Offshore Metallic Platforms Observation Using Dual-Polarimetric TS-X/TD-X Satellite Imagery: A Case Study in the Gulf of Mexico

Satellite-based synthetic aperture radar (SAR) has been proven to be an effective tool for ship monitoring. Offshore platforms monitoring is a key topic for both safety and security of the maritime domain. However, the scientific literature oriented to the observation of offshore platforms using SAR imagery is very limited. This study is mostly focused on the analysis and understanding of the multipolarization behavior of platforms’ backscattering using dual-polarization X-band SAR imagery. This study is motivated by the fact that under low incidence angle and moderate wind conditions, copolarized channels may fail in detecting offshore platforms even when fine-resolution imagery is considered. This behavior has been observed on both medium- and high-resolution TerraSAR-X/TanDEM-X SAR imagery, despite the fact that platforms consist of large metallic structures. Hence, a simple multipolarization model is proposed to analyze the platform backscattering. Model predictions are verified on TerraSAR-X/TanDEM-X SAR imagery, showing that for acquisitions under low incidence angle, the platforms result in a reduced copolarized backscattered intensity even when fine resolution imagery is considered. Finally, several solutions to tackle this issue are proposed with concluding remark that the performance of offshore observation

Editorial for the Special Issue "Remote Sensing of Target Detection in Marine Environment"

First paragraph: Remote sensing is a powerful tool used to obtain an unprecedented amount of information about the ocean from a distance, usually from satellites or aircrafts. Measurements collected by active and passive remote sensing instruments can be used for both marine and maritime applications. They allow monitoring of vast areas of the Earth that are difficult to access and sample using traditional methods. Within this context, the observation of targets at sea, e.g.; man-made targets (ships or oil/gas rigs/platforms and wind turbines) and natural targets (icebergs, surfactants, etc.) is nowadays a very hot-topic in the field of global monitoring of environment and security

Exploring The Use Of SAR Remote Sensing To Detect Microplastics Pollution In The Oceans

The increase in plastic pollution is advancing micro level pollution and the total weight of microplastics (8 tons and in the North Atlantic as 10.4 x 108 tons. The plastic in marine environment will eventually degrade and it will be promptly colonized by bacteria releasing surfactants. Such surfactants will have the effect of damping the capillary and small gravitational waves on the ocean surface. Since SAR is sensitive to roughness induced by capillary waves, it may be exploited to detect bacterial activities related to plastic pollution. In this work we used Sentinel-1A and COSMO SkyMed radar images acquired in the Atlantic and Pacific gyres to detect surfactants that may be associated to plastic pollution. We are using SAR, because the damping properties of surfactants produce dark areas in images. Since area of low backscattering in SAR images could also be produced by other oceanographic/meteorological event, we exploited geophysical remote sensing products associated to time and locations synchronised to SAR acquisitions. Among other products we considered sea surface temperature, surface wind, chlorophyll, surface reflectance, turbidity and wave heights. Additionally we made sure that the areas were not within busy shipping routes. The result of the analysis is that, including effects due to colocation errors of SAR and meteorological data, we could identify a large amount of linear slicks in SAR images that were not directly related to apparent meteorological conditions. Such slicks in the gyres have the appearance of oil slicks, however in some areas they are in large amount and they are not connected to large ship traffic. At the moment these slicks seems to only be visible when the wind conditions are moderate (e.g. 6m/s) as it happen for ordinary oil slicks. Besides the work on radar data, we are making controlled experiments with micro-plastic pollution in sea water, to understand the amount and type of surfactants produced by microbes colonising plastics. The conclusion of our study is that radar remote sensing has the potential to detect plastic pollution areas under sorter meteorological conditions

Detecting microplastics pollution in world oceans using SAR remote sensing

Plastic pollution in the world’s oceans is estimated to have reached 270.000 tones, or 5.25 trillion pieces. This plastic is now ubiquitous, however due to ocean circulation patterns, it accumulates in the ocean gyres, creating “garbage patches”. This plastic debris is colonized by microorganisms which create unique bio-film ecosystems. Microbial colonization is the first step towards disintegration and degradation of plastic materials: a process that releases metabolic by-products from energy synthesis. These by-products include the release of short-chain and more complex carbon molecules in the form of surfactants, which we hypothesize will affect the fluid dynamic properties of waves (change in viscosity and surface tension) and make them detectable by SAR sensor. In this study we used Sentinel-1A and COSMO-SkyMed SAR images in selected sites of both the North Pacific and North Atlantic oceans, close to ocean gyres and away from coastal interference. Together with SAR processing we conducted contextual analysis, using ocean geophysical products of the sea surface temperature, surface wind, chlorophyll, wave heights and wave spectrum of the ocean surface. In addition, we started experiments under controlled conditions to test the behaviour of microbes colonizing the two most common pollutants, polyethylene (PE) and polyethylene terephthalate (PET) microplastics. The analysis of SAR images has shown that a combination of surface wind speed and Langmuir cells- ocean circulation pattern is the main controlling factor in creating the distinct appearance of the sea-slicks and microbial bio-films. The preliminary conclusion of our study is that SAR remote sensing may be able to detect plastic pollution in the open oceans and this method can be extended to other areas

Detection of Wind Turbines in Intertidal Areas Using SAR Polarimetry

The detection of wind turbines in a strong clutter background is analyzed at variance of polarimetric synthetic-aperture radar (SAR) configurations. The area of interest is the intertidal zone near Jiangsu, China and two detectors are used, the polarimetric notch filter (PNF) and a change detector that optimizes the ratio between covariance matrices. The detection performance is quantitatively analyzed using the receiver operating characteristic (ROC) curve, while the scattering mechanisms that characterize wind turbines are analyzed using the Yamaguchi decomposition. Experimental analysis shows that: 1) wind turbines result in a nontrivial scattering mechanism and 2) full-polarimetric measurements achieve the best detection performance independently of the two detectors

Wind speed retrieval from the Gaofen-3 synthetic aperture radar for VV- and HH-polarization using a re-tuned algorithm

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

Detecting aquaculture platforms using COSMO SkyMed

Aquaculture are a very valuable asset for many coastal countries and in the future they will play an important role in food security. Satellite remote sensing can improve the temporal and geo-spatial analysis of such marine facilities. Detecting platforms used for fish and shellfish farming provides a way to monitor assets and check they do not get damaged by storms. It also allows to identify illegal placement of structures in areas which should not host farms. In this work, we want to evaluate the use of COMSO SkyMed polarimetric acquisitions. In particular, we want to use a novel methodology called intensity Dual-Pol Ratio Anomaly Detector (iDPolRAD). Extensive work has been carried out on detecting ships using space-borne Synthetic Aperture Radar (SAR) systems. However, the identification of smaller and non-metallic targets is still challenging especially when the sea conditions are rough. This work presents an assessment of different detectors and polarimetric information for the detection of wooden mussels platforms. The results show that the use of dual polarimetric information can improve the detection performance. © VDE VERLAG GMBH Berlin Offenbac

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

A dual-polarimetric approach to earthquake damage assessment

In this study, a novel physical approach is proposed to detect damages due to earthquakes using dual polarimetric (DP) coherent Synthetic Aperture Radar (SAR) imagery. An optimization method, aimed at enhancing scattering basis differences between measurements collected before and after the event, is designed exploiting Lagrange optimization of the difference between two polarimetric covariance matrices. A meaningful showcase is presented to demonstrate the soundness of the proposed approach that consists of processing Sentinel–1 C–band scenes related to 2016 Central Italy Earthquake. The proposed approach, which is contrasted with the conventional coherence based single– and dual–polarization approaches, results in the best sensitivity to damages

On the Ability of PolSAR Measurements to Discriminate Among Mangrove Species

In this article, a polarimetric 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