C-band SAR Images Research Articles

Assessing flash flood erosion following storm Daniel in Libya

The eastern Mediterranean basin is witnessing increased storm activity impacting populous urban coastal areas that historically were not prone to catastrophic flooding. In the fall of 2023, Storm Daniel struck the eastern coast of Libya, causing unprecedented flash floods with a tragic death toll and large-scale infrastructure damages. We use Sentinel-1A C-band SAR images to characterize the resulting flash flood erosion and sediment load dynamics across the watersheds and to map damages within coastal cities at their outlets. Our results suggest that sediment loading, resulting from surface erosion, increased the density of turbid streams. The above exacerbated the catastrophic impact of the flash floods in the coastal cities of Derna and Susah, where 66% and 48% of their respective urban surface have experienced moderate-to-high damages. Our findings highlight the increased vulnerability of coastal watersheds in arid areas within the eastern Mediterranean basin due to the forecasted increase in hydroclimatic extremes and call for a transformative coastal management approach to urgently implement nature-based solutions and land-use changes to mitigate these rising risks.

On the physical mechanism causing strongly enhanced radar backscatter in C-Band SAR images of convective rain over the ocean

ABSTRACT Radar signatures of rain over the ocean have a complex structure since they receive contributions from surface scattering and volume scattering and attenuation by hydrometeors in the atmosphere. These contributions overlap and are often difficult to detangle. While most of the mechanisms contributing to radar signatures of rain over the ocean are well understood, there is one remaining issue that has been discussed controversially in the literature for a long time. It is the question what scattering mechanism causes the areas of strongly enhanced radar backscatter, also called ‘bright blobs’ or ‘bright patches’, which are frequently observed on spaceborne C-band SAR images acquired over tropical and subtropical oceans in the presence of convective rain. Recently, papers have been published in which it is hypothesized that they are caused by radar backscattering at hydrometeors in the melting layer (ML). Although many observational facts seem to support this hypothesis, there exists one strong argument against this hypothesis: It is the observation that the position of the ML radar signatures (bright blob) in the SAR image is not shifted in anti-range from the position, where the rain column hits the sea surface. This absence of a shift is observed when 1) comparing Sentinel-1 SAR images on which rain cells are visible with quasi-concurrently acquired weather radar images and 2) when inter-comparing of SAR images of rain cells acquired concurrently at different frequencies and polarizations. Based on these observations, we discard the hypothesis that the bright blobs are due to volume scattering at hydrometeors in the ML and hypothesize instead that they are due to scattering at splash products at the sea surface. This hypothesis is supported by radar backscattering measurements carried out in the laboratory and from a shore-based platform, which show that, at C-and X-band, strong rain can give rise to strong radar returns also at cross-polarization.

Deep Learning Model Transfer in Forest Mapping Using Multi-Source Satellite SAR and Optical Images

Deep learning (DL) models are gaining popularity in forest variable prediction using Earth observation (EO) images. However, in practical forest inventories, reference datasets are often represented by plot- or stand-level measurements, while high-quality representative wall-to-wall reference data for end-to-end training of DL models are rarely available. Transfer learning facilitates expansion of the use of deep learning models into areas with sub-optimal training data by allowing pretraining of the model in areas where high-quality teaching data are available. In this study, we perform a “model transfer” (or domain adaptation) of a pretrained DL model into a target area using plot-level measurements and compare performance versus other machine learning models. We use an earlier developed UNet based model (SeUNet) to demonstrate the approach on two distinct taiga sites with varying forest structure and composition. The examined SeUNet model uses multi-source EO data to predict forest height. Here, EO data are represented by a combination of Copernicus Sentinel-1 C-band SAR and Sentinel-2 multispectral images, ALOS-2 PALSAR-2 SAR mosaics and TanDEM-X bistatic interferometric radar data. The training study site is located in Finnish Lapland, while the target site is located in Southern Finland. By leveraging transfer learning, the SeUNet prediction achieved root mean squared error (RMSE) of 2.70 m and R2 of 0.882, considerably more accurate than traditional benchmark methods. We expect such forest-specific DL model transfer can be suitable also for other forest variables and other EO data sources that are sensitive to forest structure.

Construction of Time-series Displacement Data of Yongdam Dam Based on PSInSAR Analysis of Satellite C-band SAR Images

The increase in water-related disasters due to climate change has a significant impact on the stability of water resource facilities. The displacement of a water resource facility is one of the important indicators to evaluate the stability of the facility. In this study, the time-series displacement of the Yongdam Dam was constructed by applying the persistent scatter interferometric synthetic aperture radar (PSInSAR) technique to the Sentinel-1 C-band SAR images. A sufficient number of persistent scatterers were derived to enable local deformation monitoring of the Yongdam Dam, and the dam showed very small displacement velocity except during the heavy rainfall in August 2020. In the future, C-band SAR imagery from the water resources satellite (Next Generation Medium Satellite 5) is expected to provide accurate displacement data for water resource facilities.

A Novel Polarimetric Channel Imbalance Phase Estimation Method Based on the Rotated Double-Bounce Backscatters in Urban Areas

Polarization calibration without artificial calibrators has been one of the focuses of research and discussion for PolSAR communities. However, there is limited research on the treatment of dual-polarization systems and the calibration methods for getting rid of distributed targets. In this paper, we contribute to proposing a new and convenient method for estimating the polarimetric channel imbalance phase at the transmitter and receiver, which can be used for both quad-pol and dual-pol SAR systems. We found a brand-new reference object in the urban area scene, namely the effective dihedrals. A statistical calculation method was proposed correspondingly, which obtained an effective estimation of the channel imbalance phases. The theoretical explanation of the proposed method was consistent with the statistical phenomena presented in the experiments. The technique was illustrated and verified through C-band SAR images, including GaoFen-3 (GF-3) data and Sentinel-1 data. The technique was also validated and successfully applied in airborne SAR data of P, L, S, C, and X bands. The estimation error could be within 7° when crosstalk items were less than −30 dB. The method realizes a fast and low-cost dual-polarization phase imbalance estimation and provides a new technical approach to supplement the traditional tropical-rainforest-based quad-pol system calibration. The method can be conveniently applied to the monitoring of polarization distortion parameters, ensuring good polarization SAR data quality.

Analysis of the Temporal Evolution of Ice Aprons in the Mont-Blanc Massif Using X and C-Band SAR Images

This paper investigates the backscatter evolution and surface changes of ice aprons (IAs) by exploiting time series of X- and C-band SAR images from PAZ and Sentinel-1 satellites. IAs are extremely small ice bodies of irregular shape present on steep slopes and complex topographies in all the major high-Alpine environments of the world. Due to their small size and locations in complex topographies, they have been very poorly studied, and very limited information is known about their evolution and responses to climate change. SAR datasets can provide handy information about the seasonal behaviour of IAs since physical changes of IA surfaces modify the backscattering of RaDAR waves. The analysis of the temporal variations of the backscatter coefficient illustrates the effects of increasing temperatures on the surface of the IAs. All IAs considered in the analysis show a strong decrease in backscatter coefficient values in the summer months. The backscattering patterns are also supported by the annual evolution of the coefficient of variation, which is an appropriate indicator to evaluate the heterogeneity of the surface. Higher mean backscatter values in the X-band than in the C-band indicate surface scattering phenomena dominate the IAs. These features could provide key information for classifying IAs using SAR images in future research.

Mapping stressed wheat plants by soil aluminum effect using C-band SAR images: implications for plant growth and grain quality

Under toxic aluminum (Al) levels in the soil, wheat (Triticum aestivum L.) suffers stress and plant growth is affected. A method for diagnosis of plants is proposed that includes the following as a strategy: to analyze total Al in the soil, employ satellite radar imagery and calculate a vegetation index. The objective of this research, conducted at the field scale, was to explore how radar backscattering coefficients from a winter wheat canopy, combined with the normalized difference vegetation index (NDVI) and geographic information system (GIS) technology, can be used as a mapping tool for the variability of Al-stressed canopies. As a result, an analysis of covariance showed significant differences, and the lowest plant height was obtained at a high level of soil Al, as well as the minimum grain weight and magnesium content. It was found that a simple model could be used to estimate plant height from the backscattering coefficient of vertical transmit-vertical receive polarization (σ°VV), with a strong correlation (r − 0.84). In turn, a third-order polynomial regression model (R2 0.70) was proposed to estimate the NDVI from σ°VV. This model provided a good estimate of the NDVI at the stem elongation stage of growth (50 days after sowing). Detected NDVI patterns were associated with variation in canopy stress depending on polarimetric information, which, in turn, was related to soil Al levels. Thus, the maps derived from the model can monitor spatial variability, where NDVI values < 0.68 indicate stressed areas. This study provides guidance for in-season stress spatial variability caused by Al.

DS-InSAR Based Long-Term Deformation Pattern Analysis in the Mining Region With an Improved Phase Optimization Algorithm

The environment management and land utilization of the abandoned mining region is critically dependent on precise monitoring and comprehensive understanding of mining subsidence. In order to overcome the shortcomings of the traditional distributed target phase optimization method in the space continuity constraints of adjacent pixels, an improved phase optimization algorithm was proposed, which combines region growing and time series interferometric synthetic aperture radar with distributed scatterers (DS-InSAR). By using 17 L-band and 51 C-band SAR images, the characteristics of temporal and spatial distribution in Peibei mining area of Xuzhou, China, were obtained during the period from 2007 to 2011 and the period from 2017 to 2020. With the long-term monitoring, the evolution of deformation in the mining area was carried out. The modified phase optimization technique has proven its ability in the density of measurement points and the influence of noise in space, which is promising for the detection of large gradient deformation and the accurate analysis of surface deformation in mining areas. The study has been concentrated toward detecting continuous subsidence in the mining region. Coal mines in operation are usually accompanied by unstable ground, and the uplift or second subsidence has sometime occurred in the closed mine region. Conclusively, the presented methodology is practically feasible for long-term deformation pattern analysis in coal-exhausted mining areas.

Assessing Urban Landslide Dynamics through Multi-Temporal InSAR Techniques and Slope Numerical Modeling

Landslides threaten more than before the urbanized areas and are a worldwide growing problem for the already affected communities and the local authorities committed to landslide risk management and mitigation. For this reason, it is essential to analyze landslide dynamics and environmental conditioning factors. Various techniques and instruments exist for landslide investigation and monitoring. Out of these, Multi-temporal Synthetic Aperture Radar Interferometry (MT-InSAR) techniques have been widely used in the last decades. Their capabilities are enhanced by the availability of the active Sentinel-1 mission, whose 6-day revisiting time enables near real-time monitoring of landslides. Interferometric results, coupled with ground measurements or other approaches such as numerical modeling, significantly improve the knowledge of the investigated surface processes. In this work, we processed the C-band SAR images of the available European Space Agency (ESA) satellite missions, using MT-InSAR methods to identify the surface deformations related to landslides affecting the Iași Municipality (Eastern Romania). The results (i.e., velocity maps) point out the most active landslides with velocities of up to 20 mm/year measured along the satellite Line of Sight (LOS). Following, we focused on the most problematic landslide that affects the Țicău neighborhood and is well-known for its significant implications that it had. To better understand its behavior and the sensitivity of the displacements to the environmental factors (i.e., rainfall), we carried out 2D numerical modeling using a finite difference code. The simulated displacement field is consistent with the InSAR displacements and reveals the most active sectors of the landslide and insights about its mechanism.

A Machine Learning-Based Approach for Surface Soil Moisture Estimations with Google Earth Engine

Due to its relation to the Earth’s climate and weather and phenomena like drought, flooding, or landslides, knowledge of the soil moisture content is valuable to many scientific and professional users. Remote-sensing offers the unique possibility for continuous measurements of this variable. Especially for agriculture, there is a strong demand for high spatial resolution mapping. However, operationally available soil moisture products exist with medium to coarse spatial resolution only (≥1 km). This study introduces a machine learning (ML)—based approach for the high spatial resolution (50 m) mapping of soil moisture based on the integration of Landsat-8 optical and thermal images, Copernicus Sentinel-1 C-Band SAR images, and modelled data, executable in the Google Earth Engine. The novelty of this approach lies in applying an entirely data-driven ML concept for global estimation of the surface soil moisture content. Globally distributed in situ data from the International Soil Moisture Network acted as an input for model training. Based on the independent validation dataset, the resulting overall estimation accuracy, in terms of Root-Mean-Squared-Error and R², was 0.04 m3·m−3 and 0.81, respectively. Beyond the retrieval model itself, this article introduces a framework for collecting training data and a stand-alone Python package for soil moisture mapping. The Google Earth Engine Python API facilitates the execution of data collection and retrieval which is entirely cloud-based. For soil moisture retrieval, it eliminates the requirement to download or preprocess any input datasets.

Spatial search and a three level model based water layer extraction from C-band SAR image

ABSTRACT This paper describes a spatial search and a three-level model-based approach for automatic extraction of surface water layers from Sentinel-1 C-band SAR images at 10 m spatial resolution. The technique incorporates a connected component spatial search for segmenting low backscatter regions and uses the segmented image object for characterizing the segments. The water body is described here as a collection of different spatially connected segments. A three-level model is used to describe the connected segments of a water body in SAR data. Noise tolerance is achieved in this method by incorporating a speckle noise level into the model. The segmentation process further calculates contextual information which includes shadow estimated from DEM, polarization angle of the segment, and a boundary co-occurrence in both polarization to qualify the detected segments as a water body. The proposed method is found to have an accuracy of 94% in terms of f1 score. The algorithm, estimation of different parameters, and the results obtained in selected regions are explained in this paper.

Wide-Area Land Cover Mapping With Sentinel-1 Imagery Using Deep Learning Semantic Segmentation Models

Land cover (LC) mapping is essential for monitoring the environment and understanding the effects of human activities on it. Recent studies demonstrated successful applications of specific deep learning models to small-scale LC mapping tasks (e.g., wetland mapping). However, it is not readily clear which of the existing state-of-the-art models for natural images are the best candidates to be taken for the particular remote sensing task and data. In this article, we answer that question for mapping the fundamental LC classes using the satellite imaging radar data. We took ESA Sentinel-1 C-band SAR images acquired during the whole summer season of 2018 in Finland, which are representative of the land cover in the country. CORINE LC map was used as a reference, and the models were trained to distinguish between the five major CORINE-based classes. We selected seven among the state-of-the-art semantic segmentation models so that they cover a diverse set of approaches: U-Net, DeepLabV3+, PSPNet, BiSeNet, SegNet, FC-DenseNet, and FRRN-B, and further fine-tuned them. Upon evaluation and benchmarking, all the models demonstrated solid performance with overall accuracy between 87.9% and 93.1%, with good to a very good agreement (Kappa statistic between 0.75 and 0.86). The two best models were fully convolutional DenseNets (FC-DenseNet) and SegNet (encoder-decoder-skip), with the latter having a much shorter inference time. Overall, our results indicate that the semantic segmentation models are suitable for efficient wide-area mapping using satellite SAR imagery and provide baseline accuracy against which the newly proposed models should be evaluated.

Despeckling of SAR Images Using Shrinkage of Two-Dimensional Discrete Orthonormal S-Transform

Speckles are inherent to SAR. They hide and undermine several relevant information contained in the SAR images. In this paper, a despeckling algorithm using the shrinkage of two-dimensional discrete orthonormal S-transform (2D-DOST) coefficients in the transform domain along with shock filter is proposed. Also, an attempt has been made as a post-processing step to preserve the edges and other details while removing the speckle. The proposed strategy involves decomposing the SAR image into low and high-frequency components and processing them separately. A shock filter is used to smooth out the small variations in low-frequency components, and the high-frequency components are treated with a shrinkage of 2D-DOST coefficients. The edges, for enhancement, are detected using a ratio-based edge detection algorithm. The proposed method is tested, verified, and compared with some well-known models on C-band and X-band SAR images. A detailed experimental analysis is illustrated.

Monitoring of Ground Movement and Groundwater Changes in London Using InSAR and GRACE

Groundwater-induced land movement can cause damage to property and resources, thus its monitoring is very important for the safety and economics of a city. London is a heavily built-up urban area and relies largely on its groundwater resource and thus poses the threat of land subsidence. Interferometric Synthetic Aperture Radar (InSAR) can facilitate monitoring of land movement and Gravity Recovery and Climate Experiment (GRACE) gravity anomalies can facilitate groundwater monitoring. For London, no previous study has investigated groundwater variations and related land movement using InSAR and GRACE together. In this paper, we used ENVISAT ASAR C-band SAR images to obtain land movement using Persistent Scatterer InSAR (PSInSAR) technique and GRACE gravity anomalies to obtain groundwater variations between December 2002 and December 2010 for central London. Both experiments showed long-term, decreasing, complex, non-linear patterns in the spatial and temporal domain. The land movement values varied from −6 to +6 mm/year, and their reliability was validated with observed Global Navigation Satellite System (GNSS) data, by conducting a two-sample t-test. The average groundwater loss estimated from GRACE was found to be 9.003 MCM/year. The ground movement was compared to observed groundwater values obtained from various boreholes around central London. It was observed that when large volumes of groundwater is extracted then it leads to land subsidence, and when groundwater is recharged then surface uplift is witnessed. The results demonstrate that InSAR and GRACE complement each other and can be an excellent source of monitoring groundwater for hydrologists.

A novel hybrid quantum-PSO and credal decision tree ensemble for tropical cyclone induced flash flood susceptibility mapping with geospatial data

Flash flood is considered as one of the most destructive natural hazards worldwide, especially in tropical countries, where tropical cyclones with torrential rains are recurrent problems yearly. Therefore, an accurate prediction of susceptible areas to flash floods is crucial for developing measures to prevent, avoid, and minimize damages associated with flash floods. The aim of this research is to propose a new state-of-the-art model based on hybridizing Quantum Particle Swarm Optimization (QPSO) and the Credal Decision Tree (CDT) ensemble, namely the QPSO-CDTreeEns model, for spatial prediction of the flash flood. The concept of the proposed model is to build a forest tree of the CDT established through the Random Subspace ensemble. Therein, QPSO is integrated to optimize the three parameters, the subspace size, number of trees, and the maximum depth of trees. A district suffered from a high frequency of flash floods in the north-western mountainous area of Vietnam was selected as a case study. In this regard, a geospatial database that includes a total of 1698 flash flood and inundation polygons derived from Sentinel-1 C-band SAR images and ten input indicators were used to construct and to verify the proposed model. The result shows that the QPSO-CDT-Ens model performed well (Overall accuracy = 90.4, Kappa coefficient = 0.807) and outperformed the five machine learning algorithms in flash flood susceptibility mapping. Among the ten factors, the land-use/land-cover (LULC), the slope, the curvature, and the TWI are the most important indicators. We conclude that the proposed model is a promising tool for flash flood susceptibility mapping in the tropics and may assist decision-makers in sustainable land-use planning in the national disaster mitigation strategies.

Accuracy verification and evaluation of Sentinel-1A repeat track differential interferometric synthetic aperture radar in monitoring mining subsidence

The accuracy of differential interferometric synthetic aperture radar (DInSAR) in monitoring the ground subsidence is a major challenge to be addressed urgently. Using the repeat track DInSAR and GIS spatial analysis tools, eight C-band Sentinel-1A SAR images of the Guotun coal mine (China) were processed to determine the mining subsidence from November 27, 2015 to July 24, 2016. The mining data of 13 working faces and the DInSAR- and leveling-monitored results were compared. A method was proposed to solve the problem of time inconsistency between DInSAR- and leveling-monitored results. The location, spatial distribution, scope, and variations of mining subsidence monitored by Sentinel-1A repeat track DInSAR were consistent with the mining progress of the working faces. The accuracy of the DInSAR-monitored subsidence values was directly related to the coherence of the subsidence zones, and the absolute difference from the leveling-monitored values was small at the subsidence edge but large at the subsidence center.

Integration of multi-sensor analysis and decision tree for evaluation of dual and quad-Pol SAR in L- and C-bands applied for marsh delineation

Marsh is a wetland type characterized by hydromorphic soils, herbaceous vegetation, aquatic and emergent vegetation; usually, the apparent water surface does not exceed 25% of the area. Multi-polarized active remote sensors with different frequencies have characteristics that make them ideal for mapping and delineating marsh areas since they provide information on canopy roughness, vegetation moisture and amount of biomass. Therefore, the main objective of this study is to develop a method based on multi-frequency radar satellites images to delineate marsh areas using decision tree classification. In order to reach this objective, we sought to answer the following questions: (1) Are L-band SAR images more efficient for marshes delineation than C-band SAR images? (2) Is multi-sensor (L and C-band) integration more accurate for marsh areas delineation than a single sensor? and (3) What are the most efficient channels for marshes delineation? Our findings showed that L-band images present greater proportion correct (PC) for marshes delineation compared to C-band images. However, the greatest PC was found using integration of Alos Palsar 1 and Sentinel 1 satellites images, reaching more than 72% of correctness. Regarding the polarization importance to Alos Palsar 1 image, HVVH presented the highest importance, with 29%, followed by VH and HV polarizations, both with 28%. For Sentinel 1 image, the most important polarization was VH, with 22%, followed by VV + VH that presented 20%. HVVH polarization was the most important in Alos and Sentinel images integration, with 35%, followed by Alos Palsar HV and VH, with 34 and 33%, respectively. Thus, we concluded that the method based on SAR multi-frequency data integration used in this study can be easily applied by other researchers interested in marsh delineation since the radar images used are freely available and can be processed and manipulated in free GIS software.

National geohazards mapping in Europe: Interferometric analysis of the Netherlands

The launch of Copernicus, the largest Earth Observation program to date, is significant due to the regular, reliable and freely accessible data to support space-based geodetic monitoring of physical phenomena that can result in natural hazards. In this study, wide area interferometric synthetic aperture radar (InSAR) capability is demonstrated by processing 435 Copernicus Sentinel-1 C-Band SAR images (May 2015–May 2017) using the Intermittent Small Baseline Subset (ISBAS) method to produce a wide-area-map (WAM) covering 53,000 km2 of the Netherlands, Belgium and Germany. The ISBAS-WAM contains over 19 million measurements, achieving a ground coverage of 94%. The retrieval of measurements over soft surfaces (i.e. agricultural fields, forests and wetlands) was crucial due the dominance of non-urban land cover. A statistical analysis of the velocities reveals that intermittently coherent measurements in rural areas can provide reliable, additional deformation information with a very high degree of confidence (5σ), which spatially correlates to known deformation features associated with compressible soils, infrastructure settlement, peat oxidation, gas production, salt mining and underground and opencast mining. The spatial distribution of deformations concurs with independent data sources, such as previous persistent scatterer interferometry (PSI) deformation maps, models of subsidence and settlement susceptibility, and quantitatively with GPS measurements over the Groningen gas field.Remotely derived deformation products, with near complete spatial coverage, provide a powerful screening tool for mitigation and remediation of geological and geotechnical issues to help in the protection of assets, property and life. The ISBAS-WAM demonstrates that routine generation of such products on a continental scale is now theoretically achievable, given the establishment of the Copernicus programme and the development of state-of-the-art InSAR methods, such as ISBAS.

Boreal Forest Snow Damage Mapping Using Multi-Temporal Sentinel-1 Data

Natural disturbances significantly influence forest ecosystem services and biodiversity. Accurate delineation and early detection of areas affected by disturbances are critical for estimating extent of damage, assessing economical influence and guiding forest management activities. In this study we focus on snow load damage detection from C-Band SAR images. Snow damage is one of the least studied forest damages, which is getting more common due to current climate trends. The study site was located in the southern part of Northern Finland and the SAR data were represented by the time series of C-band SAR scenes acquired by the Sentinel-1 sensor. Methods used in the study included improved k nearest neighbour method, logistic regression analysis and support vector machine classification. Snow damage recordings from a large snow damage event that took place in Finland during late 2018 were used as reference data. Our results showed an overall detection accuracy of 90%, indicating potential of C-band SAR for operational use in snow damage mapping. Additionally, potential of multitemporal Sentinel-1 data in estimating growing stock volume in damaged forest areas were carried out, with obtained results indicating strong potential for estimating the overall volume of timber within the affected areas. The results and research questions for further studies are discussed.

A quad polarimetric SAR calibration algorithm using rotation symmetry

ABSTRACTPolarimetric calibration, precisely deciphering the polarimetric information hidden in the polarized characteristics of a radar scene, is the core step before applying quad polarimetric synthetic aperture radar (PolSAR) data for ground parameter inversion and classification. The previously published techniques for polarimetric calibration generally use at least one known point calibration target to completely determine the polarimetric distortions (cross-talks and channel imbalances) between the various polarized channels. This paper describes a novel method that solely relies on the image itself with the property of rotation symmetry. The algorithm derives the entire cross-talk and channel imbalance parameters using an iterative operation to circularly modify the observed average covariance matrix, which is independent on the known point calibration targets in the illuminated scene. The proposed method is validated to be reliable and efficient through polarimetric calibration experiments using the airborne C-band and RadarSat-2 quad polarimetric SAR images. The experimental results indicate that the new technique achieves similar calibration precision to the Ainsworth algorithm but without using any known calibration point target.