Synthetic Aperture Radar Parameters Research Articles

Assessing the influence of different Synthetic Aperture Radar parameters and Digital Elevation Model layers combined with optical data on the identification of argan forest in Essaouira region, Morocco

Assessing the influence of different Synthetic Aperture Radar parameters and Digital Elevation Model layers combined with optical data on the identification of argan forest in Essaouira region, Morocco

Random forest and support vector machine classifiers for coastal wetland characterization using the combination of features derived from optical data and synthetic aperture radar dataset

Abstract Mapping mangrove forests is crucial for their conservation, but it is challenging due to their complex characteristics. Many studies have explored machine learning techniques that use Synthetic Aperture Radar (SAR) and optical data to improve wetland classification. This research compares the random forest (RF) and support vector machine (SVM) algorithms, employing Sentinel-1 dual polarimetric C-band data and Sentinel-2 optical data for mapping mangrove forests. The study also incorporates various derived parameters. The Jeffries–Matusita distance and Spearman’s rank correlation are used to evaluate the significance of commonly used spectral indices and SAR parameters in wetland classification. Only significant parameters are retained, reducing data dimensionality from 63 initial features to 23–33 essential features, resulting in an 18% improvement in classification accuracy. The combination of SAR and optical data yields a substantial 33% increase in the overall accuracy for both SVM and RF classification. Consistently, the fusion of SAR and optical data produces higher classification accuracy in both RF and SVM algorithms. This research provides an effective approach for monitoring changes in Pichavaram wetlands and offers a valuable framework for future wetland monitoring, supporting the planning and sustainable management of this critical area.

Multi-band SAR intercomparison study in the Antarctic Peninsula for sea ice and iceberg detection

Synthetic aperture radar (SAR) systems are one of the best resources to gather information in polar environments, but the detection and monitoring of sea ice types and icebergs using them is still a challenge. Limitations using single-frequency images in sea ice characterization are well known, and using different SAR bands has been revealed to be useful. In this paper, we present the quantitative results of an intercomparison experiment conducted by the Argentine Naval Hydrographic Service (SHN) using X-, C-, and L-bands from COSMO-SkyMed, Sentinel-1, and SAOCOM satellites, respectively. The aim of the experiment was to evaluate SAOCOM for its use on SHN products. There were 25 images with different SAR parameters that were analyzed, incorporating the diversity in the information that everyday Ice Services attend to. Particularly, iceberg detections, fast first-year ice, and belts and strips were studied in the Antarctic Sound, the surroundings of Marambio Island, and Erebus and Terror Gulf. The results show that the HV polarization channel of the L-band provides useful information for iceberg detection and fast first-year ice surface feature recognition and is a promising frequency for the study of strip identification under windy sea conditions and snow accumulation on first-year ice.

Compact optical real-time imaging system for high-resolution SAR data based on autofocusing

Improved resolutions of synthetic aperture radar (SAR) have complicated real-time high-accuracy imaging. To reduce the time and power required by digital processing algorithms, we propose a novel idea of photoelectric combination and design a SAR optical imaging system. Range matched filtering is realized in the digital domain, whereas azimuth autofocusing is realized through free propagation in the optical domain, with range cell migration correction at the speed of light. To adapt to changeable SAR parameters, a compound lens is formed by combining spatial light modulators. The system is simple, compact, efficient, and adaptable, with nano-second processing delays.

Space-Time Varying Plasma Sheath Effect on Hypersonic Vehicle-Borne SAR Imaging

Hypersonic vehicle-borne synthetic aperture radar (SAR) imaging has enormous potential in civilian and military application due to high speed and strong survivability of the hypersonic vehicle. However, owing to high temperature and high pressure, a plasma flow, called plasma sheath, covers the hypersonic vehicle. The electron density of the plasma sheath varies with space and time, and space-time varying plasma sheath will affect hypersonic vehicle-borne SAR imaging severely. In this article, the effect of space-time varying plasma sheath on hypersonic vehicle-borne SAR imaging is investigated. First, the SAR signal model coupling space-time varying plasma sheath effect is established using the transmission line method. The variation law of electron density is based on flow field theory and experiment. The double propagation for SAR signal in plasma sheath and the time sequence of electron density for transmitting and receiving period are investigated and included in the SAR signal model. Moreover, the modulation effect of SAR signal under space-time varying plasma sheath is studied, and the range compressed signal under plasma sheath is derived. Based on the SAR signal model, hypersonic vehicle-borne SAR imaging defocus phenomenon with the presence of space-time varying plasma sheath is systematically analyzed. The simulation shows that the point target imaging result under plasma sheath is composed of several azimuth defocus strips, and in each strip the range dimension defocuses. The results of point target imaging and a SAR image of an area under plasma sheath verifies theory and analysis in this article. Finally, the SAR imaging degradation under different SAR and plasma sheath parameters are studied and the condition that serious defocus occurs is analyzed. The work in this article will benefit the study of hypersonic vehicle-borne SAR imaging method under plasma sheath in the future.

Methodology for groundnut discrimination based on timeseries of dual-polarimetric SAR parameters

The groundnut, also known as peanut, is a legume crop that is classified as both a grain legume and an oil crop (due to its high oil content). About 85% of the total groundnut in India is sown in the kharif season under rainfed conditions. This makes it extremely difficult to apply the optical remote sensing methodologies for groundnut discrimination and acreage estimation. We develop a remote sensing strategy based on timeseries synthetic aperture radar (SAR) imagery, which is immune to the rainy conditions of the observation period. Further, our work is distinct from the traditional multitemporal SAR backscatter analysis as it incorporates polarimetric parameters such as the alpha angle (α) and the polarimetric radar vegetation index to aid discrimination. These parameters were shortlisted on the basis of maximal sensitivity to groundnut growth stages. Random forest classifier, an ensemble learning-based classifier, is used to carryout the crop classification using these parameters. This approach is contrasted with SAR backscatter-based analysis (using the same RF classifier) and with a Euclidean distance (ED)-based model matching method (with the same polarimetric parameters). A detailed comparative analysis of classification accuracy of groundnut discrimination is carried out using extensive ground truth data over the agricultural fields of Junagadh district in the Gujarat state of Western India. The backscatter-based analysis with RF achieves 86.2% overall accuracy in discriminating groundnut (with the competing cotton crop), whereas polarimetric parameters with ED model matching achieves 76%. Using the proposed method, with dual-pol parameters derived out of Sentinel-1 SAR data, we report 92.7% overall accuracy with a kappa coefficient of 0.81. The major insight is that combining timeseries polarimetric information together with a good machine learning classifier improves the classification accuracy of short crops compared to nonpolarimetric approach.

Assessment of rice crop biophysical parameters using Sentinel-1 C-band SAR data

Evaluation of crop growth parameters is crucial to understand the crop phenology and its relation with Synthetic Aperture Radar (SAR) scattering mechanism. The study explores the potential of dual polarization Sentinel–1 backscatter parameters to investigate its sensitivity with various biophysical parameters of paddy crop. Temporal profile of crop growth parameters such as fresh biomass, dry biomass, vegetation water content (VWC) and plant height of paddy crop are assessed with respect to SAR parameters such as VV, VH, VH/VV and Radar Vegetation Index (RVI). Temporal Sentinel–1A SAR data during June to November 2018 was considered for this study. Regression analysis using linear and logarithmic models reveal that the sensitivity of Sentinel–1A backscatter parameters with crop biophysical parameters is best when the crop is at early vegetation stage i.e. between tillering to booting stages with maximum R2 value of 0.68 for liner regression with VH and 0.69 for multiple regression for crop height followed by other biophysical parameters. At later vegetation stage i.e. ripening stage maximum R2 value of 0.52 for liner regression with VH and multiple regression for crop biomass is observed. There is no significant correlation for peak vegetation stage. The results are indicative for the potential use of dual-polarimetric SAR parameters as input parameters for multiple regression models to improve the prediction of biophysical parameters at different phenolgical stages.

Retrieving the Soil Moisture in Bare Farmland Areas Using a Modified Dubois Model

Soil moisture is an important parameter for global soil moisture transport, environmental evaluation, and precision agricultural research. The accurate retrieval of soil moisture in farmland areas using Synthetic Aperture Radar (SAR) depends on the accurate description of surface and SAR parameters. In these parameters, surface roughness and incidence angle are the key factors that affect the accuracy of the soil moisture retrieval model. This article proposes a modified Dubois model to retrieve soil moisture suitable for the bare surface of farmland area. The model eliminates the incidence angle parameters and uses polarization parameters to depict the surface roughness parameters in the Dubois model. To eliminate the incidence angle, the backscattering coefficients gamma0, which eliminates the effect of the incidence angles, are used to replace the sigma0. Under rain and no rain condition, the trend of backscattering coefficients (VH and VV) and cross-polarization ratio (VH-VV) of different soil texture with the soil moisture are compared. Then, the polarization parameter based on VH backscattering coefficients is used to describe surface roughness. The model is evaluated with time-series soil moisture observation data in situ of the study area. The results indicate that the modified model can retrieve soil moisture with high accuracy, and the total RMSE can reach 0.064 cm3cm−3 while the Dubois model is 0.124 cm3cm−3. Under rain and no rain condition, the retrieval accuracy of the modified model is 0.066 cm3cm−3 and 0.063 cm3cm−3. The retrieval accuracy is 0.060 cm3cm−3 and 0.067 cm3cm−3 under high and low incidence angles conditions, respectively. These results indicate that the modified Dubois model can retrieve soil moisture with high accuracy under different conditions.

Analysis of Hypersonic Platform-Borne SAR Imaging: A Physical Perspective

The usage of a hypersonic platform for remote sensing application has promising prospects, especially for hypersonic platform-borne synthetic aperture radar (SAR) imaging. However, the high-speed of hypersonic platform will lead to extreme friction between the platform and air, which will cause the ionization of air. The ionized gas forms the plasma sheath wrapped around the hypersonic platform. The plasma sheath will severely affect the propagation of SAR signal and further affect the SAR imaging. Therefore, hypersonic platform-borne SAR imaging should be studied from a physical perspective. In this paper, hypersonic platform-borne SAR imaging under plasma sheath is analyzed. The SAR signal propagation in plasma sheath is computed using scatter matrix method. The proposed SAR signal model is verified by using a ground experiment system. Moreover, the effect of attenuation caused by plasma sheath on SAR imaging is studied under different SAR parameters and plasma sheath. The result shows that attenuation caused by plasma sheath will degrade the SAR imaging quality and even cause the point and area targets to be submerged into the noise. The real SAR images under plasma sheath also illustrate this phenomenon. Furthermore, by studying imaging results under different SAR and plasma parameters, it can be concluded that the severe degradation of SAR imaging quality appears at condition of high plasma sheath electron density and low SAR carrier frequency. The work in this paper will be beneficial for the study of hypersonic platform-borne SAR imaging and design of hypersonic SAR imaging systems in the future.

Sensitivity of Compact Polarimetric SAR Parameters to Modeled Lake Ice Growth

Synthetic aperture radar (SAR) is a valuable tool for lake ice monitoring. The recently proposed SAR configuration for Earth observation called compact polarimetric (CP) SAR could be a good compromised choice between conventional (single or dual) and fully polarimetric (FP) SAR for operational ice applications, including lake ice. Given its enhanced radar target information compared with conventional SAR systems over wider swath coverage compared with FP SAR, CP systems could play important role in the new generation of Earth observation systems. Herein, we study the evolution of CP SAR parameters from simulated CP SAR data in relation to early ice growth. Focus of the study is on four lakes located in Cornwallis Island, Canadian Central Arctic. We adopt parameters extracted from dual circular polarimetric and right circular transmit, linear (horizontal and vertical) receive configurations. In this study, we consider the ice thickness calculated from an established empirical model. Meteorological and ice climatological data were used to support the analysis. Results demonstrated a potential connection between a number of CP parameters and lake ice growth. Furthermore, we were able to highlight the relationship between the density of air bubbles in ice layer and the intensity of volume scattering mechanism, leading to the identification of lakes with increased gas production activities. Thus, differences between lakes in terms of density of air bubbles were detected and statistically evaluated.

Remote characterization of chloride content in oven-dried concrete specimens by using synthetic aperture radar image models

Synthetic aperture radar (SAR) imaging is a remote sensing technique capable of performing noncontact subsurface inspection of dielectric materials like Portland cement concrete. Such techniques can be applied to field inspection of concrete structures and laboratory material characterization. The objective of this paper is to demonstrate the application of SAR imaging on characterizing chloride content inside oven-dried concrete specimens, using a 10-GHz central frequency SAR imaging sensor. Twelve concrete panel specimens (0.3x0.3x0.05 m3) with a 0.45 water-to-cement ratio were manufactured in six groups of different chloride contents (0%, 2%, 4%, 6%, 8% and 10% of cement weight). Five SAR image-based parameters were developed from each SAR image of concrete specimens for chloride, including integrated SAR amplitude of back reflection Iintb, integrated amplitude Iint, average maximum amplitude I-max, critical contour area Ac, and average Gaussian curvature of critical contour Kavg. A parametric analysis on the combined use of different numbers of SAR image parameters was carried out to determine the optimal application of SAR image parameters on the chloride detection problem in this paper. From our result, it is found that, for the purpose of chloride detection inside concrete specimens using single SAR parameter, integrated amplitude Iint shows the best performance. When using two SAR image parameters, combination of integrated amplitude Iint and critical contour area Ac shows the best performance among other two-parameter combinations. When using three SAR image parameters, combination of integrated amplitude Iint, critical contour area Ac, and average maximum amplitude I-max shows the best performance among other three-parameter combinations. Furthermore, it is interesting to report that combination of three SAR image parameters (Iint, Ac, and I-max) provides the overall best performance among all other combinations for the chloride detection problem in oven-dried concrete specimens.

Statistical Exploration of SENTINEL-1 Data, Terrain Parameters, and in-situ Data for Estimating the Near-Surface Soil Moisture in a Mediterranean Agroecosystem

Reliable near-surface soil moisture (θ) information is crucial for supporting risk assessment of future water usage, particularly considering the vulnerability of agroforestry systems of Mediterranean environments to climate change. We propose a simple empirical model by integrating dual-polarimetric Sentinel-1 (S1) Synthetic Aperture Radar (SAR) C-band single-look complex data and topographic information together with in-situ measurements of θ into a random forest (RF) regression approach (10-fold cross-validation). Firstly, we compare two RF models' estimation performances using either 43 SAR parameters (θNovSAR) or the combination of 43 SAR and 10 terrain parameters (θNovSAR+Terrain). Secondly, we analyze the essential parameters in estimating and mapping θ for S1 overpasses twice a day (at 5 a.m. and 5 p.m.) in a high spatiotemporal (17 × 17 m; 6 days) resolution. The developed site-specific calibration-dependent model was tested for a short period in November 2018 in a field-scale agroforestry environment belonging to the “Alento” hydrological observatory in southern Italy. Our results show that the combined SAR + terrain model slightly outperforms the SAR-based model (θNovSAR+Terrain with 0.025 and 0.020 m3 m−3, and 89% compared to θNovSAR with 0.028 and 0.022 m3 m−3, and 86% in terms of RMSE, MAE, and R2). The higher explanatory power for θNovSAR+Terrain is assessed with time-variant SAR phase information-dependent elements of the C2 covariance and Kennaugh matrix (i.e., K1, K6, and K1S) and with local (e.g., altitude above channel network) and compound topographic attributes (e.g., wetness index). Our proposed methodological approach constitutes a simple empirical model aiming at estimating θ for rapid surveys with high accuracy. It emphasizes potentials for further improvement (e.g., higher spatiotemporal coverage of ground-truthing) by identifying differences of SAR measurements between S1 overpasses in the morning and afternoon.

Monitoring crop growth using a canopy structure dynamic model and time series of synthetic aperture radar (SAR) data

ABSTRACT Normalized Difference Vegetation Index (NDVI) time series data are used by agricultural agencies for many essential operational crop monitoring programmes. But optical sensors often miss key growth stages due to cloud cover interference, impacting the performance of operational activities. Although the synergistic use of optical and Synthetic Aperture Radar (SAR) imagery can provide time series data, any SAR-optical integration necessitates building a relationship between these two data sources. The objective of this study was to use a semi-empirical Canopy Structure Dynamics Model (CSDM), Growing Degree Days (GDD), and SAR parameters calibrated to optical NDVI to derive daily estimates of canola crop condition over an entire growing season. RADARSAT-2 Fine Quad-pol and RapidEye images were collected over three years for a study site in western Canada. Object-based image analysis was applied to study the relationship between the optical and SAR time series data. Significant correlations were documented between a number of SAR parameters and optical NDVI, specifically a ratio of backscatter intensities (HH-HV)/(HH+HV), a ratio of volume to surface scattering extracted from the Freeman Durden decomposition, and Entropy from the Cloude-Pottier decomposition. Correlations (r-values) between these SAR parameters and optical NDVI ranged from 0.63 to 0.84 for the three years of data. Based on this analysis, a simple statistical model was used to relate SAR parameters to optical NDVI, creating a SAR-calibrated NDVI (SARcal-NDVI). A CSDM was fit to the SARcal-NDVI for each canola field, constructing a temporal vegetation index curve which captured canopy development from emergence to senescence. Coefficients of determination (R2) were 0.87 0.86 and 0.82 for entropy, the volume-surface scattering ratio, and the ratio of backscatter intensities (HH-HV)/(HH+HV), respectively, demonstrating a good model fit. The CSDM describes well the temporal evolution of SARcal-NDVI. Using the CSDM, SARcal-NDVI and GDD, the canola condition can be estimated for any given day in the growing season. In fact when the CSDM was used to estimate SARcal-NDVI for the exact days of RapidEye acquisitions, correlations with optically derived NDVI were high. The strongest correlations with RapidEye NDVI were reported for the volume-surface scattering ratio (R2 of 0.69 and RMSE of 0.15). The SARcal-NDVI estimated from the CSDM was also physically meaningful. Field-based biomass was significantly correlated (R2 of 0.79) with the SARcal-NDVI calculated using the volume-surface scattering ratio. Although further research is needed to extend this method to other crops, these results demonstrate that SAR data can be used to estimate vegetation conditions and when coupled with a CSDM, integrated into current monitoring operations based on optical NDVI. As a next step, the research team will be assessing SARcal-NDVI in a national operational programme which reports on crop yields using modelling with optical-based NDVI.

Mapping Floods in Lowland Forest Using Sentinel-1 and Sentinel-2 Data and an Object-Based Approach

The impact of floods on forests is immediate, so it is necessary to quickly define the boundaries of flooded areas. Determining the extent of flooding in situ has shortcomings due to the possible limited spatial and temporal resolutions of data and the cost of data collection. Therefore, this research focused on flood mapping using geospatial data and remote sensing. The research area is located in the central part of the Republic of Croatia, an environmentally diverse area of lowland forests of the Sava River and its tributaries. Flood mapping was performed by merging Sentinel-1 (S1) and Sentinel-2 (S2) mission data and applying object-based image analysis (OBIA). For this purpose, synthetic aperture radar (SAR) data (GRD processing level) were primarily used during the flood period due to the possibility of all-day imaging in all weather conditions and flood detection under the density of canopy. The pre-flood S2 imagery, a summer acquisition, was used as a source of additional spectral data. Geographical information system (GIS) layers—a multisource forest inventory, habitat map, and flood hazard map—were used as additional sources of information in assessing the accuracy of and interpreting the obtained results. The spectral signature, geometric and textural features, and vegetation indices were applied in the OBIA process. The result of the work was a developed methodological framework with a high accuracy and speed of production. The overall accuracy of the classification is 94.94%. Based on the conducted research, the usefulness of the C band of the S1 in flood mapping in lowland forests in the leaf-off season was determined. The paper presents previous research and describes the SAR parameters and characteristics of floodplain forest with a significant impact on the accuracy of classification.

Mapping of wheat lodging susceptibility with synthetic aperture radar data

Crop lodging reduces yield quantity and grain quality of cereal crops. Understanding seasonal variation in crop lodging susceptibility enables lodging risk assessments and predictions of associated crop yield losses. We demonstrate a novel remote sensing-based approach, using sparse field observations and widely available synthetic aperture radar (SAR) satellite imagery, to map a safety factor against root lodging (SFA) in wheat. SFA quantifies the ability of the rooting system to support the self-weight moment of the whole plant and can be used as an indicator of in-season root lodging susceptibility. SAR satellite images, from Sentinel-1 and RADARSAT-2, were acquired synchronously with field measurements in Jolanda di Savoia, Ferrara, Italy during the 2018 winter wheat growing season. The field data included measurements from non-lodged (healthy) wheat such as plant height, height at the centre of gravity, self-weight moment of the whole plant, soil anchorage strength, SFA and those from lodged wheat such as crop angle of inclination, lodged area and the point of plant failure (stem or root). Field measurements confirmed that SFA decreased progressively through the season and was consistent with the observed lodging. Strong and significant correlations through the season were observed between SFA and SAR satellite image metrics. The validated regression models showed a strong relationship between field-measured SFA and the metrics from RADARSAT-2 (R2CV = 0.84, RMSECV = 0.54) and Sentinel-1 data (R2CV = 0.73, RMSECV = 0.59). However, it is possible that the relationship between SFA and SAR parameters is confounded by the variations in crop biomass, which is highly correlated with SFA (r = −0.71, p < 0.0001). Our study, for the first time, demonstrates the use of remote sensing SAR data for lodging susceptibility assessment. Current and planned satellite platforms have the potential for large scale, operational assessment of lodging susceptibility in cereal crops.

Image translation between high-resolution optical and synthetic aperture radar (SAR) data

ABSTRACT This paper presents a novel study: remote-sensing image translation between high-resolution optical and Synthetic Aperture Radar (SAR) data through machine learning approaches. To this end, conditional Generative Adversarial Networks (cGANs) have been proposed with the guide of high-level image features. Efficiency of the proposed methods have been verified with different SAR parameters on three regions from the world: Toronto, Vancouver in Canada and Shanghai in China. The generated SAR and optical images have been evaluated by pixel-based image classification with detailed land cover types including: low and high-density residential area, industry area, construction site, golf course, water, forest, pasture and crops. Results showed that the translated image could effectively keep many land cover types with compatible classification accuracy to the ground truth data. In comparison with state-of-the-art image translation approaches, the proposed methods could improve the translation results under the criteria of common similarity indicators. This is one of first study on multi-source remote-sensing data translation by machine learning.

Ship Velocity Automatic Estimation Method Via Two-Dimensional Spectrum Pattern of Kelvin Wakes in SAR Images

In this article, an automatic method is proposed for ship velocity estimation based on the two-dimensional spectrum pattern of Kelvin wakes in synthetic aperture radar (SAR) images of which the effectiveness is proved with both simulation and experimental results. The proposed method has the following advantages. First, the velocity can be estimated automatically, which will not introduce extra error caused by manual operations. Second, the prior knowledge of SAR parameters is not needed and the only factor that matters is the pixel spacing of the SAR image. Then, the effects of ship speed, wind speed, the angle between ship heading, and SAR platform flight direction on the spectrum of Kelvin wakes, and the ability of the proposed method are studied. It is found that ship velocity can be estimated precisely with the proposed spectrum-based method with a larger ship speed or a low wind speed. In addition, it is easier to estimate ship speed when the SAR platform flies across the ship heading.

Analysis of Combining SAR and Optical Optimal Parameters to Classify Typhoon-Invasion Lodged Rice: A Case Study Using the Random Forest Method.

Lodging, a commonly occurring rice crop disaster, seriously reduces rice quality and production. Monitoring rice lodging after a typhoon event is essential for evaluating yield loss and formulating suitable remedial policies. The availability of Sentinel-1 and Sentinel-2 open-access remote sensing data provides large-scale information with a short revisit time to be freely accessed. Data from these sources have been previously shown to identify lodged crops. In this study, therefore, Sentinel-1 and Sentinel-2 data after a typhoon event were combined to enable monitoring of lodging rice to be quickly undertaken. In this context, the sensitivity of synthetic aperture radar (SAR) features (SF) and spectral indices (SI) extracted from Sentinel-1 and Sentinel-2 to lodged rice were analyzed, and a model was constructed for selecting optimal sensitive parameters for lodging rice (OSPL). OSPL has high sensitivity to lodged rice and strong ability to distinguish lodged rice from healthy rice. After screening, Band 11 (SWIR-1) and Band 12 (SWIR-2) were identified as optimal spectral indices (OSI), and VV, VV + VH and Shannon Entropy were optimal SAR features (OSF). Three classification results of lodging rice were acquired using the Random Forest classification (RFC) method based on OSI, OSF and integrated OSI–OSF stack images, respectively. Results indicate that an overall level of accuracy of 91.29% was achieved with the combination of SAR and optical optimal parameters. The result was 2.91% and 6.05% better than solely using optical or SAR processes, respectively.

On the Calculation of the Geometric Resolution and Energy Potential of a Bistatic Synthetic Aperture Radar in Geosynchronous Orbit

A model and a software module were developed to analyze the shape of the trajectory described by the subsatellite point on the Earth’s surface depending on eccentricity, perigee angle, and inclination of the geosynchronous orbit (GEO). For various parameters of the satellite’s orbit and synthetic aperture radar (SAR) equipment, we built the dependences of the geometric resolution in the longitudinal (azimuthal) and transverse (relative to the trajectory of the subsatellite point) direction, as well as the radiometric sensitivity of a SAR operating in GEO in the bistatic oblique quasi-specular mode of wave reflection from the Earth’s surface. The optimal time shifts of the orbital positions of two satellites constituting a bistatic pair were found, at which the specified restrictions on the SAR parameters are fulfilled for observation points in a wide area of ​​the Earth’s surface for a long time interval. A generalized assessment of a set of SAR parameters (transmitter and receiver antenna areas, size of the resolution element, and average transmitter power) that provides a given level of radiometric sensitivity is performed.

Compact Polarimetry Response to Modeled Fast Sea Ice Thickness

Compact Polarimetric (CP) Synthetic Aperture Radar (SAR) is expected to gain more and more ground for Earth observation applications in the coming years. This comes in light of the recently launched RADARSAT Constellation Mission (RCM), which uniquely provides CP SAR imagery in operational mode. In this study, we present observations about the sensitivity of CP SAR imagery to thickness of thermodynamically-grown fast sea ice during early ice growth (September–December 2017) in the Resolute Bay area, Canadian Central Arctic. Fast ice is most suitable to use for this preliminary study since it exhibits only thermodynamic growth in absence of ice mobility and deformation. Results reveal that ice thickness up to 30 cm can be retrieved using several CP parameters from the tested set. This ice thickness corresponds to the thickness of young ice. We found the surface scattering mechanism to be dominant during the early ice growth, exposing an increasing tendency up to 30 cm thickness with a correlation coefficient with the thickness equal to 0.86. The degree of polarization was found to be the parameter with the highest correlation up to 0.95. While thickness retrieval within the same range is also possible using parameters from Full Polarimetric (FP) SAR parameters as shown in previous studies, the advantage of using CP SAR mode is the much larger swath coverage, which is an operational requirement.