Spaceborne Synthetic Aperture Radar Data Research Articles

Integrating post-event very high resolution SAR imagery and machine learning for building-level earthquake damage assessment

AbstractEarthquakes have devastating effects on densely urbanised regions, requiring rapid and extensive damage assessment to guide resource allocation and recovery efforts. Traditional damage assessment is time-consuming, resource-intensive, and faces challenges in covering vast affected areas, often limiting timely decision-making. Space-borne synthetic aperture radars (SAR) have gained attention for their all-weather and day-night imaging capabilities. These advantages, coupled with wide coverage, short revisits and very high resolution (VHR), have created opportunities for using SAR data in disaster response. However, most SAR studies for post-earthquake damage assessment rely on change detection methods using pre-event SAR images, which are often unavailable in operational scenarios. Limited studies using solely post-event SAR data primarily concentrate on city-block-level damage assessment, thus not fully exploiting the VHR SAR potential. This paper presents a novel method integrating solely post-event VHR SAR imagery and machine learning (ML) for regional-scale post-earthquake damage assessment at the individual building-level. We first used supervised learning on case-specific datasets, and then introduced a combined learning approach, incorporating inventories from multiple case studies to assess generalisation. Finally, the ML model was tested on unseen study areas, to evaluate its flexibility in unfamiliar contexts. The method was implemented using datasets collected during the Earthquake Engineering Field Investigation Team (EEFIT) reconnaissance missions following the 2021 Nippes earthquake and the 2023 Kahramanmaraş earthquake sequence. The results demonstrate the method’s ability to classify standing and collapsed buildings, achieving up to 72% overall accuracy on unseen regions. The proposed method has potential for future disaster assessments, thereby contributing to more effective earthquake management strategies.

A Multicomponent Linear Frequency Modulation Signal-Separation Network for Multi-Moving-Target Imaging in the SAR-Ground-Moving-Target Indication System

Multi-moving-target imaging in a synthetic aperture radar (SAR) system poses a significant challenge owing to target defocusing and being contaminated by strong background clutter. Aiming at this problem, a new deep-convolutional-neural-network (CNN)-assisted method is proposed for multi-moving-target imaging in a SAR-GMTI system. The multi-moving-target signal can be modeled by a multicomponent LFM signal with additive perturbation. A fully convolutional network named MLFMSS-Net was designed based on an encoder–decoder architecture to extract the most-energetic LFM signal component from the multicomponent LFM signal in the time domain. Without prior knowledge of the target number, an iterative signal-separation framework based on the well-trained MLFMSS-Net is proposed to separate the multi-moving-target signal into multiple LFM signal components while eliminating the residual clutter. It works well, exhibiting high imaging robustness and low dependence on the system parameters, making it a suitable solution for practical imaging applications. Consequently, a well-focused multi-moving-target image can be obtained by parameter estimation and secondary azimuth compression for each separated LFM signal component. The simulations and experiments on both airborne and spaceborne SAR data showed that the proposed method is superior to traditional imaging methods in both imaging quality and efficiency.

Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference

Space-borne synthetic aperture radar (SAR), as an all-weather observation sensor, is an important means in modern information electronic warfare. Since SAR is a broadband active radar system, radio frequency interference (RFI) in the same frequency band will affect the normal observation of the SAR system. Untangling the above problem, this research explores a quick and accurate method for detecting and segmenting RFI-contaminated images. The purpose of the current method is to quickly detect the existence of RFI and to locate it in massive SAR data. Based on deep learning, the method shown in this paper realizes the existence of RFI by determining the presence or absence of interference in the image domain and then performs pixel-level image segmentation on Sentinel-1 RFI-affected quick-look images to locate RFI. Considering the need to quickly detect RFI in massive SAR data, an improved network based on MobileNet is proposed, which replaces some inverted residual blocks in the network with ghost blocks, reducing the number of network parameters and the inference time to 6.1 ms per image. Further, this paper also proposes an improved network called the Smart Interference Segmentation Network (SISNet), which is based on U2Net and replaces the convolution of the VGG blocks in U2Net with a residual convolution and introduces attention mechanisms and a modified RFB module to improve the segmentation mIoU to 87.46% on average. Experiment results and statistical analysis based on the MID dataset and PAIS dataset show that the proposed methods can achieve quicker detection than other CNNs while ensuring a certain accuracy and can significantly improve segmentation performance under the same conditions compared to the original U2Net and other semantic segmentation networks.

Two Mw ≥ 6.5 Earthquakes in Central Pamir Constrained by Satellite SAR Observations

The Pamir, situated in central Asia, is a result of the ongoing northward advance of the Indian continent, leading to compression of the Asian landmass. While geodetic and seismic data typically indicate that the most significant deformation in Pamir is along its northern boundary, an Mw 7.2 earthquake on 7 December 2015 and an Mw 6.8 earthquake on 23 February 2023 have occurred in the remote interior of Pamir. These two Mw ≥ 6.5 earthquakes, with good observations of satellite synthetic aperture radar data, provide a rare opportunity to gain insights into rupture mechanics and deformation patterns in this challenging-to-reach region. Here, we utilize spaceborne synthetic aperture radar data to determine the seismogenic faults and finite slip models for these two earthquakes. Our results reveal that the 2015 earthquake ruptured a ~88 km long, left-lateral strike-slip fault that dips to northwest. The rupture of the 2015 earthquake extended to the ground surface over a length of ~50 km with a maximum slip of ~3.5 m. In contrast, the 2023 earthquake did not rupture the ground surface, with a maximum slip of ~2.2 m estimated at a depth of ~9 km. Notably, the seismogenic fault of the 2015 earthquake does not align with the primary strand of the Sarez–Karakul fault system (SKFS), and the 2023 earthquake occurred on a previously unmapped fault. The well-determined seismogenic faults for the 2015 and 2023 earthquakes, along with the SKFS and other distributed faults in the region, suggest the existence of a wide shear zone extending from south to north within the central Pamir.

Segmented continuously varying pulse interval sequence design for extremely high-resolution spaceborne sliding spotlight SAR

ABSTRACT Synthetic aperture radar (SAR) is an active microwave remote sensing device used to obtain high-resolution SAR images.With the development of SAR, extremely high-resolution SAR (i.e. resolution reaching 0.1 m) plays an increasingly important role in applications such as target recognition. In extremely high-resolution spaceborne SAR, the variation of slant range history becomes extremely severe, resulting in echoes of interest overlapping with transmitted pulses and nadir echoes when transmitting the pulses at constant pulse intervals (PIs), further causing the loss of echo data. Although a continuously varying pulse interval (CVPI) system can expand the tolerance of slant range variation to some extent, overlap still occurs when the variation in slant range exceeds a certain threshold. Thus, this paper proposes segmented CVPI sequence design methods to solve the data acquisition problem in the situation of severe variation in slant range history. The timing diagram- and strip layering diagram-based segmented CVPI sequence design methods, which are suitable for the acquisition of spaceborne SAR data with any variation of slant range, are given. The characteristics of the two methods are analysed. Finally, several computer simulations indicate that the echoes generated by the proposed methods are not overlapped, which verifies the validity of the proposed methods.

Vertical autofocus for the phase screen in a turbulent ionosphere

The performance of spaceborne synthetic aperture radars (SARs) is affected by the Earth’s ionosphere. In particular, the ionospheric turbulence causes phase perturbations of the SAR signals, which may lead to image distortions. A convenient way to model those phase perturbations is by means of a phase screen. The latter is an infinitesimally thin layer positioned at a certain elevation above the Earth’s surface. The radar signal acquires an instant perturbation once its trajectory intersects the screen. The trajectory is a ray between the antenna and the target, and the magnitude of the perturbation is equal to the screen density at the intersection point. The density is a bivariate function of the coordinates along the screen. The coordinates of a specific intersection point are determined by the ray itself, as well as the screen elevation. Thus, the magnitude of the phase perturbation explicitly depends on the screen elevation. Accordingly, to compensate for the resulting image distortions one should be able to determine the elevation of the screen. In the paper, we develop an algorithm of vertical autofocus that derives this elevation from the received SAR data, given a pair of point scatterers in the target area. The proposed algorithm exploits a modification of the coherent interferometric imaging that was previously employed to reduce the effect of phase errors due to the trajectory uncertainty. In our analysis, we highlight the differences between this case and transionospheric propagation.

Mitigate the LFM-PRFI in SAR Data: Joint Down-Range and Cross-Range Filtering

Synthetic aperture radar (SAR), an active remote sensing equipment, shares the spectrum with devices in the same frequency band and is therefore easy to affect by pulse radio frequency interference (PRFI). The wideband version of PRFI, i.e., linear-frequency-modulation PRFI (LFM-PRFI), derived from the ground- and space-based radar sensors is a challenging issue for SAR because it usually has a large bandwidth and pulse width compared with the traditional PRFI. The well-known notch filtering methods, including time- and frequency-domain versions, are robust approaches against PRFI, which has been integrated into some SAR ground processing systems. However, the down-range frequency domain notch filtering will increase the sidelobes of the targets when mitigating wideband LFM-PRFI, whereas the time-domain version will introduce ghosts when notching large pulse-width ones. This paper proposed a filtering method that consists of three steps to tackle the above problems. The first step is focusing the energy of LFM-PRFI down-range and cross-range simultaneously, which can be done by match filtering and Fourier transform. The second step is mitigating the LFM-PRFI, which can be done by a designed filter. The last step is data restoration, which can be done by multiplying the conjugate of the phase function used before. Numerical experiments based on simulated SAR data and measured spaceborne SAR data acquired by European Sentinel-1 are performed to test the mitigation performance, which verified the effectiveness and superiority of the proposed approach.

Dynamic Predictive Quantization for Staggered SAR: Experiments With Real Data

Present and future spaceborne synthetic aperture radar (SAR) missions are designed to acquire an increasingly large amount of onboard data. This is a consequence of the use of large bandwidths, multiple polarizations, and the acquisition of large swath widths at fine spatial resolutions, which result in challenging requirements in terms of onboard memory and downlink capacity. In this scenario, SAR raw data quantization represents an essential aspect, as it affects the volume of data to be stored and transmitted to the ground as well as the quality of the resulting SAR products. Dynamic predictive block-adaptive quantization (DP-BAQ) is a novel technique, recently proposed by the authors, consisting of a low-complexity data compression method, and its application is particularly suitable for staggered SAR systems. DP-BAQ exploits the existing correlation among the azimuth raw data samples by applying linear predictive coding (LPC). This results in a data rate reduction of up to 25% with respect to state-of-the-art SAR quantization methods. In this letter, we test and validate the potential of DP-BAQ on airborne SAR data which emulates the system scenario of Tandem-L, a German Aerospace Center (DLR) mission proposal for a bistatic L-band system. For this purpose, an experimental SAR image has been acquired at the L-band by the airborne DLR flugzeug-SAR (F-SAR) sensor over the Kaufbeuren area, in Southern Germany. In order to simulate the staggered SAR acquisition mode, we implemented a dedicated resampling and filtering of the data. Our analyses confirm the effectiveness of DP-BAQ for efficient data volume reduction, exhibiting a consistent and promising performance when tested on areas characterized by different land cover types and backscatter statistics.

An Improved Imaging Algorithm for HRWS Space-Borne SAR Data Processing Based on CVPRI

In space-borne synthetic aperture radar (SAR), the sliding spotlight mode can acquire images with both high-resolution and wide-swath in azimuth direction. Due to the significant two-dimensional spatial variance of Doppler parameters, the traditional imaging algorithms based on the conventional range models is not available. In this article, the strategy of continuously varying pulse repetition interval (CVPRI) is likely to lead to a novel approach to dealing with the azimuth variance problem to realize high-resolution wide-swath (HRWS) imaging in azimuth direction for sliding spotlight SAR. First, the eighth-order Taylor expansion of the modified equivalent squint range model (MESRM-TE8) is adopted, and the accuracy of MESRM-TE8 is accordingly explained. Then, the properties of the spatial variance for the MESRM-TE8 are analyzed in detail, based on which the strategy of CVPRI is given theoretically to eliminate the azimuth variance. An improved imaging algorithm based on CVPRI is subsequently proposed to address the azimuthal-variant Doppler parameters and realize a batch data processing of a large scene in azimuth frequency domain. The extended scaling method is integrated in this algorithm to uniformly compensate cubic phase modulation introduced by CVPRI and circumvent azimuth time folding caused by subaperture processing in the focused image. Finally, the effectiveness of the CVPRI strategy and the proposed algorithm is demonstrated by the simulation results.

A Semi-Empirical Retrieval Method of Above-Ground Live Forest Fuel Loads by Combining SAR and Optical Data

Forest fuel load is the key factor for fire risk assessment, firefighting, and carbon emissions estimation. Remote sensing technology has distinct advantages in fuel load estimation due to its sensitivity to biomass and adequate spatiotemporal observations for large scales. Many related works applied empirical methods with individual satellite observation data to estimate fuel load, which is highly conditioned on local data and limited by saturation problems. Here, we combined optical data (i.e., Landsat 7 ETM+) and spaceborne Synthetic Aperture Radar (SAR) data (i.e., ALOS PALSAR) in a proposed semi-empirical retrieval model to estimate above-ground live forest fuel loads (FLAGL). Specifically, optical data was introduced into water cloud model (WCM) to compensate for vegetation coverage information. For comparison, we also evaluated the performance of single spaceborne L-band SAR data (i.e., ALOS PALSAR) in fuel load estimation with common WCM. The above two comparison experiments were both validated by field measurements (i.e., BioSAR-2008) and leave-one-out cross-validation (LOOCV) method. WCM with single SAR data could achieve reasonable performance (R2 = 0.64 or higher and RMSEr = 35.3% or lower) but occurred an underestimation problem especially in dense forests. The proposed method performed better with R2 increased by 0.05–0.13 and RMSEr decreased by 5.8–12.9%. We also found that the underestimation problem (i.e., saturation problem) was alleviated even when vegetation coverage reached 65% or the total FLAGL reached about 183 Tons/ha. We demonstrated our FLAGL estimation method by validation in an open-access dataset in Sweden.

Mapping and Scaling of In Situ Above Ground Biomass to Regional Extent With SAR in the Great Slave Region

AbstractGlobal forests are increasingly threatened by disturbance events such as wildfire. Spaceborne Synthetic Aperture Radar (SAR) missions at L‐ (or P‐) band, such as the upcoming NASA ISRO SAR (NISAR), have great potential to advance global mapping of above‐ground biomass (AGB). AGB mapping with SAR is challenging due to lack of available L‐ or P‐ band data, and because SAR data are sensitive to confounding factors such as hydrology and terrain. This study uses recently collected AGB validation site data (AGBV) to produce a 1 ha biomass map about the Great Slave Lake in Canada using SAR data, and reports on NISAR's anticipated performance. This study addresses errors inherent to the representativeness of AGBVs with coarser grid/landscape scale processes by evaluating model performance as data are aggregated over increasingly larger areas (AOAs). Air and spaceborne SAR data were found to be interoperable after processing them according to analysis ready data specifications, improving data availability. Owing to poor model performance at two AGBVs, root‐mean‐square errors (RMSEs) were ∼60 Mg/ha, irrespective of AOA. When instead using NISAR's more lenient assessment criteria, RMSEs decreased to 32, 15, and 21 Mg/ha for the small (∼0.1 ha), medium (∼3.5 ha), and large (∼14 ha) AOA. Thus, AGB mapping in this region appears to benefit significantly from coarser data aggregations than to be used by NISAR's. This approach is practical for identifying a suitable scale of correspondence between the AGBV and SAR data and the landscape‐scale processes and can substantially improve AGB mapping accuracy.

Forest foliage fuel load estimation from multi-sensor spatiotemporal features

• Spatiotemporal features of spaceborne SAR and optical data were combined first time. • The addition of spatiotemporal features greatly improves the estimation of FFL. • Temporal features seem more important than spatial features. • Both spatial and temporal changes in FFL can indicate forest fire risk. Foliage fuel is the most flammable component in crown fires. Spatiotemporal dynamics of foliage fuel load (FFL) are important for fire managers to assess fire risk. Here, we integrated optical data from the Landsat 8 Operational Land Imager (OLI) with synthetic aperture radar (SAR) data from Sentinel-1 to estimate FFL. We first reconstructed seamless time series from the Landsat 8 and Sentinel-1 imagery by accounting for unequal time intervals between image observations and outliers. We then extracted temporal features that are proxies of the intra- and inter-annual dynamics from these time series. In addition, we derived spatial features from the imagery that quantify spatial context and therefore used varying window sizes. The random forest regression was implemented to assess the importance of the spatiotemporal features, reduce errors, and derive robust FFL estimates. The satellite estimates were validated against 96 field measurements from Pinus yunnanensis forests in the Liangshan Yi Autonomous Prefecture, Sichuan Province, China. Both the spatiotemporal features of SAR and optical data importantly contributed to FFL estimation. When only optical data was used, the model achieved a R 2 of 0.75 (relative Root Mean Squared Error (rRMSE) = 25.3 %), while when only SAR data was used the R 2 was 0.76 (rRMSE = 25.6 %). However, when optical and SAR data were combined, the R 2 increased to 0.81 (rRMSE = 23.2 %). We also found that temporal features were more important predictors of FFL than features that captured spatial context. We demonstrated our FFL mapping method by a case study in the Chinese Sichuan Province, in relation to the occurrence of a fire. Our method needs additional validation over different tree species and forest types, yet has potential for mapping forest fuel loads and fire risk.

Detection of wet snow in the Arctic tundra from time-series fully-polarimetric RADARSAT-2 images

Snow melt onset detection and mapping of snow extent during the spring melt period using spaceborne Synthetic Aperture Radar (SAR) is important for various applications including understanding and predicting snowmelt runoff, flood hazard risk, and freshwater resource supply. In this study, we investigated the time-series evolution of RADARSAT-2 C-band HH, VV, VH and HV radar backscattering coefficients and scattering contributions during the spring melt seasons of 2017 and 2018 over the Niaqunguk (Apex) River, Nunavut, Canada. Meteorological observations and optical Planet satellite images were analyzed to support interpretations of SAR data in relation to snowpack warming and melt processes at the high spatial resolution of 6 m. Our results suggest that the cross-polarization (HV and VH) backscatter from snow significantly dropped by ∼10.7 dB when the air temperatures became positive and snowpack moisture content began to increase (i.e., melt onset). Co-polarization (HH and VV) backscatter from snow also decreased, but by a smaller magnitude of ∼7.4 dB. Freeman–Durden decomposition analysis suggested that the volume scattering contribution dropped drastically, and surface scattering dominated at the melt onset. The timing of melt onset can be readily inferred from the temporal changes in backscatter signal, which was particularly pronounced in the HV time-series. The backscatter decrease (e.g., 2.5 dB for HH and 3.4 dB for HV) associated with melt onset was also pronounced for large areas, where both snow-covered and snow-free surfaces were present, and water was masked out. Cross-polarization signal demonstrated better separability between snow-free and snow-covered surfaces than co-polarization signal throughout the melt season. This suggests that the HV backscatter signal is more suitable for snow melt onset and snow/ground detection compared to conventionally used HH and VV, given the instrument noise floor is sufficiently low to capture the HV signal variations. Our classification results demonstrated slightly higher overall accuracy for HV (83.9%) than for HH (82.7%) signal. However, snow and bare-ground classification accuracies were limited to the late season period when class separabilities were lower compared to the earlier melt season, preventing robust assessment of classification accuracy using HV backscatter due to limited ground truth data.

A High-Resolution, Wide-Swath SAR Imaging System Based on Tandem SAR Satellites.

For the spaceborne synthetic aperture radar (SAR), it is difficult to obtain high resolution and wide width at the same time. This paper proposes a novel imaging system based on tandem SAR satellites, where one obtains coarse resolution and wide swath by the scanning mode, and the other obtains the undersampled echo from the same swath. The high resolution is achieved by associating the tandem SARs’ echo and using the minimum-energy-based algorithm. Finally, a high-resolution wide-swath SAR system is designed, and its imaging performance is verified by simulated data and real airborne SAR data.

A Nonlinear Polynomial Filter for Sidelobe Suppression of Spaceborne and Airborne Synthetic Aperture Radar (SAR) Images With High Resolution

A nonlinear polynomial filter (NPF) was proposed for the sidelobe suppression in synthetic aperture radar (SAR) imagery with high resolution. The single-sinc and multiple-sinc cases were examined. In the single-sinc case, one target existed, and the other surrounding targets were absent or weak scatterers that can be ignored. Otherwise, the cases were considered as multiple-sinc cases. A judge function was computed using the underlying sample and two adjacent samples for every sample in the image domain. This function was the basis to determine whether the related sample fell under the single-sinc case or not. If it was the single-sinc case, the judge function was also used to reduce the sidelobes. A polynomial filter was used for the sidelobe suppression in the multiple-sinc cases. Using the polynomial filtering, the energy of sidelobes was reduced by dividing a polynomial function. The unequivocal determination of sidelobe suppression was achieved for SAR data. The performance of the proposed algorithm was evaluated using acquired spaceborne SAR and airborne SAR data with high resolution. The effectiveness of the sidelobe suppression was qualitatively and quantitatively examined, and the results were satisfactory.

Built-Up Area Mapping for the Greater Bay Area in China from Spaceborne SAR Data Based on the PSDNet and Spatial Statistical Features

Built-up areas (BAs) information acquisition is essential to urban planning and sustainable development in the Greater Bay Area in China. In this paper, a pseudo-Siamese dense convolutional network, namely PSDNet, is proposed to automatically extract BAs from the spaceborne synthetic aperture radar (SAR) data in the Greater Bay Area, which considers the spatial statistical features and speckle features in SAR images. The local indicators of spatial association, including Moran’s, Geary’s, and Getis’ together with the speckle divergence feature, are calculated for the SAR data, which can indicate the potential BAs. The amplitude SAR images and the corresponding features are then regarded as the inputs for PSDNet. In this framework, a pseudo-Siamese network can independently learn the BAs discrimination ability from the SAR original amplitude image and the features. The DenseNet is adopted as the backbone network of each channel, which can improve the efficiency while extracting the deep features of the BAs. Moreover, it also has the ability to extract the BAs with multi-scale sizes by using a multi-scale decoder. The Sentinel-1 (S1) SAR data for the Greater Bay Area in China are used for the experimental validation. Our method of BA extraction can achieve above 90% accuracy, which is similar to the current urban extraction product, demonstrating that our method can achieve BA mapping for spaceborne SAR data.

A Simulation Method of Two-Dimensional Sea-Surface Current Field for Trajectory Crossing Spaceborne SAR

The trajectory-crossing method is an important and effective method for spaceborne SAR (synthetic aperture radar) to detect a two-dimensional current field. However, in practical applications, there are few spaceborne SAR data that meet the requirements of close acquisition time and overlapping trajectories, which makes it difficult to comprehensively analyze the impact of different systems and environmental parameters on current measurement results from real data processing. With the proposal of a SAR constellation plan in the future, the data resources will be constantly enriched, and the trajectory-crossing method will be widely used. It is, thus, necessary to lay some theoretical foundation at present. A two-dimensional sea-surface-current-field simulation method for trajectory crossing spaceborne SAR is proposed in this paper. Based on the principles of the trajectory-crossing method, the proposed method can realize the simulation of two-dimensional sea-surface-current field under given spaceborne SAR system and environmental parameters. In this paper, the simulation process of this method is given, and the simulation experiment is performed. Compared with the current measurement results of SAR data, the simulation experiment shows that the current velocity error is less than 0.03 m/s and the direction error is less than 10 degrees, which proves the reliability of the proposed simulation method. The proposed method lays a foundation for analyzing the influence of various parameters in the application of the trajectory-crossing method.

Monitoring the spatial dispersion of an oil slick by enhancing and noise-filtering SAR images using SENTINEL-1 satellite repeat-pass observations

ABSTRACT This article presents a method for monitoring the spatial dispersion of an oil slick and the temporal changes it experiences by enhancing and noise-filtering synthetic aperture radar (SAR) amplitude images. When accidental oil spills occur, spatial dispersion of the initial oil cover and assessing the temporal changes that affect it are important factors for recovery operations. Herein, the method proposed aims to facilitate a better understanding of the potential oil spread and oil-float flow from the source of the spill to adjacent coastal zones. Space-borne wide-swath SAR data is used in this regard, as it covers relatively large areas compared with other ground-based remote-sensing techniques and offer day and night observation under all weather conditions in coastal zones. The backscattered radar signal from a VV polarimetric channel, which showed a higher sensitivity for changes in the sea-surface roughness, was used. Heterogeneous surface-scattering phenomena were observed, and the amplitude image was a prominent factor in the formation of a Bragg surface around the seashore, which facilitated a better understanding of the spatial dispersion of a spilt oil layer and its temporal changes. The proposed method was applied to an oil-slick spread that happened after an oil tanker ran aground on a coral reef near Blue Bay Marine Park, south-east of the Mauritius Island, in July 2020. The method for deriving the separability between the oil and the non-oil sea surface was to render per-pixel classifiers more efficient for both speckle smoothing and texture preservation in satellite images. Furthermore, sea wind speed and direction, derived from SAR amplitude images, were used to assess the limitations and reliability of the proposed method. Based on the results, the proposed method indicated a substantial impact for facilitating a better understanding of the spatial dispersion of oil slicks and the temporal changes that affect them.

Field-scale soil moisture estimation using sentinel-1 GRD SAR data

Soil moisture is a critical land variable that controls the energy and mass balance in land–atmosphere interactions. Spaceborne Synthetic Aperture Radar (SAR) sensors offer an efficient way to map and monitor soil moisture because of their sensitivity towards the dielectric and geometric properties of the target. In addition, SAR acquisitions are weather-independent, providing a significant advantage over optical imaging during periods of cloud cover. However, vegetation cover makes these processes more complex and influences the interaction of SAR backscatter resulting from combined soil matrix and vegetation cover. Therefore, using SAR data, it is necessary to compensate for vegetation contribution in total backscatter while estimating soil moisture over the vegetated soil surface. This study presents a technique that utilizes a vegetation index derived from SAR data to generate high-resolution soil moisture maps. It is noteworthy that this proposed soil moisture retrieval method uses only the dual-polarimetric Ground Range Detected (GRD) SAR product, i.e., only backscatter intensities. Hence, the proposed method has a high potential for operational soil moisture monitoring globally. We validated over 34 soil moisture stations of the Texas Soil Observation Network (TxSON) using time-series Sentinel-1 SAR data. The Root Mean Square Error (RMSE) values for estimated volumetric soil moisture are within the range of 0.048 m3 m−3 to 0.055 m3 m−3 with the Pearson correlation coefficient r>0.79. The code to generate DpRVIc in Google Earth Engine is available at: https://github.com/Narayana-Rao/dual_pol_descriptors.

Multifrequency Spaceborne Synthetic Aperture Radar Data for Backscatter-Based Characterization of Land Use and Land Cover

Polarimetric synthetic aperture radar remote sensing extracts the information about the target using decomposition models to separate the polarimetric information into single-bounce (contributed by smooth surfaces), double-bounce (contributed by urban structure), and volume (mainly due to vegetation cover) scattering components. The penetration capacity of the electromagnetic wave into the surface increases with the decrease in its frequency. This study explores and compares the polarimetric decomposition models for scattering-based characterization of land use and cover using multifrequency spaceborne synthetic aperture radar sensor datasets that were acquired over San Francisco, CA, USA. The present work compares the scattering parameters of coherent (Pauli), roll-invariant (Barnes), eigenvalue–eigenvector (Cloude), and compact-polarimetric (Raney) decomposition modeling approaches for scattering-based characterization of urban structures, waterbody, and vegetation cover. The land use/cover classification was performed based on the scattering response of the scatterers using a support vector machine classifier. The outputs of the classification approach on multisensor, multifrequency, and multi-polarization polarimetric synthetic aperture radar data have shown reasonable accuracy in classifying the land use and land cover. The decomposition models fail to characterize the oriented urban structures that cause misclassification of urban structures as vegetation. The higher-order roll-invariant decomposition modeling approaches could improve the interpretation of different targets and accuracy in land use and land cover classification.