C-band Synthetic Aperture Radar Images Research Articles

Seismic Risk Regularization for Urban Changes Due to Earthquakes: A Case of Study of the 2023 Turkey Earthquake Sequence

Damage identification soon after a large-magnitude earthquake is a major problem for early disaster response activities. The faster the damaged areas are identified, the higher the survival chances of inhabitants. Current methods for damage identification are based on the application of artificial intelligence techniques using remote sensing data. Such methods require a large amount of high-quality labeled data for calibration and/or fine-tuning processes, which are expensive in the aftermath of large-scale disasters. In this paper, we propose a novel semi-supervised classification approach for identifying urban changes induced by an earthquake between images recorded at different times. We integrate information from a small set of labeled data with information from ground motion and fragility functions computed on large unlabeled data. A relevant consideration is that ground motion and fragility functions can be computed in real time. The urban changes induced by the 2023 Turkey earthquake sequence are reported as an evaluation of the proposed method. The method was applied to the interferometric coherence computed from C-band synthetic aperture radar images from Sentinel-1. We use only 39 samples labeled as changed and 9000 unlabeled samples. The results show that our method is able to identify changes between images associated with the effects of an earthquake with an accuracy of about 81%. We conclude that the proposed method can rapidly identify affected areas in the aftermath of a large-magnitude earthquake.

Recognizing the Shape and Size of Tundra Lakes in Synthetic Aperture Radar (SAR) Images Using Deep Learning Segmentation

Permafrost tundra contains more than twice as much carbon as is currently in the atmosphere, and it is warming six times as fast as the global mean. Tundra lakes dynamics is a robust indicator of global climate processes, and is still not well understood. Satellite data, particularly, from synthetic aperture radar (SAR) is a suitable tool for tundra lakes recognition and monitoring of their changes. However, manual analysis of lake boundaries can be slow and inefficient; therefore, reliable automated algorithms are required. To address this issue, we propose a two-stage approach, comprising instance deep-learning-based segmentation by U-Net, followed by semantic segmentation based on a watershed algorithm for separating touching and overlapping lakes. Implementation of this concept is essential for accurate sizes and shapes estimation of an individual lake. Here, we evaluated the performance of the proposed approach on lakes, manually extracted from tens of C-band SAR images from Sentinel-1, which were collected in the Yamal Peninsula and Alaska areas in the summer months of 2015–2022. An accuracy of 0.73, in terms of the Jaccard similarity index, was achieved. The lake’s perimeter, area and fractal sizes were estimated, based on the algorithm framework output from hundreds of SAR images. It was recognized as lognormal distributed. The evaluation of the results indicated the efficiency of the proposed approach for accurate automatic estimation of tundra lake shapes and sizes, and its potential to be used for further studies on tundra lake dynamics, in the context of global climate change, aimed at revealing new factors that could cause the planet to warm or cool.

Flood Depth Estimation in Agricultural Lands From L and C-Band Synthetic Aperture Radar Images and Digital Elevation Model

Flood depth is one of the important attributes in damage assessment especially in areas where agro-based activities takes place. Assessing impacted region turns out to be exceptionally difficult in the case of low-lying catchments with arable lands, which inundates due to extreme precipitation and flood modelling becomes impossible. Under these circumstances, SAR derived flood maps are valuable in removing different constraints related to flood modelling with high degree of profundity. Depth estimation from non-contact based methods requires, flood boundary as a primary input. However, detecting flood depth in emerging vegetation poses a complex challenge in terms of boundary estimation due to composite signatures that are manifested on SAR data. In this paper, a new approach is proposed to extract flood boundaries by fusing SAR data of two different frequencies, which are sensitive to water level changes. In the first step, wavelet fusion is applied to combine L- and C- band SAR data followed by Otsu’s segmentation method to extract varying levels of flood boundaries. In the second step, SRTM Digital Elevation Model (DEM) with 30m horizontal resolution is used on each boundary for statistical analysis based on which water surface levels are extracted. In the final step, depth levels are calculated from the water surface elevation and DEM. Floodwater Depth Estimation tool (FwDET) derived depth measurements are used to calibrate the statistical thresholds for derived flood depths. The study carried out on 2016 Assam flood event shows maximum flood depth of 1.56 meters in the selected study area and the results are verified with evidence-based ground truth collections which showed RMSE error of 0.25 meters from the measured values.

Combination of Hyperspectral and Quad-Polarization SAR Images to Classify Marsh Vegetation Using Stacking Ensemble Learning Algorithm

Combinations of multi-sensor remote sensing images and machine learning have attracted much attention in recent years due to the spectral similarity of wetland plant canopy. However, the integration of hyperspectral and quad-polarization synthetic aperture radar (SAR) images for classifying marsh vegetation has still been faced with the challenges of using machine learning algorithms. To resolve this issue, this study proposed an approach to classifying marsh vegetation in the Honghe National Nature Reserve, northeast China, by combining backscattering coefficient and polarimetric decomposition parameters of C-band and L-band quad-polarization SAR data with hyperspectral images. We further developed an ensemble learning model by stacking Random Forest (RF), CatBoost and XGBoost algorithms for marsh vegetation mapping and evaluated its classification performance of marsh vegetation between combinations of hyperspectral and full-polarization SAR data and any of the lone sensor images. Finally, this paper explored the effect of different polarimetric decomposition methods and wavelengths of radar on classifying wetland vegetation. We found that a combination of ZH-1 hyperspectral images, C-band GF-3, and L-band ALOS-2 quad-polarization SAR images achieved the highest overall classification accuracy (93.13%), which was 5.58–9.01% higher than that only using C-band or L-band quad-polarization SAR images. This study confirmed that stacking ensemble learning provided better performance than a single machine learning model using multi-source images in most of the classification schemes, with the overall accuracy ranging from 77.02% to 92.27%. The CatBoost algorithm was capable of identifying forests and deep-water marsh vegetation. We further found that L-band ALOS-2 SAR images achieved higher classification accuracy when compared to C-band GF-3 polarimetric SAR data. ALOS-2 was more sensitive to deep-water marsh vegetation classification, while GF-3 was more sensitive to shallow-water marsh vegetation mapping. Finally, scattering model-based decomposition provided important polarimetric parameters from ALOS-2 SAR images for marsh vegetation classification, while eigenvector/eigenvalue-based and two-component decompositions produced a great contribution when using GF-3 SAR images.

Integration of C-band SAR and high-resolution optical images for delineating palaeo-channels in Nagaur and Barmer districts, western Rajasthan, India.

Identifying hitherto unknown palaeo-channels, especially in the arid regions of the Thar Desert, is crucial since these channels may form excellent aquifers, and are also associated with valuable ore deposits of many precious minerals. This study employed integrated C-band Synthetic Aperture Radar (SAR) of Sentinel-1A and high-resolution multispectral Sentinel-2A data of pre- and post-monsoon seasons (June and November) to delineate playas and palaeo-channels. This approach is the first of its kind for this area. The palaeo-channels were delineated through a detailed visual inspection of colour composite (CC) images of Sentinel-2A data, SAR backscatter (VH) images and fused SAR and optical images. Then, we studied the topographic profiles generated from the Shuttle Radar Topography Mission -Digital Elevation Model (SRTM-DEM) across the identified palaeo-channels, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) to further confirm the existence of a palaeo-channel's course and playas. As a result, several playas and palaeo-channels in the area were successfully identified, some of which were previously unmapped and undetected. The results indicate that the post-monsoon datasets are more useful for the precise delineation of palaeo-channels due to the presence of relatively higher moisture along the palaeo-channels' courses.

Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images

This article aims at performing maritime target classification in SAR images using machine learning (ML) and deep learning (DL) techniques. In particular, the targets of interest are oil platforms and ships located in the Campos Basin, Brazil. Two convolutional neural networks (CNNs), VGG-16 and VGG-19, were used for attribute extraction. The logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbours (kNN), decision tree (DT), naive Bayes (NB), neural networks (NET), and AdaBoost (ADBST) schemes were considered for classification. The target classification methods were evaluated using polarimetric images obtained from the C-band synthetic aperture radar (SAR) system Sentinel-1. Classifiers are assessed by the accuracy indicator. The LR, SVM, NET, and stacking results indicate better performance, with accuracy ranging from 84.1% to 85.5%. The Kruskal–Wallis test shows a significant difference with the tested classifier, indicating that some classifiers present different accuracy results. The optimizations provide results with more significant accuracy gains, making them competitive with those shown in the literature. There is no exact combination of methods for SAR image classification that will always guarantee the best accuracy. The optimizations performed in this article were for the specific data set of the Campos Basin, and results may change depending on the data set format and the number of images.

Global seasonal Sentinel-1 interferometric coherence and backscatter data set

This data set is the first-of-its-kind spatial representation of multi-seasonal, global C-band Synthetic Aperture Radar (SAR) interferometric repeat-pass coherence and backscatter signatures. Coverage comprises land masses and ice sheets from 82° Northern to 79° Southern latitudes. The data set is derived from multi-temporal repeat-pass interferometric processing of about 205,000 Sentinel-1 C-band SAR images acquired in Interferometric Wide-Swath Mode from 1-Dec-2019 to 30-Nov-2020. The data set encompasses three sets of seasonal (December-February, March-May, June-August, September-November) metrics produced with a pixel spacing of three arcseconds: 1) Median 6-, 12-, 18-, 24-, 36-, and 48-days repeat-pass coherence at VV or HH polarizations, 2) Mean radiometrically terrain corrected backscatter (γ0) at VV and VH, or HH and HV polarizations, and 3) Estimated parameters of an exponential coherence decay model. The data set has been produced to obtain global, spatially detailed information on how decorrelation affects interferometric measurements of surface displacement and is rich in spatial and temporal information for a variety of mapping applications.

Machine learning for satellite-based sea-state prediction in an offshore windfarm

Accurate wave forecasts are essential for the safe and efficient maritime operations and, in particular, the maintenance of offshore wind farms. Here, machine learning and remote monitoring from satellites are integrated to provide uniquely detailed predictions of significant wave height (SWH). C-Band Synthetic Aperture Radar images from European Space Agency Sentinel-1 satellites were combined with wave-buoy data from around the UK, using the CEFAS Wavenet. A total of 240 images in wide swarth mode were collected, that represent significant wave height ranging from 0 to 4.7 m. Image properties related to sea surface roughness in dual-polarization mode, together with the wave buoy data, trained an ensemble of artificial neural networks. The trained networks were shown to provide an effective method for the estimation of the SWH, having an RMSE = 0.23 m for SWH<3 m, which is the region of interest for offshore wind energy applications. The methodology enables information on the spatial distribution of wave height in very high resolution to be obtained. Sea-state resolved from the satellite data, using the artificial neural network shows that windfarm infrastructure directly influences wave propagation. The overall variance of significant wave height throughout a wind farm was calculated, providing information on regions of interest with considerably different wave heights as compared to the nearest wave buoy. The new model will help towards improved downscaling of general sea state forecasts, locating hotspots of different wave height properties and correct prioritization of maintenance jobs to perform in wind turbines.

Integration of L-Band Derived Soil Roughness into a Bare Soil Moisture Retrieval Approach from C-Band SAR Data

Surface soil moisture (SSM) is a key variable for many environmental studies, including hydrology and agriculture. Synthetic aperture radar (SAR) data in the C-band are widely used nowadays to estimate SSM since the Sentinel-1 provides free-of-charge C-band SAR images at high spatial resolution with high revisit time, whereas the use of L-band is limited due to the low data availability. In this context, the main objective of this paper is to develop an operational approach for SSM estimation that mainly uses data in the C-band (Sentinel-1) with L-bands (ALOS/PALSAR) as additional data to improve SSM estimation accuracy. The approach is based on the use of the artificial neural networks (NNs) technique to firstly derive the soil roughness (Hrms) from the L-band (HH polarization) to then consider the L-band-derived Hrms and C-band SAR data (VV and VH polarizations) in the input vectors of NNs for SSM estimation. Thus, the Hrms estimated from the L-band at a given date is assumed to be constant for a given times series of C-band images. The NNs were trained and validated using synthetic and real databases. The results showed that the use of the L-band-derived Hrms in the input vector of NN in addition to C-band SAR data improved SSM estimation by decreasing the error (bias and RMSE), mainly for SSM values lower than 15 vol.% and regardless of Hrms values. Based on the synthetic database, the NNs that neglect the Hrms underestimate and overestimate the SSM (bias ranges between −8.0 and 4.0 vol.%) for Hrms values lower and higher than 1.5 cm, respectively. For Hrms &lt;1.5 cm and most SSM values higher than 10 vol.%, the use of Hrms as an input in the NNs decreases the underestimation of the SSM (bias ranges from −4.5 to 0 vol.%) and provides a more accurate estimation of the SSM with a decrease in the RMSE by approximately 2 vol.%. Moreover, for Hrms values between 1.5 and 2.0 cm, the overestimation of SSM slightly decreases (bias decreased by around 1.0 vol.%) without a significant improvement of the RMSE. In addition, for Hrms &gt;2.0 cm and SSM between 8 to 22 vol.%, the accuracy on the SSM estimation improved and the overestimation decreased by 2.2 vol.% (from 4.5 to 2.3 vol.%). From the real database, the use of Hrms estimated from the L-band brought a significant improvement of the SSM estimation accuracy. For in situ SSM less than 15 vol.%, the RMSE decreased by 1.5 and 2.2 vol.% and the bias by 1.2 and 2.6 vol.%, for Hrms values lower and higher than 1.5 cm, respectively.

Mapping potential signs of gas emissions in ice of Lake Neyto, Yamal, Russia, using synthetic aperture radar and multispectral remote sensing data

Abstract. Regions of anomalously low backscatter in C-band synthetic aperture radar (SAR) imagery of lake ice of Lake Neyto in northwestern Siberia have been suggested to be caused by emissions of gas (methane from hydrocarbon reservoirs) through the lake’s sediments. However, to assess this connection, only analyses of data from boreholes in the vicinity of Lake Neyto and visual comparisons to medium-resolution optical imagery have been provided due to a lack of in situ observations of the lake ice itself. These observations are impeded due to accessibility and safety issues. Geospatial analyses and innovative combinations of satellite data sources are therefore proposed to advance our understanding of this phenomenon. In this study, we assess the nature of the backscatter anomalies in Sentinel-1 C-band SAR images in combination with very high resolution (VHR) WorldView-2 optical imagery. We present methods to automatically map backscatter anomaly regions from the C-band SAR data (40 m pixel spacing) and holes in lake ice from the VHR data (0.5 m pixel spacing) and examine their spatial relationships. The reliability of the SAR method is evaluated through comparison between different acquisition modes. The results show that the majority of mapped holes (71 %) in the VHR data are clearly related to anomalies in SAR imagery acquired a few days earlier, and similarities to SAR imagery acquired more than a month before are evident, supporting the hypothesis that anomalies may be related to gas emissions. Further, a significant expansion of backscatter anomaly regions in spring is documented and quantified in all analysed years 2015 to 2019. Our study suggests that the backscatter anomalies might be caused by lake ice subsidence and consequent flooding through the holes over the ice top leading to wetting and/or slushing of the snow around the holes, which might also explain outcomes of polarimetric analyses of auxiliary L-band Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) data. C-band SAR data are considered to be valuable for the identification of lakes showing similar phenomena across larger areas in the Arctic in future studies.

Urban objects detection from C-band synthetic aperture radar (SAR) satellite images through simulating filter properties

Satellite-based remote sensing has a key role in the monitoring earth features, but due to flaws like cloud penetration capability and selective duration for remote sensing in traditional remote sensing methods, now the attention has shifted towards the use of alternative methods such as microwave or radar sensing technology. Microwave remote sensing utilizes synthetic aperture radar (SAR) technology for remote sensing and it can operate in all weather conditions. Previous researchers have reported about effects of SAR pre-processing for urban objects detection and mapping. Preparing high accuracy urban maps are critical to disaster planning and response efforts, thus result from this study can help to users on the required pre-processing steps and its effects. Owing to the induced errors (such as calibration, geometric, speckle noise) in the radar images, these images are affected by several distortions, therefore these distortions need to be processed before any applications, as it causes issues in image interpretation and these can destroy valuable information about shapes, size, pattern and tone of various desired objects. The present work aims to utilize the sentinel-1 SAR datasets for urban studies (i.e. urban object detection through simulation of filter properties). The work uses C-band SAR datasets acquired from Sentinel-1A/B sensor, and the Google Earth datasets to validate the recognized objects. It was observed that the Refined-Lee filter performed well to provide detailed information about the various urban objects. It was established that the attempted approach cannot be generalised as one suitable method for sensing or identifying accurate urban objects from the C-band SAR images. Hence some more datasets in different polarisation combinations are required to be attempted.

Sentinel-1 Big Data Processing with P-SBAS InSAR in the Geohazards Exploitation Platform: An Experiment on Coastal Land Subsidence and Landslides in Italy

The growing volume of synthetic aperture radar (SAR) imagery acquired by satellite constellations creates novel opportunities and opens new challenges for interferometric SAR (InSAR) applications to observe Earth’s surface processes and geohazards. In this paper, the Parallel Small BAseline Subset (P-SBAS) advanced InSAR processing chain running on the Geohazards Exploitation Platform (GEP) is trialed to process two unprecedentedly big stacks of Copernicus Sentinel-1 C-band SAR images acquired in 2014–2020 over a coastal study area in southern Italy, including 296 and 283 scenes in ascending and descending mode, respectively. Each stack was processed in the GEP in less than 3 days, from input SAR data retrieval via repositories, up to generation of the output P-SBAS datasets of coherent targets and their displacement histories. Use-cases of long-term monitoring of land subsidence at the Capo Colonna promontory (up −2.3 cm/year vertical and −1.0 cm/year east–west rate), slow-moving landslides and erosion landforms, and deformation at modern coastal protection infrastructure in the city of Crotone are used to: (i) showcase the type and precision of deformation products outputting from P-SBAS processing of big data, and the derivable key information to support value-adding and geological interpretation; and (ii) discuss potential and challenges of big data processing using cloud/grid infrastructure.

GA-SVR Algorithm for Improving Forest Above Ground Biomass Estimation Using SAR Data

Synthetic aperture radar (SAR) features have been demonstrated that they have the potentiality to improve forest above ground biomass (AGB) estimation accuracy, especially including polarimetric information. Genetic algorithms (GAs) have been successfully implemented in optimal feature identification, while support vector regression (SVR) has great robustness in parameter estimation. The use of combined GAs and SVR can improve the accuracy of forest AGB estimation through simultaneously identifying the optimal SAR features and selecting the SVR model parameters. In this article, 14 SAR polarimetric features were extracted from C-band and L-band full-polarization SAR images and worked as input SAR features, respectively. C-band data was acquired on GaoFen-3 mission, we also call it GF-3 image. L-band data was ALOS-2 PALSAR-2 data. Both feature subsets from GF-3 and ALOS-2 PALSAR-2 and SVR hyper parameters used in the forest AGB estimation were optimized by a GA processing, where 8 different settings of 3 kinds of parameters, as 512 kind of different combinations were applied for SVR hyper parameters searching field. The results of GA-SVR performance using the two datasets were presented and compared with two traditional methods: the algorithm of GA feature selection companied with default SVR parameters (GA+ default SVR), and the algorithm of GA feature selection companied with grid searching for SVR parameter selection (GA+Grid SVR). The results showed that the proposed GA-SVR algorithm improved the forest AGB estimation accuracy with cross-validation coefficient of 80.21% for GF-3 and 71.41% for ALOS-2 PALSAR-2 data.

Satellite InSAR survey of structurally-controlled land subsidence due to groundwater exploitation in the Aguascalientes Valley, Mexico

To address increasing water demands in expanding cities, many aquifers in Mexico are overexploited and deplete. The resulting land subsidence often combines with ground faulting/fracturing and damage to infrastructure. This study provides the longest Synthetic Aperture Radar (SAR) survey ever undertaken for the Aguascalientes Valley, aimed to constrain its structurally-controlled subsidence process and the induced risk. 275 ERS-1/2 1996–2002, ENVISAT 2003–2010 and Sentinel-1 2014–2020 C-band SAR images are processed with change detection, differential Interferometric SAR (InSAR) and Small Baseline Subset (SBAS) methods. Aguascalientes notably expanded over the last four decades, as revealed by Seasat 1978 L-band SAR, Landsat 1985–2010 and Sentinel-2 2020 optical imagery. The observed subsidence pattern involves alluvial/fluvial deposits within the N-S trending graben. Maximum settlement rates are −14 cm/year in 1996, −10 cm/year in 2000–2010 and over −12 cm/year in 2015–2020. An acceleration (−0.70 cm/year2) is recorded in 2015–2020 close to recently developed industrial plants and housing districts. Satellite estimates agree with in-situ observations, static GPS surveying and continuous GPS monitoring data. Rough correlation is found with piezometric level drop rates, whereas aquifer thickness plays a stronger role in the subsidence process. While these outcomes align with the existing literature, this InSAR survey: (i) unveils previously unknown E-W deformation affecting two N-S oriented bands within the valley, with up to ~ ±3 cm/year in 2015–2020 towards its center; (ii) identifies zones of sagging and hogging with horizontal strain (ε) of up to 0.05–0.1%; (iii) retrieves differential rates reaching 6–8 cm/year and angular distortions (β) of 1/500 along the Oriente fault; and (iv) investigates the statistical distribution of β across field surveyed faults and fissures, and marks areas with potentially yet-unmapped ground discontinuities. A new surface faulting risk matrix embedding β and ε is therefore proposed to estimate subsidence impact on properties and population. Given its scale-dependency, the risk assessment provides a lower bound to the percentage of urban areas at risk within the Aguascalientes state: at least 2% of the urban areas were at high and very high risk in 2003–2010 (involving ~12,000 properties and ~39,000 inhabitants), but this increased to 6% in 2015–2020 (~25,600 properties, ~85,200 inhabitants). The evidence of a subsidence process evolving spatially and temporally highlights the need for continuous updating of hazard information.

A Method for Classifying Land and Ocean Area by Removing Sentinel-1 Speckle Noise

Han, H.G. and Lee, M.J., 2020. A method for classifying land and ocean area by removing sentinel-1 speckle noise. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 33-38. Coconut Creek (Florida), ISSN 0749-0208.In Korea, satellite image-based land cover maps are limited because they are based on time-consuming pixel value-based classification techniques. The main categories of land cover classification are water and land; therefore, synthetic-aperture radar (SAR) images with high water reflectivity may be used to improve land cover map classification accuracy. In this study, C-band SAR images obtained by the Sentinel-1 satellite are used, which include various noises including speckle noise. To remove speckle noise, this paper applied Lee, Gamma, and Frost filters, and found that the Lee filter offered the best performance. By combining a stacking technique and the Lee filter, this paper successfully classified the target water system using image dichotomy and histogram analyses of the region of interest (ROI). The resulting land cover map showed 90 % accuracy compared to the pixel-based map, and comparison with the optical image showed that water coverage was effectively classified. This classification of forest reservoirs, which are difficult to distinguish in optical images, was rated as excellent. Thus, this speckle noise-removal technique will facilitate the improvement of land cover classification accuracy, particularly for flood boundaries and shorelines.

A note on radar signatures of hydrometeors in the melting layer as inferred from Sentinel-1 SAR data acquired over the ocean

Synthetic aperture radar (SAR) images acquired over the ocean often show radar signatures of rain, which are not easy to interpret. The scattering mechanisms causing radar signatures are usually attributed to surface scattering due to sea surface roughness variations caused by raindrops impinging onto the sea surface and/or by up- and downdraft winds. In this paper, we address another radar signature of rain, which is often observed in C-band (and also in X-band) SAR images, but whose origin has been a matter of debate in the ocean remote sensing community since long time and has not been solved yet. This radar signature consists of areas of very high radar backscatter (bright patches) at co- as well as well as at cross-polarization. This paper aims at providing evidence that it is not caused by surface scattering, but by volume scattering from wobbling, non-spherical, oblate hydrometeors within the melting layer. To this end, we first review the theory of radar backscattering from the melting layer as developed by D'Amico et al. (1998) and then present historic radar backscatter data from the melting layer carried out by ground-based and airborne radars, which validate this theory. Then we show four representative Sentinel-1 SAR images acquired over the sea area close to Hong Kong and a SIR-C/X-SAR image acquired over the Gulf of Mexico, which show pronounced radar signatures of rain (bright patches) at co-polarization (VV) and cross-polarization (VH). The analysis of the SAR images yields the result that within the bright patches the ratio of the radar backscatter at cross-polarization to the one at co-polarization shows the same characteristics as the linear depolarization ratio (LDR) measured by radar meteorologist in radar backscattering from the melting layer. Furthermore, we show that radar signatures of rain due to volume scattering may interfere with co-polarization radar signatures of rain due to surface scattering. Thus, cross-polarization SAR images are better suited to detect radar backscattering from the melting layer than co-polarization SAR images, This investigation is of relevance for ocean surface wind retrieval using C-band SARs, since scattering at hydrometeors in the melting layer can cause significant errors in ocean wind retrieval. Areas with simultaneously high co- and cross-polarization NRCS values of around −10 dB and − 20 dB, respectively, have to be flagged as areas where the conventional wind retrieval algorithm cannot be applied.

CMODH Validation for C-Band Synthetic Aperture Radar HH Polarization Wind Retrieval Over the Ocean

A new HH-polarized geophysical model function (GMF), called CMODH, has been proposed recently for C-band synthetic aperture radar (SAR) ocean surface wind speed retrieval. CMODH has the potential to be directly used to retrieve wind speed from HH-polarized SAR images without converting the normalized radar cross section (NRCS) from HH- into VV-polarization using various empirical and theoretical polarization ratio (PR) models. However, the capability of CMODH for wind speed retrieval has not been comprehensively validated. In this letter, as a case study, two C-band HH-polarized SAR images acquired in RADARSAT-2 (RS-2) quad-polarization and Sentinel-1A (S1-1A) dual-polarization modes are first used to examine the CMODH performance. The wind speeds estimated using CMODH are shown to be consistent with buoy winds. A statistical comparison is then carried out to further validate CMODH using collocated 1457 C-band RS-2 and 284 S1-1A/B HH-polarized SAR images and 110 in situ buoys. The results show that the CMODH-retrieved wind speeds are in good agreement with buoy measurements, with a bias of 0.07 and 0.49 m/s and a root mean square error (RMSE) of 1.66 and 2.05 m/s for RS-2 and S1-1A/B, respectively. Compared to hybrid models, such as CMOD5.N and various PR models, CMODH achieves the smallest RMSE for wind speed retrieval. Moreover, for the first time, it is shown that the wind speed retrieval capability of CMODH is better than that of CMOD5.N for incidence angles between 30° and 49° and wind speeds between 10 and 20 m/s, which provides a new prospective on wind retrieval improvement using a combination of VV- and HH-polarized SAR observations.

Sentinel-1 SAR interferometry for agriculture: description of an experiment in Oryol, Russia

In this work we describe an experiment to be carried out in the basin of Suhaya Orlitsa river (Oryol region, central part of European Russia) to compare in-situ measurements of soil moisture with estimates obtained using Synthetic Aperture Radar (SAR) interferometry. The Sentinel-1 mission of the European Space Agency (ESA), acquiring C-band SAR images regularly over all Earth regions since 2014 with a mean revisiting time of 6 days, is used. In-situ measurements of soil moisture are planned in a time interval of 3 hours in coincidence of each Sentinel-1 passage, using a temporal sampling of 15 minutes. Test measurements are planned at the end of the month of April, when the soil accumulates water. The aim of the experiment is to demonstrate the feasibility of using Sentinel-1 images to densify the network of in-situ measurements of soil moisture on the territory of Russia. The application of SAR interferometry is investigated as it requires less in-situ measurements than methods based on the use of radar cross-section and the inversion of models of electromagnetic scattering from natural surfaces. Examples of interferometric coherence and phase images obtained by processing Sentinel-1 images acquired on 20th September 2019 and 2nd October 2019 over the study area are shown.

Detection of urban flood inundation using RISAT-1 SAR images: a case study of Srinagar, Jammu and Kashmir (North India) floods of September 2014

Microwave data from SAR (synthetic aperture radar) sensors are used extensively in providing operational support for riverine floods, unlike for urban flood disaster. Current approaches for flood mapping using SAR basically aim at detecting for regions of low backscatter due to specular reflection from water surface. However, flooding in urban areas may appear bright in SAR images, because of the enhancement of the double-bounce effect. The present communication presents a case study of the use of moderate-resolution C-band SAR images from Indian Remote Sensing Satellite RISAT-1 to assess the changes in backscattering coefficient (σ0) values (flood σ0 − non-flood σ0) for catastrophic floods of September 2014. SAR images considered for evaluation are with similar acquisition parameters (orbit, look direction, incidence angle, polarization, imaging mode, resolution, and acquisition time) to have a consistency in comparison of backscatter values. From the backscattering coefficient (σ0) comparison analysis and visual inspection between pre-flood and flood duration images, it is observed that there is a significant increase in the σ0 values of about 8.0 dB and corresponding increase in the tonal brightness of flooded locations. The areas identified with high backscatter response are also validated for flooding through high-resolution Pleiades (spatial resolution 0.5 m) optical satellite data and ground information from open sources. The observations made from this study highlight the fact that moderate-resolution SAR images could be an important source in indicating urban flooded areas during rapid response.

Assessing the Usefulness of Iceberg Electromagnetic Backscatter Modeling Using a C-Band SAR Classifier

This letter presents the validation of an electromagnetic (EM) backscatter model of icebergs at C-band by comparing the performances of target classifiers trained with both modeled and real synthetic aperture radar (SAR) data. Simulated SAR data were obtained in a combination of imaging beam modes and scene parameters to produce 216 simulated Sentinel-1 C-band SAR images. Parameters consisted of Sentinel-1 IW1 (33.1°) and IW3 (43.1°) beam modes with varying wind speed (5 and 10 m/s), wind direction (0°, 45°, and 90°), and target orientation (0°, 45°, and 90°). Simulations were created from an EM SAR simulator called GRECOSAR, which took 3-D profiles of iceberg and ship targets and parameters necessary to closely mimic the real scenes. 3-D models of three icebergs were captured in a field study off the coast of Bonavista, Newfoundland, and Labrador, Canada in June 2017. Three generic ship models were sourced from an online inventory and scaled to a size equivalent to that of the iceberg targets. Real SAR image data were drawn from in-house data set collected from a complementary research program. Classifiers including support vector machine (SVM), Random Forest (RanFor), k-nearest neighbor (kNN), and neural network (NN) were trained with targets from modeled SAR data and then gradually mixed with real SAR data. Target classifier performance from the modeled target data was shown to be similar to classifiers trained entirely from real SAR data. The similarity in accuracy provides an indication of the validity of the modeled SAR data for this specific application.