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The relationship between chasmata (rift zones) and spatially associated volcanism (mons and coronae) on Venus has been extensively discussed but remains enigmatic. One region where these features are prominently displayed is along the 10,000 km long, WNW trending, Parga Chasmata, which connects Atla Regio with Themis Regio. The Mbokomu Mons area (located about 2200 km SE of Atla Regio) was selected for detailed study to provide insight into these relationships. More than 39,000 extensional lineaments (grabens, fissures and fractures) were mapped at 1:500,000 scale using full resolution Magellan Synthetic Aperture Radar (SAR) images and grouped into radiating, circumferential and linear systems. They are (except where noted) interpreted to represent the surface expression of underlying mafic dyke swarms, on the basis of associated volcanic features and terrestrial analogues. Radiating and/or circumferential swarms are associated with Mbokomu Mons and the four coronae in the surrounding area, Among Corona (AC), Repa Corona (RC) and two unnamed coronae (UC1 and UC2). Mbokomu Mons is unique among the tectono-magmatic features in this region of Parga Chasmata, in having both corona and mons characteristics. The initial Corona Phase consists of radiating and circumferential systems mainly preserved in an unflooded annular uplift, while the Mons Phase includes a second radiating swarm associated with a central edifice, and smaller circumferential fracture pattern near the summit that could overlie a magma reservoir. The plume or diapir that is interpreted to have been responsible for the initial Corona Phase is estimated to have had a radius of ∼150 km. Cross-cutting relationships indicate that Mbokomu Mons is younger than nearby Among, Oduduwa and Onenhtse coronae. All four centres are aligned along a WNW-trend parallel to the Parga Chasmata (rift system). Mbokomu Mons is located at, and its emplacement may be linked to, the intersection of this WNW-trending zone of weakness and the orthogonal Jokwa Linea rift system. Mbokumo Mons is also younger than the nearby parallel Penthesilia Fossa (PF) (part of the Great Dyke of Atla Regio). • Mapping grabens for dyke swarm history of Mbokomu Mons (MM) region, Parga Chasmata. • Dyke swarms show 2 stages in MM evolution: Corona and Mons stages. • Domal uplift (radial swarms), then plume spreading (circumferential swarms). • Swarms feed flows of MM at ∼1700 m elevation - imply magma reservoir at 400 m depth. • Alignment of 4 centres shows zone of lithospheric weakness parallel to Parga rift. [ABSTRACT FROM AUTHOR]

Radar loads, especially Synthetic Aperture Radar (SAR) image reconstruction loads use a large volume of data collected from satellites to create a high-resolution image of the earth. To design near-real-time applications that utilise SAR data, speeding up the image reconstruction algorithm is imperative. This can be achieved by deploying a set of distributed computing infrastructures connected through a network. Scheduling such complex and large divisible loads on a distributed platform can be designed using the Divisible Load Theory (DLT) framework. We performed distributed SAR image reconstruction experiments using the SLURM library on a cloud virtual machine network using two scheduling strategies, namely the Multi-Installment Scheduling with Result Retrieval (MIS-RR) strategy and the traditional EQual-partitioning Strategy (EQS). The DLT model proposed in the MIS-RR strategy is incorporated to make the load divisible. Based on the experimental results and performance analysis carried out using different pixel lengths, pulse set sizes, and the number of virtual machines, we observe that the time performance of MIS-RR is much superior to that of EQS. Hence the MIS-RR strategy is of practical significance in reducing the overall processing time, and cost, and in improving the utilisation of the compute infrastructure. Furthermore, we note that the DLT-based theoretical analysis of MIS-RR coincides well with the experimental data, demonstrating the relevance of DLT in the real world. • The article uses distributed computing to improve SAR image reconstruction efficiency. • Multi-instalment strategies (MIS-RR and EQS) were compared using SLURM on a cloud network using the divisible load model • The study shows the practical significance of MIS-RR in improving compute infrastructure utilization. • DLT-based theoretical analysis of MIS-RR coincides with experimental results. [ABSTRACT FROM AUTHOR]

The Mediterranean Basin has experienced substantial land use changes as traditional agriculture decreased and population migrated from rural to urban areas, which have resulted in a large forest cover increase. The combination of Landsat time series, providing spectral information, with lidar, offering three-dimensional insights, has emerged as a viable option for the large-scale cartography of forest structural attributes across large time spans. Here we develop and test a comprehensive framework to map forest above ground biomass, canopy cover and forest height in two regions spanning the most representative biomes in the peninsular Spain, Mediterranean (Madrid region) and temperate (Basque Country). As reference, we used lidar-based direct estimates of stand height and forest canopy cover. The reference biomass and volume were predicted from lidar metrics. Landsat time series predictors included annual temporal profiles of band reflectance and vegetation indices for the 1985–2023 period. Additional predictor variables including synthetic aperture radar, disturbance history, topography and forest type were also evaluated to optimize forest structural attributes retrieval. The estimates were independently validated at two temporal scales, i) the year of model calibration and ii) the year of the second lidar survey. The final models used as predictor variables only Landsat based metrics and topographic information, as the available SAR time-series were relatively short (1991–2011) and disturbance information did not decrease the estimation error. Model accuracies were higher in the Mediterranean forests when compared to the temperate forests (R2 = 0.6–0.8 vs. 0.4–0.5). Between the first (1985–1989) and the last (2020–2023) decades of the monitoring period the average forest cover increased from 21 ± 2% to 32 ± 1%, mean height increased from 6.6 ± 0.43 m to 7.9 ± 0.18 m and the mean biomass from 31.9 ± 3.6 t ha−1 to 50.4 ± 1 t ha−1 for the Mediterranean forests. In temperate forests, the average canopy cover increased from 55 ± 4% to 59 ± 3%, mean height increased from 15.8 ± 0.77 m to 17.3 ± 0.21m, while the growing stock volume increased from 137.8 ± 8.2 to 151.5 ± 3.8 m3 ha−1. Our results suggest that multispectral data can be successfully linked with lidar to provide continuous information on forest height, cover, and biomass trends. • Active/passive synergies were evaluated for forest structural attributes estimation. • Four decades of forest structural attributes were generated over two Spanish regions. • Since 1985 the average forest canopy cover increased by 11% in the Madrid region. [ABSTRACT FROM AUTHOR]

One reason arid and semi-arid environments have been used to store waste is due to low groundwater recharge, presumably limiting the potential for meteoric water to mobilize and transport contaminants into groundwater. The U.S. Department of Energy Office of Legacy Management (LM) is evaluating selected uranium mill tailings disposal cell covers to be managed as evapotranspiration (ET) covers, where vegetation is used to naturally remove water from the cover profile via transpiration, further reducing deep percolation. An important parameter in monitoring the performance of ET covers is soil moisture (SM). If SM is too high, water may drain into tailings material, potentially transporting contaminants into groundwater; if SM is too low, radon flux may increase through the cover. However, monitoring SM via traditional instrumentation is invasive, expensive, and may fail to account for spatial heterogeneity, especially over vegetated disposal cells. Here we investigated the potential for non-invasive SM monitoring using radar remote sensing and other geospatial data to see if this approach could provide a practical, accurate, and spatially comprehensive tool to monitor SM. We used theoretical simulations to analyze the sensitivity of multi-frequency radar backscatter to SM at different depths of a field-scale (3 ha) drainage lysimeter embedded within an in-service LM disposal cell. We then evaluated a shallow and deep form of machine learning (ML) using Google Earth Engine to integrate multi-source observations and estimate the SM profile across six soil layers from depths of 0–2 m. The ML models were trained using in situ SM measurements from 2019 and validated using data from 2014 to 2018 and 2020–2021. Model predictors included backscatter observations from satellite synthetic aperture radar, vegetation, temperature products from optical infrared sensors, and accumulated, gridded rainfall data. The radar simulations confirmed that the lower frequencies (L- and P-band) and smaller incidence angles show better sensitivity to deeper soil layers and an overall larger SM dynamic range relative to the higher frequencies (C- and X-band). The ML models produced accurate SM estimates throughout the soil profile (r values from 0.75 to 0.94; RMSE = 0.003–0.017 cm3/cm3; bias = 0.00 cm3/cm3), with the simpler shallow-learning approach outperforming a selected deep-learning model. The ML models we developed provide an accurate, cost-effective tool for monitoring SM within ET covers that could be applied to other vegetated disposal cell covers, potentially including those with rock-armored covers. • First data-driven application for mapping soil moisture within a uranium waste cover. • Machine learning and public remote sensing datasets predict soil moisture dynamics with high accuracy. • Plant greenness best explains soil moisture dynamics in a vegetated disposal cell cover. • In-situ soil moisture probes may not completely capture soil moisture heterogeneity. [ABSTRACT FROM AUTHOR]

Remote sensing satellites acquire a continuous stream of data on a daily basis. As most of those data are unlabeled, the development of algorithms requiring weak supervision is of paramount importance. In this paper, we show that the need for annotation for Synthetic Aperture Radar data can be reduced by coupling a despeckling task (self-supervised) and a segmentation task (supervised). The proposed self-supervised learning framework, called MERLIN-Seg, has been trained for building footprint extraction and achieves favorable performances even with 1% of annotated data. We show that conditioning the network on despeckling without labels is beneficial for supervised segmentation. Our experiments demonstrate that the joint training of the two tasks achieves better performances than a vanilla segmentation network in terms of IoU, F1 score, and accuracy on both simulated and real SAR images. [Display omitted] • Self-supervised despeckling learns a semantically-meaningful latent space. • Scarcity of data for supervised tasks is mitigated by joint learning of despeckling and downstream task. • Efficient building footprint extraction with few labeled data is achieved on simulated and real images. • Extension to any downstream task is possible. [ABSTRACT FROM AUTHOR]

Since the late 1970s, remote sensing techniques have been proven to be suitable for characterizing and monitoring plants and crops. In particular, synthetic aperture radar (SAR) missions contribute considerably to this prediction effort. However, the main issue when using SAR image series together with field observations is the scarcity of data due to the difficulty of acquiring field measurements. This research aimed to contribute to solving this problem with an alternative statistical model that can overcome the lack of a long, robust series of field-based ground truth observations. The main novelty of this research is the evaluation of the potential of a panel data approach to radar remote sensing imagery for predicting crop biophysical variables. For this purpose, RADARSAT-2 imagery was acquired over the study area in central Spain. Simultaneously, a field campaign was deployed to estimate crop parameters in the same area and to validate the results of the modelling. The analysis of the influence of the crop type on the incidence angle and the polarimetric parameters showed a strong influence of the co-polar channels (HH, VV), the entropy (H) and the coherence between the co-polar channels (γ HHVV), with the differences being higher at 25°. The panel data analysis method demonstrated that good predictions, with R2 greater than 0.78, were achieved for all biophysical variables analysed in this study. Overall, this novel statistical approach with remote sensing data showed great applicability for the prediction of crop variables, even with a short series of observations. • Advance temporal and spatial statistical methods applied to radar remote sensing. • Radar parameters and statistical modelling can predict crop biophysical variables. • Differences were found between the parameters at different incidence angles. • The physical parameters related to water content were better predicted. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest in the development of ground-based Synthetic Aperture Radars (GBSAR) for the purpose of monitoring structural displacements. GBSAR offers high-resolution monitoring over a wide area and can capture data every few minutes. However, compact high-frequency multiple input multiple output (MIMO) radars have emerged as an alternative for monitoring sub-second displacements, such as structural vibrations. MIMO radar has sub-second acquisition interval. However, it has limited cross-range resolution compared to GBSAR, and interference between antennas and presence of multiple scatterers in the scene can cause strong sidelobes in the processed data. On the other hand, GBSAR utilizes a long synthetic aperture to achieve high cross-range resolution. However, due to its longer data acquisition time compared to MIMO radar, conventional methods are insufficient for detecting scatterers' sub-second displacements that occur during the data acquisition process. This study proposes a method to effectively monitor sub-second or sub-minute displacements using GBSAR signals. The proposed method enhances the conventional radar interferometric processes by employing spectral estimation, allowing for multi-dimensional detection of targets' azimuth angle, linear displacement, and vibrational characteristics. Consequently, this method improves both the processing of MIMO radar data and enables high-resolution fast displacement monitoring from GBSAR signals. The paper presents the theoretical details and mathematical formulations of the proposed method for both MIMO radar and GBSAR imaging modes. To evaluate the effectiveness of the proposed method, numerical simulations and real experiments are conducted. The experimental results validate the capability of the proposed method in both GBSAR and MIMO configuration modes for high-resolution monitoring of fast linear displacements and vibrations. The results exhibit promising signal-to-noise ratio (SNR) and peak-to-sidelobe ratio (PSLR) values. [ABSTRACT FROM AUTHOR]

High-resolution mapping of rice fields is crucial for understanding and managing rice cultivation in countries like Bangladesh, particularly in the face of climate change. Rice is a vital crop, cultivated in small scale farms that contributes significantly to the economy and food security in Bangladesh. Accurate mapping can facilitate improved rice production, the development of sustainable agricultural management policies, and formulation of strategies for adapting to climatic risks. To address the need for timely and accurate rice mapping, we developed a framework specifically designed for the diverse environmental conditions in Bangladesh. We utilized Sentinel-1 and Sentinel-2 time-series data to identify transplantation and peak seasons and employed the multi-Otsu automatic thresholding approach to map rice during the peak season (April–May). We also compared the performance of a random forest (RF) classifier with the multi-Otsu approach using two different data combinations: D1, which utilizes data from the transplantation and peak seasons (D1 RF) and D2, which utilizes data from the transplantation to the harvest seasons (D2 RF). Our results demonstrated that the multi-Otsu approach achieved an overall classification accuracy (OCA) ranging from 61.18% to 94.43% across all crop zones. The D2 RF showed the highest mean OCA (92.15%) among the fourteen crop zones, followed by D1 RF (89.47%) and multi-Otsu (85.27%). Although the multi-Otsu approach had relatively lower OCA, it proved effective in accurately mapping rice areas prior to harvest, eliminating the need for training samples that can be challenging to obtain during the growing season. In-season rice area maps generated through this framework are crucial for timely decision-making regarding adaptive management in response to climatic stresses and forecasting area-wide productivity. The scalability of our framework across space and time makes it particularly suitable for addressing field data scarcity challenges in countries like Bangladesh and offers the potential for future operationalization. [Display omitted] • We developed a novel framework to map boro rice at peak season using Sentinel images. • Boro rice maps in Bangladesh showed high classification accuracy (mean of 87.90%). • No requirement of sample data collection for training the classification model. • Multi-Otsu effectively maps rice in low-data areas, outperforming other ML methods. • Provide stakeholders rice area statistics to support food security management. [ABSTRACT FROM AUTHOR]

Polarimetric radar backscattering coefficients depend on the impinging frequency, dielectric constant, incidence angle, polarization, and surface roughness. Therefore, surface roughness can be estimated using high-resolution polarimetric radar datasets. For this purpose, we examine the utility of model-based and eigenvalue-based decomposition approaches in this work. While using model-based decomposition, the dielectric constant is estimated at the outset. Thereafter, the rms height is derived from a scalar multiplier that models surface roughness. We also propose a modified single-bounce eigenvalue relative difference (SERD) and establish that it is a better indicator of surface roughness than the circular polarization coherence and the original SERD for non-reflection-symmetric lunar terrain. The Chandrayaan-2 Dual Frequency Synthetic Aperture Radar (DFSAR) datasets acquired over three Apollo mission landing sites are used for demonstration. • Estimation of lunar surface roughness at cm scales from Chandrayaan-2 data. • RMS height estimated using model-based decomposition approach. • Also proposed a modified SERD parameter as a descriptor of lunar surface roughness • Modified SERD found superior to circular polarization coherence and original SERD • Useful study for identifying optimal landing sites for future lunar missions [ABSTRACT FROM AUTHOR]

Abstract Imaging via synthetic aperture radar (SAR) is a well-established technique for effective scene reconstruction, with resolution up to a few centimeters. The measurement process requires the round trip time for the electromagnetic waves to travel to the scene and return back to the sensing mechanism. While hypothetically the round trip time can be exactly determined, in practice this distance can only be approximated, leading to errors in the round trip time estimates. These errors manifest as phase errors on the data and produce defocused imagery, making information extraction difficult. This investigation develops an autofocusing technique that exploits the correlation of the phase error on both the azimuth angle and spatial (cycles/meter) frequencies while also enforcing the piecewise smooth nature of the image. Our method estimates the phase error correction and the image through a joint optimization procedure. Specifically, our method incorporates a phase synchronization technique to estimate the unknown two-dimensional phase error. High order regularization is used in the optimization procedure, which helps to reduce speckle in the SAR image. Highlights • Auto-focusing corrects the phase error using phase synchronization. • Not a post-processing technique, so minimal information loss. • HOTV regularization reduces speckle effects. • Algorithm can be employed directly on SAR phase history data. [ABSTRACT FROM AUTHOR]

The modeling and mapping of soil organic carbon (SOC) has advanced through the rapid growth of Earth observation data (e.g., Sentinel) collection and the advent of appropriate tools such as the Google Earth Engine (GEE). However, the effects of differing optical and radar sensors on SOC prediction models remain uncertain. This research aims to investigate the effects of different optical and radar sensors (Sentinel-1/2/3 and ALOS-2) on SOC prediction models based on long-term satellite observations on the GEE platform. We also evaluate the relative impact of four synthetic aperture radar (SAR) acquisition configurations (polarization mode, band frequency, orbital direction and time window) on SOC mapping with multiband SAR data from Spain. Twelve experiments involving different satellite data configurations, combined with 4027 soil samples, were used for building SOC random forest regression models. The results show that the synthesis mode and choice of satellite images, as well as the SAR acquisition configurations, influenced the model accuracy to varying degrees. Models based on SAR data involving cross-polarization, multiple time periods and "ASCENDING" orbits outperformed those involving copolarization, a single time period and "DESCENDING" orbits. Moreover, combining information from different orbital directions and polarization modes improved the soil prediction models. Among the SOC models based on long-term satellite observations, the Sentinel-3-based models (R2 = 0.40) performed the best, while the ALOS-2-based model performed the worst. In addition, the predictive performance of MSI/Sentinel-2 (R2 = 0.35) was comparable with that of SAR/Sentinel-1 (R2 = 0.35); however, the combination (R2 = 0.39) of the two improved the model performance. All the predicted maps involving Sentinel satellites had similar spatial patterns that were higher in northwest Spain and lower in the south. Overall, this study provides insights into the effects of different optical and radar sensors and radar system parameters on soil prediction models and improves our understanding of the potential of Sentinels in developing soil carbon mapping. • Our results revealed that the composite mode, radar data utilization strategies and sensor selection influenced outputs. • The mixed information of different orbital directions and polarization modes improved the mapping accuracy. • The predictive performance of Sentinel-1 and Sentinel-2 was comparable. • The accuracy rankings based on satellite observations were as follows: Sentinel-3 > Sentinel-2 > Sentinel-1 > ALOS-2. • The synergistic utilization of the Sentinel series gave better prediction models. [ABSTRACT FROM AUTHOR]

Highlights • A novel approach is proposed for the nonparametric blind single image super resolution of SAR. • Combination of an adaptive compressive sensing and sparse priors is the fundamental idea. • A new approach based on the conjugate gradient least squares method is proposed. • Proposed method achieves the state-of-the-art performance in the PSF kernel estimation accuracy. This paper by proposing a novel approach, is one the first works that addresses the highly ill-posed problem of nonparametric blind single image super resolution (SISR) of the synthetic aperture radar (SAR) images. Combination of an adaptive compressive sensing (CS) technique and some effective sparse priors, as a powerful regularizer in the both high resolution (HR) image reconstruction and the point spread function (PSF) estimation domains is the fundamental idea of the proposed method. This task is formulated as a new cost function to be minimized with respect to an intermediate reconstructed HR image patch and a nonparametric PSF kernel, according to the alternative minimization (AM) algorithm. To solve the optimization of cost function, a numerical scheme based on the conjugate gradient least squares (CGLS) method is proposed. Experimental results for the both synthetic and realistic low resolution (LR) SAR images demonstrate that the proposed method achieves the state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Microwave remote sensing data acquired with Magellan Venus orbiter are the main source of information about the surface of the planet. We analyze variability of the backscattering function (dependence of radar cross-section on incidence angle) for steep incidence angles 0.25°–4.75° in the 75°N–55°S latitude zone with data from the Magellan radar altimeter at 12.6 cm wavelength. We show that all variability of the backscattering function can be described by three parameters, describing (1) surface reflectivity, (2) relative proportion of horizontal facets, and (3) general roughness. Analysis of maps of these parameters revealed that surficial deposits, for example, microdune fields, are abundant on Venus even in places, where they are not readily seen in the synthetic aperture radar images. Properties of surficial deposits rather than original volcanic flow roughness define the shape of the backscattering function on the majority of regional plains. A large radar-dark flow in Bereghinia Planitia has anomalously high proportion of horizontal facets, which is consistent with it being formed by a relatively recent plain-forming volcanic episode. Some crater-associated radar-dark diffuse features and splotches are also characterized by increased proportion of horizontal faces, which indicate the presence of mantles deposited from fluidized granular material. The backscattering functions of the anomalous radar-bright material of mountaintops are more consistent with the strong internal scattering hypothesis rather than the exotic surficial material hypothesis. Obtained maps can be useful for planning future lander missions to sites with access to surface material with known provenance. [ABSTRACT FROM AUTHOR]

Large-scale forest clear-cut identification is one of the major application of remote sensing techniques. ALOS-2/PALSAR-2 is the latest SAR satellite providing multi-polarized L-band SAR data. With increasing deforestation, it is important to assess the potential of SAR data for identifying clear-cuts in forest regions. In this research work, multi-temporal ALOS-2/PALSAR-2 SAR data and supplementary Landsat-8 optical data sets are acquired over Indian tropical forest, and SAR parameters are analysed over a progressively clear-cut Teak plantation. Sensitivity of the SAR parameters to progressive clear-cuts is estimated and found that the cross-polarized backscatter σ H V 0 and entropy parameter H are most sensitive to both partial and complete clear-cut in forest compartments. An entropy thresholding based classification is carried out to identify clear-cut regions with a good accuracy. The study highlights the utility of SAR parameters to monitor forest clear-cuts for better forest management. [ABSTRACT FROM AUTHOR]

This paper proposes a sparsity constraint nearest subspace classifier (SNSC) for target recognition of synthetic aperture radar (SAR) images. Unlike optical images, SAR images are highly sensitive to target azimuth. Therefore, the global dictionary collaborated by samples from different classes has high between-class correlation, which will impair the performance of sparse representation-based classification (SRC). Furthermore, even on the subspace spanned by a single class, only a small number of samples with similar azimuths to the test image are highly correlated with the test image. Thus, the linear coefficients over the subspace are actually sparse ones. Therefore, in this paper we impose sparsity constraint on nearest subspace classifier (NSC) classifier and apply it to SAR target recognition. The target label of the test sample is decided to be the class with the minimum reconstruction error. The proposed method is tested on moving and stationary target acquisition and recognition (MSTAR) dataset and compared with several state-of-the-art methods and the experimental results verify the validity and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

We present new range-Doppler images of the Moon using 6-mwavelength. The radar images were obtained using the Jicamarca Radio Observatory 49.92  MHz radar. The observations were performed using circular polarization on transmit and two orthogonal linear polarizations on receive, allowing scattering images to be obtained with the polarization matched to the transmitted wave (polarized), and at a polarization orthogonal to the transmitted wave (depolarized). Due to the long wavelength that penetrates efficiently into the subsurface of the Moon, the radar images are especially useful for studies of subsurface composition. Two antenna interferometry on receive was used to remove the Doppler north-south ambiguity. The images have approximately 10  km resolution in range 20  km resolution in Doppler, allowing many large scale features, including maria, terrae, and impact craters to be identified. Strong depolarized return is observed from relatively new larger impact craters with large breccia and shallow regolith. Terrae regions with less lossy surface material also appear brighter in both depolarized and polarized images. A large region in the area near the Mare Orientale impact basin has overall higher than mean radar backscatter in both polarized and depolaried returns, indicating higher than average presence of relatively newly formed large breccia in this region. Mare regions are characterized by lower polarized and depolarized return, indicating that there is higher loss of the radio wave in the subsurface, reducing the echo. We also report unexpected low polarized and depolarized backscatter from an old impact basin in the Schiller–Schickard region, as well as from the region poleward from Mare Imbrium. [ABSTRACT FROM AUTHOR]

This paper investigates accurate and efficient ℓ 1 regularization methods for generating synthetic aperture radar (SAR) images. Although ℓ 1 regularization algorithms are already employed in SAR imaging, practical and efficient implementation in terms of real time imaging remain a challenge. Here we demonstrate that fast numerical operators can be used to robustly implement ℓ 1 regularization methods that are as or more efficient than traditional approaches such as back projection, while providing superior image quality. In particular, we develop a sequential joint sparsity model for composite SAR imaging which naturally combines the joint sparsity methodology with composite SAR. Our technique, which can be implemented using standard, fractional, or higher order total variation regularization, is able to reduce the effects of speckle and other noisy artifacts with little additional computational cost. Finally we show that generalizing total variation regularization to non-integer and higher orders provides improved flexibility and robustness for SAR imaging. [ABSTRACT FROM AUTHOR]

The present study highlights the potential of multi-temporal X-band Synthetic Aperture Radar (SAR) moisture products to be used for the calibration of a model reproducing soil moisture (SM) variations. We propose the MHYSAN model (Modèle de bilan HYdrique des Sols Agricoles Nus) for simulating soil water balance of bare soils. This model was used to simulate surface evaporation fluxes and SM content at daily time scale over a semi-arid, bare agricultural site in Tunisia (North Africa). Two main approaches are considered in this study. Firstly, the MHYSAN model was successfully calibrated for seven sites using continuous thetaprobe measurements at two depths. Then the possibility to extrapolate local SM simulations at distant sites, based on soil texture similarity only, was tested. This extrapolation was assessed using SAR estimates and manual thetaprobe measurements of SM recorded at these distant sites. The results reveal a bias of approximately 0.63% and 3.04%, and an RMSE equal to 6.11% and 4.5%, for the SAR volumetric SM and manual thetaprobe measurements, respectively. In a second approach, the MHYSAN model was calibrated using seven very high-resolution SAR (TerraSAR-X) SM outputs ranging over only two months. The simulated SM were validated using continuous thetaprobe measurements during 15 months. Although the SM was measured on only seven different dates for the purposes of calibration, satisfactory results were obtained as a result of the wide range of SM values recorded in these seven images. This led to good overall calibration of the soil parameters, thus demonstrating the considerable potential of Sentinel-1 images for daily soil moisture monitoring using simple models. [ABSTRACT FROM AUTHOR]

Landforms on Titan include features hypothesized to be alluvial fans. Terrestrial alluvial fans form via two processes: fluid-gravity flows (sheetfloods) and sediment-gravity flows (debris flows). Along the Panamint Mountain Range in Death Valley, California, USA, seven fans formed primarily by debris flows are located adjacent to seven fans formed primarily by sheetfloods. The causal difference between these two groupings stems from their catchment lithologies; the debris flow fan catchments are clay-rich and relatively sand-poor, and the sheetflood fan catchments are clay-poor and sand-rich. On Titan, the low and mid latitudes are dominated by sand seas, demonstrating that sand is available for transport. At high latitudes, these sand seas are absent, suggesting that transportable sand is scarce. Based on the sedimentology of the two Panamint Range fan types, we hypothesize that possible fans at lower latitudes on Titan are formed by sheetfloods, whereas those at higher latitudes formed primarily by debris flows. To test these hypotheses, we measured and analyzed the mean normalized radar cross sections ( σ °) and changes in σ ° with downfan distance for debris flow and sheetflood fans along the Panamint Range. We then compared the results with the same measurements for possible fans on Titan. We find that, in the Panamint Range, debris flow fans are brighter than sheetflood fans and have greater change in σ ° with downfan distance, and that on Titan, low-latitude possible fans are likewise brighter than the fans at high latitudes with greater change in σ ° with downfan distance. Consequently, our findings suggest that low-latitude possible fans on Titan are formed primarily by debris flows, whereas high-latitude possible fans on Titan are formed primarily by sheetfloods. Thus, our results do not support our hypotheses. Scenarios to explain these results include: (1) high-latitude possible fans are dominated by radar-dark debris flow deposits, (2) low- and mid-latitude possible fans are dominated by radar-bright sheetflood deposits, (3) sand-sized sediments were relatively scarce at the time of low- and mid-latitude possible fan formation, (4) bedrock composition varies as a function of latitude on Titan, (5) alluvial fans form differently on Titan because of the lower gravity conditions, and (6) fan-like features may result from non-alluvial processes, such as form distributary fluvial systems on Earth. [ABSTRACT FROM AUTHOR]

The Miniature Radio Frequency (Mini-RF) instrument aboard NASA's Lunar Reconnaissance Orbiter (LRO) is a hybrid dual-polarized synthetic aperture radar (SAR) that operated in concert with the Arecibo Observatory to collect bistatic radar data of the lunar nearside from 2012 to 2015. The purpose of this bistatic campaign was to characterize the radar scattering properties of the surface and near-surface, as a function of bistatic angle, for a variety of lunar terrains and search for a coherent backscatter opposition effect indicative of the presence of water ice. A variety of lunar terrain types were sampled over a range of incidence and bistatic angles; including mare, highland, pyroclastic, crater ejecta, and crater floor materials. Responses consistent with an opposition effect were observed for the ejecta of several Copernican-aged craters and the floor of the south-polar crater Cabeus. The responses of ejecta material varied by crater in a manner that suggests a relationship with crater age. The response for Cabeus was observed within the portion of its floor that is not in permanent shadow. The character of the response differs from that of crater ejecta and appears unique with respect to all other lunar terrains observed. Analysis of data for this region suggests that the unique nature of the response may indicate the presence of near-surface deposits of water ice. [ABSTRACT FROM AUTHOR]

We present a geomorphologic map of Titan's polar terrains. The map was generated from a combination of Cassini Synthetic Aperture Radar (SAR) and Imaging Science Subsystem imaging products, as well as altimetry, SARTopo and radargrammetry topographic datasets. In combining imagery with topographic data, our geomorphologic map reveals a stratigraphic sequence from which we infer process interactions between units. In mapping both polar regions with the same geomorphologic units, we conclude that processes that formed the terrains of the north polar region also acted to form the landscape we observe at the south. Uniform, SAR-dark plains are interpreted as sedimentary deposits, and are bounded by moderately dissected uplands. These plains contain the highest density of filled and empty lake depressions, and canyons. These units unconformably overlay a basement rock that outcrops as mountains and SAR-bright dissected terrains at various elevations across both poles. All these units are then superposed by surficial units that slope towards the seas, suggestive of subsequent overland transport of sediment. From estimates of the depths of the embedded empty depressions and canyons that drain into the seas, the SAR-dark plains must be >600 m thick in places, though the thickness may vary across the poles. At the lowest elevations of each polar region, there are large seas, which are currently liquid methane/ethane filled at the north and empty at the south. The large plains deposits and the surrounding hillslopes may represent remnant landforms that are a result of previously vast polar oceans, where larger liquid bodies may have allowed for a sustained accumulation of soluble and insoluble sediments, potentially forming layered sedimentary deposits. Coupled with vertical crustal movements, the resulting layers would be of varying solubilities and erosional resistances, allowing formation of the complex landscape that we observe today. [ABSTRACT FROM AUTHOR]

Our closest neighbor (Moon) is full of wonders and potentials that can help us learn more about the solar system and provide a habitat for space colonies. The lunar surface contains several potential volatiles implanted by the solar wind that can be used for In-Situ Resource Utilization (ISRU). Also by characterizing the lunar surface we can comprehend many solar system phenomenon. Radar has been proven to be very effective in characterizing and understanding the physical properties of the lunar surface. The success of Chandrayaan-2 (CH-2) in 2019 opened numerous doors to better understanding the lunar surface. The Dual Frequency Synthetic Aperture Radar (DFSAR), is the first L-band SAR ever used for lunar mapping. We approximated the dielectric values for the Permanently Shadowed Regions (PSRs) by combining the Integral Equation Model (IEM) and Artificial Neural Network (ANN). The two-dimensional Gaussian model proved to be more accurate when correlated with in-situ measurements made by the Apollo 17 mission out of four different configurations, but the validation process has its limitations, thus we analyzed the result from all the models. The real part of complex dielectric values of unknown craters near Malapert crater (PSRs SP 83260 0090770 and SP 841420 0059530) and Faustini crater (PSR SP 871460 0840750) were high in certain sections of the region and moderate in others. The high dielectric values of 11.2 and 10.9 were estimated by the two-dimensional Gaussian and Exponential model for the right-side walls of the PSRs SP 83260 0090770 and SP 841420 0059530, respectively. For three-dimensional Gaussian and Exponential models, the same region but with fewer pixels showed high dielectric values of approximately. However, the estimated dielectric value for the left side wall was around 3.4 for two-dimensional models and 2.3 for three-dimensional models. On the left side of the region, the PSR SP 871460 0840750 had high dielectric values of 10.6 and 10.2, as calculated by the two-dimensional Gaussian and Exponential models and approximately 13 for the three-dimensional models. The right side of the region showed approximately 3.6 dielectric values for two-dimensional models and 4 for three-dimensional models. We noticed that some pixel values showed ambiguous dielectric values (<1) during the retrieval and validation process, notably in three-dimensional models, which could be linked to incidence angle variability and model sensitivity. This indicates the model's limitation and is treated as outliers, which we rectified by optimizing the threshold. Overall, the research brought attention to the intriguing dielectric constant variation in PSRs. • Dielectric values for the Permanently Shadowed Regions (PSRs). • Chandrayaan-2 Dual Frequency Synthetic Aperture Radar (DFSAR) data. • Combining Integral Equation Model (IEM) and Artificial Neural Network (ANN). • Two- and three-dimensional Gaussian and Exponential models. [ABSTRACT FROM AUTHOR]

Coastal ecosystems offer substantial support and space for the sustainable development of human society, and hence the ecological risk evaluation of coastal ecosystems is of great significance. In this article, we propose an innovative framework for evaluating coastal ecological risk by considering oil spill risk information and environmental vulnerability information. Specifically, a deep learning based marine oil spill monitoring method is presented to obtain the oil spill risk information from Sentinel-1 polarimetric synthetic aperture radar (PolSAR) images. The environmental vulnerability information is then obtained from biological sample data and habitat information. Finally, a weighted probability model is introduced to utilize the oil spill risk and environmental vulnerability information, to evaluate the coastal ecological risk. In the experimental part, the proposed oil spill monitoring method shows its reliability in global ocean areas, and the proposed model is adopted to evaluate the ecological risk in Jiaozhou Bay, China. The results show that the ecological situation of more than half of the areas in Jiaozhou Bay is unstable, and the areas with high risk are mainly concentrated in the ports, shipping channels, and those areas with high biodiversity. This study provides some new perspectives on ecological risk assessment for coastal ecosystems, facilitating the planning process and the actions to be taken in response to the accidents that occur in the ocean, especially oil spill accidents. • Current studies about ecological risk evaluation of coastal ecosystems cannot reflect the ecological status objectively. • The model proposed in this paper comprehensively considers the influence of oil spills on the marine biology and habitat. Oil spill event has been considered a very important disaster-causing factor in many studies of the evaluation of coastal ecosystem risk (Adler and Inbar, 2007; Atlas and Hazen, 2011; Feng et al., 2021). With the rapid development of marine trade, marine oil pollution is becoming increasingly severe. Statistically, 5.86 million tons of oil have leaked from seaborne vessels in the past 50 years (Ma et al., 2022).There are a large amount of toxic compounds and heavy metals in crude oil (Atlas and Hazen, 2011), which will rapidly enter the marine ecological cycle through the food chain and damage the health of the marine ecosystem once the oil spill occurs (Fahd et al., 2021). [ABSTRACT FROM AUTHOR]

Venus’ highlands appear much brighter than its lowland plains in reflected radar, which has been explained by several conflicting hypotheses. We study this transition at higher spatial and elevation resolution than previously possible by combining Magellan synthetic aperture radar (SAR) images with Magellan SAR stereo elevations. We confirm that SAR backscatter over Ovda Regio (5°N to 15°S) grades from low to high as elevation increases (2–4.5 km above the datum), and then drops precipitously above ∼4.5 km (T= ∼702 K). This pattern is consistent with presence of a substance that undergoes a phase transition from ferroelectric to normal dielectric at ∼700 K; the mineral chlorapatite is a likely candidate. This pattern is seen across Ovda, on other near-equatorial highlands, and on some shield volcanoes like the Tepev Montes. We also confirm that Maxwell Montes (60–68°N) shows a different pattern; its surface transitions abruptly from low backscatter to high backscatter at ∼4.5 km above the datum, and remains so to nearly its highest elevations (∼10 km). This pattern is consistent with the presence of a semiconductor material either precipitated from the atmosphere (e.g., a frost) or produced by atmosphere–surface interaction. If a ferroelectric substance were in the rock at Maxwell (as at Ovda), it could be invisible beneath the coating of semiconductor material. However, the absence of a semiconductor material on Ovda requires either that [1] the atmosphere compositions at Maxwell and Ovda are substantially different, or [2] that the semiconductor at Maxwell forms by atmosphere–surface reaction (not as an atmospheric precipitate) and that the surface materials at Ovda and Maxwell are substantially different. [ABSTRACT FROM AUTHOR]

Object detection in SAR images is a challenging task as these images are inherently affected with speckle noise. This paper presents a novel algorithm based on bandlet transform for object detection in Synthetic Aperture Radar (SAR) images. Here first a bandlet based despeckling scheme is employed on the input SAR image and then a constant false alarm rate (CFAR) detector is used for object detection. The input image is first decomposed using Bandlet transform and the bandlet coefficients so obtained are modified using soft thresholding rule on all sub bands, except for low frequency sub band. The optimum thresholds for each sub bands are computed using generalized cross-validation (GCV) technique which doesn’t require the information on noise variance of the input image. The method takes advantage of the geometrical features of bandlet transform for retaining the edges and boundaries of the objects present in SAR images while removing the speckle effectively. Thus CFAR detection on despeckled image can effectively find an optimum threshold for object detection to maintain a constant false alarm rate. The proposed Bandlet transform based scheme surpasses the traditional despeckling and object detection schemes in wavelet domain, in terms of numerical and visual quality. [ABSTRACT FROM AUTHOR]

Limited-aperture data brings great challenge for inverse scattering problems. The limited-aperture problem we are particularly interested in is the limited-aperture "backscattering" problem where both the incident and observation directions are on the same aperture. Other than directly using the limited-aperture data, we introduce two data completion algorithms to recover the full-aperture data. With the reconstructed full-aperture data, we then apply the factorization method and direct sampling method for the object reconstruction. Both of the data completion algorithms, though derived in different ways, share the same key ingredient: the limited-aperture data is related to the full-aperture data via the prolate matrix. In the first algorithm we represent the full-aperture data in the form of double Fourier series, and relate the corresponding Fourier coefficients to the limited-aperture data via two prolate matrices. This fundamental idea and algorithm motivate the second algorithm: for each incident direction, we represent the full-aperture data by the single layer potential, and relate the Fourier coefficients of the density to the limited-aperture data via the prolate matrix. The second algorithm can be seen as a "direction-wise" algorithm. Both of the data completion algorithms are independent of the topological and physical properties of the unknown scatterers. A variety of numerical examples are presented to illustrate the effectiveness and robustness of the data completion algorithms with the sampling methods. • Two data completion algorithms are investigated to treat the inverse scattering problems with limited-aperture backscattering data. • Both the algorithms relate the limited-aperture data to the full-aperture data via the prolate matrix. • The data completion algorithms can be easily implemented since only the approximate inversion of the prolate matrix is involved. • The data completion algorithms make sampling methods completely applicable. • Both the data completion algorithms and the sampling methods are independent of the physical and geometrical properties of the unknown objects. [ABSTRACT FROM AUTHOR]

We use repeat synthetic aperture radar (SAR) observations and complementary altimetry passes acquired by the Cassini spacecraft to study the scattering properties of Titan’s empty lake basins. The best-fit coefficients from fitting SAR data to a quasi-specular plus diffuse backscatter model suggest that the bright basin floors have a higher dielectric constant, but similar facet-scale rms surface facet slopes, to surrounding terrain. Waveform analysis of altimetry returns reveals that nadir backscatter returns from basin floors are greater than nadir backscatter returns from basin surroundings and have narrower pulse widths. This suggests that floor deposits are structurally distinct from their surroundings, consistent with the interpretation that some of these basins may be filled with evaporitic and/or sedimentary deposits. Basin floor deposits also express a larger diffuse component to their backscatter, which is likely due to variations in subsurface structure or an increase in roughness at the wavelength scale (Hayes, A.G. et al. [2008]. Geophys. Res. Lett. 35, 9). We generate a high-resolution altimetry radargram of the T30 altimetry pass over an empty lake basin, with which we place geometric constraints on the basin’s slopes, rim heights, and depth. Finally, the importance of these backscatter observations and geometric measurements for basin formation mechanisms is briefly discussed. [ABSTRACT FROM AUTHOR]