L-band Interferometric Synthetic Aperture Radar Research Articles

The Prospect of Typhoon Landfall Observation Utilizing L-band InSAR

The cyclonic phenomenon, particularly typhoons within the 100° E to 180° E longitude region, bring significant devastation through intense rainfall and strong winds, causing secondary effects like landslides and floods upon landfall. This results in infrastructural damage and loss of life, ranking storm-related disasters, including typhoons, second only to earthquakes in casualties, emphasizing the need for a robust observation system. While higher income countries benefit from established radar and meteorological stations for continuous monitoring, this study highlights the critical necessity for an additional monitoring system in lower income countries. We investigate the applicability of Interferometric Synthetic Aperture Radar (InSAR) during typhoon landfall, particularly in densely vegetated areas where longer wavelengths like L-band (for instance, the Advanced Land Observing Satellite (ALOS/PALSAR) and ALOS-2/PALSAR-2) are suitable. By analyzing typhoon occurrences and confirming landfall using weather radar from 2007 to 2011 (ALOS/PALSAR observation period) and 2014 to 2023 (ALOS-2/PALSAR-2 observation period), our study successfully identifies the landfall of the Jebi typhoon on September 4, 2018. InSAR processing reveals significant phase gradients linked to dense water vapor during landfall, corroborated by comparisons with sea-level Zenith Wet Delay (ZWD) from Global Navigation Satellite System (GNSS) data and hourly precipitation rates from the Automated Meteorological Data Acquisition System (AMeDAS). The concurrence of InSAR results with ZWD values and rainfall data from meteorological stations validates the potential of L-band InSAR in observing typhoon landfalls.

Heavy Rain Episodes Identified by L-band InSAR and Limitations of Split-Spectrum Method in Indonesia

Located in a tropical area with abundant precipitation, Indonesia is highly prone to heavy rain hazards, in particular landslides and floods. Thus, rainfall observation is vital. Nonetheless, the topography, the fund availability, as well as the archipelagic state of Indonesia may raise difficulties for in-situ observation, such as rain gauge and weather radar. Currently, the advance of radiometer satellites, such as the Global Precipitation Mission delivers rain estimation and has proven to show good association with in-situ observation on a monthly basis, not daily over the Indonesia area. Therefore, it is vital to have additional measurement methods. For the first time, we apply L-band Interferometric Synthetic Aperture Radar (InSAR) to observe heavy rain in Indonesia. From our three study cases, we successfully identified localized anomalies due to the dense water vapor during heavy rain in the InSAR images. The localized anomalies vary from 10.9 cm in West Java, 7.8 cm in East Kalimantan, and 7.7 cm in West Kalimantan. Furthermore, we utilize the split-spectrum method for our InSAR result; the high-water vapor occurrence in the troposphere associated with heavy rain should be identified in the non-dispersive term. Nevertheless, due to long temporal separation and thinner bandwidth, the split-spectrum method results display unsatisfactory results. We conclude that, while InSAR has the ability to identify heavy rain, having SSM to distinguish between non-dispersive and dispersive phases is not currently practical in Indonesia.

Assessment of L-band InSAR snow estimation techniques over a shallow, heterogeneous prairie snowpack

Snow water equivalent (SWE) is a critical input for weather, climate, and water resource management models at local to global scales. Despite its importance, global SWE measurements that are accurate, consistent, and at sufficiently high spatiotemporal resolutions are not currently available. L-band interferometric synthetic aperture radar (InSAR) techniques have been used to measure SWE at local to regional scales, and two upcoming L-band SAR satellite missions have renewed interest in these techniques to provide regular SWE measurements at the global scale. However, previous research demonstrating the capabilities of L-band InSAR-SWE measurement has been limited to mountain or tundra snowpack regimes. Here we examine the feasibility of applying the same techniques over a prairie snowpack, which are typically characterized by shallow snow depths (mean snow depth of 0.22 m in this study), exposed agricultural vegetation, and high spatial variability over short distances. Our study area in central Montana, USA (47.060, -109.951) was a validation site for NASA SnowEx 2021, as part of the UAVSAR snow timeseries. Airborne L-band SAR imagery was acquired by the UAVSAR platform while concurrent snow measurements were collected using uncrewed aerial vehicle (UAV)-based LiDAR, UAV-based photogrammetry, and ground-based manual techniques. This validation dataset enables an investigation of the effects of sub-pixel snow cover heterogeneity and exposed agricultural vegetation stubble on SAR data and the resulting SWE estimations. Results based on repeated application of the Kolmogorov–Smirnov test show that UAVSAR VV phase change is sensitive to differences in snow cover but relatively unaffected by differences in agricultural stubble height. However, we did not find similarly definitive results when we used the same phase change data to estimate SWE. Although broad spatial patterns were similar in both LiDAR-derived and InSAR-derived SWE estimates, considerable differences in the two estimates were apparent in areas with large sub-pixel snow depth variability. Our results indicate that additional work is necessary to derive accurate SWE estimates in prairie environments. Regular measurements from L-band SAR satellites will provide an excellent opportunity to refine InSAR-based snow estimation techniques over shallow, heterogeneous snowpacks.

Estimating snow accumulation and ablation with L-band interferometric synthetic aperture radar (InSAR)

Abstract. Snow is a critical water resource for the western United States and many regions across the globe. However, our ability to accurately measure and monitor changes in snow mass from satellite remote sensing, specifically its water equivalent, remains a challenge. To confront these challenges, NASA initiated the SnowEx program, a multiyear effort to address knowledge gaps in snow remote sensing. During SnowEx 2020, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) team acquired an L-band interferometric synthetic aperture radar (InSAR) data time series to evaluate the capabilities and limitations of repeat-pass L-band InSAR for tracking changes in snow water equivalent (SWE). The goal was to develop a more comprehensive understanding of where and when L-band InSAR can provide SWE change estimates, allowing the snow community to leverage the upcoming NASA–ISRO (NASA–Indian Space Research Organization) SAR (NISAR) mission. Our study analyzed three InSAR image pairs from the Jemez Mountains, NM, between 12 and 26 February 2020. We developed a snow-focused multi-sensor method that uses UAVSAR InSAR data synergistically with optical fractional snow-covered area (fSCA) information. Combining these two remote sensing datasets allows for atmospheric correction and delineation of snow-covered pixels within the radar swath. For all InSAR pairs, we converted phase change values to SWE change estimates between the three acquisition dates. We then evaluated InSAR-derived retrievals using a combination of fSCA, snow pits, meteorological station data, in situ snow depth sensors, and ground-penetrating radar (GPR). The results of this study show that repeat-pass L-band InSAR is effective for estimating both snow accumulation and ablation with the proper measurement timing, reference phase, and snowpack conditions.

Error Evaluation of L-Band InSAR Precipitable Water Vapor Measurements by Comparison with GNSS Observations in Japan

Interferometric synthetic aperture radar (InSAR) enables us to obtain precipitable water vapor (PWV) maps with high spatial resolution through the phase difference caused by refraction in the atmosphere. Although previous studies have evaluated the error level of InSARPWV observations, they validated it only with C-band InSARPWV observations. Since ionospheric disturbance seriously contaminates the InSAR phase in the case of the lower-frequency SAR system, it is necessary for a PWV error level evaluation correcting the ionospheric effect appropriately if we use lower-frequency SAR systems, such as the Advanced Land Observing Satellite-2 (ALOS-2). In this paper, we evaluated the error level of the L-band InSARPWV observation obtained from ALOS-2 data covering four areas in Japan. We compared the InSAR observations with global navigation satellite system (GNSS) atmospheric observations and estimated the L-band InSARPWV error value by utilizing the error propagation theory. As a result, the L-band InSARPWV absolute error reached 2.83 mm, which was comparable to traditional PWV observations. Moreover, we investigated the impacts of the seasonality, the interferometric coherence, and the height dependence on the PWV observation accuracy in InSAR.

P-Band InSAR for Geohazard Detection over Forested Terrains: Preliminary Results

Decorrelation of X, C, and L-band InSAR (Interferometric Synthetic Aperture Radar) over densely vegetated regions is a common obstacle for detecting ground deformation beneath forest canopies. Using long-wavelength P-band SAR sensors (wavelength of 69.72 cm), which can penetrate through dense forests and collect relatively consistent signals from ground surface, is one potential solution. Here, we experimented using the NASA JPL (Jet Propulsion Laboratory)’s P-band AirMOSS (Airborne Microwave Observatory of Subcanopy and Subsurface) radar system to collect repeat-pass P-band SAR data over densely vegetated regions in Oregon and California (USA), and generated by far the first P-band InSAR results to test the capability of P-band InSAR for geohazard detection over forested terrains. Our results show that the AirMOSS P-band InSAR could retain coherence two times as high as the L-band satellite ALOS-2 (Advanced Land Observing Satellite-2) data, and was significantly more effective in discovering localized geohazards that were unseen by the ALOS-2 interferograms over densely vegetated areas. Our results suggest that the airborne P-band InSAR could be a revolutionary tool for studying geohazards under dense forest canopies.

Tropospheric dispersive phase anomalies during heavy rain detected by L-band InSAR and their interpretation

The split-spectrum method (SSM) can largely isolate and correct for the ionospheric contribution in the L-band interferometric synthetic aperture radar (InSAR). The standard SSM is performed on the assumption of only the first-order ionospheric dispersive effect, which is proportional to the total electron content (TEC). It is also known that during extreme atmospheric events, either originated from the ionosphere or in the troposphere, other dispersive effects do exist and potentially provide new insights into the dynamics of the atmosphere, but there have been few detection reports of such signals by InSAR. We apply L-band InSAR into heavy rain cases and examine the applicability and limitation of the standard SSM. Since no events such as earthquakes to cause surface deformation took place, the non-dispersive component is apparently attributable to the large amount of water vapor associated with heavy rain, whereas there are spotty anomalies in the dispersive component that are closely correlated with the heavy rain area. The ionosonde and Global Navigation Satellite System (GNSS) rate of total electron content index (ROTI) map both show little anomalies during the heavy rain, which suggests few ionospheric disturbances. Therefore, we interpret that the spotty anomalies in the dispersive component of the standard SSM during heavy rain are originated in the troposphere. While we can consider two physical mechanisms, one is runaway electron avalanche and the other is the dispersive effect due to rain, comparison with the observations from the ground-based lightning detection network and rain gauge data, we conclude that the rain dispersive effect is spatiotemporally favorable. We further propose a formulation to examine if another dispersive phase than the first-order TEC effect is present and apply it to the heavy rain cases as well as two extreme ionospheric sporadic-E events. Our formulation successfully isolates the presence of another dispersive phase during heavy rain that is in positive correlation with the local rain rate. In comparison with other dispersive phases during Sporadic-E episodes, the dispersive heavy rain phases seem to have the same order of magnitude with the ionospheric higher order effects.

Ground Displacement in East Azerbaijan Province, Iran, Revealed by L-band and C-band InSAR Analyses.

Iran, as a semi-arid and arid country, has a water challenge in the recent decades and underground water extraction has been increased because of improper developments in the agricultural sector. Thus, detection and measurement of ground subsidence in major plains is of great importance for hazard mitigation purposes. In this study, we carried out a time series small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) analysis of 15 L-band PALSAR-2 images acquired from ascending orbits of the ALOS-2 satellite between 2015 and 2020 to investigate long-term ground displacements in East Azerbaijan Province, Iran. We found that two major parts of the study area (Tabriz and Shabestar plains) are subsiding, where the mean and maximum vertical subsidence rates are −10 and −98 mm/year, respectively. The results revealed that the visible subsidence patterns in the study area are associated with either anthropogenic activities (e.g., underground water usage) or presence of compressible soils along the Tabriz–Shabestar and Tabriz–Azarshahr railways. This implies that infrastructure such as railways and roads is vulnerable if progressive ground subsidence takes over the whole area. The SBAS results deduced from L-band PALSAR-2 data were validated with field observations and compared with C-band Sentinel-1 results for the same period. The C-band Sentinel-1 results showed good agreement with the L-band PALSAR-2 dataset, in which the mean and maximum vertical subsidence rates are −13 and −120 mm/year, respectively. For better visualization of the results, the SBAS InSAR velocity map was down-sampled and principal component analysis (PCA) was performed on ~3600 randomly selected time series of the study area, and the results are presented by two principal components (PC1 and PC2).

Coseismic Slip Distribution of the 2019 Mw 7.5 New Ireland Earthquake from the Integration of Multiple Remote Sensing Techniques

The 2019 Mw 7.5 New Ireland earthquake occurred at an equatorial area where the dense vegetation prevents remote sensing techniques such as C- or X-band interferometric synthetic aperture radar (SAR) from acquiring coherent phase measurements. Therefore, in this paper, multiple remote sensing techniques including the L-band interferometric SAR, the range and azimuth offset tracking of SAR intensities, and the offset tracking of optical images were employed to map its co-seismic deformation field and to determine the slip distribution. The surface rupture was clearly and consistently captured by all offset observations, with the ground fault trace striking at an angel of 315° and extending over 10 km. An iterative weighting strategy based on the residual root mean square of inversions using individual datasets was developed to determine the relative weight of each dataset, allowing for the joint inversion of the fault geometry, the refinement of the dip angle, and the determination of the best fitting slip distribution. The resultant model indicates a nearly left-lateral strike-slip motion on the Weitin fault that ruptured to the surface with a maximum slip of 6.10 m, occurring at a depth of ~10 km, and a geodetic moment release of 1.03 × 1020 Nm, corresponding to a magnitude of Mw 7.31. The distribution of aftershocks shows about 70% of aftershocks were located in the area with increased Coulomb failure stress and few aftershocks in the subduction zone to the south of the Weitin fault were triggered by this event.

The Total Electron Content From InSAR and GNSS: A Midlatitude Study

The total electron content (TEC) measured from the interferometric synthetic aperture radar (InSAR) and from a dense network of global navigation satellite system (GNSS) receivers are used to assess the capability of InSAR to retrieve ionospheric information, when the tropospheric contribution to the interferometric phase is reasonably negligible. With this aim, we select three night-time case studies over Italy and investigate the correlation between TEC from advanced land observing satellite-phased array type L-band synthetic aperture radar (ALOS-PALSAR) and from the Rete Integrata Nazionale GPS (RING) network, the latter considered as the reference true ionospheric TEC. To retrieve the TEC variability from ALOS-PALSAR, we first investigate the correlation between the integral of the azimuth shifts and the interferometric phase in the absence of ground motions (e.g., earthquakes) and/or heavy rain events. If correlation exists (as in two out of three case studies under investigation), we can assume the tropospheric contribution to the interferometric phase as negligible and the TEC variability from L-band InSAR can be retrieved. For these two case studies, the comparison between the TEC from the InSAR images and from the RING network is quite encouraging as the correlation coefficient is R ∼ 0.67 in the first case and R ∼ 0.83 in the second case. This result highlights the potential to combine InSAR and GNSS experimental measurements to investigate small-scale spatial variability of TEC, in particular over regions scarcely covered by ground-based GNSS receivers.

An L-band interferometric synthetic aperture radar study on the Ganos section of the north Anatolian fault zone between 2007 and 2011: Evidence for along strike segmentation and creep in a shallow fault patch

We utilize L-band interferometric synthetic aperture radar (InSAR) data in this study to retrieve a ground velocity map for the near field of the Ganos section of the north Anatolian fault (NAF) zone. The segmentation and creep distribution of this section, which last ruptured in 1912 to generate a moment magnitude (Mw)7.3 earthquake, remains incompletely understood. Because InSAR processing removes the mean orbital plane, we do not investigate large scale displacements due to regional tectonics in this study as these can be determined using global positioning system (GPS) data, instead concentrating on the close-to-the-fault displacement field. Our aim is to determine whether, or not, it is possible to retrieve robust near field velocity maps from stacking L-band interferograms, combining both single and dual polarization SAR data. In addition, we discuss whether a crustal velocity map can be used to complement GPS observations in an attempt to discriminate the present-day surface displacement of the Ganos fault (GF) across multiple segments. Finally, we characterize the spatial distribution of creep on shallow patches along multiple along-strike segments at shallow depths. Our results suggest the presence of fault segmentation along strike as well as creep on the shallow part of the fault (i.e. the existence of a shallow creeping patch) or the presence of a smoother section on the fault plane. Data imply a heterogeneous fault plane with more complex mechanics than previously thought. Because this study improves our knowledge of the mechanisms underlying the GF, our results have implications for local seismic hazard assessment.

Spatio-temporal analysis of ground subsidence due to underground coal mining in Huainan coalfield, China

Underground coal mining produces ground subsidence that adversely impacts the environment. Huainan coalfield, one of the largest coalfields in East China, experiences fast ground subsidence that restricts agricultural, urban and rural developments in this region. We used stacking and the small baseline subset interferometric synthetic aperture radar (InSAR) techniques to perform a spatio-temporal analysis of ground subsidence in Huainan coalfield caused by underground coal mining activities. Fourteen L-band ALOS PALSAR images acquired during 2007–2010 were used to retrieve high-resolution and high-precision time series of ground deformation and the mean deformation rate. The results show that subsidence with a rate of up to 15 cm/year is widely spread over the studied area in response to mining operation. In regions where mining lasted for many years, ground subsidence continues at a steady rate during the entire observation period. In regions of recent (i.e., since 2007–2009) mining activities, ground subsidence is rapidly accelerating. Accumulated subsidence already caused submerging of the agriculture lands, limiting further mining activities and farming. The InSAR results are consistent with the ground leveling measurements and observations. It is confirmed that the L-band InSAR can be successfully utilized for mapping long-term subsidence in Huainan coalfield in China caused by underground coal mining activities.

Quantification of L-band InSAR coherence over volcanic areas using LiDAR and in situ measurements

Interferometric Synthetic Aperture Radar (InSAR) is a powerful tool to monitor large-scale ground deformation at active volcanoes. However, vegetation and pyroclastic deposits degrade the radar coherence and therefore the measurement of 3-D surface displacements. In this article, we explore the complementarity between ALOS–PALSAR coherence images, airborne LiDAR data and in situ measurements acquired over the Piton de La Fournaise volcano (Reunion Island, France) to determine the sources of errors that may affect repeat-pass InSAR measurements. We investigate three types of surfaces: terrains covered with vegetation, lava flows (a′a, pahoehoe or slabby pahoehoe lava flows) and pyroclastic deposits (lapilli). To explain the loss of coherence observed over the Dolomieu crater between 2008 and 2009, we first use laser altimetry data to map topographic variations. The LiDAR intensity, which depends on surface reflectance, also provides ancillary information about the potential sources of coherence loss. In addition, surface roughness and rock dielectric properties of each terrain have been determined in situ to better understand how electromagnetic waves interact with such media: rough and porous surfaces, such as the a′a lava flows, produce a higher coherence loss than smoother surfaces, such as the pahoehoe lava flows. Variations in dielectric properties suggest a higher penetration depth in pyroclasts than in lava flows at L-band frequency. Decorrelation over the lapilli is hence mainly caused by volumetric effects. Finally, a map of LAI (Leaf Area Index) produced using SPOT 5 imagery allows us to quantify the effect of vegetation density: radar coherence is negatively correlated with LAI and is unreliable for values higher than 7.5.

Landslide observation and volume estimation in central Georgia based on L-band InSAR

Abstract. The republic of Georgia is a mountainous and tectonically active area that is vulnerable to landslides. Because landslides are one of the most devastating natural hazards, their detection and monitoring is of great importance. In this study we report on a previously unknown landslide in central Georgia near the town of Sachkhere. We used a set of Advanced Land Observation Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data to generate displacement maps using interferometric synthetic aperture radar (InSAR). We detected a sliding zone of dimensions 2 km north–south by 0.6 km east–west that threatens four villages. We estimated surface displacement of up to ∼30 cm/yr over the sliding body in the satellite line-of-sight (LOS) direction, with the largest displacement occurring after a local tectonic earthquake. We mapped the morphology of the landslide mass by aerial photography and field surveying. We found a complex set of interacting processes, including surface fracturing, shear and normal faults at both the headwall and the sides of the landslide, local landslide velocity changes, earthquake-induced velocity peaks, and loss in toe support due to mining activity. Important implications that are applicable elsewhere can be drawn from this study of coupled processes. We used inverse dislocation modelling to find a possible dislocation plane resembling the landslide basal décollement, and we used that plane to calculate the volume of the landslide. The results suggest a décollement at ∼120 m depth, dipping at ∼10° sub-parallel to the surface, which is indicative of a translational-type landslide.

Detection of vertical slope movement in highly vegetated tropical area of Gunung pass landslide, Malaysia, using L-band InSAR technique

Landslides are one of the most dangerous natural hazards in the world which have significant negative impact on so many lives and properties. Interferometric Synthetic Aperture Radar (InSAR) is an imaging technique for measuring changes in the detailed characteristics of the surface which is timely and cost effective. This research aimed to detect the landslide that occurred in Gunung pass area, Malaysia using InSAR generated from ALOSPALSAR repeat pass data. The signals information was converted into amplitude and phase for both scenes where the phases were used to construct the InSAR. Goldstein filter was used to reduce the phase noise and the results were used as an input for phase unwrapping. Using the unwrapped phase, the vertical displacement was measured and landslide was recognized. Results showed the efficiency of InSAR in detecting the movement of landslide in Gunung pass without the differential having to generate DInSAR. The results were validated using the observed reference point of the landslides and the root mean square error (RMSE) was 0.19. Furthermore, advance 3D processing was performed for measuring the volume of the landslides. The achievements of current research represented that PALSAR data yield excellent performance to generate the interferometric and landslide could be detected very precisely in highly vegetated tropical forest.

LIDAR-Aided SAR Interferometry Studies in Boreal Forest: Scattering Phase Center and Extinction Coefficient at X- and L-Band

Scattering phase center (SPC) location in boreal forests was studied in order to assist forest inventory with single- and quad-pol synthetic aperture radar (SAR) interferometry. Airborne X- and L-band interferometric SAR data collected by the DLR E-SAR instrument in southern Finland during the FINSAR campaign was used in the study. A simple Random Volume over Ground (RVoG) model was employed as the theoretical framework for inversion of forest parameters and interpretation of the obtained results. LIDAR measurements of the canopy height and terrain elevation were used as reference and auxiliary data. The RVoG model was found to satisfactorily explain the SPC location inside the canopy in boreal forests. We show that when using X-band, the height of the SPC is typically about 75% of the canopy height, as predicted by the RVoG model; however, the retrieved extinction was found to be rather low. The feasibility of highly accurate tree height inversion using single-polarization X-band interferometry (with RMSE approaching 1.5 m) is demonstrated using a digital terrain model. For this purpose, the traditional polarimetric interferometry SAR technique for phase center retrieval is modified to include a complementary LIDAR measured terrain model. At L-band, the phase center height was determined to be around 50% of the canopy height and even lower, indicating that the ground contribution is significant. Moreover, several simplified inversion approaches for tree height and extinction coefficient retrieval were considered based on several boundary cases of the RVoG model, describing the canopy frequently encountered in boreal forest environments. These analyses allowed developing a combined approach for simultaneous estimation of both forest height and extinction in the boreal zone when an accurate elevation model of the terrain is available.

The Use of C- and L-Band Repeat-Pass Interferometric SAR Coherence for Soil Moisture Change Detection in Vegetated Areas

Soil moisture estimation studies using Synthetic Aperture Radar (SAR) routinely utilise only the amplitude part of the received echo. In this study, repeat-pass C- and L-band interferometric SAR coherence from 2007 - 2009 was evaluated for the detection of surface soil moisture changes in the presence of vegetation using two different approaches. In the first analysis, the association between low coherence and large in situ soil moisture changes was investigated using 24 interferometric pairs and the decorrelation effects due to vegetation and weather were also assessed. Results reveal that, in very few cases soil moisture differences between acquisitions contributed to the signal decorrelation. For the majority of cases, particularly in C-band, the change in vegetation tended to be the predominant source of decorrelation, suppressing the influence of any soil moisture changes. The second analysis applied thresholds to both coherence and intensity data to determine if a coalesced coherence (�) and intensity change (�� 0 ) approach could improve detection of changes in measured soil moisture content. The aim was to test the usefulness of a � > 0.3 and �� 0 > 1.5 dB thresholding approach to separate the effects of a vegetation change and a soil moisture change on the SAR signal. Results suggest that the approach improves the reliability of the soil moisture change detection although clearly limits the use of available image pairs. These analyses demonstrate the increased information the coherence adds to SAR studies over agricultural areas.

Ionospheric Artifacts in Simultaneous L-Band InSAR and GPS Observations

Phase artifacts in interferometric synthetic aperture radar (InSAR) images frequently degrade the interpretability of the phase and correlation signatures of terrain. Often, these distortions are attributed to spatially variable ionospheric propagation delays at two different SAR acquisition times. We present here L-band InSAR data from Iceland, California, and Hawaii. The California and Hawaii interferograms show no significant ionospheric artifacts, while the Iceland interferogram shows a maximum misregistration of three pixels in the azimuth direction, which leads to severe phase decorrelation artifacts in the InSAR image. We relate the misregistration of complex pixels seen in the interferograms to the gradient of the ionospheric total electron content (TEC) observed by global positioning system (GPS) data and confirm that indeed the phase artifacts in the Iceland interferogram are due to dispersive ionospheric propagation rather than other decorrelation factors such as neutral atmospheric delays. We develop a method to measure the spatial TEC variation at synthetic aperture length scales using dual-frequency GPS carrier phase data. We solve for the GPS data ambiguities using a low-resolution ionosphere reference derived from either available ionospheric observations or the GPS carrier phase data themselves. GPS observations show directly the level of ionospheric variability, and the spatial TEC gradient as observed by GPS predicts the misregistration of complex pixels in interferograms in all three areas. This confirmation of the cause of the image artifacts suggests that they can be routinely corrected from the InSAR data alone, provided that the sensor measures the change in TEC along the radar swath.

Mapping arctic landfast ice extent using L-band synthetic aperture radar interferometry

In recent years methods have been developed to extract the seaward landfast ice edge from series of remote sensing images, with most of them relying on incoherent change detection in optical, infrared, or radar amplitude imagery. While such approaches provide valuable results, some still lack the required level of robustness and all lack the ability to fully automate the detection and mapping of landfast ice over large areas and long time spans. This paper introduces an alternative approach to mapping landfast ice extent that is based on coherent processing of interferometric L-band Synthetic Aperture Radar (SAR) data. The approach is based on a combined interpretation of interferometric phase pattern and interferometric coherence images to extract the extent and stability of landfast ice. Due to the low complexity of the base imagery used for landfast ice extraction, significant improvements in automation and reduction of required manual interactions by operators can be achieved. A performance analysis shows that L-band interferometric SAR (InSAR) data enable the mapping of landfast ice with high robustness and accuracy for a wide range of environmental conditions.

Monitoring snowpack evolution using interferometric synthetic aperture radar on the North Slope of Alaska, USA

This research investigates the use of Interferometric Synthetic Aperture Radar (InSAR) to generate a time-series of snow water equivalent (SWE) for dry snow within the Kuparuk watershed, North Slope, Alaska, during the winter of 1993/1994. Maps depicting relative change in phase and the theoretical relative change in SWE between satellite acquisitions are created for 3-day periods at the end of March 1994 using both ascending and descending ERS-1 overpasses. The theoretical coefficient relating relative change in phase and relative change in SWE for C-band is found to be at least twice as large as what is expected when using a simple single-layer snow model for this study area and time period. Without any direct measurements of SWE on the ground, station measurements of snow depth and hourly wind are linked to each 3-day relative change in phase map. Along with a qualitative assessment, quantitative measures of the rate and magnitude of phase change around these stations are directly compared to the hourly wind data for a given 3-day period. InSAR-derived maps acquired around a measured precipitation event show a considerable relationship to the predominant direction of strong winds over each 3-day period while maps acquired around no measureable precipitation depict much less correlation between phase change and predominant direction of strong winds. Despite limited ground measurements to infer snowpack conditions, these results show continued promise for the InSAR technique to measure changes in snowpack conditions (e.g. SWE) at much higher resolutions than manual sampling methods or passive microwave remote sensing. The extension of this technique to current L-band InSAR satellite platforms is also discussed.