Sentinel-1A Synthetic Aperture Radar Research Articles

Sentinel-1 EW mode dataset for Antarctica from 2014–2020 produced by the CASEarth Cloud Service Platform

ABSTRACT Antarctica plays an important role in research on global change, and its unique geography, ocean, climate, and environment provide an ideal place for humankind to understand Earth’s evolution. Remote sensing provides an effective means to monitor and observe large-scale processes on the continent. Synthetic aperture radar (SAR) in particular provides the capability for all-weather Earth observation. The Sentinel-1A and Sentinel-1B SAR satellites have ideal ground coverage and imaging frequency for observing Antarctica. This study developed a dataset of 69,586 Sentinel-1 EW mode satellite images of the Antarctic ice sheet from October 2014 to December 2020. The dataset was processed with the European Space Agency Sentinel Application Platform (SNAP) and a Python batch scheduling tool on the Big Earth Data Cloud Service Platform of the Chinese Academy of Sciences Big Earth Data Science Engineering Program (CASEarth). Several data processing operations were implemented to process the raw dataset, including radiometric calibration, invalid edge removal, geocoding, data re-projection to an Antarctic projection, data compression to TIFF format, and construction of image pyramids. The dataset is available at http://www.doi.org/10.11922/sciencedb.j00076.00085.

The deadliest tropical cyclone ‘Amphan’: investigate the natural flood inundation over south 24 Parganas using google earth engine

Climate change is hammering the environment, socio-economic condition, vegetation degradation, earth surface change and over the land losses. In between 2019 and 2020, West Bengal people are facing three major cyclones like, Fani, Bulbul and the last was Amphan. Those natural disasters were affected in vegetation, hammering crop production, water quality change, life losses and affected in the ecosystem. 20th May 2020 Amphan was entering in West Bengal and occur several damaged. Seventy two people of West Bengal were lost their life and several months over the southern parts of West Bengal are facing huge land scarcity and salt-water intrusion on the mainland. Over 13.6 billion dollar was losses due to this cyclone. Google Earth Engine (GEE) is a cloud-based platform to detect earth observation using high spatial satellite data with planetary-scale analysis capabilities. C-band Sentinel-1A Synthetic Aperture Radar is used to identifying the flood inundation on Google Earth Engine (GEE). Landsat 8 OLI data is used for MNDWI calculation on this area. Pre-Amphan and during Amphan two satellite data are clearly showing the water increased area. The Coastal area like Sagar Island (154.254 Sq.km), Ghoramara Island, Mousuni Island, G-Plot, L-Block and some parts of riverside most affected by this cyclone. Kultali (187.254 Sq.km) followed by Kulpi (156.86 Sq.km), Namkhana (198.485 Sq.km) and Basanti (129.53 Sq.km) area under flooded during this cyclone. During COVID-19 pandemic, social distancing is the main problem on cyclone shelter house and relief camp. This area has faced those two disasters at the same time.

Automatic Subsidence Troughs Detection in SAR Interferograms Using Circlet Transform.

This article presents the results of automatic detection of subsidence troughs in synthetic aperture radar (SAR) interferograms. The detection of subsidence troughs is based on the circlet transform, which is able to detect features with circular shapes. Compared to other methods of detecting circles, the circular transform takes into account the finite data frequency. Moreover, the search shape is not limited to a circle but identified on the basis of a certain width. This is especially important in the case of detection of subsidence troughs whose shapes may not be similar to circles or ellipses but to their fragments. The transformation works directly on the image gradient; it does not require further binary segmentation or edge detection as in the case of other methods, e.g., the Hough transform. The entire processing process can be automated to save time and increase reliability compared to traditional methods. The proposed automatic detection method was tested on a differential interferogram that was generated based on Sentinel-1A SAR images of the Upper Silesian Coal Basin area. The test carried out showed that the proposed method is 20% more effective in detecting troughs that than the method using Hough transform.

Displacement Analysis of Geothermal Field Based on PSInSAR And SOM Clustering Algorithms A Case Study of Brady Field, Nevada—USA

The availability of free and high temporal resolution satellite data and advanced SAR techniques allows us to analyze ground displacement cost-effectively. Our aim was to properly define subsidence and uplift areas to delineate a geothermal field and perform time-series analysis to identify temporal trends. A Persistent Scatterer Interferometry (PSI) algorithm was used to estimate vertical displacement in the Brady geothermal field located in Nevada by analyzing 70 Sentinel-1A Synthetic-Aperture Radar (SAR) images, between January 2017 and December 2019. To classify zones affected by displacement, an unsupervised Self-Organizing Map (SOM) algorithm was applied to classify points based on their behavior in time, and those clusters were used to determine subsidence, uplift, and stable regions automatically. Finally, time-series analysis was applied to the clustered data to understand the inflection dates. The maximum subsidence is –19 mm/yr with an average value of –6 mm/yr within the geothermal field. The maximum uplift is 14 mm/yr with an average value of 4 mm/yr within the geothermal field. The uplift occurred on the NE of the field, where the injection wells are located. On the other hand, subsidence is concentrated on the SW of the field where the production wells are located. The coupling of the PSInSAR and the SOM algorithms was shown to be effective in analyzing the direction and pattern of the displacements observed in the field.

Change detection based flood mapping using multi-temporal Earth Observation satellite images: 2018 flood event of Kerala, India

ABSTRACT The future projections of climate change envisage a global increase in extreme precipitation events and subsequent flooding. The reliable and rapid flood maps are the critical parameters in preparing the disaster management plans. This study demonstrated an effective flood mapping framework using freely available multi-temporal Earth Observation (EO) images, including C-band Sentinel-1A & 1B Synthetic Aperture Radar (SAR) images and optical WorldView-3 images, for analyzing the 2018 flood event of Kerala, India. Two Change Detection (CD) techniques, i.e. Ratio Index (RI) and Normalized Change Index (NCI) combined with semi-automatic thresholding are implemented on temporal descending pass SAR images for flood identification. For ascending pass SAR images, the statistical-based thresholding method is implemented. The results indicate that combined use of ascending and descending pass SAR images contributed to a better understanding of flood conditions. It is also inferred that the use of a pre-flood image can enhance flood area estimation and helps in minimizing the overestimation errors. The results also found that NCI outperforms RI for Kerala flood event. Flood area extracted from these techniques is plotted against the Indian Meteorological Department (IMD) rainfall datasets, which showed a similar trend. Field photographs and optical images are used for validation purposes.

Rice-Field Mapping with Sentinel-1A SAR Time-Series Data

This study proposed a feature-based decision method for the mapping of rice cultivation by using the time-series C-band synthetic aperture radar (SAR) data provided by Sentinel-1A. In this study, a model related to crop growth was first established. The model was developed based on a cubic polynomial function which was fitted by the complete time-series SAR backscatters during the rice growing season. From the developed model, five rice growth-related features were introduced, including backscatter difference (BD), time interval (TI) between vegetative growth and maturity stages, backscatter variation rate (BVR), average normalized backscatter (ANB) and maximum backscatter (MB). Then, a decision method based on the combination of the five extracted features was proposed to improve the rice detection accuracy. In order to verify the detection performance of the proposed method, the test data set of this study consisted of 50,000 rice and non-rice fields which were randomly sampled from a research area in Taiwan for simulation verification. From the experimental results, the proposed method can improve overall accuracy in rice detection by 6% compared with the method using feature BD. Furthermore, the rice detection efficiency of the proposed method was compared with other four classifiers, including decision tree (DT), support vector machine (SVM), K-nearest neighbor (KNN) and quadratic discriminant analysis (QDA). The experimental results show that the proposed method has better rice detection accuracy than the other four classifiers, with an overall accuracy of 91.9%. This accuracy is 3% higher than fine SVM, which performs best among the other four classifiers. In addition, the consistency and effectiveness of the proposed method in rice detection have been verified for different years and studied regions.

Modelling above ground biomass of Indian mangrove forest using dual-pol SAR data

Usage of remote sensing technology has increased for estimation of Above Ground Biomass (AGB) due to accessibility issues within the forests. Current study is piloted for mangroves of Mundra taluka in Kachchh district, western India. The study aims to validate and compare various allometric models for mangrove Above Ground Biomass (AGB) estimation to find out best suitable model. Models are validated by extensive ground truth. The most suitable allometric model is then used to develop a novel methodology to estimate AGB using dual-pol Sentinel-1A Synthetic Aperture Radar (SAR) data. This study is first of its kind, implemented for Indian mangroves, using Polarimetric Radar Vegetation Index (PRVI) for modelling AGB. The study also incorporates time series change detection in AGB within the study area. Our results suggest that amalgamation of field measurements, allometric equations and remote sensing technology can be the vital solution for modelling Above Ground Biomass of mangrove forest.

Spectral linear mixing model applied to data from passive microwave radiometers for sea ice mapping in the Antarctic Peninsula

This study proposes the use of a Spectral Linear Mixing Model (SLMM) on passive microwave data for mapping the concentration and area of young and/or first-year ice in the oceanic region located in the northwest of the Antarctic Peninsula. Sentinel-1A Synthetic Aperture Radar (SAR) data were used to estimate fraction images needed for subpixel analysis of passive microwave data acquired by the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder (SSMIS) in the 19H, 19 V, 37H, and 37 V channels and polarizations. Sea ice concentrations estimated by the SLMM approach had a difference of 1.4% (13.6% standard deviation, 15.3% root mean square error) when compared to sea ice concentrations mapped using Sentinel-1A SAR data. The evolution in the total area covered by young and/or first-year ice with concentrations ≥ 15% showed a negative linear trend for the studied region from 1979 to 2018.

Superpixel-based time-series reconstruction for optical images incorporating SAR data using autoencoder networks

ABSTRACT Time-series reconstruction for cloud/shadow-covered optical satellite images has great significance for enhancing the data availability and temporal change analysis. In this study, we proposed a superpixel-based prediction transformation-fusion (SPTF) time-series reconstruction method for cloud/shadow-covered optical images. Central to this approach is the incorporation between intrinsic tendency from multi-temporal optical images and sequential transformation information from synthetic aperture radar (SAR) data, through autoencoder networks (AE). First, a modified superpixel algorithm was applied on multi-temporal optical images with their manually delineated cloud/shadow masks to generate superpixels. Second, multi-temporal optical images and SAR data were overlaid onto superpixels to produce superpixel-wise time-series curves with missing values. Third, these superpixel-wise time series were clustered by an AE-LSTM (long short-term memory) unsupervised method into multiple clusters (searching similar superpixels). Four, for each superpixel-wise cluster, a prediction-transformation-based reconstruction model was established to restore missing values in optical time series. Finally, reconstructed data were merged with cloud-free regions to produce cloud-free time-series images. The proposed method was verified on two datasets of multi-temporal cloud/shadow-covered Landsat OLI images and Sentinel-1A SAR data. The reconstruction results, showing an improvement of greater than 20% in normalized mean square error compared to three state-of-the-art methods (including a spatially and temporally weighted regression method, a spectral–temporal patch-based method, and a patch-based contextualized AE method), demonstrated the effectiveness of the proposed method in time-series reconstruction for multi-temporal optical images.

Integration of Thermal Infrared and Synthetic Aperture Radar Images to Identify Geothermal Steam Spots Under Thick Vegetation Cover

Identifying physical properties of geothermal features such as surface temperature and roughness is crucial for geothermal assessment, hydrology, and environmental studies. Surface temperature and roughness-related rock properties strongly influence the thermal infrared and back-scattering intensities, respectively, of synthetic aperture radar (SAR) images. This study aimed to identify geothermal steam spots based on the surface temperature and roughness of altered surfaces, mud pools, and hot springs in tropical conditions. The thermal emissivity separation method was adopted to calculate kinetic temperature using the thermal infrared (TIR) of the advanced spaceborne thermal emission and reflection radiometer (ASTER) data as well as ground temperature data. Surface temperatures calculated by the thermal emissivity separation method were corrected using measured ground temperatures to eliminate the effects of atmospheric absorption, thermal inertia of materials, and/or diurnal temperature in the images. In addition, the linear fitting method was applied to obtain the final surface roughness model based on the calculated determination coefficient (R2) between the initial surface roughness model derived by Sentinel-1A SAR and field data. The highest R2 between surface roughness values from Sentinel-1A SAR and field data were selected to calculate spatially the surface roughness of the Wayang Windu Geothermal Field, West Java, Indonesia. The satellite imagery and field measurements showed that surface temperature and roughness features correlate with each other, and high and low thermal features correlate with high and low surface roughness values, respectively. Accordingly, we integrated the TIR and SAR data to propose the thermal resistivity index (TRI) as an indicator of the hydrothermal fluid paths to the surface. Low TRI was encountered at geothermal features and intersections of subsurface faults, which indicated weak zones that radiate heat from the subsurface to the surface.

On the impact of C-band in place of L-band radar for SMAP downscaling

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) mission was launched on 31st January 2015, with the aim of providing global soil moisture maps at 9 km spatial resolution by combining L-band radar and radiometer observations. However, after the SMAP radar became inoperable, NASA decided to utilize the Sentinel 1A/1B C-band SAR data in its place. The new version of baseline brightness temperature (Tb) downscaling algorithm for SMAP is tested using L-band airborne data to evaluate the capabilities of the C-band Sentinel-1A SAR relative to L-band radar data in downscaling the SMAP Tb for achieving high resolution brightness temperature. In this study, the downscaling algorithm used L-band airborne Synthetic Aperture Radar (SAR) backscatter (σ) collected from the fifth Soil Moisture Active Passive Experiment (SMAPEx-5) in south-eastern Australia to downscale 36 km L-band SMAP radiometer Tb pixels to 3 km and 9 km. The downscaling results were then compared with the published results using Sentinel-1A C-band backscatter, and evaluated against airborne 1 km resolution L-band passive microwave brightness temperature collected from SMAPEx-5. The results show that for vertical polarization the average Root Mean Square Error (RMSE) of downscaled Tb when compared with reference airborne Tb across 4 days at 9 km resolution were 4.9 K for L-band and 6.0 K for C-band, and increased to 9.3 K for L-band and 9.6 K for C-band at 3 km spatial resolution. Moreover, the correlation coefficient (R) of downscaled and reference Tb across the 4 days was 0.92 for L-band and 0.88 for C-band at 9 km, decreasing to 0.75 for L-band and 0.72 for C-band at 3 km spatial resolution. Accordingly, the RMSE increased and the correlation coefficient decreased when using C-band radar data in place of that at L-band. However, overall there is expected to be only a slight decrease in performance of the downscaling algorithm by using the Sentinel 1A data in place of the SMAP radar.

Flood Inundation Mapping by Combining GNSS-R Signals with Topographical Information

The Cyclone Global Navigation Satellite System (CYGNSS) mission collects near-global hourly, pseudo-randomly distributed Global Navigation Satellite System - Reflectometry (GNSS-R) signals in the form of signal-to-noise ratio (SNR) point data, which is sensitive to the presence of surface water, due to their operating frequency at L-band. However, because of the pseudo-random nature of these points, it is not possible to obtain continuous flood inundation maps at adequately high resolution. By considering topological indicators, such as height above nearest drainage (HAND) and slope of nearest drainage (SND), which indicate the probability of a certain area being prone to flooding, we hypothesize that combining static topographic information with the dynamic GNSS-R signals can result in large-scale, high-resolution flood inundation maps. Flood mapping was performed and validated with flood extent derived using available Sentinel-1A synthetic aperture radar (SAR) data for flooding in Kerala during August 2018, and North India during August 2017. The results obtained after thresholding indicate that the model exhibits a flooding accuracy ranging from 60% to 80% for lower threshold values. We observed significant overestimation error in mapping inundation across the flooding period, resulting in an optimal critical success index of 0.22 for threshold values between 17–19.

Mapping Paddy Rice Fields by Combining Multi-Temporal Vegetation Index and Synthetic Aperture Radar Remote Sensing Data Using Google Earth Engine Machine Learning Platform

The knowledge of the area and spatial distribution of paddy rice fields is important for water resource management. However, accurate map of paddy rice is a long-term challenge because of its spatiotemporal discontinuity and short duration. To solve this problem, this study proposed a paddy rice area extraction approach by using the combination of optical vegetation indices and synthetic aperture radar (SAR) data. This method is designed to overcome the data-missing problem due to cloud contamination and spatiotemporal discontinuities of the traditional optical remote sensing method. More specifically, the Sentinel-1A SAR and the Sentinel-2 multispectral imager (MSI) Level-2A imagery are used to identify paddy rice with a high temporal and spatial resolution. Three vegetation indices, namely normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and land surface water index (LSWI), are estimated from optical bands. Two polarization bands (VH (vertical-horizontal) and VV (vertical-vertical)) are used to overcome the cloud contamination problem. This approach was applied with the random forest machine learning algorithm on the Google Earth Engine platform for the Jianghan Plain in China as an experimental area. The results of 39 experiments uncovered the effect of different factors. The results indicated that the combination of VV and VH band showed a better performance compared with other polarization bands; the average producer’s accuracy of paddy rice (PA) is 72.79%, 1.58% higher than the second one VH. Secondly, the combination of three indices also showed a better result than others, with average PA 73.82%, 1.42% higher than using NDVI alone. The classification result presented the best combination is EVI, VV, and VH polarization band. The producer’s accuracy of paddy rice was 76.67%, with the overall accuracy (OA) of 66.07%, and Kappa statistics of 0.45. However, NDVI, EVI, and VH showed better performance in mapping the morphology. The results demonstrated the method developed in this study can be successfully applied to the cloud-prone area for mapping paddy rice to overcome the data missing caused by cloud and rain during the paddy growing season.

Pre- and Postcollapse Ground Deformation Revealed by SAR Interferometry: A Case Study of Foshan (China) Ground Collapse

On the evening of 7 February 2018, a deadly collapse of a metro tunnel under construction in the Southern China city of Foshan caused 11 deaths, 8 injuries, and 1 missing person. For disaster prevention and mitigation, the spatiotemporal ground deformations before and after the collapse event were derived from 55 Sentinel-1A synthetic aperture radar (SAR) images spanning from March 2017 to January 2019. The results showed that prominent ground subsidence in the shape of a funnel with a maximum rate of 42 mm/year was observed in the vicinity of the collapse area before the accident. After the accident, the area and magnitude of subsidence decreased compared with precollapse subsidence. This decrease is related to the progress of tunnel excavation and groundwater changes. In the temporal domain, continuous subsidence was observed over a year before and after the accident, and accelerated subsidence appeared one month before the collapse accident. Soft soil consolidation and tunnel-induced soil losses were the main reasons for the subsidence over the study area. The leakage of groundwater accounted for the collapse event. The leaked groundwater eroded the soil, resulting in the formation of an arched hole. The connection between the arched hole and the tunnel reduced the bearing capacity of the soil layer above the arched hole, triggering the collapse event. The findings provide scientific evidence for future collapse monitoring and early warning due to tunnel excavation.

Google Earth Engine for concurrent flood monitoring in the lower basin of Indo-Gangetic-Brahmaputra plains.

The present study focused on the recent flood inundation (July 2020) that occurred in the lower Indo-Gangetic-Brahmaputra plains (IGBP) using concurrent C-band Sentinel-1A Synthetic Aperture Radar images in Google Earth Engine. The study exhibited that a substantial proportion of IGBP (40,929 km2) was inundated primarily in Bangladesh (9.09% of the total inundation), Assam (8.99%), and Bihar (6.29%) during June–July 2020. The severe impact of flood inundation was observed in croplands (4.41% of the total cropland), followed by settlements (20.98% of the total settlements) that affected a large population (~ 10,046,262) in IGBP. The prevailing COVID-19 pandemic has debilitated the efforts of mitigation and responses to flooding risks. The study necessitates adopting an integrated, multi-hazard, multi-stakeholder approach with an emphasis on self-reliance of the community for sustenance with local resources and practices.

The feasibility of assessing swell-based bathymetry using SAR imagery from orbiting satellites

Remote sensing instruments, especially synthetic aperture radar (SAR), have become valuable tools for monitoring coastal bathymetry in both space and time. In this study, we investigate the viability of using data from spaceborne SAR satellites to detect shallow water depths from swell patterns. Using data from four Sentinel-1A SAR images, we employed numerical simulations to quantify the sensitivity of the bathymetry results to different initial input parameters such as wavelength, swell period, and initial water depth. Our sensitivity experiments indicate that the swell period is most sensitive to the water depth, while the initial water depth has the lowest impact on the shallow water depth estimation. Once we quantified the sensitivity of the estimated water depth to these parameters, we calculated the minimum and maximum detectable shallow water depth ranges of six SAR orbiting satellites (L-band: ALOS-2, C-band: GF-3, RADARSAT-2 and Sentinel-1, X-band: TerraSAR-X and COSMO-SkyMed). Because of its single-polarization SAR data, the TerraSAR-X satellite is ultimately the best source of SAR data for swell-based bathymetry analyses. With an incidence angle of 20° and significant wave height of 0.6 m, the TerraSAR-X satellite has the largest theoretical depth detection range (with a minimum detectable depth of less than 2 m, and a maximum detectable depth of more than 200 m) for swell waves with a period of 15 s. The results of this study demonstrate that for low to moderate wave height, satellites with lower slant range to platform velocity ratios (RRV) can be successfully applied to the task of large-scale bathymetric mapping at shallow depths where there are weak tidal currents or where no recent or accurate ground truth data is available. Based on these results, we can make recommendations that will enable future users to get reliable, robust bathymetry information from their SAR data sets.

Statistical analyses of eddies in the Western Mediterranean Sea based on Synthetic Aperture Radar imagery

We analysed 7188 Sentinel-1A Synthetic Aperture Radar (SAR) images acquired in a 3-year period, between October 2014 and September 2017, in the Western Mediterranean Sea and found imprints of about 14,300 oceanic eddies with diameters ranging from 0.4 km to 160.1 km. Those eddies manifest on the SAR imagery either through the accumulation of surfactants or through wave-current interaction, and considering the favourable wind-speed ranges for both mechanisms we performed statistical analyses to gain insight into the eddies' spatio-temporal distribution, their typical sizes and shapes, and into the predominant mechanisms that are responsible for their formation. The vast majority of the detected eddies is elliptical, with a tendency for only large mesoscale eddies towards a circular shape. We found no general influence of the wind on the eddies' orientation, although mesoscale eddies tend to orient into the main current direction in each subregion. Submesoscale eddies do not show such tendency. Submesoscale eddies in the Western Mediterranean Sea are predominantly cyclonic (>90%), and with increasing eddy diameter there is a transition from cyclonic to anticyclonic sense of rotation. In general, we found highest densities of submesoscale eddies in autumn, when the biological activity is still high and when mean regional winds are already strong, but variable. In addition, instabilities of thermal fronts during summer and autumn and instabilities of the main and secondary currents influence the formation of submesoscale eddies. Some of our results confirm those previously obtained for other regions.

Freezing and thawing of lakes on the Nelson and King George Islands, Antarctic, using Sentinel 1A synthetic aperture radar images.

This article aims to analyze the dynamics of freezing and thawing of Antarctic lakes located in ice-free areas on Nelson Island and Fildes Peninsula, where response to changes in air temperature and precipitation rates occur rapidly, during the period from July 2016 to December 2018. In these places, which are difficult to access, remote sensing is an important alternative, especially considering the use of active remote sensors such as the Synthetic Aperture Radar (SAR), which has less restriction regarding the presence of clouds over the study area. Three backscatter thresholds were defined (σ) for the identification of the physical state of the water of the lakes of the study region, applied in Sentinel 1A SAR (S1A) images under Horizontal Horizontal (HH) polarization and Interferometric Wide (IW) imaging mode. These images, along with the air temperature data obtained by the Interim Re-Analysis (ERA-Interim) atmospheric reanalysis model, provided the evidence for the interpretation of the freezing and thawing periods of the lakes. The thresholds applied for the definition of the physical state of the lake water were greater than - 14dB for frozen water, between - 14 and - 17dB for the surface, with up to 60% of their frozen area, and less than - 17dB for open water. The temporal analysis revealed that the lakes start to thaw in October, become completely thawed in February, and freeze again in March. Nevertheless, it can be said that the S1A satellite allows a satisfactory identification of the liquid and solid phases of the water in the lakes of the study region.

Spectral attenuation of ocean waves in pack ice and its application in calibrating viscoelastic wave-in-ice models

Abstract. We investigate a case of ocean waves through a pack ice cover captured by Sentinel-1A synthetic aperture radar (SAR) on 12 October 2015 in the Beaufort Sea. The study domain is 400 km by 300 km, adjacent to a marginal ice zone (MIZ). The wave spectra in this domain were reported in a previous study (Stopa et al., 2018b). In that study, the authors divided the domain into two regions delineated by the first appearance of leads (FAL) and reported a clear change of wave attenuation of the total energy between the two regions. In the present study, we use the same dataset to study the spectral attenuation in the domain. According to the quality of SAR-retrieved wave spectrum, we focus on a range of wave numbers corresponding to 9–15 s waves from the open-water dispersion relation. We first determine the apparent attenuation rates of each wave number by pairing the wave spectra from different locations. These attenuation rates slightly increase with increasing wave number before the FAL and become lower and more uniform against wave number in thicker ice after the FAL. The spectral attenuation due to the ice effect is then extracted from the measured apparent attenuation and used to calibrate two viscoelastic wave-in-ice models. For the Wang and Shen (2010b) model, the calibrated equivalent shear modulus and viscosity of the pack ice are roughly 1 order of magnitude greater than that in grease and pancake ice reported in Cheng et al. (2017). These parameters obtained for the extended Fox and Squire model are much greater, as found in Mosig et al. (2015) using data from the Antarctic MIZ. This study shows a promising way of using remote-sensing data with large spatial coverage to conduct model calibration for various types of ice cover.Highlights. Three key points: The spatial distribution of wave number and spectral attenuation in pack ice are analyzed from SAR-retrieved surface wave spectra. The spectral attenuation rate of 9–15 s waves varies around 10−5 m2 s−1, with lower values in thicker semicontinuous ice fields with leads. The calibrated viscoelastic parameters are greater than those found in pancake ice.

Land subsidence in Isfahan metropolitan and its relationship with geological and geomorphological settings revealed by Sentinel-1A InSAR observations

Recent reports from Isfahan, Iran, have proclaimed cases of land subsidence arising from persistence of drought events and groundwater overexploitation. In this light, the present study attempts to conduct an PS-InSAR analysis of subsidence events using the Sentinel–1A Synthetic Aperture Radar imagery technique, in order to develop a subsidence map of the target study region, covering the period from October 16, 2014 to November 22, 2019 as well as to investigate its relation to geological/geomorphological factors. Toward this end, the methodology proceeds to detect land subsidence phenomenon across the Isfahan Metropolitan area, situated on the Zayande-Rud River Terrace (ZRT) and Isfahan Alluvium Plain (IAP). A total of 198 Sentinel images were used to derive deformation specifics observed throughout ZRT & IAP by dint of incorporating ascending and descending passes of the InSAR technique. As maintained by spatial and temporal variation maps of surface deformations in the urban regions of Isfahan, ZRT & IAP have undergone various orders of subsidence throughout the observation period, at an estimated rate of −5 to −100 mm/year. Overall, land subsidence appears to increase from south towards the northern, northeastern, and eastern sectors of Isfahan Metropolitan, while the southern sectors are nearly a stable area. This study indicates that the spatial subsidence pattern during the study period corresponds closely to the spatial distribution of ZRT & IAP in the Isfahan Metropolitan area.