C-band SAR Data Research Articles

Evaluation of speckle filtering configurations on Sentinel-1 SAR backscatter Analysis Ready Data (S1ARD) preparation framework on the Google Earth Engine platform for supporting rice monitoring activities

Implementing the Sentinel-1 SAR backscatter analysis ready data (S1ARD) preparation framework to Sentinel-1 C-band SAR data in the Google Earth Engine platform potentially enhances the quality of SAR data, facilitating the advancement of wide-scale, large-impact, and continuous SAR-supported RS applications such those for rice monitoring activities. Nevertheless, there is a lack of published works assessing different speckle filtering configurations available within the S1ARD preparation framework, particularly those directly associated with rice monitoring activities. This study quantitatively evaluated the performance of available speckle filtering parameters on the S1ARD preparation framework, analyzed their derived backscatter values over a rice-growing cycle, and utilized produced datasets as inputs to classify distinct classes of rice transplanting periods in two study areas by applying the random forest classifier. The backscatter analysis demonstrated that the mono-temporal speckle filtering frameworks yielded elevated backscatter values compared to the unfiltered dataset, which exhibited higher values than those derived by the multi-temporal frameworks. Furthermore, filtered datasets increased classification accuracies ranging from 9.30 - 13.95% and 17.23 - 25.94% in study area 1 and between 4.69 - 15.63% and 9.28 - 29.75% in study area 2, for OA and Kappa, respectively, than those produced by unfiltered datasets. Overall, the multi-temporal speckle filtering framework with a Lee filter, 15 number-of-image, and a 7 x 7 window configuration was recommended to apply to the S1ARD preparation framework to assist SAR-supported RS-based rice monitoring activities. Finally, the findings of this work offer direct guidance and recommendations about the behavior and contributions of Sentinel-1 C-band SAR data applied with distinct speckle filtering configurations yielded by benefiting the S1ARD preparation framework for aiding SAR-supported RS-based rice monitoring activities.

Innovative soil fluoride estimation method: dual polarimetric saline-associated fluoride for agricultural patches.

Fluoride and its constituents in soil affect plant growth and public health. In this study, soil fluoride was measured for the semi-arid regions in southern India, using Sentinel-1 data in conjunction with the dual polarimetric saline-associated fluoride model (also known as fluoride model). A loss angle was estimated from laboratory-based dielectric components of soil samples with strong electrical conductivity under high and low fluoride conditions. The conductivity loss angle and real and imaginary dielectric constants were used to study fluoride salt's dielectric behavior. The imaginary dielectric component sensitive to dielectric loss could predict fluoride across large areas over time. This was statistically analyzed with R2 = 0.86, RMSE = 1.90, and bias = 0.35 showing a promising depiction that C-band SAR data can distinguish fluoride levels over varied clay soil and soil with varying vegetation development. Moreover, the association between biomass and simulated fluoride helped to identify fluoride-tolerant and non-tolerant crops. The study found that Sorghum and Oryza sativa tolerate saline-associated fluoride, whereas Peanut and Allium do not. Furthermore, the model successfully retrieves fluoride from saline salts based on tangent loss.

Forest Snow Depth Estimation Based on Optimized Features and DNN Network Using C-Band SAR Data

Forest Snow Depth Estimation Based on Optimized Features and DNN Network Using C-Band SAR Data

Current land subsidence in Jakarta: a multi-track SBAS InSAR analysis during 2017–2022 using C-band SAR data

In Jakarta, Indonesia, rapid urbanization and intensive groundwater extraction have led to significant land subsidence, posing challenges for sustainable urban management. This study utilized Interferometry Synthetic Aperture Radar (InSAR) integrated with multi-track data analysis to generate refined time-series and velocities of 2D displacement across Greater Jakarta. This study reveals notable variation in subsidence rates across different areas, with the most significant subsidence observed in Cikarang and the coastal regions of North Jakarta, which remains linear to this day. Notably, while the 2D approach improved accuracy by up to 53% at some locations, discrepancies at others indicated that simpler descending projections might sometimes yield better results. This underscores the necessity for a nuanced application of geodetic methods based on specific site conditions to effectively monitor and manage land subsidence in complex urban environments. Our findings highlight the critical importance of integrating multiple monitoring approaches to comprehensively address both vertical and horizontal displacement.

A Semi-Empirical Threshold Model for Oil Spill Detection by Analyzing Microwave Backscatter of Ocean Surface From Sentinel-1 C-Band SAR Data

A Semi-Empirical Threshold Model for Oil Spill Detection by Analyzing Microwave Backscatter of Ocean Surface From Sentinel-1 C-Band SAR Data

Forest Characterization Using C-band SAR Data—Initial Results of EOS-04 Data

Forest Characterization Using C-band SAR Data—Initial Results of EOS-04 Data

Interferometric Synthetic Aperture Radar Applicability Analysis for Potential Landslide Identification in Steep Mountainous Areas with C/L Band Data

Landslides frequently occur in the mountainous area of southwest China, resulting in infrastructure damage, as well as a loss of life and property. The use of interferometric synthetic aperture radar (InSAR) technology has become increasingly popular due to its wide coverage, high precision, and efficiency in identifying potential landslides in steep mountainous regions to mitigate risks. This study focused on the Mao County region in China and utilized a small baseline subset of InSAR (SBAS−InSAR) technology with Sentinel-1 and ALOS-2 data to identify the potential landslides and analyze their applicability. To ensure accuracy, the findings were verified using optical image and field surveys. Additionally, a comparative analysis was performed on C-band and L-band SAR data to examine differences in the coherence, geometric distortion, and displacement results, revealing that the L-band has clear advantages in the coherence, suitable observation coverage, and displacement results, while C-band can detect relatively slight displacements. This study aimed to determine the applicability of different SAR satellites for early landslide identification in steep mountainous areas, which can serve as a technical reference for selecting appropriate SAR data and enhancing InSAR identification abilities for potential landslides in the future.

Estimation of Surface and Subsurface Soil Moisture Using Microwave Remote Sensing: A Typical Analysis

Accurate measurement and monitoring of surface and subsurface soil moisture is essential for understanding hydrological processes, crop growth modeling, crop water requirement, and climate studies. Accurate measurement of the soil moisture content (SMC) in the root zone is essential for precise irrigation authority and plant water stress evaluation. However, the existing passive microwave satellite missions, Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), that operate at L-band, can only estimate the top 5 cm of soil moisture. Microwave remote sensing has proven to be a valuable tool for non-invasive soil moisture estimation. This research aims to investigate and develop a methodology for estimating surface and subsurface soil moisture using microwave data from Sentinel-1. The study was conducted to establish the relationship between surface & the backscatter coefficient derived using the Sentinel-1 SAR microwave remote sensing satellite imagery, and relationship between surface and subsurface soil moisture at different depths, in the Godhra region. Two seasons namely summer (Zaid) and monsoon (Kharif) were taken into consideration to build up the relationship between surface soil moisture and co-polarization backscatter coefficient ( For the summer (Zaid) and monsoon (Kharif) seasons, the co-polarization backscatter coefficient ( and surface soil moisture (0-5, cm) were found to have a correlation in terms of R2 as 0.91 and 0.90, respectively. The study explores the relationship between microwave signals and surface soil moisture content (0-5, cm) and then the relationship between surface soil moisture and soil moisture at various depths were also modeled thereby contributing to improved soil moisture estimation techniques and applications. The value of the coefficient of determination (R2) of surface soil moisture (0-5, cm) to subsurface soil moisture at 6-20 cm, 21-40 cm, and 41-60 cm depths were found to be 0.60, 0.51, and 0.46, respectively, in the summer (Zaid) season. The value of the coefficient of determination (R2) of surface soil moisture (0-5, cm) to subsurface soil moisture at 6-20 cm, 21-40 cm, 41-60 cm, 61-80 cm, and 81-100 cm depths were found to be 0.83, 0.61, 0.51, 0.26, and 0.13, respectively. According to the study, it is observed that the relationship between co-polarization backscatter coefficient ( and soil moisture weakens as the depth of soil moisture increases. Overall, the regression models developed between the co-polarization backscatter coefficient ( and surface soil moisture showed very good results, whereas the regression models developed between the surface soil moisture and soil moisture at various depths showed reasonably acceptable results up to the depth of 60 cm. The findings in the present study suggest that Sentinel-1A C-band SAR data can be used to estimate surface soil moisture. It is also shown in this study that the surface soil moisture can be correlated with the subsurface soil moisture up to the depth of 60 cm, satisfactorily using regression equations.

Ground subsidence associated with mining activity in the Ningdong coal base area, northwestern China revealed by InSAR time series analysis

Ningdong coal base area located in northwestern China is one of the largest coal-producing bases in China. The aim of this work is to investigate a regional-scale mining subsidence over the Ningdong coal base area, by using both conventional and advanced Differential Synthetic Aperture Radar Interferometry (DInSAR) methods. Fifteen L-band SAR images from ALOS-2 satellite and 102 C-band images from Sentinel-1A satellite spanning between November 2014 and July 2019 were used for the analysis. To increase the spatial extent of the displacement signal because of decorrelated effects, we modified the traditional Small Baseline Subset (SBAS) method to incorporate the coherence into the inverse problem, hereafter we call it coherence-based SBAS method. Instead of excluding decorrelated pixels present in the interferograms, we keep all the pixels in the time series analysis and down-weighted the decorrelated pixels with coherence. We performed the coherence-based SBAS method to both the two SAR datasets to obtain the subsidence rate maps and displacement time-series over the mining areas, and compared the results with that from the traditional stacking InSAR method. We evaluated the effectiveness of L-band and C-band DInSAR for monitoring mining subsidence by comparing differential interferograms and displacements derived from SBAS method between ALOS-2 and Sentinel-1A data. Compared to C-band, L-band SAR are less affected by phase aliasing due to large displacement gradients. The most significant subsidence was found at Maliantai mine with −264 mm/year detected by SBAS method from Sentinel-1 data. We validated the InSAR displacement accuracy by comparing both ALOS-2 and Sentinel-1 results with 18 GPS stations above five active mining regions. The average RMSE between InSAR and GPS measurements is 28.4 mm for Sentinel-1 data and 21 mm for ALOS-2 data. Our results demonstrate that the combined exploitation of L-band and C-band SAR data through both conventional and advanced DInSAR methods could be crucial to monitor ground subsidence in mining areas, which provides insights into subsidence dynamics and determine the characteristic surface response to longwall advance.

Soil moisture estimation underneath crop cover using high incidence angle C-band Sentinel-1 SAR data

SAR's return signal sensitivity to dielectric constant and penetration through vegetation makes it ideal for large-scale soil moisture studies. High incidence angle SAR data is especially useful for understanding crop characteristics. However, interpreting the return signal requires considering both crop characteristics and underlying soil moisture. In this study, an attempt has been made to assess the potential of high incidence angle C-band SAR data for soil moisture retrieval by incorporating effects of crop on Radar signal using water cloud model. The simulated VV backscatter from water cloud model by considering Leaf Area Index (LAI) and plant water content as vegetation descriptors has shown Root Mean Square Error (RMSE) of 1.28 ​dB with actual VV backscatter. Later, a two-layer feedforward neural network comprising one hidden layer with sigmoid neurons has been considered to develop retrieval models. It is observed that the ANN with input of high incidence angle C-band VV backscatter over wheat crop has soil moisture retrieval performance with correlation coefficient (R) and RMSE of 0.55 and 9.18 ​m3/m3 respectively. Another ANN model is developed to incorporate effects of crop on Radar signal by using VH backscatter, Radar Vegetation Index (RVI) and two-way attenuation factor derived from water cloud model along with VV backscatter. The overall performance of this model has been observed with R and RMSE of 0.77 and 5.81 ​m3/m3 respectively. The study results indicate that the high incidence angle SAR data may be considered for soil moisture retrieval underneath crop cover by effectively incorporating the crop effects on Radar signal. This may further extend to develop a model to extract crop biophysical parameters as well as soil moisture from Sentinel-1 SAR data.

Target Scattering Feature Extraction Based on Parametric Model Using Multi-Aspect SAR Data

The multi-aspect SAR observation can obtain the backscattering information of the illuminated scene target. There are targets with different structures in the scene, and their backscattering responses are also different. Using backscattering amplitude information to analyze the differences between targets is a conventional method. For point target types, one-dimensional backscattering curves can be used to analyze scattering characteristics, but it is difficult to analyze the overall structure of the target. Therefore, it is necessary to perform statistical analysis on the backscattering information combinating with the multi-aspect target area, and establish parameters to model the target area. In this paper, the algorithm uses the G0 distribution based on expectation maximization (EM) to fit the target area of the SAR scene. For different target types in the scene, the β and σ parameters obtained by the model combined with the backscattering amplitude information are used to perform the target. The results show that full-target in multi-aspect SAR image can be differentiated by two parameters. The scattering of partial-target slices can be characterized using two parameters (amplitude difference from surrounding points, scattering energy). The parametric model quantitatively characterizes the scattering feature of the target level, and the parameters changing corresponds to the change of the target image feature. C-band circular SAR data is used to validate our method. The experimental results give the parameter representation with sampling window based on the analysis of the target scattering, and give parameter estimates to characterize the partial target scattering.

Comparison of Three Methods for Distinguishing Glacier Zones Using Satellite SAR Data

Changes in glacier zones (e.g., firn, superimposed ice, ice) are good indicators of glacier response to climate change. There are few studies of glacier zone detection by SAR that are focused on more than one ice body and validated by terrestrial data. This study is unique in terms of the dataset collected—four C- and L-band quad-pol satellite SAR images, Ground Penetrating Radar data, shallow glacier cores—and the number of land ice bodies analyzed, namely, three tidewater glaciers in Svalbard and one ice cap in Iceland. The main aim is to assess how well popular methods of SAR analysis perform in distinguishing glacier zones, regardless of factors such as the morphologic differences of the ice bodies, or differences in SAR data. We test and validate three methods of glacier zone detection: (1) Gaussian Mixture Model–Expectation Maximization (GMM-EM) clustering of dual-pol backscattering coefficient (sigma0); (2) GMM-EM of quad-pol Pauli decomposition; and (3) quad-pol H/α Wishart segmentation. The main findings are that the unsupervised classification of both sigma0 and Pauli decomposition are promising methods for distinguishing glacier zones. The former performs better at detecting the firn zone on SAR images, and the latter in the superimposed ice zone. Additionally, C-band SAR data perform better than L-band at detecting firn, but the latter can potentially separate crevasses via the classification of sigma0 or Pauli decomposition. H/α Wishart segmentation resulted in inconsistent results across the tested cases and did not detect crevasses on L-band SAR data.

Identifying Wet and Dry Snow With Dual-Polarized C-Band SAR Data Based on Markov Random Field Model

Quad-pol SAR is one of the most effective approaches for dry and wet snow identification data, but its applicability is limited by the high cost of quad-pol SAR data. Dual-pol SAR such as Sentinel-1 has larger spatial coverage, longer time sequences and freely accessible data, but there is still a highly uncertainty in dual-pol SAR to distinguish dry and wet snow due to limited polarimetric information. In this study, a pixel neighborhood-based snow identification algorithm was developed and verified using dual-pol C-band SAR data in Northern Xinjiang, China. A total of six decomposed parameters were obtained to characterize the polarimetric information of dual-pol SAR data by modifying the H-α decomposition applicable to dual-pol SAR data. In the case of limited training samples, polarimetric features that were most sensitive to snow identification were selected as the optimal features for support vector machine (SVM), and the result derived from SVM was employed as the initial labels of markov random field (MRF) model to separate dry and wet snow using iterative conditional mode (ICM). Then, the proposed algorithm, dual-pol SVM-MRF (DSVM-MRF), was validated and compared with previously published methods. The results show that the DSVM-MRF acquires the superior snow recognition with the overall accuracy and kappa coefficient of 84.5% and 0.58, respectively.

Soil Moisture Mapping Along Climatic Gradient by Dual-Polarization Sentinel-1 C-Band Data

Soil moisture plays an important role in agriculture, hydrology, plants’ seasonal growth cycle, and their sustainability under climate change. This letter presents a novel method for regional mapping of soil moisture with partial vegetation cover by using dual-polarized Sentinel-1 C-band SAR data. Model-based retrieval of soil moisture from radar backscatter under partial vegetation cover first necessitates the separation of the backscatter of the underlying soil from total radar backscatter and then the implementation of an inversion technique to estimate its soil moisture content from the backscattering coefficient of the underlying soil. The regional mapping of soil moisture was verified with in situ measurements along a climatic gradient (650–100 mm/year) in central Israel. A good agreement between the estimated soil moisture and the measured from ground samples was found, with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 0.793 with an RMSE of 0.047.

Mapping Samudra Tapu glacier: A holistic approach utilizing radar and optical remote sensing data for glacier radar facies mapping and velocity estimation

Himalayan glaciers have shown more sensitivity and visible changes to the climate change and global warming in the last 150 years. The highly rugged topography and inaccessible remote areas makes satellite images as the most appropriate source of information retrieval. We performed remote sensing based glacier change study for Samudra Tapu glacier, located in the Chandra basin of North-West Himalaya. In the present study, the capabilities of both optical and microwave remote sensing data was analysed in glacier change study in terms of its coverage, shift in equilibrium line altitude (ELA) and surface velocity over a period from 2000 to 2021. Multi Sensor (RISAT-1, Sentinel-1) time series of C-band SAR data along with a object oriented classification technique were used to identify different glacier facies such as percolation facies, icefalls, bare ice facies, refreeze snow and supraglacial debris. These classified maps were also used to detect the snow line and firn line along with ELA, aided with elevation information from digital elevation model (DEM). It was identified that more than 50 % of the total glacier area still lies into accumulation region. Further, we estimated the glacier surface velocity using Differential Interferometric Synthetic Aperture Radar (DInSAR) technique using European Remote Sensing Satellite (ERS-1/2) tandem data of 1996. High value of coherence was observed from the SAR return signal for one-day temporal difference. A mean velocity of 17–24 cm/day was found for the months of March and May 1996, highest flow rates were seen in the high accumulation area located in the Eastern and Southern Aspect of glacier. Spatial analysis of velocity patterns with respect to slope and aspect show that high rates of flow was found in southern slopes and movement rates generally increase with increase in slope. Feature tracking approach was used to estimate the glacier flow for long term and seasonal basis using optical and SAR datasets (IRS-1C, 1D PAN, Landsat-7, 8 PAN, and TANDEM-x) during 1999–2020 period. The results suggest that glacier flow varies with season, i.e., high velocity during spring-summer season, as compared to late summer or winter and, the rate of ice flow changes over the years. The mean glacier velocity reduced to 49.5 m/year during 2013–2020 time, as compared to 67.67 m/year during 1999–2003 time. These results of reducing glacier velocity and changing snow line altitude indicates enhanced glacier’s melt rate and overall negative mass balance for Smudra tapu glacier.

Cover classifications in wetlands using Sentinel-1 data (Band C): a case study in the Parana river delta, Argentina

With the launch of the Sentinel-1 mission, for the first time, multitemporal and dual-polarization C-band SAR data with a short revisit time is freely available. How can we use this data to generate accurate vegetation cover maps on a local scale? Our main objective was to assess the use of multitemporal C-Band Sentinel-1 data to generate wetland vegetation maps. We considered a portion of the Lower Delta of the Paraná River wetland (Argentina). Seventy-four images were acquired and 90 datasets were created with them, each one addressing a combination of seasons (spring, autumn, winter, summer, complete set), polarization (VV, HV, both), and texture measures (included or not). For each dataset, a Random Forest classifier was trained. Then, the kappa index values (κ) obtained by the 90 classifications made were compared. Considering the datasets formed by the intensity values, for the winter dates the achieved kappa index values (κ) were higher than 0.8, while all summer datasets achieved κ up to 0.76. Including feature textures based on the GLCM showed improvements in the classifications: for the summer datasets, the κ improvements were between 9% and 22% and for winter datasets improvements were up to 15%. Our results suggest that for the analyzed context, winter is the most informative season. Moreover, for dates associated with high biomass, the textures provide complementary information.

Mapping the Spatial Distribution of Fern Thickets and Vine-Laden Forests in the Landscape of Bornean Logged-Over Tropical Secondary Rainforests

Forest degradation has been most frequently defined as an anthropogenic reduction in biomass compared with reference biomass in extant forests. However, so-defined “degraded forests” may widely vary in terms of recoverability. A prolonged loss of recoverability, commonly described as a loss of resilience, poses a true threat to global environments. In Bornean logged-over forests, dense thickets of ferns and vines have been observed to cause arrested secondary succession, and their area may indicate the extent of slow biomass recovery. Therefore, we aimed to discriminate the fern thickets and vine-laden forests from those logged-over forests without dense ferns and vines, as well as mapping their distributions, with the aid of Landsat-8 satellite imagery and machine learning modeling. During the process, we tested whether the gray-level co-occurrence matrix (GLCM) textures of Landsat data and Sentinel-1 C-band SAR data were helpful for this classification. Our study sites were Deramakot and Tangkulap Forest Reserves—commercial production forests in Sabah, Malaysian Borneo. First, we flew drones and obtained aerial images that were used as ground truth for the supervised classification. Subsequently, a machine-learning model with a gradient-boosting decision tree was iteratively tested in order to derive the best model for the classification of the vegetation. Finally, the best model was extrapolated to the entire forest reserve and used to map three classes of vegetation (fern thickets, vine-laden forests, and logged-over forests without ferns and vines) and two non-vegetation classes (bare soil and open water). The overall classification accuracy of the best model was 86.6%; however, by combining the fern and vine classes into the same category, the accuracy was improved to 91.5%. The GLCM texture variables were especially effective at separating fern/vine vegetation from the non-degraded forest, but the SAR data showed a limited effect. Our final vegetation map showed that 30.7% of the reserves were occupied by ferns or vines, which may lead to arrested succession. Considering that our study site was once certified as a well-managed forest, the area of degraded forests with a high risk of loss of resilience is expected to be much broader in other Bornean production forests.

Rice Crop Monitoring Using Sentinel-1 SAR Data: A Case Study in Saku, Japan

Global warming affects rice crop production, causing deterioration of rice grain quality. This study used C-band microwave images taken by the Sentinel-1 satellites to monitor rice crop growth with the aim to understand microwave backscatter behavior, focusing on decreases in panicle water contents with ripening, which affect C-band backscatter. Time-series changes illustrated a similar tendency across all four analysis years, showing that VV/VH ratio at an incidence angle of 45–46° stopped decreasing to be stable over the reproductive and ripening periods due to reductions in the panicle water content that allowed for greater microwave penetration into the canopy, thereby increasing panicle-related backscatter. Furthermore, multivariate regression analysis combined with field observations showed that VV and VH with the shallow incidence angles were significantly negatively correlated with panicle water content, which well demonstrated backscatter increases with plant senescence. Furthermore, it was observed that backscatter behaviors were highly consistent with changes in crop phenology and surface condition. Accordingly, Sentinel-1 images with shallow incidence angles and high revisit observation capabilities offer a strong potential for estimating panicle water content. Therefore, it seems reasonable to conclude that C-band SAR data is capable of retrieving grain filling conditions to estimate proper harvesting time.

INSAR-BASED DETECTION AND MAPPING OF SEISMICALLY INDUCED GROUND SURFACE DISPLACEMENT AND DAMAGE IN PAMPANGA, PHILIPPINES

On April 22, 2019, an earthquake with a magnitude MW 6.1 struck the municipality of Castillejos in Zambales, the Philippines, and severely affected the province of Pampanga, which caused damage to commercial and residential structures reaching over 40 victims. This paper presents an approach for creating a pixel-based proxy damage assessment and displacement field maps to delineate the extent of ground surface displacements due to an earthquake. Specifically, this paper explored two change detection methods: the interferometric synthetic aperture radar (InSAR) technique and the coherence difference analysis method, using an open-source remote sensing software package and free SAR image data acquired by Sentinel-1 missions. Ground truth data were collected to substantiate the findings of the generated maps after the earthquake. Out of 7 surveyed damaged structures that were included in the National Disaster Risk Reduction and Management Council (NDRRMC) of the Philippines Situational Report, four damaged structures were successfully targeted using the proxy damage assessment map that had a coherence difference value ranging from 0.7-0.9 and damage grades of 3-5 based on the European Macroseismic Scale 1998 (EMS-98) damage classification system. This study confirms that change detection methods applied to C-band Sentinel-1 SAR data are valuable for mapping damaged areas and estimating ground surface displacements toward better hazard mitigation and disaster response.

Detection and mapping co-seismic deformation induced by a light earthquake (M&lt;5) using C-band SAR data

Detection and mapping co-seismic deformation induced by a light earthquake (M&lt;5) using C-band SAR data