Revisit of a Case Study of Spilled Oil Slicks Caused by the Sanchi Accident (2018) in the East China Sea

Journal of Marine Science and Engineering

Article Revisit of a Case Study of Spilled Oil Slicks Caused by the Sanchi Accident (2018) in the East China Sea

Zhehao Yang 1, Weizeng Shao 2,3,* , Yuyi Hu 1, Qiyan Ji 1,*, Huan Li 4 and Wei Zhou 5

1 Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China; [email protected] (Z.Y.); [email protected] (Y.H.) 2 College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China 3 National Satellite Ocean Application Service, Beijing 100081, China 4 National Marine Data and Information Service, Tianjin 300171, China; [email protected] 5 South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China; [email protected] * Correspondence: [email protected] (Q.J.); [email protected] (W.S.); Tel.: +86-0580-2550-753 (W.S.)

Abstract: Marine oil spills occur suddenly and pose a serious threat to ecosystems in coastal waters. Oil spills continuously affect the ocean environment for years. In this study, the oil spill caused by the accident of the Sanchi ship (2018) in the East China Sea was hindcast simulated using the oil particle-tracing method. Sea-surface winds from the European Centre for Medium-Range Weather Forecasts (ECMWF), currents simulated from the Finite-Volume Community Ocean Model (FVCOM), and waves simulated from the Simulating WAves Nearshore (SWAN) were employed as background marine dynamics ﬁelds. In particular, the oil spill simulation was compared with the detection from Chinese Gaofen-3 (GF-3) synthetic aperture radar (SAR) images. The validation of the SWAN- simulated signiﬁcant wave height (SWH) against measurements from the Jason-2 altimeter showed Citation: Yang, Z.; Shao, W.; Hu, Y.; a 0.58 m root mean square error (RMSE) with a 0.93 correlation (COR). Further, the sea-surface Ji, Q.; Li, H.; Zhou, W. Revisit of a current was compared with that from the National Centers for Environmental Prediction (NCEP) Case Study of Spilled Oil Slicks Climate Forecast System Version 2 (CFSv2), yielding a 0.08 m/s RMSE and a 0.71 COR. Under Caused by the Sanchi Accident (2018) these circumstances, we think the model-simulated sea-surface currents and waves are reliable in the East China Sea. J. Mar. Sci. Eng. for this work. A hindcast simulation of the tracks of oil slicks spilled from the Sanchi shipwreck 2021, 9, 279. https://doi.org/ was conducted during the period of 14–17 January 2018. It was found that the general track of 10.3390/jmse9030279 the simulated oil slicks was consistent with the observations from the collected GF-3 SAR images.

Academic Editor: Merv Fingas However, the details from the GF-3 SAR images were more obvious. The spatial coverage of oil slicks between the SAR-detected and simulated results was about 1 km2. In summary, we conclude that Received: 31 January 2021 combining numerical simulation and SAR remote sensing is a promising technique for real-time oil Accepted: 26 February 2021 spill monitoring and the prediction of oil spreading. Published: 4 March 2021 Keywords: oil slick; Sanchi accident; Gaofen-3; synthetic aperture radar Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. 1. Introduction Marine oil spills are serious marine disasters that occur in marine ports, docks, and oil drilling platforms. Oil spills cause damage to the marine ecological environment and are a direct threat to public safety. Moreover, oil slicks drift with the movements of ocean Copyright: © 2021 by the authors. circulations and ocean waves, causing oil pollution to persist in the ocean for a long time. Licensee MDPI, Basel, Switzerland. In recent years, due to rapid economic development in China, the Chinese demand for This article is an open access article crude oil and refined products is increasing year by year, leading to a high potential risk distributed under the terms and of marine oil spills. On 16 July 2010, an oil spill occurred in Dalian Xingang Port, leading conditions of the Creative Commons to more than 1500 tons of oil spilling into the sea. The oil spill seriously affected the local Attribution (CC BY) license (https:// fisheries, aquaculture, tourism, and shipping industry and caused huge economic losses creativecommons.org/licenses/by/ to Dalian City. Oil slicks spilled from ships account for the major proportion of oil spill 4.0/).

J. Mar. Sci. Eng. 2021, 9, 279. https://doi.org/10.3390/jmse9030279 https://www.mdpi.com/journal/jmse J. Mar. Sci. Eng. 2021, 9, 279 2 of 15

accidents. For example, the pollution in surrounding areas for up to 180 days and more than 50% of bulk oil particles remaining in the ocean after the Sanchi ship accident (2018) in the East China Sea were due to oil slicks [1]. Owing to the frequent occurrence of major oil spill accidents, the development of oil spill monitoring techniques has progressed in the last few decades. It is necessary to promote emergency responses to marine oil spills in China and improve oil slick simulation techniques. Most studies involving spilled oil slick simulations have focused on the development of numerical models [2–5]. Several oil slick prediction models have been developed during the past 20 years and the state of the art in oil slick modeling has been reviewed by various studies [6–8], e.g., Monte Carlo stochastic simulation, Lagrangian transport, and the oil particle-tracing method. Generally, oil transportation is affected by marine environmental dynamic factors as well as the physical and chemical properties of oil, such as weathering [9], evaporation, and emulsification [10]. Compared with the previous algorithms for solving the convection diffusion equation, the oil particle-tracing method can not only solve the deformation and breaking process of oil spill under the influence of marine dynamic environment, but also accurately forecast the expansion process of oil film and the obvious stretch of oil film shape in the wind direction. Therefore, the most widely used oil spill model is based on the particle-tracing method, which simulates the drift and diffusion process of oil slicks in water by dispersing oil into a large number of small oil droplets [11,12]. In principle, the motion of oil particles is expressed by the Lagrange tracing method, and the implementation of this method could directly resolve the physical movement of oil slicks. The advantage of the oil particle-tracing method is that the shape changes and breaking process of oil slicks under the action of complex ocean environments is described while effectively eliminating the numerical divergence problem. However, prior knowledge of the oil particle-tracing method relies on sea-surface dynamics. It is well known that marine dynamic factors, such as sea-surface winds [13], currents [14], and waves [15–17], can be numerically simulated by high-resolution meteorological or hydrodynamic models. With the development of remote-sensing technology, oceanic characteristics such as sea-surface temperature [18], chlorophyll-a [19], and sediment [20] content can be impres- sively imaged through optical satellites, including advanced very high-resolution radiometer (AVHRR) [21] and moderate-resolution imaging spectro-radiometer (MODIS) [22]. Satel- lites carrying microwave sensors, such as altimeters [23], scatterometers [24], and synthetic aperture radar (SAR) [25,26], can quantitatively detect information on ocean dynamics under all types of weather and in real time. In particular, it has been proven that SAR is greatly useful in oil spill monitoring [27–31], especially polarmetric SAR (PolSAR) [32], as well as the X-band marine radar [33] and the GNSS-R technique [34]. As for co-polarized SAR, the constant false alarm rate (CFAR) is a well-known method for oil spill detection [35]; the details are described in the AppendixA. Utilizing amplitude coherence and the co-polarized phase difference (CPD) standard deviation to detect the spilled oil slicks in dual-polarimetric TerraSAR-X imagery is proposed in [36]. After analyzing the backscattering differences between oil slicks and sea clutter, the feature pedestal height is used to observe oil slicks in quad-polarimetric SAR imagery [37]. Different from these two works, in [38], an advanced method for realizing oil spill detection from the compact polarimetric SAR image was recently developed. It is noteworthy that the accuracy of SAR detection for oil slicks relies on prior information about background dynamics to remove false alarms. The efficiency of oil spill modeling based on the particle-tracing method for the case of the Dalian accident in July 2010 has been confirmed [39]. In recent studies of the Sanchi Event (2018), two individual methods, optical/microwave satellite and numerical modeling, have been widely employed [1,40]. In recent decades, these methods have been improved significantly. Therefore, the present work focused on the real-time monitoring and prediction of oil spills using SAR and numerical models, which are sound and can be practically applied for oil spills. It has also been revealed that the prediction accuracy of oil spill transportation depends on simulations from meteorological or hydrodynamic J. Mar. Sci. Eng. 2021, 9, x FOR PEER REVIEW 3 of 15

J. Mar. Sci. Eng. 2021, 9, 279 3 of 15 practically applied for oil spills. It has also been revealed that the prediction accuracy of oil spill transportation depends on simulations from meteorological or hydrodynamic models. As for driving the oil spill model, the sea-surface wind, current, and wave fields models. As for driving the oil spill model, the sea-surface wind, current, and wave fields are are necessary. The oil particle drift mainly depends on sea-surface winds, tides, and cir- necessary. The oil particle drift mainly depends on sea-surface winds, tides, and circulation culation currents [41]. However, Stokes drift transport-induced ocean waves are also an currents [41]. However, Stokes drift transport-induced ocean waves are also an important important factor [42]. In this work, the coastal currents and waves associated with sea- factor [42]. In this work, the coastal currents and waves associated with sea-surface surface wind were fully considered. The European Centre for Medium-Range Weather wind were fully considered. The European Centre for Medium-Range Weather Forecasts Forecasts (ECMWF) winds were directly employed, which have a good performance com- (ECMWF) winds were directly employed, which have a good performance compared pared to moored measurements and satellite observations [43]. The ocean currents and to moored measurements and satellite observations [43]. The ocean currents and waves waveswere simulatedwere simulated using using the Finite-Volume the Finite-Volume Community Community Ocean Ocean Model Model (FVCOM) (FVCOM) [44] [44] and andthe Simulatingthe Simulating WAves WAves Nearshore Nearshore (SWAN) (SWAN) [45] model, [45] model, respectively, respectively, which which have a have unique a uniqueunstructured unstructured grid. grid. TheThe rest rest of of this this paper paper is isorganized organized as as follows. follows. The The data data-set-set collection collection is described is described in Sectionin Section 2, e.g.,2, e.g., the theforcing forcing fields fields and and open open boundary boundary data data for modeling. for modeling. In particular, In particular, the fourthe fourChinese Chinese Gaofen Gaofen-3-3 (GF-3) (GF-3) SAR SARimages images acquired acquired during during the period the period of the ofSanchi the Sanchi acci- dentaccident event event were werecollected collected for this for study. this study. The method The method for spilled for spilled oil slick oil simulation slick simulation using theusing particle the particle-tracing-tracing model modelis introduced is introduced in Section in Section 3. Also,3. Also,the settings the settings for the for SWAN the SWAN and FVCOMand FVCOM model model are briefly are briefly described, described, and and the the validations validations of of se sea-surfacea-surface currents currents and and waveswaves are are presen presented.ted. Moreover, Moreover, the the oil spill oil spill simulations simulations are compared are compared with the with GF the-3 SAR GF-3- detectedSAR-detected results. results. The conclusions The conclusions are summarized are summarized in Section in Section 4. 4.

2.2. Dataset Dataset Collection Collection AtAt 15:51 15:51 UTC UTC on on 6 6 January January 2018, 2018, the the oil oil tanker tanker called called Sanchi, Sanchi, carrying carrying more more than than 100,000100,000 tons tons of of o oilil and and natural natural gas, gas, suddenly suddenly collided collided with with a a cargo cargo ship ship called called CF CF Crystal Crystal inin the the Zhoushan Zhoushan islands islands of of the the E Eastast China China Sea Sea [46], [46], which which is is at at the the mouth mouth of of the the Yan Yangtzegtze EstuaryEstuary and and Hangzhou Hangzhou Bay Bay in in China. China. Together Together with with the the collision, collision, a a large large amount amount of of spilled spilled oiloil floated ﬂoated on on the the sea sea surface surface and and spread spread as slicks. as slicks. The Thegeographic geographic map mapof the of spatial the spatial coveragecoverage of the of the Sanchi Sanchi event event is shown is shown in in Figure Figure 1,1 , in in which which the the unstructur unstructureded grids are uniquelyuniquely used used for for sea sea-surface-surface current current and and wave wave simulations simulations using using FVCOM FVCOM and and SWAN, SWAN, resprespectively.ectively. The The tidal tidal current current is is relatively relatively strong strong between between Hangzhou Hangzhou Bay Bay and and the the island island chainschains due due to to the the trumpet trumpet terrain. terrain. Moreover, Moreover, it has it has been been found found that that there there are area series a series of sofmaller smaller islands, islands, resulting resulting in the in therapid rapid change change of wave of wave properties. properties. These These backgrou backgroundnd dy- namicdynamic features features of the of theregional regional marine marine environment environment cause cause difficulties difﬁculties in the in theprediction prediction of oilof- oil-slicks.slicks.

FigureFigure 1. 1. ((aa)) The The large large-scale-scale map map of of the the study study area area with with the the depth depth level; level; the the black black rectangle rectangle represents represents the the location location of of (b (b).). ((bb)) The The geographic geographic map of the spatial coveragecoverage ofof thethe SanchiSanchi accident, accident, in in which which the the unstructured unstructured grids grids are are uniquely uniquely used used for for sea-surface current and wave simulations using the Finite-Volume Community Ocean Model (FVCOM) and the Sim- sea-surface current and wave simulations using the Finite-Volume Community Ocean Model (FVCOM) and the Simulating ulating WAves Nearshore (SWAN) model, respectively. WAves Nearshore (SWAN) model, respectively.

J. Mar. Sci. Eng. 2021, 9, 279 4 of 15

In this study, the quick-look maps of four GF-3 SAR images during the period of 14–18 January 2018 after the shipwreck were collected, in which the pinkish marks represent the shipwreck positions, as shown in Figure2. These images were acquired in Fine Stripmap (FS) mode with a spatial resolution of 10 m and a swath coverage of 100 km or Stander Stripmap (SS) mode with a spatial resolution of 25 m and a swath coverage of 130 km [47,48]. Generally, oil slicks contaminate the sea surface, where non-Bragg scattering dominates at such area whereas Bragg scattering dominates. In other words, the relatively low backscattering signal in a SAR image, expressed by normalized radar cross section (NRCS), is probably caused by oil slicks [49]. Collectively, the algorithms for the oil spill detection from a SAR image have been well established. The CFAR method is a mature method for co-polarized SAR application. In the collected SAR images, the irregular oil slicks are clearly visible, illustrated by low NRCS in Figure2b to Figure2c, which is useful for confirming the robustness of the spilled oil slick simulation using the oil particle- tracing method. However, other marine dynamics phenomena including eddies, fronts, and up-welling/down-welling can be also featured by low NRCS. As shown in Figure 2a, the cyclonic patterns of low NRCS are probably caused by the background marine dynamics, because the corresponding image was taken on the first day of shipwreck and the quantity of oil slicks are too small to produce the relatively large coverage of ‘low-NRCS’ compared to that on 15 January. In particular, the black oil-like patches with ‘low-NRCS’ in the GF-3 SAR image on 18 January (Figure2d) were far away from shipwreck. Therefore, the images on 15 and 17 January were taken to confirm the oil slick simulation. Since 1979, the ECMWF has continuously provided reanalysis wind data at 10 m above the sea surface with a 0.125◦ grid of spatial resolution at interval of 6 h each day, which is reliable for atmospheric and marine research. In particular, the ECMWF winds are popularly treated as the forcing field for running hydrodynamic models, such as FVCOM [44] for current simulation and SWAN [50] for wave simulation in this study. The case map of ECMWF wind taken at 00:00 on 16 January 2018 is shown in Figure3a. The Climate Forecast System Version 2 (CFSv2) from the National Center of Atmospheric Research (NCAR) provided the sea-surface current data with about 50 km of spatial resolution, which was used for confirming the accuracy of the model-simulated currents from the FVCOM. Figure3b shows the sea-surface current map from CFSv2 taken at 22:00 on 15 January 2018, in which the relatively strong current is clearly visible around the Zhoushan islands in the Yangtze Estuary. Similarly, the wave measurements from the Jason-2 altimeter were collocated with the model-simulated waves from SWAN in January 2018 in order to validate the model-simulated waves, although Jason-measured waves were available at the footprints following the satellite track, as shown in Figure4, in which the pinkish marks represent the shipwreck position, and the black rectangle represents the available measurements for validating the model-simulated SWH from SWAN. J. Mar. Sci. Eng. 2021, 9, x FOR PEER REVIEW 4 of 15

In this study, the quick-look maps of four GF-3 SAR images during the period of 14– 18 January 2018 after the shipwreck were collected, in which the pinkish marks represent the shipwreck positions, as shown in Figure 2. These images were acquired in Fine Strip- map (FS) mode with a spatial resolution of 10 m and a swath coverage of 100 km or Stander Stripmap (SS) mode with a spatial resolution of 25 m and a swath coverage of 130 km [47,48]. Generally, oil slicks contaminate the sea surface, where non-Bragg scattering dominates at such area whereas Bragg scattering dominates. In other words, the relatively low backscattering signal in a SAR image, expressed by normalized radar cross section (NRCS), is probably caused by oil slicks [49]. Collectively, the algorithms for the oil spill detection from a SAR image have been well established. The CFAR method is a mature method for co-polarized SAR application. In the collected SAR images, the irregular oil slicks are clearly visible, illustrated by low NRCS in Figure 2b to 2c, which is useful for confirming the robustness of the spilled oil slick simulation using the oil particle-tracing method. However, other marine dynamics phenomena including eddies, fronts, and up- welling/down-welling can be also featured by low NRCS. As shown in Figure 2a, the cyclonic patterns of low NRCS are probably caused by the background marine dynamics, because the corresponding image was taken on the first day of shipwreck and the quantity of oil slicks are too small to produce the relatively large coverage of ‘low-NRCS’ compared J. Mar. Sci. Eng. 2021, 9, 279 to that on 15 January. In particular, the black oil-like patches with ‘low-NRCS’ in the5 ofGF 15- 3 SAR image on 18 January (Figure 2d) were far away from shipwreck. Therefore, the images on 15 and 17 January were taken to confirm the oil slick simulation.

J. Mar. Sci. Eng. 2021, 9, x FOR PEER REVIEW 5 of 15

Since 1979, the ECMWF has continuously provided reanalysis wind data at 10 m above the sea surface with a 0.125° grid of spatial resolution at interval of 6 h each day, which is reliable for atmospheric and marine research. In particular, the ECMWF winds are popularly treated as the forcing field for running hydrodynamic models, such as FVCOM [44] for current simulation and SWAN [50] for wave simulation in this study. The case map of ECMWF wind taken at 00:00 on 16 January 2018 is shown in Figure 3a. The Climate Forecast System Version 2 (CFSv2) from the National Center of Atmospheric Re- search (NCAR) provided the sea-surface current data with about 50 km of spatial resolution, which was used for confirming the accuracy of the model-simulated currents from the FVCOM. Figure 3b shows the sea-surface current map from CFSv2 taken at 22:00 on 15 January 2018, in which the relatively strong current is clearly visible around the Zhoushan islandsFFigureigure in the 2. TheYangtze quick quick-look Estuary.-look maps Similarly, of four Gaofen-3Gaofen the wave-3 (GF-3)(GF measureme-3) syntheticsyntheticnts apertureaperture from the radar radar Ja- (SAR) (SAR) images images of of the son-2 altimeter weretheSanchi Sanchi collocat shipwreck shipwrecked with in 2018,in the 2018, model in whichin which-simulated the the pinkish pinkish waves marks marks from represent represent SWAN the intheshipwreck January shipwreck positions. positions (.a ()a The) The image taken acquired in Fine Stripmap (FS) mode on 14 January at 22:20; (b) The image taken 2018 in order toimage validate taken the acquired model- insimulated Fine Stripmap waves, (FS) although mode onJason 14 January-measured at 22:20; waves (b ) The image taken acquired in FS mode on 15 January at 21:39; (c) The image taken acquired in Stander Stripmap (SS) were available atacquired the footprints in FS mode following on 15 Januarythe satellite at 21:39; track, (c )as The shown image in taken Figure acquired 4, in which in Stander Stripmap (SS) mode on 17 January at 09:42; and (d) The image taken acquired in SS mode on 18 January at 09:01. the pinkish marksmode repre on 17sent January the shipwreck at 09:42; and position, (d) The and image the taken black acquired rectangle in SS represents mode on 18 January at 09:01. the available measurements for validating the model-simulated SWH from SWAN.

Figure 3. (aF)igure The sea-surface3. (a) The sea wind-surface map wind from map European from European Centre for Centre Medium-Range for Medium Weather-Range Weather Forecasts Fore- (ECMWF) taken at casts (ECMWF) taken at 00:00 on 16 January 2018; (b) The sea-surface current map from Climate 00:00 on 16 January 2018; (b) The sea-surface current map from Climate Forecast System Version 2 (CFSv2) taken at 22:00 on Forecast System Version 2 (CFSv2) taken at 22:00 on 15 January 2018. 15 January 2018.

J. Mar.J. Mar. Sci. Sci. Eng. Eng. 20212021, ,99,, x 279 FOR PEER REVIEW 6 of 15 6 of 15

FigureFigure 4. 4.The The signiﬁcant significant wave wave height height (SWH) (SWH) following following the footprintsthe footprint of thes of Jason-2 the Jason altimeter-2 altimeter in in JanuaryJanuary 2018 2018 overlaid overlaid with with the the water water depth, depth, in which in which the pinkish the pinkish marks marks represent represent the shipwreck the shipwreck position,position, and and the the black black rectangle rectangle represents represents the availablethe available measurements measuremen for validatingts for validating the model- the model- simulatedsimulated SWH SWH from from SWAN. SWAN. 3. Method and Results 3. Method and Results In this section, we present the simulated sea-surface current from the FVCOM model and theIn sea-surfacethis section, wave we present from the the SWAN simulated model. sea The-surface oil spill current simulated from using the FVCOM the oil model particle-tracingand the sea-surface method wave is then from compared the SWAN with SAR-detectedmodel. The oil slicksspill simulated from GF-3 SARusing the oil images.particle-tracing method is then compared with SAR-detected oil slicks from GF-3 SAR images. 3.1. Simulation of Sea-Surface Current 3.1. InSimulation this work, of the Sea FVCOM-Surface model Current is used to simulate the sea-surface current, which follows the principle [44]: In this work, the FVCOM model is used to simulate the sea-surface current, which follows the principle [dV44]: 1 ∂p ∂ ∂V + fV = − + Km + F, (1) dt dV ∂y 1∂z∂퐩 ∂∂z ∂퐕 + f퐕 = − + (Km ) + 퐅, (1) dtdT ∂ ∂T∂y ∂z ∂z = K + F , (2) dt ∂z h ∂z T dT ∂ ∂퐓 dS ∂ = ∂S(Kh ) + 퐅퐓, (2) = Kh∂z +∂F퐳S, (3) dt dt∂z ∂z ρ d=S ρ (T∂, S),∂퐒 (4) = (퐊퐡 ) + 퐅퐒, (3) ∂u dt∂v ∂w∂z ∂퐳 + + = 0 , (5) ∂x ∂y ∂z 훒 = 훒 (퐓, 퐒), (4)

∂u ∂v ∂w + + = 0 , ∂x ∂y ∂z (5) where V is the velocity vector; u, v, and w are the components at x, y, and z coordinates;  is the sea-surface pressure; f is the Coriolis parameter; T is the sea temperature; S is the

J. Mar. Sci. Eng. 2021, 9, 279 7 of 15

J. Mar. Sci. Eng. 2021, 9, x FOR PEER REVIEW 7 of 15

where V is the velocity vector; u, v, and w are the components at x, y, and z coordinates; ρ is the sea-surface pressure; f is the Coriolis parameter; T is the sea temperature; S is the salinity; ρis is the the sea sea water water density; density; Km Kandm and Kh Kareh are the the vertical vertical eddy eddy viscosity viscosity coefficient coefficient and heand thermal he thermal vertical vertical eddy eddy diffusion diffusion coefficient, coefficient, respectively; respectively; FT is F theT is the thermal thermal term; term; and and FS ◦ isFS theis the salt salt diffusion diffusion term. term. The The winds winds from from ECMWF ECMWF with with a 0.125 a 0.125°grid grid at aat time a time interval interval of 6of h 6 were h were the the forcing forcing field field and and the the water water depth depth was was derived derived from from bathymetric bathymetric topography topogra- fromphy from the Generalthe General Bathymetric Bathymetric Chart Chart of theof the Oceans Oceans (GEBCO) (GEBCO) at at a 0.1a 0.1°◦ grid. grid. TheThe openopen boundary conditions included water tide data from TOPX.5; and sea sea-surface-surface t temperature,emperature, sea surfacesurface salinity,salinity, andand seasea surfacesurface currentcurrent valuesvalues fromfrom thethe HYbridHYbrid CoordinateCoordinate OceanOcean Model (HYCOM) with 1/121/12°◦ gridgrid at at a a time time interv intervalal of of 1 1 h h,, as as described described in in [14]. [14]. To confirmconfirm the the applicability applicability of of the the simulations, simulations, FVCOM-modeled FVCOM-modeled sea-surface sea-surface currents cur- wererents comparedwere compared with thosewith those from fro NCEPm NCEP CFSv2. CFSv2. As an As example, an example, Figure Figure5a shows 5a shows the sea- the surfacesea-surface current current map frommap FVCOMfrom FVCOM taken ontaken 14 January on 14 January at 22:30, at in 22:30, which in the which black rectanglethe black representsrectangle represents the spatial the coverage spatial coverage of the corresponding of the corresponding GF-3 SAR GF image-3 SAR and image the pinkishand the markspinkish represent marks represent the shipwreck the shipwreck position. position. Although Although the sea-surface the sea current-surface speed current was speed less thanwas less 1 m/s, than the 1 m/s, distribution the distribution of sea-surface of sea-surface current current was complex. was complex. Figure Figure3b shows 3b shows the comparisonthe comparison of available of available matchups matchups between between sea-surface sea-surface current current speeds speeds simulated simulated by the by FVCOMthe FVCOM and and the the CFSv2 CFSv2 data data in in the the black black rectangle rectangle in in Figure Figure3. 3. Generally, Generally, it it was was also also found that that the the model-simulated model-simulated sea-surface sea-surface current speeds were were less less than than those those from from CFSv2, which was supposedly caused by the underestimation of the ECMWF winds [13]. [13]. However, the statistical statistical analysi analysiss yielded yielded a a0.08 0.08 m/s m/s root root mean mean square square (RMSE) (RMSE) with with a 0.71 a 0.71correlation correlation (COR), (COR), indicating indicating that that the the sea sea-current-current simulated simulated from from FVCOM FVCOM was suitable for conductingconducting thethe spilledspilled oiloil slickslick hindcastinghindcasting forfor thethe SanchiSanchi event.event.

FigureFigure 5.5. ((aa)) TheThe sea-surfacesea-surface current current map map from from FVCOM FVCOM taken taken on on 14 14 January January at 22:30,at 22:30, in which in which the the black black rectangle rectangle represents repre- thesents spatial the spatial coverage coverage of the correspondingof the corresponding GF-3 SAR GF- image3 SAR andimage the and pinkish the pinkish marks representmarks represent the shipwreck the shipwreck position; position (b) The; (b) The comparison between sea-surface current speeds simulated by the FVCOM and the CFSv2 data for a 0.03-m/s bin, comparison between sea-surface current speeds simulated by the FVCOM and the CFSv2 data for a 0.03-m/s bin, in which in which the error bars represent the standard deviations of each bin for the matchups in the black rectangle in Figure 3. the error bars represent the standard deviations of each bin for the matchups in the black rectangle in Figure3. 3.2. Simulation of Sea-Surface Wave 3.2. SimulationIt is well known of Sea-Surface that the Wave SWAN model has a good performance for sea-surface wave simulationIt is well in coastal known waters. that the The SWAN governing model hasequation a good of performancethe SWAN model for sea-surface is expressed wave as: simulation in coastal waters.D The governing equation of the SWAN model is expressed as: (N(k,θ;x,t)) = 퐒퐢퐧 + 퐒퐛퐨퐭 + 퐒퐧퐥 + 퐒퐭퐪 + 퐒퐝퐛, (6) D Dt (N(k, θ; x, t)) = S + S + S + S + S , (6) where N is the waveDt-density spectrum; k inis the botwave number;nl tq θ isdb the wave-propagation direction; x is the spatial vector; t is the temporary coordinate; Sin represents the wind-

J. Mar. Sci. Eng. 2021, 9, 279 8 of 15

J. Mar. Sci. Eng. 2021, 9, x FOR PEER REVIEW 8 of 15

where N is the wave-density spectrum; k is the wave number; θ is the wave-propagation induceddirection; wave x is growth; the spatial Sbot vector; represents t is thethe temporarybottom-induced coordinate; friction;S inSnlrepresents represents the the wind- non- linearinduced wave wave–wave growth; interactionSbot represents term; Stq the represents bottom-induced the three friction;- (triad)S nlandrepresents four-wave the com- non- ponentslinear wave–wave (quadruplets) interaction of the wave term;–Swavetq represents interactions; the three- and S (triad)db represents and four-wave dissipation compo- in- ducednents (quadruplets)by wave breaking. of the The wave–wave parametric interactions; schemes follow and Sdb [50represents]: wave interactions dissipation (QUAD- induced rupl)by wave; triad breaking. wave–wave The interactions parametric schemes(TRIad); followwave breaking [50]: wave (WESTHuysen); interactions (QUADrupl); and bottom frictiontriad wave–wave (JONWAP) interactions. ECMWF winds (TRIad); with wave a 0.125° breaking grid at (WESTHuysen); a time interval andof 6 bottomh were treated friction ◦ as(JONWAP). a forcing field ECMWF and windsthe global with bathymetric a 0.125 grid topography at a time interval from GEBCO of 6 h werewith treateda 0.1° grid as a ◦ wasforcing taken. ﬁeld The and outputs the global were bathymetric 0.1° grids with topography a 30-min fromtemporal GEBCO resolution. with a 0.1In particular,grid was ◦ astaken. propose Thed outputs in [14], werelarge 0.1 changesgrids in with sea a water 30-min level temporal play an resolution. important In role particular, in wave as sim- pro- ulation,posed in as [14 much], large as changes−0.5 m at in the sea low water sea level state play around an important Zhoushan role islands. in wave Therefore simulation,, the seaas much water as level−0.5 simulated m at the low from sea FVCOM state around was Zhoushan included islands.in the wave Therefore, simulation the sea by water the SWANlevel simulated model. from FVCOM was included in the wave simulation by the SWAN model. AccordingAccording to to the the wave wave theory, theory, SWH SWH is isa atypical typical variable variable representing representing the the energy energy of waveof wave fields. ﬁelds. The SWH The SWH map taken map takenon 14 January on 14 January at 22:30 atis presented 22:30 is presented in Figure in6a, Figure in which6a, in which the black rectangle represents the spatial coverage of the corresponding GF-3 SAR the black rectangle represents the spatial coverage of the corresponding GF-3 SAR image image and the pinkish marks represent the shipwreck position. The model-simulated SWH and the pinkish marks represent the shipwreck position. The model-simulated SWH from from SWAN is compared with the collocated measurements from the Jason-2 altimeter, SWAN is compared with the collocated measurements from the Jason-2 altimeter, as as shown in Figure6b. It was found that the RMSE of SWH was 0.58 with a 0.93 COR. shown in Figure 6b. It was found that the RMSE of SWH was 0.58 with a 0.93 COR. Again, Again, the under-estimation of SWH, as SWH greater than 1.5 m, was probably caused by the under-estimation of SWH, as SWH greater than 1.5 m, was probably caused by the the inherent error of ECMWF winds. inherent error of ECMWF winds.

FigureFigure 6. ((aa)) The The model model-simulated-simulated SWH SWH ma mapp from from SWAN SWAN taken taken on on 14 14 January January at at 22:30, 22:30, in in which which the the black rectangle representsrepresents the the spatial spatial co coverageverage of of the the corresponding GF GF-3-3 SAR SAR image image and and the the pinkish pinkish marks marks represent represent the the shipwreck position; (b) The comparison between SWHs simulated by the SWAN and the Jason altimeter data for a 0.3-m bin, in position; (b) The comparison between SWHs simulated by the SWAN and the Jason altimeter data for a 0.3-m bin, in which which the error bars represent the standard deviations of each bin for the matchups in the black rectangle in Figure 3. the error bars represent the standard deviations of each bin for the matchups in the black rectangle in Figure3. 3.3. Simulation of Spilled Oil-Slicks 3.3. SimulationTraditionally, of Spilled the empirical Oil-Slicks formula developed in [51] was adopted to simulate oil slicksTraditionally, spreading: the empirical formula developed in [51] was adopted to simulate oil slicks spreading: 1 S = 1 , (7) S = 4πMN, (7) 4πMN where S is the oil slick area, and M and N are the lengths of the minor and major ellipse axis, respectively, given by:

J. Mar. Sci. Eng. 2021, 9, 279 9 of 15

where S is the oil slick area, and M and N are the lengths of the minor and major ellipse axis, respectively, given by:

1/3 ρw − ρ0 1/3 1/4 M = 1.13 V0 t , (8) ρ0

4/3 1/4 N = M + 0.0034Uwindt , (9)

where ρw is water density, ρ0 is oil density, V0 is the initial volume of spilled oil, t is the time after the oil slick commences spreading (min), and Uwind is wind speed. Let the concentric and similar ellipse, on which the particle is located, have major and minor axes n and m, respectively: m n = . (10) M N If the coordinates of the particle relative to the principal axes of the ellipse are (X,Y), whose x-axis is selected in the direction of the wind, assuming X = ncosθ and Y = mcosθ, in which θ is the wind direction, then the oil particle is displaced outwards with the same elliptical angle, as follows:

X ∆N ∆X = ∆ncosθ = ∆n = X , (11) n N

Y ∆M ∆Y = ∆msinθ = ∆m = Y . (12) m M In this model, the drift and diffusion of spilled oil is solved by tracking a mass of oil particles equivalent to oil slicks. The position of each particle is affected by sea-surface winds, currents, and turbulent dispersion. The forecast model solves the following equation:

dx = V (X , t) + V (X , t), (13) dt A i D i

where VA is advective velocity, VD is diffusive velocity, and Xi is particle position. VA is calculated as the linear combination of current velocity and wind speed as follows:

VA = Vcurrent+CDVwind, (14)

where Vcurrentis the sea-surface current velocity including tidal current, ocean current, and Stokes drift transport-induced waves [52,53], Vwind is the wind speed at 10-m height and CD is the wind drag coefficient. The turbulent diffusive velocity is calculated by Monte Carlo sampling in the range of velocities [-VD,VD], which are proportional to the diffusion coefficients. The velocity fluctuation at each time step ∆t is defined as: r 6D |V | = , (15) D ∆t in which D is the diffusion coefficient and assumed to be 0.75 for this study. In the Sanchi event, the shipwreck began on 14 January, and the total tons of conden- sate oil was about 13,6000 tons. Figure7a shows the spreading track of simulated oil slicks using the oil particle-tracing method during the period 14–15 January 2018, and Figure7b shows the period of 14–17 January 2018.Similarly, the SAR-detected oil slicks from the three collected GF-3 images are presented in Figures8 and9. Although the oil slick simulation was generally consistent with SAR-detected results, the details of spilled oil slicks were clearly extracted from the GF-3 SAR images. A significant portion of the oil slicks are visible in the real GF-3 SAR image on 15 January, which is undetectable by simulations due to inherent error of numeric modeling technique. As shown in Table1, the comparison of spatial coverage between SAR-detected and model-simulated oil slicks indicated that the difference was about 0.5 km2 on 15 January and 1 km2 on 17 January. The results of J. Mar. Sci. Eng. 2021, 9, 279 10 of 15

J. Mar. Sci. Eng. 2021, 9, x FOR PEER REVIEW 10 of 15

this case study were different from the MODIS result in [37] and the 3-D oil spill model in [1]. This study suggests that taking advantage of numerical simulation and SAR is the most effective method for fast oil slick monitoring; for example, the track prediction of oil is the most effective method for fast oil slick monitoring; for example, the track prediction spreading using numerical simulation and spatial distribution of oil slicks detected from of oil spreading using numerical simulation and spatial distribution of oil slicks detected SAR images. from SAR images.

Figure 7. TheFigure spreading 7. The trackspreading of simulated track of oilsimulated slicks using oil slicks the oil using particle-tracing the oil partic methodle-tracing (a) method during the (a) perioddur- from 14 to J. Mar. Sci. Eng. 2021, 9, x FOR PEER REVIEW 11 of 15 15 January 2018ing the (b )period during from the period14 to 15 from January 14 to 2 17018 January (b) during 2018. the period from 14 to 17 January 2018.

Table 1. The comparison of spatial coverage between SAR-detected and model-simulated oil slicks.

SAR Acquisition SAR Oil Model Acquisition Model-Simulated Oil Imaging Time Area Time Area Mode (YYYY-MM-DD) (km2) (YYYY-MM-DD) (km2) FSII 2018-1-15 21:39 83.78 2018-1-15 22:00 82.31 SS 2018- 1-17 09:42 364.70 2018-1-17 10:00 363.56

Figure 8. (a) TheFigure GF-3 8. SAR (a) imageThe GF on-3 15SAR January image 2018 on 15 at January 21:39, in 2018 which at the21:39, pinkish in which marks the represent pinkish marks the shipwreck repre- position, and the sub-scenessent the b and shipwreck c are used position, to extract and spilledthe sub oil-scenes slicks; b and (b) Thec are results used to of extract extracted spilled oil slicksoil slicks; from (b sub-scene) b, The results of extracted oil slicks from sub-scene b, which are marked by black spots; and (c) The which are marked by black spots; and (c) The results of extracted oil-slicks from sub-scene c. results of extracted oil-slicks from sub-scene c.

Figure 9. (a) The GF-3 SAR image on 17 January 2018 at 09:01, in which the pinkish marks represent the shipwreck position, and sub-scenes b through g are used to extract spilled oil slicks; and (b) to (g) The results of extracted oil-slicks from sub-scenes b to g, which are marked by black spots.

4. Conclusions With the promotion of offshore oil exploration and the requirements of the oil industry, the risk of oil spill accidents in coastal areas is increasing. The rising risk is especially severe in busy ports due to the fact that 90% of international trade is undertaken by shipping. In recent years, oil spill accidents in the Gulf of Mexico have caused widespread

J. Mar. Sci. Eng. 2021, 9, x FOR PEER REVIEW 11 of 15

Figure 8. (a) The GF-3 SAR image on 15 January 2018 at 21:39, in which the pinkish marks J. Mar. Sci. Eng. 2021,represent9, 279 the shipwreck position, and the sub-scenes b and c are used to extract spilled oil slicks; 11 of 15 (b) The results of extracted oil slicks from sub-scene b, which are marked by black spots; and (c) The results of extracted oil-slicks from sub-scene c.

Figure 9. (a) TheFigure GF-3 9. SAR(a) The image GF-3 on SAR 17 January image on 2018 17 at January 09:01, in 2018 which at 09:01, the pinkish in which marks the representpinkish marks the shipwreck position, represent the shipwreck position, and sub-scenes b through g are used to extract spilled oil slicks; and sub-scenes b through g are used to extract spilled oil slicks; and (b) to (g) The results of extracted oil-slicks from and (b) to (g) The results of extracted oil-slicks from sub-scenes b to g, which are marked by black sub-scenes b to g, which are marked by black spots. spots.

Table4. Conclusions 1. The comparison of spatial coverage between SAR-detected and model-simulated oil slicks. With the promotion of offshore oil exploration andModel the Acquisitionrequirements ofModel-Simulated the oil Oil SAR Acquisition Time SAR Oil Area Imaging Modeindustry, the risk of oil spill accidents in coastal2 areas is increasing.Time The rising risk isArea (YYYY-MM-DD) (km ) 2 especially severe in busy ports due to the fact that 90% of international(YYYY-MM-DD) trade is undertaken(km ) FSIIby shipping. 2018-1-15 In recent 21:39 years, oil spill 83.78accidents in the Gulf 2018-1-15 of Mexico 22:00 have caused 82.31 SS 2018-1-17 09:42 364.70 2018-1-17 10:00 363.56

4. Conclusions With the promotion of offshore oil exploration and the requirements of the oil industry, the risk of oil spill accidents in coastal areas is increasing. The rising risk is especially severe in busy ports due to the fact that 90% of international trade is undertaken by shipping. In recent years, oil spill accidents in the Gulf of Mexico have caused widespread concern. The Sanchi ship accident near the Shanghai port caused a large oil spill after the shipwreck on 14 January 2018. Due to the influence of complicated dynamics in coastal waters, such as wind, tide, currents, and waves, the behavior of the oil spread is more complex, the spreading and drifting of oil [54]. As mentioned in the introduction, SAR can detect real-time oil spills, while the ten- dency of spills could be predictable. However, the background of dynamics is often identified to be false oil, and the prediction of spilled oil slicks depends on the accuracy of dynamic forces simulated from hydrodynamic models. Therefore, in this work, SAR- based and numerical modeling methods were combined and applied for real-time oil spill monitoring and the prediction of oil spreading. The sea-surface current and wave simulations used FVCOM and SWAN, respectively. These models share a unique unstructured grid, and the simulations are treated as forcing fields as well as the winds from ECMWF. Then, based on the well-known oil particle-tracing method, the Sanchi accident was used to simulate the spilled oil slicks during the period of 14–17 January 2018. Meanwhile, four GF-3 SAR images at the VV-polarization channel acquired in FS or SS mode were collected, in which two images were available for confirming the accuracy of simulated oil slicks. The spatial coverage of oil slicks was inverted from the GF-3 SAR images using a mature method called CFAR, considering that the SAR back-scattering signal distorted by J. Mar. Sci. Eng. 2021, 9, 279 12 of 15

the oil slicks was weaker than that under slick-free conditions. The low NRCS present in the image on 14 January was probably caused by the background marine dynamics rather than the spilled oil on the ﬁrst day of the shipwreck, and the pattern of ‘low-NRCS’ was assumed to be small-scale up-welling or down-welling. Moreover, we think the oil-like patches in the GF-3 SAR image on 18 January were probably not oil slicks as well. The comparisons between model-simulated and auxiliary data indicated that the RMSE of SWH was 0.58 m with a 0.93 COR, and the RMSE of sea-surface current speed was 0.08 m/s RMSE with a 0.71 COR. The extracted oil slicks from the collected GF- 3 SAR images were consistent with the track simulation using the oil particle-tracing method. Moreover, the comparison between SAR-detected and model-simulated oil slicks yielded about a 1 km2 difference in spatial coverage. Although a joint method combining numerical simulation and SAR is sound, this case study conﬁrms that the joint method is a robust technique for the accurate detection of spilled oil slicks in real-time and the reliable prediction of oil spreading.

Author Contributions: Conceptualization, W.S.; Z.Y., and H.L.; method, W.S.; Y.H. and Z.Y.; validation, Z.Y. and Y.H.; formal analysis, W.S. and W.Z.; investigation, Z.Y.; resources, W.S. and Q.J.; writing—original draft preparation, Z.Y.; Q.J. and W.S.; writing—review and editing, Z.Y. and Y.H.; visualization, W.S. and W.Z.; funding acquisition, W.S. and Q.J. All authors have read and agreed to the published version of the manuscript. Funding: This research was partly supported by the Science and Technology Project of Zhoushan City, China, under contract no. 2019C21008, and the National Natural Science Foundation of China under contract nos. 41806005 and 42076238. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available. Acknowledgments: We appreciate the following models and data: The Simulating WAves Nearshore (SWAN) model developed at the Delft University of Technology; the Finite-Volume Community Ocean Model (FVCOM) from the Marine Ecosystem Dynamics Modeling Laboratory (MEDML); and the winds from the European Centre for Medium-Range Weather Forecasts (ECMWF). The Hy- brid Coordinate Ocean Model (HYCOM) open datasets include the sea-surface temperature, sea- surface salinity, sea-surface current, and sea water level data, which are treated as the open condition in FVCOM model as well as the TPXO.5 tide data. The sea-surface current from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2) and the measurements from the Jason-2 altimeter are used for validating the simulated sea-surface currents and waves, respectively. In addition, we also thank Tao Zhang from Tsinghua University for the constructive discussions. Conﬂicts of Interest: The authors declare no conﬂict of interest.

Appendix A The key to oil slick detection from a co-polarized synthetic aperture radar (SAR) image is to determine the standard of image classiﬁcation based on different clusters. Traditionally, the technique used for oil slick detection is constant false alarm rate (CFAR) considering the probability density function of sea clutter as Gaussian distribution, which not only matches well with the clutter amplitude distribution in a wide range but also can accurately simulate clutter. The theoretical basis of the CFAR technique is mainly based on the following assump- tions: (1) Any scattering unit in a pixel size will not affect other scattering units to a large extent. (2) The phase of each scattering unit obeys the uniform distribution on [-π, π]. (3) The phase random variables of each scattering unit are not related to each other, and some related scattering units naturally form a scattering center. J. Mar. Sci. Eng. 2021, 9, 279 13 of 15

(4) There is no correlation between the amplitude random variable and the phase random variable of each scattering unit. In any case, the SAR-backscattering signal basically satisﬁes the above four assump- tions, therefore, it is assumed that the SAR ocean image obeys the Gaussian distribution: ! 1 (x − µ )2 ( ) = √ − i fi x exp 2 , (A1) 2σi 2σi

where fi(x) is the probability density function of the local region i; the mean value µi is the shape parameter; and the characteristic of the data distribution σi represents the standard deviation value of the image brightness in each local area, which describes the degree of dispersion of the data and the essential parameters of Gaussian shape and density function F(k). It is noteworthy that the ocean waves result in the shape parameter value in the above equation, which will reduce the detection accuracy, especially for small targets. The implementation of the CFAR technique includes two steps: (1) select a certain area, for example, 256 × 256 pixels, around any pixel xc as the reference window; and (2) determine a threshold x0 according to the statistical characteristics of the reference window satisfying that the detection with a false alarm rate Pfa,

x is the target pixel, if x > x c c 0 (A2) xc is the clutter pixel, otherwise

The false alarm rate Pfa is related to the threshold x0i of the local region i as follows: q 2 x0i = µi + −2σi ln(P fa (A3)

Obviously, as given a constant of false alarm rate Pfa, for example, 90% for oil slick detection, the threshold x0 can be determined by calculating the image mean µi and variance σi. In the inversion process, the whole SAR image is divided into a few sub-scenes and then the corresponding image mean µi and variance σi are calculated.

References 1. Qiao, F.L.; Wang, G.; Yin, L.; Zeng, K.; Zhang, Y.; Zhang, M.; Xiao, B.; Jiang, S.; Chen, H.; Chen, G. Modelling oil trajectories and potentially contaminated areas from the Sanchi oil spill. Sci. Total Environ. 2019, 685, 856–866. [CrossRef] 2. Berry, A.; Dabrowski, T.; Lyons, K. The oil spill model OILTRANS and its application to the Celtic sea. Mar. Pollut. Bull. 2012, 64, 2489–2501. [CrossRef] 3. Guo, W.; Wu, G.; Jiang, M.; Xu, T.; Yang, Z.; Xie, M.; Chen, X. A modiﬁed probabilistic oil spill model and its application to the Dalian new port accident. Ocean Eng. 2016, 121, 291–300. [CrossRef] 4. Fingas, M.; Fieldhouse, B. Formation of water-in-oil emulsions and application to oil spill modelling. J. Hazard. Mater. 2004, 107, 37–50. [CrossRef][PubMed] 5. Mccay, D.F. Development and application of damage assessment modeling: Example assessment for the north cape oil spill. Mar. Pollut. Bull. 2013, 47, 341–359. [CrossRef] 6. Yapa, P.D.; Shen, H.T. Modelling river oil spills: A review. J. Hydraul. Res. 1994, 32, 765–782. [CrossRef] 7. Fingas, M.F. A literature review of the physics and predictive modelling of oil spill evaporation. J. Hazard. Mater. 1995, 42, 157–175. [CrossRef] 8. Afenyo, M.; Veitch, B.; Khan, F. A state-of-the-art review of fate and transport of oil spills in open and ice-covered water. Ocean Eng. 2016, 119, 233–248. [CrossRef] 9. Kasimu, A.; Dong, J.; Bian, Y.; Wu, D. Simulate oil spill weathering with system dynamic model. Front. Eng. Man. 2020.[CrossRef] 10. Saha, A.; Nikova, A.; Venkataraman, P.; John, V.; Bose, A. Oil emulsiﬁcation using surface-tunable carbon black particles. ACS Appl. Mater. Inter. 2013, 5, 3094–3100. [CrossRef] 11. Elliott, A.; Hurford, N.; Penn, C. Shear diffusion and the spreading of oil slicks. Mar. Pollut. Bull. 1986, 17, 308–313. [CrossRef] 12. Guo, W.; Wang, Y. A numerical oil spill model based on a hybrid method. Mar. Pollut. Bull. 2009, 58, 726–734. [CrossRef] [PubMed] 13. Stopa, J.E.; Cheung, K.F. Intercomparison of wind and wave data from the ECMWF reanalysis interim and the NCEP climate forecast system reanalysis. Ocean Model. 2014, 75, 65–83. [CrossRef] 14. Yang, Z.H.; Shao, W.Z.; Ding, Y.Y.; Shi, J.; Ji, Q.Y. Wave simulation by the SWAN model and FVCOM considering the sea-water level around the Zhoushan islands. J. Mar. Sci. Eng. 2020, 8, 783. [CrossRef] J. Mar. Sci. Eng. 2021, 9, 279 14 of 15

15. Sheng, Y.X.; Shao, W.Z.; Li, S.Q.; Zhang, Y.M.; Yang, H.W.; Zuo, J.C. Evaluation of typhoon waves simulated by WaveWatch-III model in shallow waters around Zhoushan Islands. J. Ocean Univ. China 2019, 18, 365–375. [CrossRef] 16. Shao, W.Z.; Sheng, Y.X.; Li, H.; Shi, J.; Ji, Q.Y.; Tan, W.; Zuo, J.C. Analysis of wave distribution simulated by WAVEWATCH-III model in typhoons passing Beibu Gulf, China. Atmosphere 2018, 9, 265. [CrossRef] 17. Hu, Y.Y.; Shao, W.Z.; Shi, J.; Sun, J.; Ji, Q.Y.; Cai, L.N. Analysis of the typhoon wave distribution simulated in WAVEWATCH-III model in the context of Kuroshio and wind-induced current. J. Oceanol. Limn. 2020, 38, 1692–1710. [CrossRef] 18. Huang, S.; Liu, J.; Cai, L.N.; Zhou, M.R.; Bu, J.; Xu, J. Satellites HY-1C and Landsat 8 combined to observe the influence of bridge on sea surface temperature and suspended sediment concentration in Hangzhou Bay, China. Water 2020, 12, 2595. [CrossRef] 19. Cai, L.N.; Bu, J.; Tang, D.L.; Zhou, M.R.; Yao, R.; Huang, S. Geosynchronous Satellite GF-4 Observations of Chlorophyll-a Distribution Details in the Bohai Sea, China. Sensors 2020, 20, 5471. [CrossRef][PubMed] 20. Yao, R.; Cai, L.N.; Liu, J.; Zhou, M.R. GF-1 Satellite observations of suspended sediment injection of Yellow River Estuary, China. Remote Sens. 2020, 12, 3126. [CrossRef] 21. Nagaraja Rao, C.; Stowe, L.; Mcclain, E. Remote sensing of aerosols over the oceans using AVHRR data theory, practice and applications. Int. J. Remote Sens. 1988, 10, 743–749. [CrossRef] 22. Esaias, W.; Abbott, M.; Barton, I.; Brown, O.; Minnett, P. An overview of MODIS capabilities for ocean science observations. IEEE Trans. Geosci. Remote Sens. 1998, 36, 1250–1265. [CrossRef] 23. Zhang, H.F.; Wu, Q.; Chen, G. Validation of HY-2A remotely sensed wave heights against buoy data and Jason-2 altimeter measurements. J. Atmos. Ocean. Tech. 2015, 32, 1270–1280. [CrossRef] 24. Quilfen, Y.; Bentamy, A.; Elfouhaily, T.; Katsaros, K.; Tournadre, J. Observation of tropical cyclones by high-resolution scatterome- try. J. Geophys. Res. 1998, 103, 7767–7786. [CrossRef] 25. Alpers, W.; Ross, D.B.; Rufenach, C.L. On the detectability of ocean surface waves by real and synthetic radar. J. Geophys. Res. 1981, 86, 10529–10546. [CrossRef] 26. Li, X.F.; Zhang, J.A.; Yang, X.F.; Pichel, W.G.; DeMaria, M.; Long, D.; Li, Z.W. Tropical cyclone morphology from spaceborne synthetic aperture radar. Bull. Am. Meteorol. Soc. 2013, 94, 215–230. [CrossRef] 27. Espedal, H. Satellite SAR oil spill detection using wind speed history information. Int. J. Remote Sens. 1999, 20, 49–65. [CrossRef] 28. Migliaccio, M.; Gambardella, A.; Tranfaglia, M. SAR polarimetry to observe oil spills. IEEE Trans. Geosci. Remote Sens. 2007, 45, 506–511. [CrossRef] 29. Taghadosi, M.; Hasanlou, M.; Eftekhari, K. Soil salinity mapping using dual-polarized SAR Sentinel-1 imagery. Int. J. Remote Sens. 2018, 40, 237–252. [CrossRef] 30. Santi, F.; Luciani, G.; Bresciani, M.; Giardino, C.; Carolis, G. Synergistic use of synthetic aperture radar and optical imagery to monitor surface accumulation of cyanobacteria in the Curonian Lagoon. J. Mar. Sci. Eng. 2019, 7, 461. [CrossRef] 31. Zeng, K.; Wang, Y. A deep convolutional neural network for oil spill detection from spaceborne SAR images. Remote Sens. 2020, 12, 1015. [CrossRef] 32. Shirvany, R. Ship and oil-spill detection using the degree of polarization in linear and hybrid/compact dual-pol SAR. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 2012, 5, 885–892. [CrossRef] 33. Ramsey, E.; Rangoonwala, A.; Suzuoki, Y.; Jones, C.E. Oil Detection in a Coastal Marsh with Polarimetric Synthetic Aperture Radar (SAR). Remote Sens. 2011, 3, 2630–2662. [CrossRef] 34. Chiu, C.M.; Huang, C.J.; Wu, L.C.; Zhang, Y.L.J.; Chuang, L.Z.H.; Fan, Y.M.; Yu, H.C. Forecasting of oil-spill trajectories by using SCHISM and X-band radar. Mar. Pollut. Bull. 2018, 137, 566–581. [CrossRef] 35. Li, C.; Huang, W.M.; Gleason, S. Dual antenna space-based GNSS-R ocean surface mapping: Oil slick and tropical cyclone sensing. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2015, 8, 425–435. [CrossRef] 36. Wang, S.; Fu, X.; Zhao, Y.; Wang, H. Modification of CFAR algorithm for oil spill detection from SAR data. Intell. Autom. Soft Comput. 2015, 21, 163–174. [CrossRef] 37. Velotto, D.; Migliaccio, M.; Nunziata, F.; Lehner, S. Dual-polarized TerraSAR-X data for oil-spill observation. IEEE Trans. Geosci. Remote Sens. 2011, 49, 4751–4762. [CrossRef] 38. Nunziata, F.; Migliaccio, M.; Gambardella, A. Pedestal height for sea oil slick observation. IET Radar Sonar. Nav. 2011, 5, 103–110. [CrossRef] 39. Zhang, B.; Li, X.; Perrie, W.; Garcia-Pineda, O. Compact polarimetric synthetic aperture radar for marine oil platform and slick detection. IEEE Trans. Geosci. Remote Sens. 2017, 55, 1407–1423. [CrossRef] 40. Xu, H.; Chen, J.; Wang, S.; Liu, Y. Oil spill forecast model based on uncertainty analysis: A case study of Dalian oil spill. Ocean Eng. 2012, 54, 206–212. [CrossRef] 41. Sun, S.l.; Lu, Y.C.; Liu, Y.X.; Wang, M.Q.; Hu, C.M. Tracking an oil tanker collision and spilled oils in the East China Sea using multisensor day and night satellite imagery. Geophys. Res. Lett. 2018, 45, 3212–3220. [CrossRef] 42. Boutin, J.; Siefridt, L.; Etcheto, J.; Barnier, B. Comparison of ECMWF and satellite ocean wind speeds from 1985 to 1992. Int. J. Remote Sens. 1996, 17, 2897–2913. [CrossRef] 43. Saket, A.; Etemad-Shahidi, A.; Moeini, M. Evaluation of ECMWF wind data for wave hindcast in Chabahar zone. J. Coastal Res. 2013, 65, 380–385. [CrossRef] 44. Chen, C.S.; Huang, H.; Beardsley, R.C.; Liu, H.; Xu, Q.; Cowles, G. A finite-volume numerical approach for coastal ocean circulation studies: Comparisons with finite difference models. J. Geophys. Res. 2007, 112, C03018. [CrossRef] J. Mar. Sci. Eng. 2021, 9, 279 15 of 15

45. Akpınar, A.; Vledder, G.; Kömürcü, M.; Özger, M. Evaluation of the numerical wave model (SWAN) for wave simulation in the Black Sea. Cont. Shelf Res. 2012, 50, 80–99. [CrossRef] 46. Yin, L.; Zhang, M.; Zhang, Y.; Qiao, F.L. The long-term prediction of the oil-contaminated water from the Sanchi collision in the East China Sea. Acta Oceanol. Sin. 2018, 3, 1–4. [CrossRef] 47. Shao, W.Z.; Zhu, S.; Sun, J.; Yuan, X.Z.; Sheng, Y.X.; Zhang, Q.J.; Ji, Q.Y. Evaluation of wind retrieval from co-polarization Gaofen-3 SAR imagery around China seas. J. Ocean. Univ. China 2019, 18, 80–92. [CrossRef] 48. Shao, W.Z.; Hu, Y.Y.; Zheng, G.; Cai, L.N.; Zou, J.C. Sea state parameters retrieval from cross-polarization Gaofen-3 SAR data. Adv. Space Res. 2019, 65, 1025–1034. [CrossRef] 49. Franceschetti, G.; Iodice, A.; Riccio, D.; Ruello, G.; Siviero, R. SAR raw signal simulation of oil slicks in ocean environments. IEEE Trans. Geosci. Remote Sens. 2002, 40, 1935–1949. [CrossRef] 50. Shao, W.Z.; Jiang, X.W.; Nunziata, F.; Marino, A.; Corcione, V. Analysis of waves observed by synthetic aperture radar across ocean fronts. Ocean. Dynam. 2020, 70, 1–11. [CrossRef] 51. Lehr, W.; Fraga, R.; Belen, M.; Cekirge, H. A new technique to estimate initial spill size using a modiﬁed fay-type spreading formula. Mar. Pollut. Bull. 1984, 15, 326–329. [CrossRef] 52. Wright, J.W. Stokes drift and the fully developed sea. J. Geophys. Res. 1970, 75, 2847–2848. [CrossRef] 53. Bi, F.; Wu, K.J.; Zhang, Y.M. Effect of Stokes drift on Ekman transport in the open sea. Acta Oceanol. Sin. 2012, 6, 14–20. [CrossRef] 54. Xu, Q.; Cheng, Y.; Liu, B.; Wei, Y.Y. Modeling of oil spill beaching along the coast of the Bohai sea, China. Front. Earth Sci. 2015, 9, 637–641. [CrossRef]