Radar Backscatter Research Articles

Mapping high marsh and salt pannes/flats along the northern Gulf of Mexico coast

Coastal wetlands are predicted to undergo extensive transformation due to climate and land use change. Baseline maps of coastal wetlands can be used to help assess changes. Found in the upper portion of the estuarine zone, high marsh and salt pannes/flats provide ecosystem goods and services and are particularly important to fish and wildlife. We developed the first map of high marsh and salt pannes/flats along the northern Gulf of Mexico using regional models that included spectral indices related to greenness and wetness from optical satellite imagery, elevation data, irregularly flooded wetland probability information, and synthetic aperture radar backscatter. We found the greatest relative coverage of high marsh along the Texas coast (30% to 65%) and the Florida Panhandle (40%), whereas the greatest relative coverage of salt pannes/flats was along the lower Texas coast (74%) and the middle Texas coast (15%). As part of this effort, we also developed a map that highlighted irregularly flooded wetlands dominated by Juncus roemerianus (black needlerush) for part of the study area. Both maps had an overall accuracy of around 80%. Our results advance the understanding of estuarine marsh zonation and provide a baseline for assessing future transformations.

Crop mapping through a hybrid machine learning and deep learning method

A major function of satellite imagery is crop identification and separation, which is of key importance in acreage estimation, yield estimation, and agricultural management. This study aimed to examine the capability of the object-based method and machine learning and deep learning algorithms in combining optical and radar images to generate crop-type maps. For this purpose, Sentinel-1 and Sentinel-2 satellite images were used for the 2019 crop year in the northwest of Ardabil, Iran. Image segmentation was performed through the multiscale method and various spectral, textural, and radar backscattering characteristics. The main features were identified using Random Forest feature selection and fed to support vector machine, random forest, and convolutional neural network algorithms. The results of these algorithms were combined via the majority voting method. The results showed that the convolutional neural network algorithm performed better than other methods with an overall accuracy of 88.03% and a kappa coefficient of 85.99%. Meanwhile, the support vector machine method showed poor performance compared to the other two methods. According to the findings, combining the results of these algorithms through the majority voting method yielded the best crop identification result with an overall accuracy of 90.34% and a Kappa coefficient of 88.72%.

Limitations in the use of Sentinel-1 data for morphological change detection in rivers

ABSTRACT The identification of morphological changes occurring along river channels is essential to support river process understanding, assess sediment budgets and evaluate the effectiveness of river management. Among available remote sensing techniques, space-borne synthetic aperture radar (SAR) could potentially provide a powerful complement to optical imagery for this task. However, very few studies have been carried out on the use of SAR datasets to study erosion and deposition processes in river channels. In this work, we investigate the potential of change detection analysis based on Sentinel-1 data, by comparing variations of radar backscattering to river morphology changes identified through high-resolution drone acquisitions. We considered a time series of two years of Sentinel-1 data relative to a period where, despite a moderate fluvial event occurred, morphological changes have been significantly detected in multitemporal drone point clouds. Satellite optical imagery (planet.com) and hydro-meteorological data were used to support the analysis and interpret results. The results show that the spatial and temporal resolution of Sentinel-1 is currently not suitable for accurate discrimination of morphological changes related to river dynamics at local scale. Other spaceborne sensors with sub-metric ground sampling distance and/or daily revisit time would be probably suitable; however, so far, this option would need the use of commercial solutions with a consistent increase of the costs of the investigation.

94 GHz Radar Backscatter Characteristics of Alpine Glacier Ice

AbstractMeasuring the radar backscatter characteristics of glacier ice at different frequencies and incidence angles is fundamental to predicting the glacier mapping performance of a sensor. However, such measurements at 94 GHz do not exist. To address this knowledge gap, we collected 94 GHz radar backscatter data from the surface of Rhônegletscher in Switzerland using the All‐Weather Volcano Topography Imaging Sensor (AVTIS2) real‐aperture Frequency Modulated Continuous Wave radar. We determine the mean normalized radar cross section to be −9.9 dB. The distribution closely follows a log‐normal distribution with a high goodness of fit (R2 = 0.99) which suggests that radar backscatter is diffuse and driven by surface roughness. Further, we quantified the uncertainty of AVTIS2 3D point clouds to be 1.30–3.72 m, which is smaller than other ground‐based glacier surface mapping radars. These results demonstrate that glacier surfaces are an efficient scattering target at 94 GHz, hence demonstrating the suitability of millimeter‐wave radar for glacier monitoring.

Synthetic aperture radar polarised backscattering behaviour in partially inundated agricultural fields

ABSTRACT A methodology for classifying rainfed paddy fields based on their hydrological conditions is lacking. This study analysed the behaviour of synthetic aperture radar (SAR) backscatter coefficients at each polarisation and cross-polarisation ratio index in a rainfed rice paddy field with a partially waterlogged surface. The SAR polarisations used were the VH and VV of the C-band SAR Sentinel-1 and the HV and HH of the L-band SAR Palsar-2. The relationship between backscatter coefficient and terrains including topographic categories and local relative elevation, which affect waterlogging conditions in rainfed paddy fields, were evaluated. The VV of C-band, and HH and HV of L-band showed different patterns in the time series variation according to the topographic categories. We observed that the combination of L-band HH and HV could be used to assess waterlogging conditions because they differ depending on variations in local elevation. The study results suggest that future studies can evaluate the microtopography and associated local hydrological environment using the combination of L-band HH and HV backscatter coefficient.

Mapping Antarctic crevasses and their evolution with deep learning applied to satellite radar imagery

Abstract. The fracturing of glaciers and ice shelves in Antarctica influences their dynamics and stability. Hence, data on the evolving distribution of crevasses are required to better understand the evolution of the ice sheet, though such data have traditionally been difficult and time-consuming to generate. Here, we present an automated method of mapping crevasses on grounded and floating ice with the application of convolutional neural networks to Sentinel-1 synthetic aperture radar backscatter data. We apply this method across Antarctica to images acquired between 2015 and 2022, producing a 7.5-year record of composite fracture maps at monthly intervals and 50 m spatial resolution and showing the distribution of crevasses around the majority of the ice sheet margin. We develop a method of quantifying changes to the density of ice shelf fractures using a time series of crevasse maps and show increases in crevassing on Thwaites and Pine Island ice shelves over the observational period, with observed changes elsewhere in the Amundsen Sea dominated by the advection of existing crevasses. Using stress fields computed using the BISICLES ice sheet model, we show that much of this structural change has occurred in buttressing regions of these ice shelves, indicating a recent and ongoing link between fracturing and the developing dynamics of the Amundsen Sea sector.

Glacier melt detection at different sites of Greenland ice sheet using dual-polarized Sentinel-1 images

ABSTRACT Synectic Aperture Radar (SAR) backscatter coefficient is sensitive to glacier surface physical characteristic changes, including the states of melting and refreezing, but it is also sensitive to incidence angle variation. This study explores the capability of monitoring Greenland Ice Sheet (GrIS) melting status with Sentinel-1 dual-polarized images by referring to Automatic Weather Station (AWS) records. Sentinel-1 SAR images at five coastal regions of the GrIS are obtained from 2017 to 2021. The backscatter coefficients are normalized to an incidence angle of 30° with an empirical model. Time series of five backscatter coefficients profiles covering AWS illustrates different patterns of the ice surface dielectric constant dynamics in different elevations. The wet snow radar zone shows clear backscatter coefficients decreasing during the melting seasons, but the bare ice radar zone behaves more complexly during the melting seasons. The numbers of melting days at different elevations are also derived for each profile based on −3 dB backscatter coefficient decrease of HH and/or HV polarization, showing the heterogeneous ablation processes over the GrIS. The daily maximum 2 m air temperature on two consecutive days (before and on the SAR acquisition day) exceeds 0°C, and the daily average 2 m air temperature exceeds −0.5°C on the SAR acquisition day that was recorded by the AWS finds good agreements with the −3 dB decrease of the backscatter coefficients, suggesting the GrIS surface melting can be well captured by dual-polarized Sentinel-1 C-band SAR images. The overall agreement and Kappa coefficients are mostly better than 0.85 and 0.70, respectively, for HH images and 0.80 and 0.60, respectively, for HV images, suggesting a better performance of the co-polarized image. High temporal resolution and wide-swath SAR sequence imagery provide suitable data sources for monitoring glacier surface melting-refreezing stats; further analysis is requested to quantitatively link the volume of melting with backscatter coefficient and other SAR data sources.

Characterisation of resident space objects using multistatic interferometric inverse synthetic aperture radar imaging

AbstractThis paper illustrates an interferometric inverse synthetic aperture radar based on three‐dimensional imaging and rotational velocity estimation from a multistatic setup with three ground‐based radars with non‐orthogonal baselines. The radar backscattering along the orbit is simulated based on physical optics approximation. The paper illustrates a novel shape‐dependent and rotation‐invariant feature called normalised eigenvalues for classification. A database of interferometric point clouds is generated using radar back scattering at different aspect angles and classified using various classification algorithms. The classification accuracy at varying SNR levels was evaluated using Monte Carlo simulations.

Monitoring of deforestation events in the tropics using multidimensional features of Sentinel 1 radar data

Many countries and regions are currently developing new forest strategies to better address the challenges facing forest ecosystems. Timely and accurate monitoring of deforestation events is necessary to guide tropical forest management activities. Synthetic aperture radar (SAR) is less susceptible to weather conditions and plays an important role in high-frequency monitoring in cloudy regions. Currently, most SAR image-based deforestation identification uses manually supervised methods, which rely on high quality and sufficient samples. In this study, we aim to explore radar features that are sensitive to deforestation, focusing on developing a method (named 3DC) to automatically extract deforestation events using radar multidimensional features. First, we analyzed the effectiveness of radar backscatter intensity (BI), vegetation index (VI), and polarization feature (PF) in distinguishing deforestation areas from the background environment. Second, we selected the best-performing radar features to construct a multidimensional feature space model and used an unsupervised K-mean clustering method to identify deforestation areas. Finally, qualitative and quantitative methods were used to validate the performance of the proposed method. The results in Paraguay, Brazil, and Mexico showed that (1) the overall accuracy (OA) and F1 score (F1) of 3DC were 88.1–98.3% and 90.2–98.5%, respectively. (2) 3DC achieved similar accuracy to supervised methods without the need for samples. (3) 3DC matched well with Global Forest Change (GFC) maps and provided more detailed spatial information. Furthermore, we applied the 3DC to deforestation mapping in Paraguay and found that deforestation events occurred mainly in the second half of the year. To conclude, 3DC is a simple and efficient method for monitoring tropical deforestation events, which is expected to serve the restoration of forests after deforestation. This study is also valuable for the development and implementation of forest management policies in the tropics.

Characterising the ice sheet surface in Northeast Greenland using Sentinel-1 SAR data

AbstractOver half of the recent mass loss from the Greenland ice sheet, and its associated contribution to global sea level rise, can be attributed to increased surface meltwater runoff, with the remainder a result of dynamical processes such as calving and ice discharge. It is therefore important to quantify the distribution of melting on the ice sheet if we are to adequately understand past ice sheet change and make predictions for the future. In this article, we present a novel semi-empirical approach for characterising ice sheet surface conditions using high-resolution synthetic aperture radar (SAR) backscatter data from the Sentinel-1 satellite. We apply a state-space model to nine sites within North-East Greenland to identify changes in SAR backscatter, and we attribute these to different surface types with reference to optical satellite imagery and meteorological data. A set of decision-making rules for labelling ice sheet melting states are determined based on this analysis and subsequently applied to previously unseen sites. We show that our method performs well in (1) recognising some of the ice sheet surface types such as snow and dark ice and (2) determining whether the surface is melting or not melting. Sentinel-1 SAR data are of high spatial resolution; thus, in developing a method to identify the state of the surface from these data, we improve our capability to understand the variation of ice sheet melting across time and space.

Assimilation of ASCAT Radar Backscatter Coefficients over Southwestern France

In this work, Advanced SCATterometer (ASCAT) backscatter data are directly assimilated into the interactions between soil, biosphere, and atmosphere (ISBA) land surface model using Meteo-France’s global Land Data Assimilation System (LDAS-Monde) tool in order to jointly analyse soil moisture and leaf area index (LAI). For the first time, observation operators based on neural networks (NNs) are trained with ISBA simulations and LAI observations from the PROBA-V satellite to predict the ASCAT backscatter signal. The trained NN-based observation operators are implemented in LDAS-Monde, which allows the sequential assimilation of backscatter observations. The impact of the assimilation is evaluated over southwestern France. The simulated and analysed backscatter signal, surface soil moisture, and LAI are evaluated using satellite observations from ASCAT and PROBA-V as well as in situ soil moisture observations. An overall improvement in the variables is observed when comparing the analysis with the open-loop simulation. The impact of the assimilation is greater over agricultural areas.

Seasonality and directionality effects on radar backscatter are key to identify mountain forest types with Sentinel-1 data

Systematic Sentinel-1 acquisitions provide an unprecedented stream of SAR data which allows to describe forest temporal dynamics in detail, a powerful tool for phenological studies and forest type classification. Several studies have explored the temporal variation of backscatter intensity in this context, but none considered that scattering directionality of canopies may vary. Said directionality is related to target-sensor geometry (incidence angle), forest height, and optical depth, associated with leaf dynamics. This study explicitly models backscatter dependance on incidence angle by fitting a regression model for each Sentinel-1 image and forest type. Residuals are accumulated across the time series and used to classify pixels into the most likely forest type using the smallest accumulated residual. This modelling and classification strategy has been applied over a North-South transect across the Carpathian Mountains, including forests with different physiognomies, from deciduous broadleaf forest, to mixed broadleaf-needleleaf and pure perennial needleleaf forests. These forests were classified with increasing detail, assessing the results against in-situ forest stand data and satellite-based land cover classification products (Copernicus Forest type layer). The accuracy of our classification was K > 0.8, OA > 90% when separating broadleaf from needleleaf forest types. The accuracy decreased (K > 0.6, OA > 79%) when also separating mixed forest types. Our results suggest that incorporating directional effects into classification models can improve SAR-based forest classification of temperate forest over mountainous terrain. Furthermore, models fitted between backscatter and incidence angle provide an estimate of n, a parameter related to optical depth that has been shown to vary with leaf dynamics. ncould be used to improve image normalization in studies aiming at the estimation of biomass, or to aid the estimation of fast-changing parameters such as leaf area index or leaf moisture content.

Conceptualising a Hybrid Flying and Diving Craft

This paper introduces the conceptual design of a submersible seaplane that merges the maturity of the wing-in-ground (WIG or ekranoplan) crafts and seaplanes with covert hybrid underwater insertion, travel, and recovery. WIG crafts have a higher lift-to-drag ratio and thus improved endurance, while hybrid crafts have recently become feasible due to advances in materials, electric propulsion, and multi-medium computational fluid dynamics. The reconnaissance design can insert, loiter, and extract from underwater, surfaces if necessary; it can fly in or out of ground effect, keep watch on the sea surface while recharging, and travel underwater. This design minimizes Doppler and infrared signatures to evade the surface wave, backscatter radar systems, and cube satellite arrays typical in contested maritime areas. Five critical enabling technologies are overviewed, showing how they enable a conceptual design. This project was conducted in collaboration with two industrial partners, namely Ron Allum and Thales Australia. The conceptual design has been socialised and confirmed at technical conferences from each core discipline and partly confirmed by a recent Chinese design and testing of a similar hybrid uncrewed aerial vehicle (HUAV). Recommendations are made for improving the conceptual design before proof-of-concept prototype testing. Given the seminal nature of HUAV design and research and some of the unique innovations proposed, the lessons learned from this iteration will likely be significant to other designers and researchers.

Hardware Suitability of Complex Natural Resonances Extraction Algorithms in Backscattered Radar Signals

Complex natural resonances (CNRs) extraction methods such as matrix pencil method (MPM), Cauchy, vector-fitting Cauchy method (VCM), or Prony’s method decompose a signal in terms of frequency components and damping factors based on Baum’s singularity expansion method (SEM) either in the time or frequency domain. The validation of these CNRs is accomplished through a reconstruction of the signal based on these complex poles and residues and a comparison with the input signal. Here, we perform quantitative performance metrics in order to have an evaluation of each method’s hardware suitability factor before selecting a hardware platform using benchmark signals, simulations of backscattering scenarios, and experiments.

Estimation of lunar surface roughness using Chandrayaan-2 full-polarimetric DFSAR data

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.

Automated detection and tracking of medium-large icebergs from Sentinel-1 imagery using Google Earth Engine

Monitoring Antarctic icebergs helps us understand the interaction between ocean, atmosphere, and sea ice in the Southern Ocean. Although gigantic icebergs have been the objects of many remote sensing studies, medium icebergs in the Southern Ocean have been rarely monitored or traced. In this study, we develop an iceberg detection and tracking tool particularly for medium and large icebergs (0.4–10 km2), by using Python programming language and Sentinel-1 (S1) imagery, based on Google Earth Engine (GEE). To detect icebergs, we employ the simple non-iterative clustering (SNIC) and region adjacency graph (RAG) merging for object-based image segmentation and train/test the support vector machine (SVM) model with 6432 labeled segments of iceberg or non-icebergs from 40 images of S1 (2019–2021). Radar backscatter features and morphological features of those segments are used as the inputs of the SVM model. After icebergs are detected in two image scenes of different dates, we track the displacements of detected icebergs by comparing their 1-D shape signals. Our model shows ∼99% of accuracy in detecting icebergs and ∼ 93–98% of accuracy in tracking icebergs depending on the day difference between image scenes. When using our tool for the monitoring of icebergs in the Amundsen Sea, we find that the iceberg fraction varies from 2% to 8% in 2021 and most of icebergs move westward with a speed of <0.2 km/day.

The influence of two-dimensional currents on the sea surface radar backscattering coefficient

ABSTRACT In this paper, the current field modulation is described using a solution of the wave action equation. Using an integrated equation model (IEM), a current field modulated scattering model is established. The sea surface backscattering coefficient decreases with increasing current speed. At high wind speeds, the change of backscattering coefficient under 1 m ⋅ s−1 current field modulation is about 1 dB. An increase of 60° in the angle between wind and current direction leads to a decrease of 1.5 dB in the modulation coefficient. Finally, the model is verified using satellite data.

Tesserae: Surface differences across Venus’s “continents”

The heavily deformed upland tesserae are some of the most ancient geologic units on Venus and, as such, record the longest history of surface evolution. Our geologic understanding of these landforms is based largely on radar images from the Magellan mission, in which gross morphology and small-scale properties can be difficult to deconvolve. Here we use Magellan radar backscatter data for ridge slope surfaces in 22 highland areas to understand whether the tesserae can be subdivided in ways that differentiate surface property variations. Significant variations occur in the mean backscatter of ridge slopes, and we divide the tesserae into two groups with echoes lower (n = 15) or higher (n = 7) than an average tessera radar scattering behavior. While both few-kilometers-scale slopes and centimeter-scale roughness can modulate the radar returns, at least seven out of 15 tesserae with lower echoes are correlated with fine-grained impact crater ejecta deposits that smooth the surface. We propose that distal ejecta deposition plays a major role in creating the observed range of tessera radar properties and obscuring aspects of their original formation and in situ weathering. Our twofold classification system provides a new way of assessing the physical characteristics of tesserae from the Magellan data. Upcoming missions must consider both their original morphology and post-emplacement processes if we are to unlock the geologic record preserved in tesserae.

C and K band microwave penetration into snow on sea ice studied with off-the-shelf tank radars

AbstractSnow cover on sea ice poses a challenge for radar measurements as microwave penetration into snow is not yet fully understood. In this study, the aim is to investigate microwave penetration into snow on Arctic sea ice using commercial C (6 GHz) and K (26 GHz) band tank radars. Nadir-looking radar measurements collected at nine study locations over first-year and multiyear landfast sea ice in the Lincoln Sea in May 2018 are analysed together with detailed measurements of the physical properties of the snow cover to determine the dominant scattering horizons at both frequencies. They are evaluated for the feasibility to determine snow depth. The results show that in 39% of the measurements and only on first-year ice a major fraction of the C band radar backscatter originated closer to the snow–ice interface potentially enabling snow depth retrieval. At K band, 81% of the radar returns originated from the snow surface. Partly confirming the findings of previous studies, however, the analysis was potentially hampered by relatively warm air temperatures (up to$-0.9^\circ$C) during the study period as well as stratigraphic features and inconclusive microwave interaction with the saline basal layers found in the snow cover on first-year ice.

How Does Wind Influence Near-Nadir and Low-Incidence Ka-Band Radar Backscatter and Coherence from Small Inland Water Bodies?

While many studies have been conducted regarding wind-driven Ka-band scattering on the ocean and sea surfaces, few have identified the impacts of Ka-band scattering on small inland water bodies, and fewer have identified the influence of wind on coherence over water. These previous studies have been limited in spatial scale, covering only large water bodies &gt;25 km2. The recently launched Surface Water and Ocean Topography (SWOT) mission is the first Ka-band InSAR satellite designed for mapping water surface elevations and open water areas for rivers as narrow as 100 m and lakes as small as 0.0625 km2. Because measurements of these types are novel, there remains some uncertainty about expected backscatter amplitudes given wind-driven water surface roughness variability. A previous study using the airborne complement to SWOT, AirSWOT, found that low backscatter and low coherence values were indicative of higher errors in the water surface elevation products, recommending minimum thresholds for backscatter and coherence for filtering the data to increase the accuracy of averaged data for lakes and rivers. We determined that the global average wind speed over lakes is 4 m/s, and after comparing AirSWOT backscatter and coherence data with ERA-5 wind speeds, we found that the minimum required speed to retrieve high backscatter and coherence is 3 m/s. We examined 11,072 lakes across Canada and Alaska, with sizes ranging from 350 m2 to 156 km2, significantly smaller than what could be measured with previous Ka-band instruments in orbit. We found that small lakes (0.0625–0.25 km2) have significantly lower backscatter (3–5 dB) and 0.20–0.25 lower coherence than larger lakes (&gt;1 km2). These results suggest that approximately 75% of SWOT observable lake areas around the globe will have consistently high-accuracy water surface elevations, though seasonal wind variability should remain an important consideration. Despite very small lakes presenting lower average backscatter and coherence, this study asserts that SWOT will be able to accurately resolve the water surface elevations and water surface extents for significantly smaller water bodies than have been previously recorded from satellite altimeters. This study additionally lays the foundation for future high-resolution inland water wind speed studies using SWOT data, when the data become available, as the relationships between wind speed and Ka-band backscatter reflect those of traditional scatterometers designed for oceanic studies.