In maritime accidents leading to missing persons, acquiring accurate underwater environment information is crucial for effective search and rescue (SAR) operations. Current remote sensing techniques have limitations in providing comprehensive data beneath the sea surface. This study proposes a novel technology that enhances underwater visibility estimation by incorporating numerical modeling with high-resolution satellite data. First, turbidity was calculated utilizing the 560 nm surface reflectance data from Sentinel-2. The accuracy of turbidity values was assessed by comparison with in-situ turbidity data. To delineate detailed vertical mixing patterns influencing underwater visibility distribution, a comprehensive vertical profile database for water temperature and salinity was established using the Regional Ocean Modeling System (ROMS) model. Indices of vertical mixing, as a critical factor influencing underwater visibility, was achieved through quantitative analysis of seawater temperature and salinity data. The detailed underwater visibility distance is calculated by incorporating turbidity and vertical mixing effects. Subsequently, the underwater visibility will be validated through in-situ experimental measurements of both vertical and horizontal visibility along the coast using a Secchi disk. The outcomes of this study are anticipated to significantly contribute to enhancing the efficiency of rapid search and rescue operations for missing persons at sea.

In this study, we developed a technology for detecting small objects by conducting two aerial experiments targeting various objects, including ships, mannequins, and maritime safety equipment floating in coastal areas, thereby acquiring hyperspectral image data. By utilizing the hyperspectral data, we detected the pixels corresponding to the edges of ships and employed an ellipse fitting approach to identify the vessels, achieving a length error of 0.44 m. The N-FINDR spectral unmixing technique was applied to detect lifebuoys, buoyant apparatus, and mannequins, resulting in relatively small length errors ranging from 0.08 to 0.17 m.

Hazardous and noxious substances (HNS) are being increasingly used in various industrial fields, and the transportation of these substances using ships is also continuously increasing. Unlike traditional marine accidents, HNS accidents have high-risk characteristics that can cause fatal and complex physical and environmental damage, such as flammability, explosiveness, toxicity, and corrosiveness [1] – [6] . Therefore, there is a need to develop systematic management technology that takes these HNS accident characteristics into consideration. In the event of an actual accident, the most important factor in the response is to accurately and quickly detect HNS spills to establish an optimal and effective response strategy

According to the Protocol on Preparedness, Response and Co-operation to Pollution Incidents by Hazardous and Noxious Substances (OPRC-HNS Protocol), hazardous and noxious substances (HNS) is referred to as a chemical substance, except for oil, that impairs the ocean by destroying the environment or are harmful to humans and marine life [1] . The increase in maritime transport of HNSs from the larger and faster ships entails the potential risk of marine HNS spill accidents. Highly toxic HNSs, such as benzene, toluene, and xylene, have serious adverse effects on the human body and various organisms living in the marine ecosystems. Most colorless and transparent HNSs have limitations in visual identification and can cause secondary accidents due to explosions and fires, making human access difficult. So, HNS remote sensing based on satellites and aircraft is important for HNS detection at sea because it has the advantage of monitoring a wide area and observing it at a high resolution. To monitor this HNS spill, we designed and performed a ground HNS spill experiment using a hyperspectral sensor to detect HNS areas and estimate the spill volume. The experiment was conducted at the Centre of Documentation, Research and Experimentation on Accidental Water Pollution (CEDRE) marine pool in BREST, France. The HNS image was obtained by pouring 1 L of toluene into an outdoor marine pool and observing it with a hyperspectral sensor capable of measuring the short-wave infrared (SWIR) channel installed at a height of approximately 12 m ( Figure 1 ). At a height of 12 m, the spatial resolution of the SWIR sensor is 10.3 mm and 5.3 mm in the horizontal and vertical directions, respectively. The pure endmember spectra of toluene and seawater were extracted using principal component analysis (PCA) and N-FINDR. This method has the advantage of not requiring input variables other than the number of pure substances. And a Gaussian mixture model (GMM) was applied to the toluene abundance fraction. This model is useful when the data contain multiple Gaussian distributions. The GMM represents a normally distributed subpopulation within the entire population, and it is possible to automatically learn subpopulations without requiring data on subpopulations [2] , [3] . Considering that the histogram of the abundance fraction corresponding to all pixels constituting the HNS image is composed of two peaks containing toluene and seawater rather than a single peak, the GMM is judged to be more suitable than a single model. Empirical linear calibration based on vicarious radiometric calibration was used as an atmospheric correction method for hyperspectral images. It constructs a linear equation using the pixel value of a specific index, whose reflectance is known in advance and is applied to the total image. Rectangular-shaped target plates with white (95%), gray (50%), and black (5%) reflectance were pre-positioned for inclusion in the hyperspectral image. Consequently, a toluene area of approximately 2.4317 mm 2 was detected according to the 36% criteria suitable for HNS detection. The HNS thickness estimation was based on a three-layer two-beam interference theory model [4] . In this model, the HNS thickness was estimated based on the relationship between the reflectance and transmittance at the interface composed of three layers of air, HNS, and seawater. Because toluene has a maximum extinction coefficient of 1.3055 mm at a wavelength of 1678 nm, the closest 1676.5 nm toluene reflectance image was used for thickness estimation. Considering the detection area and ground resolution, the amount of toluene that leaked was estimated to be 0.9336 L. As the amount of toluene used in the actual ground experiment was 1 L, the accuracy of our estimation is approximately 93.36%. Although we precisely designed and constructed the HNS spill experiment and retrieved the actual amount of spill material with reasonable result, there are many possibility to affect to the estimation of HNS thickness in the complicated marine environment. One of the most important causes may be the wind speed controlling evaporation of the HNS and rapid mixing with ambient seawater. The effect of wind field on the thickness estimation should be further investigated based on more intensive experiment in both marine pool circumstances and real sea environment. In this study, the estimated total toluene volume was slightly reduced compared with the amount of toluene spilled in the experiment. The elapsed time from toluene leakage to observation with the hyperspectral sensor was up to 3 min, and there is a possibility that some toluene had evaporated or dissolved. In addition to the effect of wind speed, the evaporation of toluene is affected by other atmospheric and marine environments, such as air temperature, humidity, temperature difference between the air and sea surface temperatures, sea state including various waves. Previous studies on HNS monitoring based on remote sensing are lacking compared with those on oil spills. Considering these limitations, this study evaluated the applicability of HNS detection and thickness estimation based on oil-like reflectance spectral trends at the SWIR wavelengths. The toluene detection results based on hyperspectral remote sensing are expected to be utilized for monitoring marine HNS accidents and evaluating harmful marine ecosystem environments.

The Remotely sensed satellite or airborne images can aid rapid vessel monitoring over wide areas at high resolutions. In this study, airborne hyperspectral experiments were performed to detect marine vessels mainly including fishing boat and yacht by applying pixel-based mixture techniques and to estimate the size of the vessels through an objective ellipse fitting method. Several hyperspectral mixture algorithms, such as N-FINDR, pixel purity index (PPI), independent component analysis (ICA), and vertex component analysis (VCA), were used for the detection of vessels. The pixel-based probability of detection (POD) and false alarm ratio (FAR) for all 14 vessels were 96.40% and 4.30%, respectively. Compared with the digital mapping camera (DMC) images with resolutions of 0.10 m, the root-mean-square error of the length and width of the vessels were approximately 1.19 m and 0.81 m, respectively.

Hazardous Noxious Substance (HNS) is transported entirely through large vessels, so there is always a potential risk of marine HNS spills. In the event of an HNS accident, it can cause enormous human and property damage, so prompt detection is required. However, there is a limit to human access by ship, we need to use remote sensing data. In this study, ground experiments using hyperspectral cameras were performed to construct a spectral library of HNS. We classified the HNS and non-HNS by applying the hyperspectral mixture algorithm, and presented the HNS detection probability for every pixel by calculating the spectrum-based abundance fraction. The results of this study are expected to be used to estimate the extent of HNS spill in the event of a marine HNS accident.

Sea surface wind is one of the most important factors for waves, ocean currents, ocean circulation, and atmosphere-ocean interactions, and provides a comprehensive understanding of complex marine phenomena. Many researchers have been studying on sea surface wind using scatterometer, and the wind field data from scatteromter is accurate to within ± 2 m/s and 20 °. However, the wind field data from scatteromter have disadvantages such as lack of coastal wind field data and analysis of small scale ocean phenomenon due to low spatial resolution.

Sea surface wind is one of the most important parameters of ocean - atmosphere interaction and affects hear flux, oceanic mixed layer and oceanic surface layer circulation through continuous interaction with the atmosphere. Especially in recent years, due to rapid changes in global environment caused by rapid climate change, sea surface wind plays a major role in the understanding of seawater inflow and exchange processes and marine biogeochemical processes of atmospheric gases.

In this study, we accomplished to detect oil spill from DubaiSat-2 and Landsat OLI data using VIS/NIR channels with high spatial resolution and we classified oil spill in two types, such as thick oil and film-like oil. It is hard to recognize film-like oil even at the field and more complex to discriminate film-like oil than thick oil from the satellite data because it shows similar appearance with sea water color and their widespread distributions. In this respect, it is significant that we has developed the algorithm for film-like oil. And also we figured out the effects of tidal current and wind driven current to the movement of oil spill by particle tracking.

In this study, we investigated the accuracies of SSS in the northwest Pacific Ocean for the recent three years (2012-2014) by comparison with in situ salinity measurements from Argo floats, moored buoys, and many of ship CTD measurements. The satellite SSS errors of the northwest Pacific Ocean presented characteristic dependence on latitudes close to the global ocean but were mostly underestimated at all ranges of SST and wind speed.