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The Arctic is experiencing the greatest increase in air temperature on Earth. This significant climatic change is leading to a significant positive trend of increasing wave heights and greater coastal erosion. This in turn effects local economies and ecosystems. Increasing wave energy is one of the main drivers of this alarming trend. However, the data on spatial and temporal patterns of wave heights in the Arctic are either coarse, interpolated or limited to point measurements. The aim of this study is to overcome this shortcoming by using remote sensing data. In this study, the Synthetic Aperture Radar (SAR) satellite TerraSAR-X (TS-X) and TanDEM-X (TD-X) imagery are used to obtain sea state information with a high spatial resolution in Arctic nearshore waters in the Canadian Beaufort Sea. From the entire archive of the TS-X/TD-X StripMap mode with coverage around 30 km × 50 km acquired between 2009 and 2020 around Herschel Island, Qikiqtaruk (HIQ), all the ice-free scenes were processed. The resulting dataset of 175 collocated scenes was used to map the significant wave height ( H s ) and to link spatial and temporal patterns to local coastal processes. Sea state parameters are estimated in raster format with a 600 m step using the empirical algorithm CWAVE_EX. The statistics of the H s were aggregated according to spatial variability, seasonality and wind conditions. The results show that the spatial wave climate is clearly related to the dominant wind regime and seasonality. For instance, the aggregation of all the scenes recorded in July between 2009 and 2020 results in an average of 0.82 m H s , while in October the average H s is almost 0.40 m higher. The analysis by wind direction shows that fetch length and wind speed are likely the most important variables influencing the spatial variability. A larger fetch under NW conditions results in a mean wave height of 0.92 m, while waves generated under ESE conditions are lower at 0.81 m on average. [ABSTRACT FROM AUTHOR]

The Peace–Athabasca Delta (PAD) in western Canada is one of the largest inland deltas in the world. Flooding caused by the expansion of lakes beyond normal shorelines occurred during the summer of 2020 and provided a unique opportunity to evaluate the capabilities of remote sensing platforms to map surface water expansion into vegetated landscape with complex surface connectivity. Firstly, multi-source remotely sensed data via satellites were used to create a temporal reconstruction of the event spanning May to September. Optical synthetic aperture radar (SAR) and altimeter data were used to reconstruct surface water area and elevation as seen from space. Lastly, temporal water surface area and level data obtained from the existing satellites and hydrometric stations were used as input data in the CNES Large-Scale SWOT Simulator, which provided an overview of the newly launched SWOT satellite ability to monitor such flood events. The results show a 25% smaller water surface area for optical instruments compared to SAR. Simulations show that SWOT would have greatly increased the spatio-temporal understanding of the flood dynamics with complete PAD coverage three to four times per month. Overall, seasonal vegetation growth was a major obstacle for water surface area retrieval, especially for optical sensors. [ABSTRACT FROM AUTHOR]

The development of deep learning and remote sensing makes it possible to obtain information about oceanic internal waves from massive data. Oceanic internal waves play an important role in ocean activities. To obtain the light and dark stripes from synthetic aperture radar (SAR) images, a stripe segmentation algorithm for oceanic internal waves is proposed based on Mask RCNN. The research results show that the proposed method can not only identify whether there are oceanic internal waves, but also obtain the respective locations of the light and dark stripes in the images. The width and the direction angle of each light and dark stripe can be obtained efficiently by a separation and matching method with the sector region (SMMSR). Moreover, based on the identified relative locations of the light and dark stripes, it is possible to further determine the time when the oceanic internal waves undergo polarity conversion. [ABSTRACT FROM AUTHOR]

Precise and regularly updated maps of surface water extent are essential for wetland management. Since it is often challenging to obtain water extent information through ground surveys due to accessibility, satellite remote sensing has become a critical and cost-effective tool for acquiring this information in a temporal context. The methods that are commonly used include supervised and unsupervised multispectral classification, density slicing of a single band and spectral indices. However, optical data are substantially affected by cloud cover and solar illumination, thus, radar data comprise an ideal option. This article presents a methodological framework for rapid delineation of waterbodies' boundaries. For that purpose, an object-based approach is implemented. Sentinel-1 data were obtained and the Mean-Shift segmentation algorithm was employed. The proposed methodology produced promising results achieving an overall accuracy of 98%, a producer's accuracy for waterbodies of 90% and a user's accuracy for waterbodies of 95%. [ABSTRACT FROM AUTHOR]

In recent years, synthetic aperture radar (SAR) ship detection methods based on convolutional neural networks (CNNs) have attracted wide attention in remote sensing fields. However, these methods require a large number of labeled SAR images to train the network, where labeling for SAR images is more expensive and time-consuming than that for optical images. To address the problem of lacking labeled SAR images, in this article, we proposed an unsupervised domain adaptation (UDA) framework based on progressive transfer for SAR ship detection by transferring knowledge from the optical domain to the SAR domain. Due to the prominent difference between the optical and SAR images, our approach progressively transfers knowledge from three levels: pixel level, feature level, and prediction level. At the pixel level, considering the difference in imaging mechanism, we propose a special data augmentation method for ship targets and build the generator with skip connection based on generative adversarial networks (GANs) to generate transition domain, which can reduce the appearance discrepancy between the optical and SAR images. At the feature level, the detector is trained to learn the domain-invariant features with adversarial alignment. At the prediction level, we further use the predicted pseudo-labels obtained by the feature-aligned detector to learn more discriminative features of the SAR images directly and propose the robust self-training (RST) method to reduce the influence of noisy pseudo-labels on detector training. Specially, RST is formulated as a loss minimization problem for object detection. The experimental results based on the domain adaptation from optical dataset to SAR dataset demonstrate that our approach achieves superior SAR ship detection performance with unlabeled SAR images. [ABSTRACT FROM AUTHOR]

Global warming is causing glaciers in the Caucasus Mountains and around the world to lose mass at an accelerated pace. As a result of this rapid retreat, significant parts of the glacierized surface area can be covered with debris deposits, often making them indistinguishable from the surrounding land surface by optical remote-sensing systems. Here, we present the DebCovG-carto toolbox to delineate debris-covered and debris-free glacier surfaces from non-glacierized regions. The algorithm uses synthetic aperture radar-derived coherence images and the normalized difference snow index applied to optical satellite data. Validating the remotely-sensed boundaries of Ushba and Chalaati glaciers using field GPS data demonstrates that the use of pairs of Sentinel-1 images (2019) from identical ascending and descending orbits can substantially improve debris-covered glacier surface detection. The DebCovG-carto toolbox leverages multiple orbits to automate the mapping of debris-covered glacier surfaces. This new automatic method offers the possibility of quickly correcting glacier mapping errors caused by the presence of debris and makes automatic mapping of glacierized surfaces considerably faster than the use of other subjective methods. [ABSTRACT FROM AUTHOR]

• SAR provides 2D high-resolution images, making it a valuable tool for remote sensing. • Recent SAR systems better perceive dynamic processes due to higher mapping abilities. • SAR images are hampered by speckle noise, making data interpretation challenging. • Despeckling research trend has grown from basic filtering to advanced deep learning. • The variational methods are reviewed by stating their usage, benefits, and drawbacks. Synthetic aperture radar (SAR) has drawn significant interest from the remote sensing community due to its ability to provide high-resolution images irrespective of weather and illumination conditions. It has numerous applications in a variety of fields, including military surveillance, geological exploration, disaster management, and mapping of natural phenomena like climate change, forest management, and volcanic eruptions. The image acquired by coherent sensors such as SAR is corrupted by granular multiplicative noise, also known as speckle. It has an adverse influence on the visual interpretation of SAR images and prevents the use of various post-processing tasks. Despeckling addresses the removal of such noise from SAR images while preserving the image details. This work provides a brief summary of the theory, applications, speckle statistics, performance measures, and recent developments in despeckling filters using variational methods for SAR images. The primary focus is given to recently proposed despeckling filters, discussing their typical usage, benefits, and drawbacks. [ABSTRACT FROM AUTHOR]

• Combining different sensors performs superior to individual sensor. • SWIR bands were emphasized for forest parameters. • Adding synthetic aperture radar in forest variable research are valuable. Forest variables measurement is indispensable to supervising the dynamic of forest carbon storage. As commonly quantified forest variables, canopy cover (CC), diameter at breast height (DBH), and tree height (TH) were the predicted objects in our study. Recently, remote sensing technology, with high efficiency and wide temporal and spatial coverage, has been extensively used in practice. However, integrating open access satellite resources Sentinel-1 (S1), Sentinel-2 (S2), and Landsat 8 (L8) to quantify forest CC, DBH, and TH has not been extensively examined in the literature. In this study, we explored the potential of individual satellite bands/indices and the integration of different sensors in predicting mean CC, mean DBH, and mean TH in the plots dominated by four tree species (total area 102,887.6 ha). We acquired the satellite patterns of S1, S2, and L8 covered our research plots in 2017, and extracted the mean pixel values of the spectral images for every plot we detected. Specifically, seven sets of features derived from the satellite images of S1, S2, and L8 were used to predict CC, DBH, and TH for the survey plots. The results mainly indicated that group S2 + L8 (RMSEr (the relative root mean square error) = 17.9%, 21.0%, 23.0%) or S2 + L8 + S1 (RMSEr = 17. 8%, 21.0%, 23.1%) achieved the best accuracy for CC, DBH, and TH of all plots. Additionally, group S2 + L8 outperformed the other groups within two sensors and was superior to the individual sensors. Adding S1 synthetic aperture radar (SAR, the sensor carried by S1) had no evident improvement or deviation for most models, nevertheless, numerous bands/indices of SAR occupied important positions in variable importance and were been selected by stepwise regression, which denoted that it is available and valuable for forest parameters of some dominant tree species. Moreover, shortwave infrared (SWIR) bands played a crucial role in the three forest parameters prediction during the rainy season. SWIR bands (RMSEr = 20.5%), NIR bands (RMSEr = 24.1%), and red-edge bands (RMSEr = 27.1%) exerted the most significant implication for predicting CC, DBH, and TH apiece as univariate models. Green bands and NIR also occupied more positions of higher variable importance in multivariate models for estimating DBH and TH, while SWIR took up more for CC in all dominant species. Furthermore, non-parametric models outperformed the parametric model on the whole. In the meanwhile, the best models also were influenced by the data analysis methods. In a nutshell, a combination of multiple satellite sensors was proposed for forest variable selection, especially different optical sensors. SAR data were also inspired to be pitched in related forest measurement work, but it requires some data processing before applying to the research in many cases. [ABSTRACT FROM AUTHOR]

Various types of floating macroalgae and other floating matters have been reported in the global oceans and inland waters, and their remote detection has relied primarily on passive optical sensors. These sensors provide multiple spectral bands and frequent revisits, yet they all suffer from clouds. Synthetic aperture radar (SAR) imagers are active sensors that overcome this obstacle, yet their capacity in detecting macroalgae and other floating matters is generally unknown. Here, through statistical analysis and comparison of the Sentinel-2/MultiSpectral Instrument (MSI) and Sentinel-1/SAR imagery, we attempt to fill this knowledge gap. The types of floating matters considered in this study include macroalgae (Ulva Prolifera in the Yellow Sea, Sargassum horneri in the East China Sea, and Sargassum fluitans/natans in the Caribbean Sea), cyanobacteria (Microcystis , Nodularia spumigena , and Trichodesmium), dinoflagellates (green and red Noctiluca), organic matters (sea snots and brine shrimp cysts), and marine debris (driftwood). Of these, the only floating matter that can be definitively detected in Sentinel-1/SAR imagery is U. prolifera , followed by the occasional detection of S. fluitans/natans and driftwood. In all detection cases, the macroalgae features always appear in Sentinel-1/SAR imagery with positive contrast from the surrounding waters. Because of the all-weather measurements, SAR observations can therefore complement those from the optical sensors in monitoring and tracking U. prolifera and S. fluitans/natans in their respective regions. • The capacity of Sentinel-1 C-band SAR in detecting floating matters is evaluated. • Nearly all types of floating matters appear as positive or no contrast in SAR imagery. • The capacity is most notable for Ulva prolifera , followed by Sargassum and driftwood. • SAR can complement optical sensors in monitoring and tracking some floating matters. [ABSTRACT FROM AUTHOR]

Spring runoff measurements of Arctic watersheds are challenging given the remote location and the often dangerous field conditions. This study combines remote sensing techniques and field measurements to evaluate the applicability of synthetic aperture radar (SAR) to defining spring breakup of the braided lower Kuparuk River, North Slope, Alaska. A statistical analysis was carried out on a time series (2001 - 10) of SAR images acquired from the European Remote Sensing Satellite (ERS-2) and the Canadian RADARSAT satellite, as well as on measured runoff. On the basis of field information, the SAR images were separated into pre-breakup, breakup, and post-breakup periods. Three variables were analyzed for their suitability to bracket the river breakup period: image brightness, variance in brightness over the river length, and a sum of rank order change analysis. Variance in brightness was found to be the most reliable indicator. A combined use of that variance and sum of rank order change appeared promising when enough images were available. The temporal resolution of imagery served as the major limitation in constraining the timing of the hydrologic event. Challenges associated with spring runoff monitoring and the sensitive nature of SAR likely resulted in an earlier detection of surficial changes by the remote sensing technique compared to the field runoff observations. Given a sufficient temporal resolution, SAR imagery has the potential to improve the spatiotemporal monitoring of Arctic watersheds for river breakup investigations. [ABSTRACT FROM AUTHOR]

Environmental effects and climate change are lately representing an increasing strain on coastal areas, whose topography strongly depends on these conditions. However, the processes by which weather and environmental phenomena influence the highly variable beach morphology are still unknown. Continuous monitoring of the beach environment is necessary to implement protection strategies. This paper presents the results of an innovative study performed on a coastal area using satellite remote sensing data with the aim of understanding how environmental phenomena affect beaches. Two years of synthetic aperture radar (SAR) Sentinel-1 images are used over a test area in Noordwijk, the Netherlands. At the same time as the SAR acquisitions, information on tidal and weather conditions are collected and integrated from nearby meteorological stations. Dedicated codes are implemented in order to understand the relationship between the SAR amplitude and the considered phenomena: wind, precipitation, and tidal conditions. Surface roughness is taken into account. The results indicate a strong correlation between the amplitude and the wind. No particular correlation or trend could be noticed in the relationship with precipitation. The analysis of the amplitude also shows a decreasing trend moving from the dry area of the beach towards the sea and the correlation coefficient between the amplitude and the tide level gets negative with the increase of the water content. [ABSTRACT FROM AUTHOR]

Abstract: The arid and semi-arid sagebrush–grass ecosystem occupies a substantial portion of rangelands in the western United States. Using remote sensing techniques to map the percent of sagebrush, grass/forb, and bare ground components is necessary for forage production estimation and natural resource management over large areas. However optical data have significant deficiencies in these ecosystems because of exposed bright soil, spectrally-indeterminate vegetation, and a large dead vegetation component. Radar data also have deficiencies caused by factors such as antenna pattern calibration, local incidence angle (LIA), soil moisture, and surface roughness. With the complementary vegetation information gained from optical data and radar data, these two datasets were fused to estimate 10-m sagebrush, grass, and bare ground percent cover in the non-forested areas of Yellowstone National Park, which is a representative native western rangeland ecosystem of the US. The datasets were processed to resolve the issues of antenna pattern calibration and LIA effect. Peak green Landsat, late fall Airborne Visible and Infrared Imaging Spectrometer (AVIRIS), and Airborne Synthetic Aperture Radar (AirSAR) data were fused in this analysis. AVIRIS, Landsat, AirSAR and elevation data were used to segment the study area into two main subcategories of “pure grass” and “mixed sagebrush and grass”. Landsat Tasseled Cap Greenness (LTCG) was used to retrieve bare land and grass percentages in pure grass areas. In the areas with mixed grass and sagebrush, standardized LTCG and radar C vv were used to derive the vegetation cover percentage, and the ratio of standardized LTCG and radar L hv was further used to calculate the relative abundance of sagebrush and grass. Comparison between the field and remote sensing estimations shows the correlation coefficients were 0.838, 0.746, and 0.830 for bare land, grass, and sagebrush, respectively. When grouped into three discrete categories of “low”, “medium”, and “high”, the overall accuracies were 79.4%, 75.9%, and 77.6%, respectively. Our study shows the potential for application of global spaceborne C- and L-band radar and optical data fusion for large-area rangeland monitoring. [Copyright &y& Elsevier]

Remote-sensing-driven urban change detection has been studied in many ways for decades for a wide field of applications, such as understanding socio-economic impacts, identifying new settlements, or analyzing trends of urban sprawl. Such kinds of analyses are usually carried out manually by selecting high-quality samples that binds them to small-scale scenarios, either temporarily limited or with low spatial or temporal resolution. We propose a fully automated method that uses a large amount of available remote sensing observations for a selected period without the need to manually select samples. This enables continuous urban monitoring in a fully automated process. Furthermore, we combine multispectral optical and synthetic aperture radar (SAR) data from two eras as two mission pairs with synthetic labeling to train a neural network for detecting urban changes and activities. As pairs, we consider European Remote Sensing (ERS-1/2) and Landsat 5 Thematic Mapper (TM) for 1991–2011 and Sentinel 1 and 2 for 2017–2021. For every era, we use three different urban sites—Limassol, Rotterdam, and Liège—with at least 500 km 2 each, and deep observation time series with hundreds and up to over a thousand of samples. These sites were selected to represent different challenges in training a common neural network due to atmospheric effects, different geographies, and observation coverage. We train one model for each of the two eras using synthetic but noisy labels, which are created automatically by combining state-of-the-art methods, without the availability of existing ground truth data. To combine the benefit of both remote sensing types, the network models are ensembles of optical- and SAR-specialized sub-networks. We study the sensitivity of urban and impervious changes and the contribution of optical and SAR data to the overall solution. Our implementation and trained models are available publicly to enable others to utilize fully automated continuous urban monitoring. [ABSTRACT FROM AUTHOR]

This article considers Synthetic Aperture Radar and other synthetic-aperture imaging systems in which a backscattered wave is measured from a variety of locations. We focus on the case in which the ground-reflectivity function depends on frequency as well as on position. We begin with a (linearized) mathematical model, based on a scalar approximation to Maxwell's equations, which includes the effects of the source waveform and the antenna beam pattern. The model can also accommodate other effects such as antenna steering and motion. For this mathematical model, we use the tools of microlocal analysis to develop and analyze a three-dimensional inversion algorithm that uses measurements made on a surface and determines the frequency-dependent ground reflectivity. © 2004 Wiley Periodicals, Inc. Int J Imaging Syst Technol 14, 28–34, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20004 [ABSTRACT FROM AUTHOR]

The coastal zone offers among the world's most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land- and water-related applications in coastal zones. Compared to optical satellites, cloud-cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all-weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud-prone tropical and sub-tropical climates. The canopy penetration capability with long radar wavelength enables L-band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change-induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L-band SAR data for geoscientific analyses that are relevant for coastal land applications. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) is an established remote sensing technique that can robustly provide high-resolution imagery of the Earth's surface. However, current space-borne SAR systems are limited, as a matter of principle, in achieving high azimuth resolution and a large swath width at the same time. Digital beamforming (DBF) has been identified as a key technology for resolving this limitation and provides various other advantages, such as an improved signal-to-noise ratio (SNR) or the adaptive suppression of radio interference (RFI). Airborne SAR sensors with digital beamforming capabilities are essential tools to research and validate this important technology for later implementation on a satellite. Currently, the Microwaves and Radar Institute of the German Aerospace Center (DLR) is developing a new advanced high-resolution airborne SAR system with digital beamforming capabilities, the so-called DBFSAR, which is planned to supplement its operational F-SAR system in near future. It is operating at X-band and features 12 simultaneous receive and 4 sequential transmit channels with 1.8 GHz bandwidth each, flexible DBF antenna setups and is equipped with a high-precision navigation and positioning unit. This paper aims to present the DBFSAR sensor development, including its radar front-end, its digital back-end, the foreseen DBF antenna configuration and the intended calibration strategy. To analyse the status, performance, and calibration quality of the DBFSAR system, this paper also includes some first in-flight results in interferometric and multi-channel marine configurations. They demonstrate the excellent performance of the DBFSAR system during its first flight campaigns. [ABSTRACT FROM AUTHOR]

The area, distribution, and temporal dynamics of anthropogenic impervious surface (AIS) at large scale are significant for environmental, ecological and socio-economic studies. Remote sensing has become an important tool for monitoring large scale AIS, while it remains challenging for accurate extraction of AIS using optical datasets alone due to the high diversity of land covers over large scale. Previous studies indicated the complementary use of synthetic aperture radar (SAR) to improve the AIS estimation, while most of them were limited to local and small scales. The potential of SAR for large scale AIS mapping is still uncertain and underexplored. In this study, first, a machine learning framework incorporating both optical and SAR data based on Google Earth Engine platform was developed for mapping and analyzing the annual dynamics of AIS in China. Feature-level fusion for SAR and optical data across large scale was tested applicable considering the backscattering coefficients, texture measures and spectral characteristics. Improved accuracy (averaged 2% increased overall accuracy and averaged 4% increased Kappa coefficient) and better delineation between the bright impervious surface and bare land was observed comparing with using optical data alone. Second, comprehensive assessment was conducted using high-resolution samples from Google Earth, census data from China Statistic Yearbook and benchmark datasets from the GlobeLand30 and GHSL, demonstrating the feasibility and reliability of the proposed method and results. Last but not the least, we analyzed the spatial and temporal patterns of AIS in China from national, regional and provincial levels. • We incorporated SAR and optical images for large scale impervious surfaces mapping. • Results were validated with high resolution samples, census and benchmark data. • SAR generally improved the overall accuracy by 2%. • SAR better delineated the impervious surface from bare land. [ABSTRACT FROM AUTHOR]

North Sinai is of significant historical interest primarily because of its role since late prehistoric times as a land bridge between Egypt and the Levant. Access to this region is challenging due to its harsh geography and security concerns. Remote sensing constitutes a convenient method for archaeological prospection and monitoring over such regions with its low cost (relative to ground based sensing techniques), global coverage, and high temporal and spatial sampling. This paper describes part of a study to revisit a number of sites investigated during the North Sinai Survey (1972–1982) with very high resolution optical and Synthetic Aperture Radar satellite imagery. These were acquired throughout the summer of 2017 in the framework of a European Space Agency research project. The Synthetic Aperture Radar data includes Spotlight and Staring Spotlight modes of the TerraSAR-X mission, while the optical imagery was acquired by the Pleiades mission. The TerraSAR-X data were processed to derive filtered amplitude and consecutive coherence time series. The results of the TerraSAR-X data processing, and the pan-sharpened Pleiades data were compared with the results of the North Sinai Survey to detect possible additional buried structures in the radar data, or newly excavated sites in the optical data. While the analysis is still ongoing, results are reported here of the Qasrawet archaeological site, which was partially investigated by the North Sinai Survey expedition, but assumed to cover a much larger area. Herein, a number of newly excavated structures are apparent in the remote sensing data. The similarity of features in both the TerraSAR-X and Pleiades data suggest that all structures are surface residues, and therefore, that the subsurface mapping capabilities of the TerraSAR-X data in this area are limited. The utility of both data types for archaeological site monitoring are discussed. [ABSTRACT FROM AUTHOR]