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This paper contrasts predicted X-band sea surface backscattering from slick-free and oil-covered sea surfaces with actual measurements acquired by the X-band satellite TerraSAR-X and COSMO-SkyMed Synthetic Aperture Radar (SAR) missions. Two SAR scenes were acquired with a temporal difference of about 36 minutes, under similar met-ocean conditions, during the North Sea’s Gannet Alpha oil spill accident. The normalized radar cross section of the slick-free sea surface is predicted using the Advanced Integral Equation Model (AIEM) while the backscatter from the oiled sea surface is predicted by the AIEM augmented with the Model of Local Balance (MLB) to include the damping effect of oil slicks. Experimental results show that X-band co-polarized numerical predictions agree reasonably well with both TSX and CSK actual measurements collected over slick-free sea surfaces. When dealing with oil-covered sea surfaces, the predicted backscattering reasonably agrees with TSX measurements, while it overestimates the CSK ones. This is likely due to the different spreading conditions of the oil imaged by the two satellite missions.

This study addresses the drought phenomenon that affected the Po River, north of Italy, by jointly exploiting methods based on measurements remotely sensed by the European Sentinel-1 C-band Synthetic Aperture Radar (SAR) and meteo-climatological information derived from both in situ and satellite datasets. The SAR methods consist of a baseline approach, which estimates the water-covered area from dual-polarized SAR imagery using a constant false alarm rate (CFAR) approach, augmented with ancillary vertical ground displacement information derived from SAR interferometry (InSAR). The meteo-climatological information includes hydrometric water level, rainfall data, and the Standard Precipitation Evapotranspiration Index (SPEI). Experimental results, obtained using a seven-year time series of SAR scenes collected over the Po River close to the city of Piacenza and contrasted with ancillary external information, demonstrate the soundness of the proposed SAR-based added-value products and their satisfactory accuracy in emphasizing both seasonal trends and the drought phenomena occurred in the area. This paves the way for an operational use of SAR measurements to monitor the water-covered area of the Po River and potentially other similar environments.

The major objective of our research is to retrieve wave parameters from synthetic aperture radar (SAR) images during a tropical cyclone (TC) based on a machine learning method. In this study, more than 2000 Sentinel-1 images obtained in interferometric-wide and extra wide mode are collected during 200 TCs, which are collocated with hindcasted waves by a third-generation numeric model, namely WAVEWATCH-III (WW3). It is found that wave parameters, i.e., significant wave height (SWH), mean wave period (MWP), and mean wave length (MWL), are correlated with several SAR-measured image variables. Based on these findings, a machine learning method, namely eXtreme Gradient Boosting (XGBoost), is developed through the training dataset using 1600 images. The trained algorithm is tested over 400 images and the retrievals are compared with WW3 simulations. The statistical analysis shows that the root mean squared error (RMSE) and scatter index (SI) of SWH are 0.19 m and 0.06, respectively. The RMSE and SI of MWP are 0.19 s and 0.03, respectively. The RMSE of the MWL is 3.77 m and the SI is 0.04. Comparisons between inverted SWH by XGBoost methods and the altimeter measurements presents a 0.59 m RMSE of SWH with and 0.19 SI. This result is improved comparing to the results (i.e., a 1.44 m RMSE of SWH with a 0.45 SI) achieved by a previous algorithm. Collectively, it is considered that machine learning is a valuable method to extract wave parameters from dual-polarization SAR images.

In this study, we discuss the extra-value of polarimetric information in observing the lava flow. Dual-polarimetric Synthetic Aperture Radar (SAR) measurements are processed using a polarimetric change detector that, instead of looking at the variation of the backscatter intensity between a pair of images collected before and after the event, looks at changes in the polarimetric scattering behavior. We demonstrate that the scattering changes detected by the proposed polarimetric approach well-correlate with the footprint of the lava flow provided by external sources. In addition, we also compare the performance of the polarimetric change detector with conventional single-polarization metrics showing that the former one always outperforms the incoherent single-polarization measurements. To further demonstrate the robustness of the polarimetric change detectors, we selected two test cases that refer to vulcanic eruptions calling for completely different environments. The first one, related to the Etna volcano, calls for a lava flow over a vegetation-free environment; the second one is related to the Nyiragongo volcano and calls for a lava flow in a vegetated environment. Experimental results show that the polarimetric change detectors automatically adapt to the changing environment outperforming the single-polarization detectors.

The oceans cover roughly 2/3 of the Earth’s surface and are a fundamental ecosystem regulating climate, weather and representing a huge reservoir of biodiversity and natural resources . The preservation of the oceans is therefore not only relevant on an environmental perspective but also on an economical one. A sustainable approach is requested that cannot be simply achieved by improving technologies but calls for a shared new vision of common goods.Within such a complex and holistic problem, the role of satellite microwave remote sensing to observe marine ecosystem and to assist a sustainable development of human activities must be considered. In such a view the paper is meant. Accordingly, the key microwave sensor technologies are reviewed paying particular emphasis on those applications that can provide effective support to pursue some of the UN Sustainable Development Goals. Three meaningful sectors are showcased:oil and gas, where microwave sensors can provide continuous fine-resolution monitoring of critical infrastructures; renewable energy, where microwave satellite remote sensing allows supporting the management of offshore wind farms during both feasibility and operational stages; plastic pollution, where microwave technologies that exploit signals of opportunity offer large-scale monitoring capability to provide marine litter maps of the oceans.

This study focuses on a very complex environment, namely the Ionian coast of the Basilicata region, Southern Italy, which includes different kinds of beaches, river mouths and built-up areas. This complex environment is used as a test case to analyze the time variability of the coastline using measurements that were remotely sensed by the satellite European Copernicus Synthetic Aperture Radar (SAR) mission. First, the accuracy of the coastline, extracted by the SAR, is discussed with respect to finer-spatial-resolution drone-based light detection and ranging (LIDAR) measurements. Then, a time series of SAR dual-polarimetric measurements acquired by the European Copernicus mission is used to discuss the time variability of the coastline of the area of interest in a time period spanning from 2015 to 2021. The experimental results show that the accuracy of the SAR-based coastline is better than 15 m, which is reasonably good precision for monitoring the erosion/accretion processes that characterize the area of interest at a moderate scale. The estimated time variability of the extracted coastline suggests a dominant erosion process, which is always within 60 m.

This study investigates the extra benefit provided by the joint use of synthetic aperture radar (SAR) polarimetric diversity and ascending/descending orbit passes to quantify postearthquake damage that occurred over the area of Amatrice, an Italian city significantly damaged by the 2016 Central Italy earthquake. First, the sensitivity of PolSAR features derived from SAR scenes collected under ascending/descending orbits to the damage is investigated. Then, the damage assessment is performed using a processing chain that consists of extracting dual-polarimetric SAR features to detect damage and, then, applying a fuzzy clustering scheme, to partition the feature outputs into damage levels. This processing chain is first separately applied to Sentinel-1 SAR scenes collected under ascending/descending orbits; then the processing outputs are merged using two different approaches. To discuss the quality of the estimated damage maps, ground information collected by surveys performed by a trained team is used. Experimental results show that the joint use of ascending/descending orbit passes improves the estimation of damage levels (up to 78$\%$) with respect to the estimation performed using the orbiting passes separately.

In this study, sea surface scattering with and without surfactants is predicted using the two-scale boundary perturbation model (BPM) and the advanced integral equation model (AIEM) augmented with two different damping models, i.e., the Marangoni one and the model of local balance (MLB). Numerical predictions are showcased for both mineral oil and biogenic slicks. They are contrasted with actual satellite Synthetic Aperture Radar (SAR) measurements collected at X-band by the German TerraSAR-X sensor over mineral oil and plant oil slicks of known origin. Experimental results show that the two-scale BPM augmented with the Marangoni damping model is more suitable for predicting the normalized radar cross section and the damping ratio of plant oil (biogenic) slicks. In contrast, the AIEM combined with the damping MLB results in a better agreement with SAR measurements collected over mineral oil slicks.

The COVID-19 pandemic has affected human life worldwide and forced the implementation of lockdown periods in order to reduce the physical interactions of people. Italy has been heavily affected by a large number of deaths. The government had to impose a long lockdown period during the spring of 2020. In this paper, we provide a critical analysis of the 2020 Italian spring lockdown (ISL) through observation of the spatiotemporal NOx pattern differences in connection with the social changes imposed by such restrictions. Different freely available sources of information are used: European Space Agency (ESA) TROPOspheric Monitoring Instrument (TROPOMI) measurements, ground-based measurements, and model estimates. The results show high NOx pollution levels, even during the lockdown, that suggest new approaches to sustainable mobility policies.

This study deals with coastline extraction using multipolarization spaceborne synthetic aperture radar (SAR) imagery acquired over coastal intertidal areas. The latter are very challenging environments where mud flats lead to a large variability of normalized radar cross section, which may trigger a significant number of false edges during the extraction process. The performance of SAR-based coastline extraction methods that rely on a joint combination of multipolarization information (either single- or dual-polarization metrics) and speckle filtering (either local and nonlocal approaches) are analyzed using global positioning system (GPS) samples and colocated SAR imagery collected under different incidence angles. Our test site is an intertidal zone with a wetland (i.e., salt marsh) in the Solway Firth, south-west along the Scottish-English border. Experimental results, obtained processing a pair of RadarSAT-2 full-polarimetric and a pair of Sentinel-1 dual-polarimetric SAR imagery augmented by colocated GPS samples, show that: first, the multipolarization information outperforms the single-polarization counterpart in terms of extraction accuracy; second, among the single-polarization channels, the cross-polarized one performs best; third, both single- and dual-polarization methods perform better when nonlocal speckle filtering is applied; fourth, the joint combination of nonlocal speckle filter and dual-polarization information provides the best accuracy; and finally, the incidence angle plays a role in the extraction accuracy with larger incidence angles resulting in the best performance when dual-polarization metric is used.

In this study, a re-tuned algorithm based on the geophysical model function (GMF) C-SARMOD2 is proposed to retrieve wind speed from Synthetic Aperture Radar (SAR) imagery collected by the Chinese C-band Gaofen-3 (GF-3) SAR. More than 10,000 Vertical-Vertical (VV) and Horizontal-Horizontal (HH) polarization GF-3 images acquired in quad-polarization stripmap (QPS) and wave (WV) modes have been collected during the last three years, in which wind patterns are observed over open seas with incidence angles ranging from 18° to 52°. These images, collocated with wind vectors from the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis at 0.125° resolution, are used to re-tune the C-SARMOD2 algorithm to specialize it for the GF-3 SAR (CSARMOD-GF). In particular, the CSARMOD-GF performs differently from the C-SARMOD2 at low-to-moderate incidence angles smaller than about 34°. Comparisons with wind speed data from the Advanced Scatterometer (ASCAT), Chinese Haiyang-2B (HY-2B) and buoys from the National Data Buoy Center (NDBC) show that the root-mean-square error (RMSE) of the retrieved wind speed is approximately 1.8 m/s. Additionally, the CSARMOD-GF algorithm outperforms three state-of-the-art methods – C-SARMOD, C-SARMOD2, and CMOD7 – that, when applied to GF-3 SAR imagery, generating a RMSE of approximately 2.0–2.4 m/s.

In this article, a two-year time-series of multipolarization Sentinel-1 synthetic aperture radar (SAR) imagery is exploited to analyze the changes in the water-covered area of the Monte Cotugno (Italy) reservoir. A two-step processing chain, which includes water/land separation and waterline extraction, is proposed to accomplish this task. Experimental results, verified using independent in situ measurements, demonstrate that: first, Sentinel-1 time series can be successfully used to support the smart water management of reservoirs. In fact, the changes in the water-covered area inferred from the SAR time series agree with the seasonal behavior and they also fit anomalies; and second, multipolarization feature outperforms single-polarization ones in terms of accuracy of the extracted waterline profile.

In this article, apolarimetric synthetic aperture radar (PolSAR) feature is analyzed to discriminate among different mangrove species. This feature, which is related to the Wishart distance, maximizes the contrast among mangrove species optimizing the ratio between quadratic forms. The discrimination performance is assessed both against ground truth and by intercomparing it with conventional model-based decomposition features. Results obtained by processing actual L- and C-band full-polarimetric synthetic aperture radar scenes collected by ALOS-PALSAR-2 and RADARSAT-2 missions show that the proposed approach achieves accurate enough discrimination performance to differentiate two out of the four mangrove species. In addition, results suggest using a multifrequency PolSAR approach to maximize discrimination performance.

To monitor polar regions is of paramount importance for climatological studies. Climate change due to anthropogenic activities is inducing global warming that, for example, has resulted in glacier melting. This has had a significant impact on sea levels and ocean circulation. In this study, the temporal trend of the marine-terminated d’Iberville glacier (Ellesmere Island, Canada) is analysed using C-band synthetic aperture radar satellite imagery collected by the Radarsat-2 and Sentinel-1 missions. The data set consists of a time series of 10 synthetic aperture radar data collected from 2010 to 2022 in dual-polarimetric imaging mode, where a horizontally polarised electromagnetic wave was transmitted. An automatic approach based on a global threshold constant false alarm rate method is applied to the single- and dual-polarisation features, namely the HH-polarised normalised radar cross-section and a combination of the HH- and HV-polarised scattering amplitudes, with the aim of extracting the ice front of the glacier and, therefore, estimating its behaviour over time. Independent collocated satellite optical imagery from the Sentinel-2 multi-spectral instrument is also considered, where available, to support the experimental outcomes. The experimental results show that (1) the HH-polarised normalised radar cross-section achieved better performance with respect to the dual-polarised feature, especially under the most challenging case of a sea-ice infested sea surface; (2) when the HH-polarised normalised radar cross-section was considered, the ice front extraction methodology provided a satisfactory accuracy, i.e., a root mean square error spanning from about 1.1 pixels to 3.4 pixels, depending on the sea-surface conditions; and (3) the d’Iberville glacier exhibited, during the study period, a significant retreat whose average surface velocity was 160 m per year, resulting in a net ice area loss of 2.2 km2 (0.18 km2 per year). These outcomes demonstrate that the d’Iberville glacier is behaving as most of the marine-terminated glaciers in the study area while experiencing a larger ice loss.

The monitoring of ships is of paramount importance for ocean and coastal area surveillance. The synthetic aperture radar is shown to be a key sensor to provide effective and continuous observation of ships due to its unique imaging capabilities. When advanced synthetic aperture radar imaging systems are considered, the full scattering information is available that was demonstrated to be beneficial in developing improved ship detection and classification algorithms. Nonetheless, the capability of polarimetric synthetic aperture radar to observe marine vessels is significantly affected by several imaging and environmental parameters, including the incidence angle. Nonetheless, how changes in the incidence angle affect the scattering of ships still needs to be further investigated since only a sparse analysis, i.e., on different kinds of ships of different sizes observed at multiple incidence angles, has been performed. Hence, in this study, for the first time, the polarimetric scattering of the same ship, i.e., a small fishing trawler, which is imaged multiple times under the same sea state conditions but in a wide range of incidence angles, is analysed. This unique opportunity is provided by a premium L-band UAVSAR airborne dataset that consists of five full-polarimetric synthetic aperture radar scenes collected in the Gulf of Mexico. Experimental results highlight the key role played by the incidence angle on both coherent, i.e., co-polarisation signature and pedestal height, and incoherent, i.e., multi-polarisation and total backscattering power, polarimetric scattering descriptors. Experimental results show that: (1) the polarised scattering component is more sensitive to the incidence angle with respect to the unpolarised one; (2) the co-polarised channel under horizontal polarisation dominated the polarimetric backscattering from the fishing trawler at lower angles of incidence, while both co-polarised channels contribute to the polarimetric backscattering at higher incidence angles; (3) the HV polarisation provides the largest target-to-clutter ratio at lower incidence angles, while the HH polarisation should be preferred at higher angles of incidence.

In this study, the enhancement of the spatial resolution of microwave radiometer measurements is addressed by contrasting the accuracy of a gradient-like antenna pattern deconvolution method with its accelerated versions. The latter are methods that allow reaching a given accuracy with a reduced number of iterations. The analysis points out that accelerated methods result in improved performance when dealing with spot-like discontinuities; while they perform in a similar way to the canonical gradient method in case of large discontinuities. A key application of such techniques is the research on global warming and climate change, which has recently gained critical importance in many scientific fields, mainly due to the huge societal and economic impact of such topics over the entire planet. In this context, the availability of reliable long time series of remotely sensed Earth data is of paramount importance to identify and study climate trends. Such data can be obtained by large-scale sensors, with the obvious drawback of a poor spatial resolution that strongly limits their applicability in regional studies. Iterative gradient techniques allow obtaining super-resolution gridded passive microwave products that can be used in long time series of consistently calibrated brightness temperature maps in support of climate studies.

Sea waves constitute a natural phenomenon with a great impact on human activities, and their monitoring is essential for meteorology, coastal safety, navigation, and renewable energy from the sea. Therefore, the main measurement techniques for their monitoring are here reviewed, including buoys, satellite observation, coastal radars, shipboard observation, and microseism analysis. For each technique, the measurement principle is briefly recalled, the degree of development is outlined, and trends are prospected. The complementarity of such techniques is also highlighted, and the need for further integration in local and global networks is stressed.

Satellite synthetic aperture radar (SAR) is a unique tool to collect measurements over sea surface but the physical interpretation of such data is not always straightforward. Among the different sea targets of interest, low-backscattering areas are often associated to marine oil pollution even if several physical phenomena may also result in low-backscattering patches at sea. In this study, the effects of low-backscattering areas of anthropogenic and natural origin on the azimuth autocorrelation function (AACF) are analyzed using VV-polarized SAR measurements. Two objective metrics are introduced to quantify the deviation of the AACF evaluated over low-backscattering areas with reference to slick-free sea surface. Experiments, undertaken on six Sentinel-1 SAR scenes, collected in Interferometric Wide Swath VV+VH imaging mode over large low-backscattering areas of different origin under low-to-moderate wind conditions (speed ≤ 7 m/s), spanning a wide range of incidence angles (from about 30° up to 46°), demonstrated that the AACF evaluated within low-backscattering sea areas remarkably deviates from the slick-free sea surface one and the largest deviation is observed over oil slicks.

This study aimed at analyzing the effect of Synthetic Aperture Radar (SAR) imaging parameters and environmental conditions on the standard deviation of the co-polarized phase difference (sjC ) evaluated over sea surface[...]

Oil spills and slicks occur in the ocean around the world due to natural seeps, oil extraction, transportation, and consumption. Satellite synthetic aperture radar (SAR) has proven to be an efficient tool for identifying and classifying oil on the sea surface. This information can be used to monitor areas for potential illegal marine discharge or to respond to an oil spill incident. When used to monitor shipping lanes or drilling platforms, timely analysis can identify offending parties and lead to prosecution. Following an oil spill such as that from the Deepwater Horizon rig in the Gulf of Mexico in 2010, SAR can be used to direct response activities and optimize available resources.