the azimuthal distance (i.e., azimuth offset) between the stern of the detected ship and the vertex of the detected V-shape wake-determined as the intersection of two lines obtained through edge filtering and Radon transforms. The location and number of detected ships are then compared with an automatic identification system (AIS), and the calculated velocity of the ship is compared with the velocity obtained via along-track interferometry and AIS. Results show that our method automatically detects ships and wakes with accuracies of 91.0% and 93.2%, respectively, and estimates the velocity of ships with an accuracy of 0.13 m/s. This method is effective when wind velocities are not substantially higher than 5.5 m/s and ship velocities are not extremely low.

For open ocean environments, it is rare to find continuous, simultaneous air and sea observation records due to the challenges of instrument installation and maintenance. The Ieodo Ocean Research Station (Ieodo ORS), a remote ocean site located in the northern East China Sea with its harsh oceanic and atmospheric environment, provides a platform for the concurrent monitoring of air and sea environments. Since 2014, the Korea Hydrographic and Oceanographic Agency has run the “Ieodo ORS field trip program,” via which researchers are able to stay at the station for a week or more. This work reports technical lessons learned over 5 years from five Ieodo ORS research projects launched in 2016. Over the course of these projects, Ieodo ORS has monitored sea surface temperature, temperature and salinity in the water column, seawater pH, air pollutants, and solar radiation. The purpose of this paper is to facilitate the success of future research activities in similar environments by sharing our experiences and “best practices.”

The relative sea-level changes from tide gauges in the Korean peninsula provide essential information to understand the regional and global mean sea-level changes. Several corrections to raw tide gauge records are required to account for coastal vertical land motion (VLM), regional and local coastal variability. However, due to the lack of in-situ measurements such as leveling data and the Global Navigation Satellite System (GNSS), making precise assessments of VLM at the tide gauges is still challenging. This study aims to address the above limitation to assess the VLM in the Korean tide gauges using the time-series Interferometric Synthetic Aperture Radar (InSAR) technique. For 10 tide gauges selected in the Korean peninsula, we applied the Stanford Method for Persistent Scatterers (StaMPS)&mdash, Small Baseline Subset (SBAS) method to C-band Sentinel-1 A/B Synthetic Aperture Radar (SAR) data acquired during 2014/10&ndash, 2020/05, with the novel sequential interferograms pair selection approach to increase the slowly decorrelating filtered phase (SDFP) pixels density near the tide gauges. Our findings show that overall the tide gauges in the Korean peninsula are stable, besides the largest VLM observed at Pohang tide gauge station (East Sea) of about &minus, 26.02 mm/year, also, higher rates of uplift (&gt, 1 mm/year) were observed along the coast of Yellow Sea (Incheon TG and Boryeong TG) and higher rates of subsidence (&lt, &minus, 2 mm/year) were observed at Jeju TG and Seogwipo TG. Our approach estimates the rate of VLM at selected tide gauges with an unprecedented spatial and temporal resolution and is applicable when the in-situ and GNSS observations are not available.

A sea surface imaging technique for an emergency response using a ready-made frequency modulated continuous wave&ndash, synthetic aperture radar (FMCW SAR) system and its experimental results are described in this paper. The optimal range of radiowave incidence angle for sea surface imaging was analyzed by a theoretical scattering model and measurement data, and it was properly applied to the FMCW SAR system by readjusting the delayed-dechirp process. Raw data acquired through flight experiments were reconstructed to SAR image by the range-doppler algorithm. To verify the performance of the reconstructed sea surface image, dual-channel images collected by the configuration of the along-track interferometry were used, and then performance indicators such as signal attenuation, coherence, and phase difference were analyzed. Through this experimental study, it was confirmed that the ready-made FMCW SAR system without a function of the incident angle control can also conduct limited missions for maritime observation. It is possible to be an alternative resource for emergency response, in which the cases are requiring urgent maritime disaster detection and analysis.

Ship detection in coastal regions accompanied by machine learning could be effective in economic and martial issues. However, conventional researches on ship detection mainly focused on modifying the training model itself instead of obtaining qualified training data. In order to ameliorate the training data in the aspect of quality and quantity, this research aims to directly obtain training data from AIS information. From discrete AIS information, interpolation on SAR acquisition time was conducted, followed by adjusting the Doppler frequency shift caused by each vessel's velocity. Training data was constructed from the adjusted position using internal location of AIS sensor on each type of vessel. Extracted training data by the proposed algorithm from Sentinel-1 images was tested with CNN model. As the detection performance of the extracted training data exceeded that from visual interpretation, this study concluded that qualified training data could be extracted from the proposed algorithm.

Coastal sea level changes measured by tide gauges are affected by the severity of coastal land motion. Subsidence due to natural and anthropogenic activities in the coastal regions were directly exacerbates the tide gauge records. In this study, we aim to monitor the coastal land motion at the tide gauges in Korean peninsula using multi-temporal InSAR techniques. We present the methodology that simply employs the selection of sequential and small temporal interferograms in the StaMPS-SBAS technique for increased coherent PS pixels over coastal regions. C-band Sentinel-1 SAR data from ascending and descending mode were acquired over Pohang and Yeosu regions to map the coastal land motion. The preliminary results showed significant increased number of initial PS pixel density than conventional StaMPS-SBAS technique, chosen by Amplitude-Dispersion Index (ADI) and revealed the subsidence about 2 cm/year in Pohang tide gauge and small uplift of 1.46 mm/year near Yeosu tide gauge.

Development of convolutional neural network (CNN) optimized for object detection, led to significant developments in ship detection. Although training data critically affect the performance of the CNN-based training model, previous studies focused mostly on enhancing the architecture of the training model. This study developed a sophisticated and automatic methodology to generate verified and robust training data by employing synthetic aperture radar (SAR) images and automatic identification system (AIS) data. The extraction of training data initiated from interpolating the discretely received AIS positions to the exact position of the ship at the time of image acquisition. The interpolation was conducted by applying a Kalman filter, followed by compensating the Doppler frequency shift. The bounding box for the ship was constructed tightly considering the installation of the AIS equipment and the exact size of the ship. From 18 Sentinel-1 SAR images using a completely automated procedure, 7489 training data were obtained, compared with a different set of training data from visual interpretation. The ship detection model trained using the automatic training data obtained 0.7713 of overall detection performance from 3 Sentinel-1 SAR images, which exceeded that of manual training data, evading the artificial structures of harbors and azimuth ambiguity ghost signals from detection.

This paper presents a new damage-mapping algorithm based on coherence images estimated from multitemporal polarimetric–interferometric synthetic aperture radar (SAR) data. The interferometric coherence has been restricted in the conventional damage-mapping approaches because the decorrelation sources are too complicated to interpret accurately and temporal decorrelation effects caused by slowly occurring natural changes and disaster events are often coupled together. To overcome these limitations, we formulate a coherence model that accounts for temporal decorrelation in two simplified layers, ground and volume layers, for long-temporal repeat-pass scenarios with zero spatial baseline. The model parameters include: 1) ground-to-volume ratio, a factor to determine the relative scattering contribution of ground and volume layers; 2) temporally correlated change, which captures the exponentially decaying behavior of coherence with time; and 3) temporally uncorrelated change, which is associated with random temporal changes. We estimate the model parameters in three steps: coherence optimization, interferometric pair-invariant parameter estimation, and interferometric pair-variant parameter estimation. To isolate the effects of disaster events from background natural changes, we calculate the probability density functions of historical change pixel by pixel and produce a probability map of damage. We tested the algorithm with uninhabited aerial vehicle data acquired from 2009 to 2015 for mapping the area damaged by the 2015 Lake Fire in California. Based on performance evaluation using receiver operating characteristic curves for optimized coherences and averaged probability maps, the proposed method reduced the false alarm from 0.25 to 0.07 when the probability of detection was 0.85 compared to coherence products alone.

Land subsidence in densely urbanized areas is a global problem that is primarily caused by excessive groundwater withdrawal. The Kathmandu Basin is one such area where subsidence due to groundwater...

Irrigated rice fields in Asia have a distinct topography and scattering mechanisms. Because of these factors, many studies using radar backscattering information have been conducted to improve the mapping accuracy; however, relatively little attention has been paid to the topographic features (TF) provided by polarimetric synthetic aperture Radar (PolSAR) data. To address this issue, this letter presents a simple rice field mapping method in its late-vegetative stage using both the TF and scattering features (SF) of the PolSAR data. First, two TF, the polarization orientation angle and the dominant beta angle from the PolSAR data, were analyzed with respect to the slope from a digital elevation model. The results indicated that this feature pair had a moderately high correlation. As a result, flat areas were easily extracted using these two TF. Second, to use the SF of rice in the late-vegetative stage, the copolarized ratio and the copolarized phase difference were simply combined to map only paddy fields, of which scattering was dominated by the double-bounce scattering caused by the ground and canopy reflection. The results using C-band Radarsat-2 data were compared with a support vector machine classifier and demonstrated that the proposed mapping method provided an overall better performance. The proposed method might also be efficiently applied, with modification, for the aforementioned purpose in inaccessible areas.

Detection of changes caused by major events-such as earthquakes, volcanic eruptions, and floods-from interferometric synthetic aperture radar (SAR) data is challenging because of the coupled effects with temporal decorrelation caused by natural phenomena, including rain, snow, wind, and seasonal changes. The coupled effect of major events and natural phenomena sometimes leads to misinterpretation of interferometric coherence maps and often degrades the performance of change detection algorithms. To differentiate decorrelation sources caused by natural changes from those caused by an event of interest, we formulated a temporal decorrelation model that accounts for the random motion of canopy elements, temporally correlated dielectric changes, and temporally uncorrelated dielectric changes of canopy and ground. The model parameters are extracted from the interferometric pairs associated with natural changes in canopy and ground using the proposed temporal decorrelation model. In addition, the cumulative distribution functions of the temporally uncorrelated model parameters, which are associated with natural changes in canopy and ground, are estimated from interferometric pairs acquired before the event. Model parameters are also extracted from interferometric SAR data acquired across the event and compared with the cumulative probabilities of natural changes in order to calculate the probability of a major event. Subsequently, pixels with cumulative probabilities greater than 75% are marked as changed due to the event. A case study for detecting volcanic ash during the eruption of the Shinmoedake volcano in January 2011 was carried out using L-band Advanced Land Observation Satellite PALSAR data.

Coastal Subsidence is a global phenomenon that caused by both natural and anthropogenic activities. The impact of coastal subsidence can affect the monitoring of sea level rise by in-situ tide gauges. The Korean peninsula is one of such areas that reported considerably rapid sea level rise than the global average in the recent years. The objective of this study is to estimate the earth’s surface movements along the coastal areas of Pohang, South Korea. To achieve that, we acquired C-band Sentinel-1 data during 2016 – 2017. Time-series InSAR technique is applied to measure displacements at high-resolution with reduced decorrelation noise from interferograms. The results showed about 1.83 cm/year subsidence in the Pohang coastal area, where significant sea level rise is observed in this region. The results will provide further insight into the localized subsidence rates in the study sites and its impact on the sea level monitoring.

Monitoring land subsidence is important to prevent and manage disasters. The extent of land subsidence can be measured by various ways and one of them is PSI technique. Guatemala City has geologically weak structure and suffered from disasters such as sinkhole and landslide. The purpose of this study was to measure wide range of relative movement in Guatemala City and to analyze obtained values. C-band Sentinel-1 A/B data during November 2014 to March 2018 were used in this study. By gathering information from both ascending and descending paths, relative movements were decomposed from line of sight of each path to vertical and horizontal component. As a result 4 regions in Guatemala City showed significant subsidence patterns with average vertical movement going down with a scale of 14 mm/year.

Submarine groundwater discharge (SGD) is a widely recognized process that carries considerable amounts of groundwater and dissolved chemicals to the coastal ocean. Despite its importance, a lack of suitable tools to assess SGD's spatial and temporal variability has hampered a complete understanding of the process. Here we report, for the first time, use of an unmanned aerial vehicle (UAV or “drone”) to assess SGD variations. An octocopter UAV platform equipped with a thermal infrared (TIR) system was flown along a coastline on Jeju Island, Korea. The UAV clearly captured thermal signatures of SGD plumes and their dynamic temporal fluctuations modulated by tidal variations. Based on a plume area-SGD flux relation we developed by combining aerial and field data, we estimated that the SGD flux of the study site ranged from 33,000 to 54,000 m3 d−1. The drone approach enabled acquisition of time series plume imagery with easy control of spatial resolution, flexible field operations, and remote sensing of SGD at low cost compared to conventional aerial surveys. Combining the UAV-TIR images with on-site sampling enables one to determine fluxes of nutrients and other dissolved species. UAV-TIR mapping can thus serve as a powerful tool for study of SGD and other coastal processes.

Submarine groundwater discharge (SGD) is a global phenomenon that carries large volumes of groundwater and dissolved chemical species such as nutrient, metals, and organic compounds to coastal zones. We report the influence of SGD on the coastal waters of Jeju Island, Korea, using high-resolution aerial thermal infrared (TIR) mapping techniques and field investigations. An aircraft-based system was implemented using a cost-effective TIR camera for aerial TIR mapping. Ground-based calibrations and system integration with GPS/IMU (global positioning system/inertial measurement unit) were performed for the aerial systems. The aerial surveys showed distinct low-temperature signatures of SGD along the coasts of Jeju Island, revealing large groundwater inputs from the coastal aquifers to the ocean. Multiple aerial surveys over a range of seasons and tidal stages revealed that SGD rates dynamically affect the sea surface temperature (SST) of the coastal zone. The in-situ measurements supported that SGD has a substantial influence on the coastal water chemistry as well as SST. Our observations highlight the extent to which aerial-based TIR mapping can serve as a powerful tool for studying SGD and other coastal processes. Copyright © 2016 John Wiley & Sons, Ltd.

Doppler velocity can be derived by calculating Doppler shift anomalies between predicted and estimated Doppler centroids. The predicted Doppler centroid is calculated based on a geometric model of satellite assuming that the target is not moving. The estimated Doppler centroid can be directly extracted from the raw SAR signal data by applying the average cross-correlation coefficient method. It is known that wind-generated ocean waves can significantly contribute to Doppler velocity due to the correlation between orbital motions of the waves and (tilt and hydrodynamic) modulated radar cross sections, in addition to what sea surface current contributes. In this study, the characteristics of Doppler velocities under hurricane conditions were investigated using RADARSAT-1 ScanSAR raw data. Five different hurricanes (Hurricane Dean, Hurricane Ivan, Hurricane Kyle, Hurricane Lili, and Typhoon Xangsane) and sequential acquisitions of two hurricanes (Hurricane Kyle and Hurricane Lili) were selected to study the contribution of wind-induced waves to Doppler velocities and compared with in situ measurements of drifting buoys. The results show that hurricane-generated seas and associated winds and waves appear to be different from ordinary sea state. This leads to lower estimates of Doppler velocities than expected and much closer to sea surface current velocities.

Change detection using synthetic aperture radar (SAR) data is useful in emergency situations and unfavorable weather conditions. In this letter, change detection using multitemporal polarimetric Uninhabited Aerial Vehicle SAR data is investigated in an urban environment. The most robust polarimetric parameters are determined, and change detection techniques using a maximum likelihood ratio and a hyperbolic tangent model function are applied to the selected parameter. The model function was introduced to quantify the change characteristics and to rule out seasonal changes or those related to mobile features, and thus to only detect durable and permanent changes in urban environments. A comparison of results with historical Google Earth images showed a good level of agreement. Fitting of the hyperbolic tangent function to the multitemporal polarimetric parameters significantly reduces the false detection rate and indicates whether a building was constructed or destroyed, as well as when the detected changes occurred.

Backscattering coefficients of Sentinel-1 synthetic aperture radar (SAR) data of drifting multi-year sea ice in the western Beaufort Sea during the transition period between the end of melting and onset of freeze-up are analyzed, in terms of the incidence angle dependence and temporal variation. The mobile sea ice surface is tracked down in a 1 km by 1 km region centered at a GPS tracker, which was installed during a field campaign in August 2019. A total of 24 Sentinel-1 images spanning 17 days are used and the incidence angle dependence in HH- and HV-polarization are −0.24 dB/deg and −0.10 dB/deg, respectively. Hummocks and recently frozen melt ponds seem to cause the mixture behavior of surface and volume scattering. The normalized backscattering coefficients in HH polarization gradually increased in time at a rate of 0.15 dB/day, whereas the HV-polarization was relatively flat. The air temperature from the ERA5 hourly reanalysis data has a strong negative relation with the increasing trend of the normalized backscattering coefficients in HH-polarization. The result of this study is expected to complement other previous studies which focused on winter or summer seasons in other regions of the Arctic Ocean.

Ice rumples are locally-grounded features of flowing ice shelves, elevated tens of meters above the surrounding surface. These features may significantly impact the dynamics of ice-shelf grounding lines, which are strongly related to shelf stability. In this study, we used TanDEM-X data to construct high-resolution DEMs of the Thwaites ice shelf in West Antarctica from 2011 to 2013. We also generated surface deformation maps which allowed us to detect and monitor the elevation changes of an ice rumple that appeared sometime between the observations of a grounding line of the Thwaites glacier using Double-Differential Interferometric SAR (DDInSAR) in 1996 and 2011. The observed degradation of the ice rumple during 2011–2013 may be related to a loss of contact with the underlying bathymetry caused by the thinning of the ice shelf. We subsequently used a viscoelastic deformation model with a finite spherical pressure source to reproduce the surface expression of the ice rumple. Global optimization allowed us to fit the model to the observed deformation map, producing reasonable estimates of the ice thickness at the center of the pressure source. Our conclusion is that combining the use of multiple high-resolution DEMs and the simple viscoelastic deformation model is feasible for observing and understanding the transient nature of small ice rumples, with implications for monitoring ice shelf stability.

In recent years, subsidence due to excessive groundwater withdrawal is a major problem in the Kathmandu Basin. In addition, on 25 April 2015, the basin experienced large crustal displacements caused by Mw 7.8 Gorkha earthquake. Consequently, the effects of co-seismic deformation could affect the temporal and spatial nature of anthropogenic subsidence in the basin. In this study, we applied Small Baseline subset (SBAS)-DinSAR technique to estimate the spatio-temporal displacements in the basin, before and after the earthquake. 16 ALOS-1 L-band and 20 Sentinel-1 C-band SAR data are used for measuring pre- and post-seismic subsidence velocity, respectively. We found that the subsidence velocity in the central part of the basin is increased after the mainshock.

Continuous monitoring of topographic heights and changes in tidal flats is challenging, as it is generally difficult to observe topographic changes from on-site measurements or remote sensing techniques with high resolution and high accuracy. In this regard, an interferometric synthetic aperture radar (In-SAR) can be an effective tool to generate precise digital elevation models (DEMs) and detect large-scale topographic changes. Nevertheless, utilizing the In-SAR to detect topographic changes in tidal flats is not practical because the average slope of tidal flats is usually less than 5°, and the overall spatial and temporal variations of height are not significant. Therefore, the accuracy of In-SAR DEMs must be high to detect meaningful topographic changes. In order to minimize the error of In-SAR DEMs, height of ambiguity and random phase deviation of interferograms should be taken into account. These two factors are related to incidence angle and baseline. We simulated topographic error levels in tidal flats for a single-pass In-SAR system such as TanDEM-X. Phase error of interferograms was derived using the relationship between In-SAR coherence and the probability density function of phase deviation. Signal-to-noise ratio and geometric decorrelation were formulated by the function of baseline, incidence angle, and surface slope. The simulation results show that the height error of the DEM was minimized to lower than 15 cm when the baseline was 1500 m with an incidence angle of 29° in the TanDEM-X system. Finally, the validation of simulation results was carried out by comparing them with TanDEM-X DEM accuracies in tidal flats.

Oceanic internal waves are known to be important to the understanding of underwater acoustics, marine biogeochemistry, submarine navigation and engineering, and the Earth’s climate. In spite of the importance and increased knowledge of their ubiquity, the wave generation is still poorly understood in most parts of the world’s oceans. Here, we use satellite synthetic aperture radar images, in-situ observations, and numerical models to (1) show that wave energy (having relatively high amplitude) radiates from a shallow sill in the East China Sea in all directions, but with a significant time lag dependent on background conditions, (2) reveal that wave fronts are locally formed with often favorable conditions for re-initiation, and (3) demonstrate the resulting variety of wave patterns. These findings would be the case for any broad shelf having shallow sills with time-varying conditions, and therefore have significant implications on the redistribution of energy and materials in the global as well as regional ocean.

Definite surface topography of ice provides fundamental information for most glaciologists to study climate change. However, the topography at the marginal region of ice sheets exhibits noticeable dynamical changes from fast flow velocity and large thinning rates; thus, it is difficult to determine instantaneous topography. In this study, the surface topography of the marginal region of Thwaites Glacier in the Amundsen Sector of West Antarctica, where ice melting and thinning are prevailing, is extracted using TanDEM-X interferometry in combination with data from the near-coincident CryoSat-2 radar altimeter. The absolute height offset, which has been a persistent problem in applying the interferometry technique for generating DEMs, is determined by linear least-squares fitting between the uncorrected TanDEM-X heights and reliable reference heights from CryoSat-2. The reliable heights are rigorously selected at locations of high normalized cross-correlation and low RMS heights between segments of data points. The generated digital elevation model with the resolved absolute height offset is assessed with airborne laser altimeter data from the Operation IceBridge that were acquired five months after TanDEM-X and show high correlation with biases of 3.19 m and −4.31 m at the grounding zone and over the ice sheet surface, respectively. For practical application of the generated DEM, grounding line estimation assuming hydrostatic equilibrium was carried out, and the feasibility was seen through comparison with the previous grounding line. Finally, it is expected that the combination of interferometry and altimetery with similar datasets can be applied at regions even with a lack of ground control points.

Oh, J.H.; Kim, D.-J., and Lee, H.S., 2019. Topographic information extraction of tidal flats using remote multi-sensory data. In: Lee, J.L.; Yoon, J.-S.; Cho, W.C.; Muin, M., and Lee, J. (eds.), The 3rd International Water Safety Symposium. Journal of Coastal Research, Special Issue, No. 91, pp. 371-375. Coconut Creek (Florida), ISSN 0749-0208.Sediment from rivers has created a vast tidal flat over the west coast of Korea. The topography of the area is critical to the fishing industry and pollution purification, and it is a dangerous area causing fatal accidents due to the depth and steep slope with the tidal flow. Topographic information of the area needs to be obtained and monitored periodically for rapid changes due to periodic tides; however terrestrial surveying of the area is limited by poor accessibility. For this reason, it would be better to use remote sensors, such as satellite or aerial sensors, which differ in terms of spectral bands, spatial resolution, and coverage. This study integrated data from the three platforms, processed the data through synthetic aperture radar (SAR) interferometry and stereo aerial photogrammetry to generate separate Digital Elevation Models (DEMs), and then fused these into a final product using three-dimensional (3D) point cloud matching. The product was used for automatic extraction of tidal flat channel information by analyzing tidal flat topography and deriving channel depth and width.

This study aims to monitor the deformation of bridges, namely in the form of long-term deflection and thermal dilation, using multi-temporal interferometric synthetic aperture radar (InSAR) observations. To precisely estimate the vertical and longitudinal displacements, we used the InSAR time-series technique with multi-track stacks of Sentinel-1 SAR dataset and a single-track stack of COSMO-SkyMed SAR data over two extradosed bridge cases; Kimdaejung and Muyoung bridges between 2013 and 2017. The vertical and longitudinal displacements are estimated using multi-track Sentinel-1 SAR data and orientation angle of bridges, and we converted the displacements into thermal dilation and long-term vertical deflection. From COSMO-SkyMed data, we calculated the horizontal thermal dilation and long-term vertical deflection assuming that they dominantly contribute to the horizontal and vertical displacements, respectively. This assumption appeared reasonable based on the comparison with calculations from Sentinel-1 data. The deflection patterns exhibit downward movements at the mid-spans between towers. The results reveal that both bridges have been suffering long-term deflection over the observation period. Thus, this study verifies the potential to monitor the long-term deflection and implies that the bridges need to be monitored periodically.

Vertical land motion at tide gauges influences sea level rise acceleration; this must be addressed for interpreting reliable sea level projections. In recent years, tide gauge records for the Eastern coast of Korea have revealed rapid increases in sea level rise compared with the global mean. Pohang Tide Gauge Station has shown a +3.1 cm/year sea level rise since 2013. This study aims to estimate the vertical land motion that influences relative sea level rise observations at Pohang by applying a multi-track Persistent Scatter Interferometric Synthetic Aperture Radar (PS-InSAR) time-series analysis to Sentinel-1 SAR data acquired during 2015–2017. The results, which were obtained at a high spatial resolution (10 m), indicate vertical ground motion of −2.55 cm/year at the Pohang Tide Gauge Station; this was validated by data from a collocated global positioning system (GPS) station. The subtraction of InSAR-derived subsidence rates from sea level rise at the Pohang Tide Gauge Station is 6 mm/year; thus, vertical land motion significantly dominates the sea level acceleration. Natural hazards related to the sea level rise are primarily assessed by relative sea level changes obtained from tide gauges; therefore, tide gauge records should be reviewed for rapid vertical land motion along the vulnerable coastal areas.

Detection of changes induced by major events such as earthquakes, flooding and volcanic eruptions from interferometric SAR data is difficult due to the coupled effects with temporal decorrelation caused by natural phenomena such as rain, snow, wind and seasonal changes. In this study, we aim to separate the decorrelation caused by natural changes from the one caused by the major event by analyzing the coherence behavior using a temporal decorrelation model. We formulated the temporal decorrelation model that accounts for the random motion and dielectric changes. By applying the model into the multi-temporal coherence before the event, we extracted the temporal decorrelation components induced by natural phenomena. Based on the extracted parameters, their decorrelation probabilities related to natural changes were estimated in canopy and ground. The model parameters are also extracted from the interferometric SAR data acquired across the event. We compared probabilities between the natural phenomena and the certain event in order to assign the changed regions. Pixels with cumulative probabilities greater than 80% are selected as changed due to the event. A case study for detecting volcanic ash during the eruption of the Shinmoedake volcano in January 2011 was carried out using L-band Advanced Land Observation Satellite (ALOS) PALSAR data.

Grounding line estimation has substantial importance of determining vulnerability of ice mass in the Polar Regions. In this study, high resolution digital elevation model is utilized to estimate and update grounding line of Thwaites Glacier in West Antarctica in 2011. Estimation is done by comparing the calculated bottom topography of ice with the bathymetry model from Operation IceBridge. The bottom topography is computed by subtracting ice thickness which is also estimated from surface elevation assuming hydrostatic equilibrium. The updated grounding line infers its retreat of up to 5km inland when compared with the grounding line in 2009. The retreat is conspicuous over the bathymetric trough. Conceivable error sources are still present such as ice column density, bathymetry accuracy and firn height. Nonetheless, high resolution digital elevation model shows high practicability of estimating grounding line.

In this paper, we have investigated the relationship between the depolarization effects and the wintertime sea ice thickness in the landfast ice region where smooth thick first-year ice (FYI) and deformed old ice coexisted by using C- and X-band spaceborne polarimetric synthetic aperture radar (SAR) data (RADARSAT-2 and TerraSAR-X). We have found a strong correlation between the in situ sea ice thickness and the SAR-derived depolarization factors (copolarized correlation and cross-polarized ratio). The observed relationships have demonstrated not only a categorical difference between FYI and multiyear ice (MYI) but also a one-to-one continuity in the scatter plots, rather than being clustered. It clearly shows that the observed correlations are not merely from the categorical difference in scattering mechanism between FYI and MYI and that there might exist a one-to-one relationship between thickness and depolarization factors at least in our deformed ice case. This suggests that depolarization factors could be effective SAR parameters in the estimation of wintertime sea ice thickness. Numerical model simulations explained some portions of the correlation by employing multiple scattering on the sea ice surface and volume scattering within the low-density subsurface layer.

Melt ponds, a common feature on Arctic sea ice, absorb most of the incoming solar radiation and have a large effect on the melting rate of sea ice, which significantly influences climate change. Therefore, it is very important to monitor melt ponds in order to better understand the sea ice-climate interaction. In this study, melt pond retrieval models were developed using the TerraSAR-X dual-polarization synthetic aperture radar (SAR) data with mid-incidence angle obtained in a summer multiyear sea ice area in the Chukchi Sea, the Western Arctic, based on two rule-based machine learning approaches—decision trees (DT) and random forest (RF)—in order to derive melt pond statistics at high spatial resolution and to identify key polarimetric parameters for melt pond detection. Melt ponds, sea ice and open water were delineated from the airborne SAR images (0.3-m resolution), which were used as a reference dataset. A total of eight polarimetric parameters (HH and VV backscattering coefficients, co-polarization ratio, co-polarization phase difference, co-polarization correlation coefficient, alpha angle, entropy and anisotropy) were derived from the TerraSAR-X dual-polarization data and then used as input variables for the machine learning models. The DT and RF models could not effectively discriminate melt ponds from open water when using only the polarimetric parameters. This is because melt ponds showed similar polarimetric signatures to open water. The average and standard deviation of the polarimetric parameters based on a 15 × 15 pixel window were supplemented to the input variables in order to consider the difference between the spatial texture of melt ponds and open water. Both the DT and RF models using the polarimetric parameters and their texture features produced improved performance for the retrieval of melt ponds, and RF was superior to DT. The HH backscattering coefficient was identified as the variable contributing the most, and its spatial standard deviation was the next most contributing one to the classification of open water, sea ice and melt ponds in the RF model. The average of the co-polarization phase difference and the alpha angle in a mid-incidence angle were also identified as the important variables in the RF model. The melt pond fraction and sea ice concentration retrieved from the RF-derived melt pond map showed root mean square deviations of 2.4% and 4.9%, respectively, compared to those from the reference melt pond maps. This indicates that there is potential to accurately monitor melt ponds on multiyear sea ice in the summer season at a local scale using high-resolution dual-polarization SAR data.

Morphologic and topographic changes of tidal flats can be key indicators for monitoring environmental changes and sea level rise. Recently, a number of studies have been performed to estimate temporal topographic changes in tidal flats based on the waterline method using a number of remote sensing data that were acquired at different tidal heights. However, the effect of seasonal variation has not been taken into consideration, nor been understood so far. In this study, 18 scenes of Landsat TM and ETM+ data, covering the period 2003–2004, and corresponding tidal gauge observation data, were used to estimate seasonal topographic variations in two major tidal flats in Gomso and Hampyeong Bay in the southern part of the west sea of South Korea, using the waterline method. Our results showed that the summer deposition was dominant in Gomso Bay with overall average seasonal topographic increase of approximately 18.6 cm. In contrast, Hampyeong Bay showed more dominant summer erosion with overall average seasonal topographic subsidence of about 5.0 cm. In addition, the net overall sedimentation budget was estimated as 6,308,047 m 3 and -2,210,986 m 3 in Gomso and Hampyeong Bay, respectively. The results also indicates that although both bays of Gomso and Hampyeong are classified as semi-enclosed tidal flat, the sedimentary facies caused by formation geometry and sediment type led to different topographic changes. The results demonstrate that the amount of seasonal topographic variations is not negligible and are expected to improve the accuracy of topographic change derived by the waterline method.