Temporal Baseline Research Articles

Four bright eclipsing binaries with γ Doradus pulsating components: CM Lac, MZ Lac, RX Dra, and V2077 Cyg

ABSTRACT The study of pulsating stars in eclipsing binaries holds the promise of combining two different ways of measuring the physical properties of a star to obtain improved constraints on stellar theory. Gravity (g) mode pulsations such as those found in γ Doradus stars can be used to probe rotational profiles, mixing, and magnetic fields. Until recently few γ Doradus stars in eclipsing binaries were known. We have discovered g-mode pulsations in four detached eclipsing binary systems from light curves obtained by the Transiting Exoplanet Survey Satellite (TESS) and present an analysis of their eclipses and pulsational characteristics. We find unresolved g-mode pulsations at frequencies 1–1.5 d−1 in CM Lac, and measure the masses and radii of the component stars from the TESS data and published radial velocities. MZ Lac shows a much richer frequency spectrum, including pressure modes and tidally excited g-modes. RX Dra is in the northern continuous viewing zone of TESS so has a light-curve covering a full year, but shows relatively few pulsation frequencies. For V2077 Cyg we formally measure four pulsation frequencies, but the available data are inadequate to properly resolve the g-mode pulsations. V2077 Cyg also shows total eclipses, with which we obtain the first measurement of the surface gravity of the faint secondary star. All four systems are bright and good candidates for detailed study. Further TESS observations are scheduled for all four systems, with much improved temporal baselines in the cases of RX Dra and V2077 Cyg.

Interferometric synthetic aperture radar coherence constraints in heavily vegetated tropics

Success of Interferometry Synthetic Aperture Radar (InSAR) technique for deriving digital elevation model (DEM) in the heavily vegetated humid tropic using Sentinel-1 SAR has been controversial, owing to the difficulty in achieving good coherence. Yet achievable coherence with Sentinel-1 SAR over the heavily vegetated humid tropic has rarely been reported. Consequently, this paper evaluates coherence achievable with Sentinel-1 SAR over the heavily vegetated humid tropic. Twelve Sentinel-1 SAR over part of Johor selected based on perpendicular and temporal baselines considerations, were used as pairs of six reference and secondary images respectively and processed using Sentinel Application Platform (SNAP) to derive coherence maps and analysed accordingly. The result shows that regardless of image pairs baseline characteristics, coherence of above 0.6 over the heavily vegetated humid tropic with Sentinel-1 SAR can only be achieved in barely 10% of the study area. This result shall serve as eye-opener to geoscience community, especially SAR enthusiast looking forward to leveraging on SAR clouds penetrating ability; and Sentinel-1 SAR open access and short revisit circle to apply Sentinel-1 SAR in deriving DEM over heavily vegetated humid tropical environment. However, future studies shall focus on assessing consistency of good coherence patches and their usability in acquiring elevation.

Controversy over the decline of arthropods: a matter of temporal baseline?

Controversy over the decline of arthropods: a matter of temporal baseline?

COMPARISON AND EVALUATION OF DIFFERENT APPROACHES FOR EFFICIENT PROCESSING OF LONG GROUND-BASED SAR TIMES SERIES

Abstract. Ground-based Synthetic Aperture Radar (GB-SAR) is a monitoring tool which, once installed, acquires a large amount of data autonomously. For the IBIS-FM system, approximately 760 SAR images per day are acquired, which corresponds to more than 23 000 scenes per month. Therefore, this paper analysis different strategies for the interferometric processing of such large data stacks to find a compromise between accuracy, computational effort and the ability to (re-)process specific time intervals independently. This study compares the single master approach with the sequential approach and in addition two block-wise approaches. Moreover, a new baseline configuration called Daily Baseline Subset (DBAS) is compared which uses interferograms having a multiple of one day as temporal baseline. We evaluate them on a data stack of 30 000 images, acquired at Enguri Dam in Georgia. We check the unwrapping errors and the quality of the displacement estimation to compare the different configurations. We found that block-wise approaches show the best results considering unwrapping errors and Root Mean Square Error, while in our study the DBAS approach shows to the most plausible displacement map which is also dependent on the individual reduction of atmospheric noise.

EVALUATION OF DEM DERIVED BY REPEAT-PASS X-BAND STRIPMAP MODE PAZ DATA

Abstract. This paper, presents the initial results of digital elevation model (DEM) extraction from PAZ Synthetic Aperture Radar (SAR) satellite images using repeat-pass interferometric analysis. We used a multi-temporal high-resolution strip-map mode X-band satellite image that has a single polarization. Five main classes, i.e., volcanic structures, agriculture, settlement, sand dune and plain bareland are considered depending on the structure of the region. Within the category, the coherence value and DEM value are evaluated. In the accuracy assessment analysis, a reference map produced from aerial photogrammetry is used. Additionally, global DEM TanDEM-X data is also tested in the study region. In the analysis, quality metrics, mean error (ME), root means square error (RMSE), standard deviation (STD), and the normalized median absolute deviation (NMAD) are used. The results showed that as the temporal baseline increases the coherence values and the quality of the DEM product decrease. The RMSE values range between 2.36 m to 7.09 m in different classes. The TanDEM-X data provided high accuracies over each class range from 0.88 m to 2.40 m. Since the study area is vulnerable to sinkhole formation, sinkhole-like signals were also observed in the interferograms obtained from different and sequential pairs. The high-resolution repeat-pass PAZ data pointed out its potential for interferometric products generation.

Spatiotemporal Change Detection of Coastal Wetlands Using Multi-Band SAR Coherence and Synergetic Classification

Synthetic aperture radar (SAR) signal can penetrate clouds and some vegetation canopies in all weather, and therefore, provides an important measurement tool for change detection and sustainable development of coastal wetland environments and ecosystems. However, there are a few quantitative estimations about the spatiotemporal coherence change with multi-band SAR images in complex coastal wetland ecosystems of the Yellow River Delta (YRD). In this study, C-band Sentinel-1 and L-band ALOS-2 PALSAR data were used to detect the spatiotemporal distribution and change pattern of interferometric coherence in the coastal wetlands of the YRD. The results show that the temporal baseline has a greater impact on the interferometric coherence than the perpendicular baseline, especially for short wavelength C-band SAR. Furthermore, the OTSU algorithm was proven to be able to distinguish the changing regions. The coherence mean and standard deviation values of different land cover types varied significantly in different seasons, while the minimum and maximum coherence changes occurred in February and August, respectively. In addition, considering three classical machine learning algorithms, namely naive Bayes (NB), random forest (RF), and multilayer perceptron (MLP), we proposed a method of synergetic classification with SAR coherence, backscatter intensity, and optical images for coastal wetland classification. The multilayer perceptron algorithm performs the best in synergetic classification with an overall accuracy of 98.3%, which is superior to a single data source or the other two algorithms. In this article, we provide an alternative cost-effective method for coastal wetland change detection, which contributes to more accurate dynamic land cover classification and to an understanding of the response mechanism of land features to climate change and human activities.

The awkward question: What baseline should be used to measure biodiversity loss? The role of history, biology and politics in setting up an objective and fair baseline for the international biodiversity regime

Temporal baselines constitute common elements of good policy design and are necessary to set scenarios, targets and to measure changes over time. The choice of baselines not only can have a profound influence on outcomes but represent a political decision when included in governmental or intergovernmental instruments. Therefore, setting up baselines has technical and political consequences and they should be evidence-based to make them politically and technically stronger. Contrary to the Climate Change regime, which has an internationally agreed baseline set up at preindustrial times, there is no baseline to measure historical biodiversity loss. This feature results in the lack of a necessary reference to foster a fair implementation and monitoring of biodiversity conventions. This article looks throughout history to find regional, temporal and material circumstances related to biodiversity loss in order to suggest an evidence-based baseline. Facts show that almost all regions share a common substantially and temporally related pattern: unsustainable deforestation processes were massively conducted once the industrial revolution began, in order to respond to the external demand for raw materials. Facts also prove that other regions and/or countries had already lost their biodiversity way before industrial times to cover their internal demand for food and wood. This research aims at showing interlinkages among history, biodiversity loss and politics to suggest an evidence-based baselines to measure biodiversity loss in order to (i) increase the ambition of the Post 2020 Global Biodiversity Framework; (ii) recognize historical contributions to biodiversity loss and responsibilities to repair it; and (iii) establish conditions for a fair transition in developing countries toward sustainable development.

A Multi-Temporal Small Baseline Interferometry Procedure Applied to Mining-Induced Deformation Monitoring

This work addresses a methodology based on the interferometric synthetic aperture radar (InSAR) applied to analyze and monitor ground-motion phenomena induced by underground mining activities in the Legnica-Glogow copper district, south-western Poland. The adopted technique employs an InSAR processing chain that exploits a stack of Sentinel-1 synthetic aperture radar (SAR) images using a small baseline multitemporal approach. Interferograms with small temporal baselines are first selected, then their network is optimized and reduced to eliminate noisy data, in order to mitigate the effect of decorrelation sources related to seasonal phenomena, i.e., snow and vegetation growth, and to the radar acquisition geometry. The atmospheric disturbance is mitigated using a spatio-temporal filter based on the nonequispaced fast Fourier transform. The estimated displacement maps and time series show the effect of both linear and impulsive ground motion and are validated against global navigation satellite system (GNSS) measurements. In this context, a significant threat to the built environment is represented by seismic tremors triggered by underground mining activities, which are analyzed using the proposed method to integrate the information gathered by in situ seismometer devices.

Estimating Parkinsonism Severity in Natural Gait Videos of Older Adults With Dementia.

Drug-induced parkinsonism affects many older adults with dementia, often causing gait disturbances. New advances in vision-based human pose- estimation have opened possibilities for frequent and unobtrusive analysis of gait in long-term care settings. This work leverages spatial-temporal graph convolutional network (ST-GCN) architectures and training procedures to predict clinical scores of parkinsonism in gait from video of individuals with dementia. We propose a two-stage training approach consisting of a self-supervised pretraining stage that encourages the ST-GCN model to learn about gait patterns before predicting clinical scores in the finetuning stage. The proposed ST-GCN models are evaluated on joint trajectories extracted from video and are compared against traditional (ordinal, linear, random forest) regression models and temporal convolutional network baselines. Three 2D human pose-estimation libraries (OpenPose, Detectron, AlphaPose) and the Microsoft Kinect (2D and 3D) are used to extract joint trajectories of 4787 natural walking bouts from 53 older adults with dementia. A subset of 399 walks from 14 participants is annotated with scores of parkinsonism severity on the gait criteria of the Unified Parkinson's Disease Rating Scale (UPDRS) and the Simpson-Angus Scale (SAS). Our results demonstrate that ST-GCN models operating on 3D joint trajectories extracted from the Kinect consistently outperform all other models and feature sets. Prediction of parkinsonism scores in natural walking bouts of unseen participants remains a challenging task, with the best models achieving macro-averaged F1-scores of 0.53 ± 0.03 and 0.40 ± 0.02 for UPDRS-gait and SAS-gait, respectively. Pre-trained model and demo code for this work is available.1.

The virota and its transkingdom interactions in the healthy infant gut

SignificanceMicrobes colonizing the infant gut during the first year(s) of life play an important role in immune system development. We show that after birth the (nearly) sterile gut is rapidly colonized by bacteria and their viruses (phages), which often show a strong cooccurrence. Most viruses infecting the infant do not cause clinical signs and their numbers strongly increase after day-care entrance. The infant diet is clearly reflected by identification of plant-infecting viruses, whereas fungi and parasites are not part of a stable gut microbiota. These temporal high-resolution baseline data about the gut colonization process will be valuable for further investigations of pathogenic viruses, dynamics between phages and their bacterial host, as well as studies investigating infants with a disturbed microbiota.

Uncertainties and Perspectives on Forest Height Estimates by Sentinel-1 Interferometry

Forest height is a key parameter in forestry. SAR interferometry (InSAR) techniques have been extensively adopted to retrieve digital elevation models (DEM) to give a representation of the continuous variation of the Earth’s topography, including forests. Unfortunately, InSAR has been proven to fail over vegetation due to low coherence values; therefore, all phase unwrapping algorithms tend to avoid these areas, making InSAR-derived DEM over vegetation unreliable. In this work, a sensitivity analysis was performed with the aim of properly initializing the relevant operational parameters (baseline and multilooking factor) to maximize the theoretical accuracy of the height difference between the forest and reference point. Some scenarios were proposed to test the resulting “optimal values”, as estimated at the previous step. A simple model was additionally proposed and calibrated, aimed at predicting the optimal baseline value (and therefore image pair selection) for height uncertainty minimization. All our analyses were conducted using free available data from the Copernicus Sentinel-1 mission to support the operational transfer into the forest sector. Finally, the potential uncertainty affecting resulting height measures was quantified, showing that a value lower than 5 m can be expected once all user-dependent parameters (i.e., baseline, multilooking factor, temporal baseline) are properly tuned.

Potential of Convolutional Neural Networks for Forest Mapping Using Sentinel-1 Interferometric Short Time Series

The monitoring of land cover and land use patterns is pivotal in the joint effort to fight deforestation in the Amazon and study its relation to climate change effects with respect to anthropogenic activities. Most of the region, typically monitored with optical sensors, is hidden by a persistent cloud cover for most of the wet season. The necessity for a consistent and reliable deforestation warning system based on cloud-independent radar data is therefore of particular interest. In this paper, we investigated the potential of combining deep learning with Sentinel-1 (S-1) Interferometric Synthetic Aperture Radar (InSAR) short time series (STS), covering only 24 d of acquisitions, to map endangered areas in the Amazon Basin. To this end, we implemented a U-Net-like convolutional neural network (CNN) for multi-layer semantic segmentation, trained from scratch with different sets of input features to evaluate the viability of the proposed approach for different operating conditions. As input features, we relied on both multi-temporal backscatter and interferometric coherences at different temporal baselines. We provide a detailed performance benchmark of the different configurations, also considering the current state-of-the-art approaches based on S-1 STS and shallow learners. Our findings showed that, by exploiting the powerful learning capabilities of CNNs, we outperformed the STS-based approaches published in the literature and significantly reduced the computational load. Indeed, when considering the entire stack of Sentinel-1 data at a 6 d revisit time, we were able to maintain the overall accuracy and F1-score well above 90% and reduce the computational time by more than 50% with respect to state-of-the-art approaches, by avoiding the generation of handcrafted feature maps. Moreover, we achieved satisfactory results even when only S-1 InSAR acquisitions with a revisit time of 12 d or more were used, setting the groundwork for an effective and fast monitoring of tropical forests at a global scale.

Development and Evaluation of Statewide Prospective Spatiotemporal Legionellosis Cluster Surveillance, New Jersey, USA.

Incidence of Legionnaires’ disease is increasing, particularly in the Mid-Atlantic states in the United States; since 2015, New Jersey has documented ≈250–350 legionellosis cases per year. We used SaTScan software to develop a semiautomated surveillance tool for prospectively detecting legionellosis clusters in New Jersey. We varied temporal window size and baseline period to evaluate optimal parameter selections. The surveillance system detected 3 community clusters of Legionnaires’ disease that were subsequently investigated. Other, smaller clusters were detected, but standard epidemiologic data did not identify common sources or new cases. The semiautomated processing is straightforward and replicable in other jurisdictions, likely by persons with even basic programming skills.

Pair Selection Optimization for InSAR Time Series Processing

AbstractThe ever‐increasing amount of Synthetic Aperture Radar (SAR) data motivates the development of automatic processing chains to fully exploit the opportunities offered by these large databases. The Synthetic Aperture Radar Interferometry (InSAR) Mass processing Toolbox for Multidimensional time series is an optimized tool to automatically download SAR data, select the interferometric pairs, perform the interferometric mass processing, compute the geocoded deformation maps, invert and display the velocity maps and the 2D time series on a web page updated incrementally as soon as a new image is available. New challenges relate to data management and processing load. We address them through methodological improvements dedicated to optimizing the InSAR pair selection. The proposed algorithm narrows the classical selection based on the shortest temporal and spatial baselines thanks to a coherence proxy and balances the use of each image as Primary and Secondary images thanks to graph theory methods. We apply the processing to three volcanic areas characterized with different climate, vegetation, and deformation characteristics: the Virunga Volcanic Province (DR Congo), the Reunion Island (France), and the Domuyo and Laguna del Maule area (Chile‐Argentina border). Compared to pair selection based solely on baseline criteria, this new tool produces similar velocity maps while reducing the total number of computed differential InSAR interferograms by up to 75%, which drastically reduces the computation time. The optimization also allows to reduce the influence of DEM errors and atmospheric phase screen, which increase the signal‐to‐noise ratio of the inverted displacement time series.

An observation-based approach to calculating ice-shelf calving mass flux

In order to determine whether the calving flux of an ice shelf is changing, the long-term calving flux needs to be established. Methods used to estimate the calving flux either take into account non-steady-state behaviour by capturing movement of the calving-front location (e.g., using satellite observations), or they assume the calving front is stationary and that the ice is in steady state (e.g., flux-gate methods). Non-steady-state methods are hampered by the issue of temporal aliasing, i.e., when the satellite observation frequency is insufficient to capture the cyclic nature of the calving-front position. Methods that assume a steady state to estimate the calving flux accrue uncertainties if the ice shelf changes its physical state. In order to overcome these limitations we propose and implement a new observation-based approach that combines a time series of calving-front locations with a flux-gate method. The approach involves the creation of a unique semi-temporal domain as a mechanism to overcome the issue of temporal aliasing, and only requires easily accessible ice thickness and surface velocity estimates of the ice shelf. This approach allows for complex calving-front geometries and captures calving events of all sizes that are visible within the satellite imagery. Application of the approach allows the long-term average calving flux to be estimated (provided sufficient temporal coverage by satellite imagery), as well as identification of the minimum temporal baseline needed to produce a representative estimate of the long-term average calving flux, for any ice shelf. Implementation of the approach to multiple ice shelves would enable comparisons to be made regarding the spatial variability in the long-term calving flux of Antarctica's ice shelves, thereby highlighting calving regime change around the continent.

Spatio-temporal baseline and spatial coverage of lunar-based SAR repeat-track interferometry in single- and double-antenna configurations

ABSTRACT Spatio-temporal baseline and effective spatial coverage are key parameters for lunar-based synthetic aperture radar repeat-track interferometry (LB-SAR RTI). In the mode of lunar-based single-antenna SAR RTI (LB-SASAR RTI) Earth observation, the temporal baseline and effective spatial coverage are affected by perpendicular baseline, which depends mainly on the lunar revolution. However, the moon is a nature celestial body; multiple SAR antennas can be installed on its surface to increase the potential in Earth observation. In this study, we first designed the observation geometry of lunar-based double-antenna SAR RTI (LB-DASAR RTI) and calculate its spatio-temporal baselines and effective spatial coverage. Comparing with those of the single-antenna mode, the results show that: 1) the perpendicular baselines of LB-DASAR RTI are 0–3000 km shorter than those of LB-SASAR RTI during a year, and approximate 98% of them are between 2000 and 3000 km; 2) the amount of interferometric combinations increase by about 23.5%; 3) the amount of interferometric combinations with large swath width (4400 km) is approximately doubled, resulting in an increase of 12.3% in the percentage of large swath width (4400 km); 4) the observation duration in azimuth is increased by at least 2–3 hours, equivalent to 30° to 45° of azimuthal observation range; and 5) the repeated observation frequency is higher, the most significant increase of interferometric combinations is at 40° north and south latitudes, reaching up to 286 times a year. The study is helpful for the observation mode design of LB-SAR RTI and the implementation of observation data acquisition plan.

Surface deformation extraction from small baseline subset synthetic aperture radar interferometry (SBAS-InSAR) using coherence-optimized baseline combinations

ABSTRACT Surface deformation data can be used to provide early warnings of geohazards and are useful in a variety of research fields. The Small BAseline Subset InSAR (SBAS-InSAR) boosts the data sampling rate and improves the accuracy of deformation extraction by restricting the temporal and spatial baselines. However, various factors, such as the types of ground objects and seasons, affect the coherence between SAR images. Traditional SBAS-InSAR employs a fixed temporal baseline, which does not guarantee good coherence and might lead to decorrelation. In this paper, we propose that instead of using a fixed temporal baseline, we directly use the average coherence between SAR images as the baseline constraint index to perform an optimized selection of SBAS-InSAR interferometric pairs, ensuring good coherence of the interferometric pairs and improving the quality of the interferometric fringes. The proposed approach was used to extract surface deformation in two test experiment areas: Houston and Sydney. Compared with the conventional SBAS-InSAR and GPS data, the standard deviation of error of Houston and Sydney dropped from 0.813 to 0.589 and 0.291 to 0.246, respectively; the root mean square error (RMSE) decreased from 1.082 to 1.041 and 0.485 to 0.334, respectively, indicating that the proposed method has better surface deformation extraction accuracy. After demonstrating the accuracy of the proposed method, it was applied to Pingxiang area, a mining city in China, to effectively extract and analyze the surface deformation induced by mining activities, which proves the universality of this method in different scenarios.

Extraction of Velocity Time Series With an Optimal Temporal Sampling From Displacement Observation Networks

Today, more and more velocity observations are available online or on-demand. However, this amount of data is complex to analyze since velocity observations span different temporal baselines. Velocities obtained from a small temporal baseline are close to the derivative of the displacement but are more likely to be contaminated by noise. Velocities obtained from a long temporal baseline approximate the mean velocity between two dates but can be affected by temporal decorrelation. Having short and long temporal baselines provides a data redundancy that needs to be properly considered. In this article, we propose a method that aims to extract short-term velocity time series with regular temporal sampling from all available displacement observations. The proposed method relies on a temporal inversion based on an improved temporal closure of the displacement observation network. Two criteria are proposed to determine the optimal temporal sampling to study short-term variations. To take the unequal data uncertainty into account, the temporal inversion is done by an iterative reweighted least square using a well-established weighting function, without preprocessing. The proposed method results in velocity time series with an optimal temporal sampling, improved temporal coverage, reduced uncertainty, and no redundancy. The studied area is the Kyagar glacier, in the North of the Karakoram range that is characterized by strong velocity variations originated from a glacier surge and additional seasonal variability.

Investigation of Environmental Effects on Coherence Loss in SAR Interferometry for Snow Water Equivalent Retrieval

Interferometric Synthetic Aperture Radar (InSAR) is a promising tool for the retrieval of Snow Water Equivalent (SWE) from space. Due to refraction, the interferometric phase changes with snow depth and density, which is exploited by the InSAR method. While the method was first proposed two decades ago, qualitative research using experimental data analyzing factors affecting retrieval performance remains scarce. In this work a tower-based 1-10 GHz, fully polarimetric SAR with InSAR capabilities was used to analyze the effect of meteorological events (air temperature, precipitation intensity, and wind) on the observed temporal decorrelation of interferometric image pairs, at L-, S-, C- and X-bands. These factors were found to be causes of decorrelation in snow, being the temperature the critical variable in the case of snowmelt events. Of the analyzed bands, L-band presented the best coherence conservation properties. Additionally, the phase change between pairs with sufficient coherence was applied to generate estimates of changes in SWE, studying the retrieval errors at different bands and over different temporal baselines. SWE accumulation was calculated from 6 hours up to 12 days temporal baseline over a non-vegetated area. SWE accumulation profiles were successfully reconstructed for short temporal baselines and low frequencies, while an increase in the retrieval error was observed for high frequencies and long temporal baselines, indicating the limitations of higher frequencies for repeat-pass InSAR retrieval. The analysis was also reproduced over a forested area at L-band with similar results as to the non-vegetated area.

Earth-Based Repeat-Pass SAR Interferometry of the Moon: Spatial–Temporal Baseline Analysis

Earth-based repeat-pass synthetic aperture radar (SAR) interferometry (InSAR) is a powerful tool to obtain the high-precision lunar terrain and deformation information, due to the short revisit period as well as the all-weather, long-time, and large-coverage characteristics of radar lunar observation. In this article, the baseline formation mechanism is investigated. Theoretical analyses show that the interferometric baselines mainly come from the lunar libration; however, the lunar libration may also result in a significant change of baselines and further lead to serious geometric decorrelation. To address this problem and ensure the Earth-based repeat-pass InSAR of the Moon, the precise relative motion model between the radar and the lunar target, considering lunar libration, is established, and the subradar point (SRP) wagging phenomenon is analyzed. Then, to evaluate the effects of SRP wagging on InSAR coherence, a geometric decorrelation model based on azimuth angle and incident angle is proposed. Based on that, the spatial–temporal baseline for Earth-based repeat-pass InSAR of the Moon is analyzed based on numerical simulation. Results show that the temporal baseline has obvious periodicity with a cycle of about 27 days. When the temporal baseline is an integral multiple of 27, the spatial baseline usually has the minimum value and the geometric decorrelation effect is relatively slight. Moreover, using the geometric decorrelation criteria, we examine the effects of radar frequency, position, and target location on the quantity of viable interferometric pairs, which has guiding significance for the selection of radar system parameters, radar position, and lunar target area.