Temporal Baseline Research Articles

Long Term Interferometric Temporal Coherence and DInSAR Phase in Northern Peatlands

Peatlands of northern temperate and cold climates are significant pools of stored carbon. Understanding seasonal dynamics of peatland surface height and volume, often referred to as mire breathing or oscillation, is the key to improve spatial models of material flow and gas exchange. The monitoring of this type of dynamics over large areas is only feasible by remote sensing instruments. The objective of this study is to examine the applicability of Sentinel-1 synthetic aperture radar interferometry (InSAR) to characterize seasonal dynamics of peatland surface height and water table (WT) over open raised bog areas in Endla mire complex in central Estonia, characteristic for northern temperate bogs. Our results show that InSAR temporal coherence, sufficient for differential InSAR (DInSAR), is preserved in the open bog over more than six months of temporal baseline. Moreover, the coherence which is lost in a dry summer, make a recovery in autumn correlate with WT dynamics. The relationship between the coherence from a single master image and the corresponding WT difference is described by the second degree polynomial regression model (Root Mean Squared Error RMSE = 0.041 for coherence magnitude). It is also demonstrated that DInSAR phase is connected to bog surface dynamics and reveals differences between bogs and for ecotopes within a bog. These findings suggest that InSAR long term temporal coherence could be used to describe seasonal bog WT dynamics and differentiate between mire types and ecotopes within a bog. Moreover, DInSAR analysis has the potential to characterize seasonal mire surface oscillation which may be important for assessing the capacity of water storage or restoration success in northern temperate bogs.

Decadal Variability Survey in MACSJ1149

Abstract We present a long temporal baseline variability survey in the Frontier Field MACSJ1149. In this study, we identify active galactic nuclei (AGNs) and other transient sources via their variability using over a decade of Hubble Space Telescope (HST) images for thousands of galaxies in the cluster region and detect significant variability in galaxies extending down to an apparent nuclear magnitude of m i < 26.5. Our analysis utilizes HST images obtained in six different wavelengths from 435 nm to 1.6 microns and covers timescales 12 hr to 12 yr apart. We find that ∼2% of galaxies in these images are variable, with 49 AGN candidates and 4 new supernovae candidates detected. Half of the variables are in the cluster and these are primarily elliptical galaxies displaying variability only in the near-infrared bands. About 20% of the AGN candidates have morphologies and colors consistent with quasars, though most of the variables appear to be dominated by the host galaxy light. The structure function for these sources shows a greater amplitude of variability at shorter wavelengths, with slopes shallower than typical quasars. We also report a previously unknown Einstein cross identified in this field.

Unraveling Spatial and Temporal Heterogeneities of Very Slow Rock-Slope Deformations with Targeted DInSAR Analyses

Spaceborne radar interferometry is a powerful tool to characterize landslides at local and regional scales. However, its application to very slow rock slope deformations in alpine environments (displacement rates < 5 cm/year) remains challenging, mainly due to low signal to noise ratio, atmospheric disturbances, snow cover effects, and complexities resulting from heterogeneous displacement in space and time. Here we combine SqueeSARTM data, targeted multi-temporal baseline DInSAR, GPS data, and detailed field morpho-structural mapping, to unravel the kinematics, internal segmentation, and style of activity of the Mt. Mater deep-seated gravitational slope deformation (DSGSD) in Valle Spluga (Italy). We retrieve slope kinematics by performing 2D decomposition (2D InSAR) of SqueeSARTM products derived from Sentinel-1 data acquired in ascending and descending orbits. To achieve a spatially-distributed characterization of DSGSD displacement patterns and activity, we process Sentinel-1 A/B images (2016-2019) with increasing temporal baselines (ranging from 24-days to 1-year) and generate several multi-temporal interferograms. Unwrapped displacement maps are validated using ground-based GPS data. Interferograms derived with different temporal baselines reveal a strong kinematic and morpho-structural heterogeneity and outline nested rockslides and active sectors, that arise from the background displacement signal of the main DSGSD. Seasonal interferograms, supported by GPS displacement measurements, reveal non-linear displacement trends suggesting a complex response of different slope sectors to rainfall and snowmelt. Our analyses clearly outline a composite slope instability with different nested sectors possibly undergoing different evolutionary trends towards failure. The results herein outline the potential of a targeted use of DInSAR for the detailed investigation of very slow rock slope deformations in different geological and geomorphological settings.

Deformation Time Series and Driving-Force Analysis of Glaciers in the Eastern Tienshan Mountains Using the SBAS InSAR Method.

Glacier melting is one of the important causes of glacier morphology change and can provide basic parameters for calculating glacier volume change and glacier mass balance, which, in turn, is important for evaluating water resources. However, it is difficult to obtain large-scale time series of glacier changes due to the cloudy and foggy conditions which are typical of mountain areas. Gravity-measuring satellites and laser altimetry satellites can monitor changes in glacier volume over a wide area, while synthetic-aperture radar satellites can monitoring glacier morphology with a high spatial and temporal resolution. In this article, an interferometric method using a short temporal baseline and a short spatial baseline, called the Small Baseline Subsets (SBAS) Interferometric Synthetic-Aperture Radar (InSAR) method, was used to study the average rate of glacier deformation on Karlik Mountain, in the Eastern Tienshan Mountains, China, by using 19 Sentinel-1A images from November 2017 to December 2018. Thus, a time series analysis of glacier deformation was conducted. It was found that the average glacier deformation in the study region was −11.77 ± 9.73 mm/year, with the observation sites generally moving away from the satellite along the Line of Sight (LOS). Taking the ridge line as the dividing line, it was found that the melting rate of southern slopes was higher than that of northern slopes. According to the perpendicular of the mountain direction, the mountain was divided into an area in the northwest with large glaciers (Area I) and an area in the southeast with small glaciers (Area II). It was found that the melting rate in the southeast area was larger than that in the northwest area. Additionally, through the analysis of temperature and precipitation data, it was found that precipitation played a leading role in glacier deformation in the study region. Through the statistical analysis of the deformation, it was concluded that the absolute value of deformation is large at elevations below 4200 m while the absolute value of the deformation is very small at elevations above 4500 m; the direction of deformation is always away from the satellite along the LOS and the absolute value of glacier deformation decreases with increasing elevation.

CorPhU: an algorithm based on phase closure for the correction of unwrapping errors in SAR interferometry

SUMMARY Interferometric Synthetic Aperture Radar (InSAR) is commonly used in Earth Sciences to study surface displacements or construct high resolution topographic maps. Recent satellites such as those of the Sentinel-1 constellation allow to derive dense deformation maps with millimetric precision with high revisit frequency. However, InSAR is still limited by interferometric coherence. Interferometric phase noise resulting from a loss of coherence, due to changes in scattering properties between repeated SAR acquisitions, may lead to unwrapping errors, which then in turn lead to centimetric errors in time-series reconstruction. We present an algorithm based on interferometric phase closure to automatically correct unwrapping errors. We describe the algorithm and highlight its performances with two case studies, in Lebanon with Envisat satellite data and in Central Turkey with Sentinel-1 data. The first data set is particularly affected by unwrapping errors because of long spatial (500 m) and temporal baseline interferograms (6 yr) and decorrelation due, in particular, to vegetation. The second data set contains unwrapping errors because of temporal changes in the scattering properties of the ground. For these two examples, the algorithm allows the correction of almost all detectable unwrapping errors, without requiring visual inspection or manual deletions. Our algorithm is efficient especially on large data sets, such as with Sentinel-1 constellation, where interferometric phase is redundant and improves eventually the reconstruction of time-series.

Application of Time Series InSAR (SBAS) Method using Sentinel-1A Data for Land Subsidence Detection in Surabaya City

We studied land subsidence occurrence in Surabaya, considering the rapid urban growth in the city. It is evidenced by the increasing number of buildings (e.g., houses, apartments, offices) that have been realized before 2015 and the increasing transport activities in the industrial area. Land subsidence is a vertical deformation on the land surface that can be caused by landslides, earthquakes, soil consolidation process, or even human-made activities, depending on the material characteristics of the area. In this study, Time-series InSAR (TS-InSAR), specifically the Small Baseline Subset (SBAS) method, is applied to obtain the Line-of-Sight (LOS) velocities and time-series LOS displacements in Surabaya between May 2015 and September 2017. We used 28 Sentinel-1A data (IW mode) acquired by using Terrain Observation with Progressive Scan (TOPS) operation with the spatial resolution of 5 ´ 20 m and covers the area of 250 km2. DEM SRTM with 30 m spatial resolution was used to reduce topography effect contribution in the interferograms. Based on the SBAS method, interferogram pair selection is applied with the maximum value of 150 m for the perpendicular baseline and 100 days for the temporal baseline. The DInSAR process successfully generated seventy-four (74) interferogram pairs, and then by using SBAS algorithm, the LOS velocity and time-series LOS displacement were estimated. The results showed the varying displacements respond with the significant subsidence occurred in the North, East, and Southeast part of Surabaya. The SBAS result confirmed that the LOS velocity over the Surabaya area between May 2015 and September 2017 ranges from -40 to +30 mm/year. Significant land subsidence occurred in the Asemrowo area (North Surabaya) with the value of LOS displacement velocity up to -45 mm/yr.

Investigating the potential use of Sentinel-1 data for monitoring wetland water level changes in China's Momoge National Nature Reserve.

BackgroundInterferometric Synthetic Aperture Radar (InSAR) has become a promising technique for monitoring wetland water levels. However, its capability in monitoring wetland water level changes with Sentine-1 data has not yet been thoroughly investigated.MethodsIn this study, we produced a multitemporal Sentinel-1 C-band VV-polarized SAR backscatter images and generated a total of 28 interferometric coherence maps for marsh wetlands of China’s Momoge National Nature Reserve to investigate the interferometric coherence level of Sentinel-1 C-VV data as a function of perpendicular and temporal baseline, water depth, and SAR backscattering intensity. We also selected six interferogram pairs acquired within 24 days for quantitative analysis of the accuracy of water level changes monitored by Sentinel-1 InSAR. The accuracy of water level changes determined through the Sentinel-1 InSAR technique was calibrated by the values of six field water level loggers.ResultsOur study showed that (1) the coherence was mainly dependent on the temporal baseline and was little affected by the perpendicular baseline for Sentinel-1 C-VV data in marsh wetlands; (2) in the early stage of a growing season, a clear negative correlation was found between Sentinel-1 coherence and water depth; (3) there was an almost linear negative correlation between Sentinel-1 C-VV coherence and backscatter for the marsh wetlands; (4) once the coherence exceeds a threshold of 0.3, the stage during the growing season, rather than the coherence, appeared to be the primary factor determining the quality of the interferogram for the marsh wetlands, even though the quality of the interferogram largely depends on the coherence; (5) the results of water level changes from InSAR processing show no agreement with in-situ measurements during most growth stages. Based on the findings, we can conclude that although the interferometric coherence of the Sentinel-1 C-VV data is high enough, the data is generally unsuitable for monitoring water level changes in marsh wetlands of China’s Momoge National Nature Reserve.

Joint Exploitation of SAR and GNSS for Atmospheric Phase Screens Retrieval Aimed at Numerical Weather Prediction Model Ingestion

This paper proposes a simple and fast method to estimate Atmospheric Phase Screens (APSs) by jointly exploit a stack of Synthetic Aperture Radar (SAR) images and a dataset of GNSS-derived atmospheric product. The output of this processing is conceived to be ingested by Numerical Weather Prediction Models (NWPMs) to improve weather forecasts. In order to provide wide and dense area coverage and to respect requirements in terms of spatial resolution of ingestion products in NWPMs, both Permanent Scatterers (PSs) and Distributed Scatterers (DSs) are jointly exploited. While the formers are by definition stable targets, but unevenly distributed, the latter are ubiquitous but stable only within a certain temporal baseline that can vary depending on the operational frequency of the radar. The proposed method is thus particularly suited for C, L, and P band missions with low temporal baseline between two consecutive acquisitions of the same scene: these conditions, that are both necessary to provide the dense space-time coverage required by meteorologists, allow for a reliable and robust estimation of APSs thanks to the intrinsic limitation of temporal decorrelation. The proposed technique integrates Zenith Total Delay (ZTD) products computed on a very sparse grid from a network of GNSS stations to correct for SAR orbital errors and to provide the missing phase constant from the derived APS map. In this paper, the complete workflow is explained, and a comparison of the derived APSs is performed with phase screens derived from state-of-the-art SAR processing workflow (SqueeSAR®).

Gated Recurrent Fusion to Learn Driving Behavior from Temporal Multimodal Data

The Tactical Driver Behavior modeling problem requires an understanding of driver actions in complicated urban scenarios from rich multimodal signals including video, LiDAR and CAN signal data streams. However, the majority of deep learning research is focused either on learning the vehicle/environment state (sensor fusion) or the driver policy (from temporal data), but not both. Learning both tasks jointly offers the richest distillation of knowledge but presents challenges in the formulation and successful training. In this work, we propose promising first steps in this direction. Inspired by the gating mechanisms in Long Short-Term Memory units (LSTMs), we propose Gated Recurrent Fusion Units (GRFU) that learn fusion weighting and temporal weighting simultaneously. We demonstrate it's superior performance over multimodal and temporal baselines in supervised regression and classification tasks, all in the realm of autonomous navigation. On tactical driver behavior classification using Honda Driving Dataset (HDD), we report $10\%$ improvement in mean Average Precision (mAP) score, and similarly, for steering angle regression on TORCS dataset, we note a $20\%$ drop in Mean Squared Error (MSE) over the state-of-the-art.

Study of Systematic Bias in Measuring Surface Deformation With SAR Interferometry

This article investigates the presence of a new interferometric signal in multilooked synthetic aperture radar (SAR) interferograms that cannot be attributed to the atmospheric or Earth-surface topography changes. The observed signal is short-lived and decays with the temporal baseline; however, it is distinct from the stochastic noise attributed to temporal decorrelation. The presence of such a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fading signal</i> introduces a systematic phase component, particularly in short temporal baseline interferograms. If unattended, it biases the estimation of Earth surface deformation from SAR time series. Here, the contribution of the mentioned phase component is quantitatively assessed. The biasing impact on the deformation-signal retrieval is further evaluated. A quality measure is introduced to allow the prediction of the associated error with the fading signals. Moreover, a practical solution for the mitigation of this physical signal is discussed; special attention is paid to the efficient processing of Big Data from modern SAR missions such as Sentinel-1 and NISAR. Adopting the proposed solution, the deformation bias is shown to decrease significantly. Based on these analyses, we put forward our recommendations for efficient and accurate deformation-signal retrieval from large stacks of multilooked interferograms.

Present-day interseismic deformation characteristics of the Beng Co-Dongqiao conjugate fault system in central Tibet: implications from InSAR observations

SUMMARYNumerous V-shaped conjugate strike-slip fault systems distributed between the Lhasa block and the Qiangtang block serve as some of the main structures accommodating the eastward motion of the Tibetan Plateau. The Beng Co-Dongqiao conjugate fault system is a representative section, and determining its tectonic environment is a fundamental issue for understanding the dynamic mechanism of the V-shaped conjugate strike-slip fault systems throughout central Tibet. In this paper, we investigate the deformation rates of the Beng Co-Dongqiao conjugate faults using 3 yr of SAR data from both ascending and descending tracks of Sentinel-1 satellites. Only interferograms with a long temporal baseline were used to increase the proportion of the deformation signals. The external atmospheric delay product and the InSAR stacking strategy were employed to reduce various errors in the large-spatial-coverage Sentinel-1 data. The InSAR results revealed that the fault-parallel deformation velocities along the eastern and western segments of the Beng Co fault are 5 ± 1 mm/yr and 2.5 ± 1 mm/yr, respectively. The second invariant of the horizontal strain rates shows that the accumulated strain is centered on the eastern segment of the Beng Co Fault and Gulu rift. The velocity and strain rate fields show that the Anduo-Peng Co faults may be paired with the Beng Co fault to form a new conjugate system and the tectonic transformation between the Beng Co fault and Gulu rift. These results can better explain the tectonic deformation environment of the Beng Co-Dongqiao conjugate fault system and provide insights on the crustal dynamics throughout the entire plateau interior.

Least-Square-Based Joint Coregistration Method for SAR Image Series and Its Application for PS-InSAR Processing

Joint coregistration (JC) approach, based on least square (LS) method, is proposed to improve the synthetic aperture radar (SAR) images' coregistration precision and persistent scatterer interferometric synthetic aperture radar (PS-InSAR) performance. Firstly, the image pairs with small spatial and temporal baselines are used to estimate image shifts to reduce the decorrelation effects. The SAR images are all positioned in spatial-temporal baseline plane and connected with the Delaunay triangulation method. Those arcs with long spatial and/or temporal baselines are deleted with an experienced threshold. Secondly, the shifts are estimated with arc-linked images by conventional coregistration approaches. However, the shifts of arc-unlinked images are still unknown even the arc-linked images are accurately coregistered. For this problem, we estimate the shifts of all the slave images with respect to the reference image from the shifts estimated by the short spatial and temporal baselines. LS method is used to tackle the problem of shifts' error propagation. Finally, all the slave images are resampled according to the estimated LS-method-based images' shifts. 44 ERS-2 images are used to test the performance of JC approach for PS-InSAR processing. The validity is illustrated by the amplitude and phase stability of PS candidates, as well as the deformation performance of PS-InSAR.

Time-Series of Sentinel-1 Interferometric Coherence and Backscatter for Crop-Type Mapping

The potential use of the interferometric coherence measured with Sentinel-1 satellites as input feature for crop classification is explored in this study. A one-year time-series of Sentinel-1 images acquired over an agricultural area in Spain, in which 17 crop species are present, is exploited for this purpose. Different options regarding temporal baselines, polarization, and combination with radiometric data (backscattering coefficient) are analyzed. Results show that both radiometric and interferometric features provide notable classification accuracy when used individually (overall accuracy lies between 70% and 80%). It is found that the shortest temporal baseline coherences (6 days) and the use of all available intensity images perform best, hence proving the advantage of the 6-day revisit time provided by the Sentinel-1 constellation with respect to longer revisit times. It is also shown that dual-pol data always provide better classification results than single-pol ones. More importantly, when both coherence and backscattering coefficient are jointly used, a significant increase in accuracy is obtained (greater than 7% in overall accuracies). Individual accuracies of all crop types are increased, and an overall accuracy above 86% is reached. This proves that both features provide complementary information, and that the combination of interferometric and radiometric radar data constitutes a solid information source for this application.

From Cryptozoology to Conservation Biology: An Earlier Baseline for Entanglement of Marine Fauna in the Western Pacific Revealed from Historic “Sea Serpent” Sightings

Marine conservation biologists have increasingly acknowledged the value of non-traditional sources such as historical anecdotes for providing useful information about past conditions of social-ecological systems as part of a comprehensive management strategy for recognizing and setting trajectories toward re-establishing baseline conditions. The present study demonstrates that careful parsing of eyewitness accounts of unidentified marine objects (UMOs), which at the time had been purported to be sea serpents of the “many-humped” or “string-of-buoys” typology, reveals that marine animals in the Western Pacific have been victims of non-lethal entanglement in fishing gear for much longer than is commonly believed. The temporal baseline for onset of entanglement in this region certainly predates the mid-twentieth-century use of plastic in fisheries and other maritime operations.

Emergency Department Visits for Opioid Overdoses Among Patients With Cancer.

Patients with cancer may be at risk of high opioid use due to physical and psychosocial factors, although little data exist to inform providers and policymakers. Our aim is to examine overdoses from opioids leading to emergency department (ED) visits among patients with cancer in the United States. The Healthcare Cost and Utilization Project Nationwide Emergency Department Sample was queried for all adult cancer-related patient visits with a primary diagnosis of opioid overdose between 2006 and 2015. Temporal trends and baseline differences between patients with and without opioid-related ED visits were evaluated. Multivariable logistic regression analysis was used to identify risk factors associated with opioid overdose. All statistical tests were two-sided. Between 2006 and 2015, there were a weighted total of 35 339 opioid-related ED visits among patients with cancer. During this time frame, the incidence of opioid-related ED visits for overdose increased twofold (P < .001). On multivariable regression (P < .001), comorbid diagnoses of chronic pain (odds ratio [OR] 4.51, 95% confidence interval [CI] = 4.13 to 4.93), substance use disorder (OR = 3.54, 95% CI = 3.28 to 3.82), and mood disorder (OR = 3.40, 95% CI = 3.16 to 3.65) were strongly associated with an opioid-related visit. Patients with head and neck cancer (OR = 2.04, 95% CI = 1.82 to 2.28) and multiple myeloma (OR = 1.73, 95% CI = 1.32 to 2.26) were also at risk for overdose. Over the study period, the incidence of opioid-related ED visits in patients with cancer increased approximately twofold. Comorbid diagnoses and primary disease site may predict risk for opioid overdose.

Assessing Spatiotemporal Variations of Sentinel-1 InSAR Coherence at Different Time Scales over the Atacama Desert (Chile) between 2015 and 2018

This study investigates synthetic aperture radar (SAR) time series of the Sentinel-1 mission acquired over the Atacama Desert, Chile, between March 2015 and December 2018. The contribution analyzes temporal and spatial variations of Sentinel-1 interferometric SAR (InSAR) coherence and exemplarily illustrates factors that are responsible for observed signal differences. The analyses are based on long temporal baselines (365–1090 days) and temporally dense time series constructed with short temporal baselines (12–24 days). Results are compared to multispectral data of Sentinel-2, morphometric features of the digital elevation model (DEM) TanDEM-X WorldDEM™, and to a detailed governmental geographic information system (GIS) dataset of the local hydrography. Sentinel-1 datasets are suited for generating extensive, nearly seamless InSAR coherence mosaics covering the entire Atacama Desert (&gt;450 × 1100 km) at a spatial resolution of 20 × 20 meter per pixel. Temporal baselines over several years lead only to very minor decorrelation, indicating a very high signal stability of C-Band in this region, especially in the hyperarid uplands between the Coastal Cordillera and the Central Depression. Signal decorrelation was associated with certain types of surface cover (e.g., water or aeolian deposits) or with actual surface dynamics (e.g., anthropogenic disturbance (mining) or fluvial activity and overland flow). Strong rainfall events and fluvial activity in the periods 2015 to 2016 and 2017 to 2018 caused spatial patterns with significant signal decorrelation; observed linear coherence anomalies matched the reference channel network and indicated actual episodic and sporadic discharge events. In the period 2015–2016, area-wide loss of coherence appeared as strip-like patterns of more than 80 km length that matched the prevailing wind direction. These anomalies, and others observed in that period and in the period 2017–2018, were interpreted to be caused by overland flow of high magnitude, as their spatial location matched well with documented heavy rainfall events that showed cumulative precipitation amounts of more than 20 mm.

Identification of permanent surface water in Bengawan Solo River downstream area, Indonesia using Sentinel-1 imagery

Permanent surface water is defined as area of the Earth continental surface corresponding to an accumulation of water, showing slight variations in water levels, retaining most of their volumes over the year and that do not dry up. Bengawan Solo watershed, especially in the downstream area is situated in East Java, Indonesia and is recorded as the longest and the largest river on Java Island. Thus, this river becomes one of the biggest water resources in Java, Indonesia. As a water resources, it is important to identify spatial distribution of permanent surface water in Bengawan Solo River. Synthetic Aperture Radar (SAR) is an effective way to detect surface water over large areas. Sentinel-1 is a new available SAR and its spatial resolution and short temporal baselines have the potential to identify permanent surface water. Thus, we propose an identification of permanent surface water using Sentinel-1 data. The quantitative evaluation shows relevant results with overall accuracy of more than 97%, F-measure provides value 0.79, also very low False Positive Rate and commission error. There are also morphological and land use change detected in the area. These results are encouraging and the first step for monitoring Bengawan Solo River.

From Land to Sea, a Review of Hypertemporal Remote Sensing Advances to Support Ocean Surface Science

Increases in the temporal frequency of satellite-derived imagery mean a greater diversity of ocean surface features can be studied, modelled, and understood. The ongoing temporal data “explosion” is a valuable resource, having prompted the development of adapted and new methodologies to extract information from hypertemporal datasets. Current suitable methodologies for use in hypertemporal ocean surface studies include using pixel-centred measurement analyses (PMA), classification analyses (CLS), and principal components analyses (PCA). These require limited prior knowledge of the system being measured. Time-series analyses (TSA) are also promising, though they require more expert knowledge which may be unavailable. Full use of this resource by ocean and fisheries researchers is restrained by limitations in knowledge on the regional to sub-regional spatiotemporal characteristics of the ocean surface. To lay the foundations for more expert, knowledge-driven research, temporal signatures and temporal baselines need to be identified and quantified in large datasets. There is an opportunity for data-driven hypertemporal methodologies. This review examines nearly 25 years of advances in exploratory hypertemporal research, and how methodologies developed for terrestrial research should be adapted when tasked towards ocean applications. It highlights research gaps which impede methodology transfer, and suggests achievable research areas to be addressed as short-term priorities.

A NEW STRATEGY FOR PHASE UNWRAPPING IN INSAR TIME SERIES OVER AREAS WITH HIGH DEFORMATION RATE: CASE STUDY ON THE SOUTHERN TEHRAN SUBSIDENCE

Abstract. The primary step in all timeseries interferometric synthetic aperture radar (T-InSAR) algorithms is the phase unwrapping step to resolve the inherent cycle ambiguities of interferometric phases. In areas with a high spatio-temporal deformation gradient, phase unwrapping fails due to the aliasing problem, and so it can result in an underestimation of deformation signal. One way to handle this problem is to use the so called Small-Baseline Subset (SBAS) algorithms; in these algorithms, by using only small-baseline interferograms – hence interferograms with small deformation gradients – the chance of unwrapping error gets reduced. However, due to more number of the used interferograms, SBAS method is computationally more expensive and more time-consuming compared to algorithms that exploit Single-Master (SM) stacks. Moreover, the existence of sufficiently small temporal baseline interferograms is not guaranteed in all SAR stacks. In this paper, we propose a new method to take advantage of short temporal baseline interferograms but effectively using SM approach. We treat the phase unwrapping step as a Bayesian estimation problem while the prior information, required by the Bayesian estimator, is extracted from few short coherent interferograms that are unwrapped separately. Results from the proposed approach and a case study over the southwest of Tehran, with a high subsidence rate (reaching to 25 cm/year), demonstrates that utilizing the proposed method overcomes the aliasing problem and produces the results equal to the conventional SBAS results, while the proposed method is computationally much more efficient than SBAS.

An implicit radar convolutional burn index for burnt area mapping with Sentinel-1 C-band SAR data

Compared with optical sensors, the all-weather and day-and-night imaging ability of Synthetic Aperture Radar (SAR) makes it competitive for burnt area mapping. This study investigates the potential of Sentinel-1 C-band SAR sensors in burnt area mapping with an implicit Radar Convolutional Burn Index (RCBI). Based on multitemporal Sentinel-1 SAR data, a convolutional networks-based classification framework is proposed to learn the RCBI for highlighting the burnt areas. We explore the mapping accuracy level that can be achieved using SAR intensity and phase information for both VV and VH polarizations. Moreover, we investigate the decorrelation of Interferometric SAR (InSAR) coherence to wildfire events using different temporal baselines. The experimental results on two recent fire events, Thomas Fire (Dec., 2017) and Carr Fire (July, 2018) in California, demonstrate that the learnt RCBI has a better potential than the classical log-ratio operator in highlighting burnt areas. By exploiting both VV and VH information, the developed RCBI achieved an overall mapping accuracy of 94.68% and 94.17% on the Thomas Fire and Carr Fire, respectively.