Application of coastal acoustic tomography: calibration of open boundary conditions on a numerical ocean model for tidal currents

Coastal acoustic tomography (CAT), which measures path-averaged currents from reciprocal acoustic transmission experiments and reconstructs velocity fields from the multiple path-averaged current data, is useful for monitoring tidal currents in coastal shallow water, especially if data assimilation is employed. Previous CAT data assimilation studies have focused on state estimation problems, i.e., the reconstruction of tidal currents and following dynamical discussion. In this study, we investigate the use of path-averaged currents in a boundary control problem. Specifically, we aim to use the observed path-averaged currents to determine the parameters of a numerical ocean model, which were tidal amplitudes and phases as the open boundary conditions in this study. We investigate two methods: using the ensemble Kalman filter (EnKF) results and a linearization approach called model Green’s function method. Both calibration methods decreased the amplitudes of tidal constituents at the open boundaries. We compare the model performance between the model predictions with and without the calibration of the open boundary conditions. The model predictions with the calibrated open boundary conditions improved the agreement with the observed path-averaged current. We also implemented the sequential updates of EnKF with the two calibrated open boundary conditions. The EnKF results with the independently calibrated two open boundary conditions improved the agreement with the comparison data obtained by acoustic Doppler current profiler measurement compared with the original EnKF result with the initial open boundary conditions

Trial of Chemical Composition Estimation Related to Submarine Volcano Activity Using Discolored Seawater Color Data Obtained from GCOM-C SGLI. A Case Study of Nishinoshima Island, Japan, in 2020

This study aims to develop the relational equation between the color and chemical composition of discolored seawater around a submarine volcano, and to examine its relation to the volcanic activity at Nishinoshima Island, Japan, in 2020, using the model applied by atmospheric corrected reflectance 8 day composite of GCOM-C SGLI. To achieve these objectives, the relational equation between the RGB value of the discolored seawater in the submarine volcano and the chemical composition summarized in past studies was derived using the XYZ colorimetric system. Additionally, the relationship between the volcanic activity of the island in 2020 and the chemical composition was compared in chronological order using the GCOM-C SGLI data. The following findings were obtained. First, a significant correlation was observed between the seawater color (x) calculated by the XYZ colorimetric system and the chemical composition such as (Fe + Al)/Si. Second, the distribution of (Fe + Al)/Si in the island, estimated from GCOM-C SGLI data, fluctuated significantly just before the volcanic activity became active (approximately one month prior). These results suggest that the chemical composition estimation of discolored seawater using SGLI data may be a powerful tool in predicting submarine volcanic activity

Method for Distinguishing <i>Sargassum</i> and <i>Zostera</i> in the Seto Inland Sea Using Sentinel-2 Data

Coastal blue carbon ecosystems are crucial to mitigating global warming. To accurately calculate the blue carbon stock, the existing amount of each species in seaweed and seagrass (SWSG) beds must be estimated to calculate the amount of CO2 absorbed by each species. However, there exists no efficient and comprehensive method for separating SWSG species. Remote sensing techniques hold promise in addressing this issue. This study used satellite Sentinel-2 data to differentiate and map the areas in which Sargassum and Zostera flourish in the Seto Inland Sea. A two-step approach was proposed to separate these algae. First, the SWSG bed area was estimated using the bottom index method, which has been commonly used for sediment mapping. Consequently, using spectral characteristics obtained from field surveys, the Sargassum and Zostera distinguishing index was developed to efficiently separate Sargassum and Zostera. This algorithm was applied to Sentinel 2 data to create a distribution map of Sargassum and Zostera in the Seto Inland Sea. When the map was compared with SWSG bed maps, obtained using field survey-based methods, it showed high credibility, meaning that the proposed method can be used to repeatedly and easily understand seasonal changes in SWSG types in this area in the future

Quantitatively Mapping Discolored Seawater around Submarine Volcanoes Using Satellite GCOM-C SGLI Data: A Case Study of the Krakatau Eruption in Indonesia in December 2018

The final goal of this paper is to contribute to the difficult task of understanding and forecasting submarine volcanic eruption activity by proposing a method to quantify discolored water. To achieve this purpose, we quantitatively analyzed the discolored seawater seen before and after the eruption of the marine environment around the Indonesian submarine volcano “Anak Krakatau”, which erupted at the end of December 2018, from the viewpoint of the “dominant wavelength”. The atmospherically corrected COM-C SGLI data for 17 periods from the eruption from October 2018 to March 2019 were used. As a result, the following three main items were found. First, the average ± standard deviation of the entire dominant wavelength was 497 nm ± 2 nm before the eruption and 515 nm ± 35 nm after the eruption. Second, the discolored water area around the island derived from SGLI was detected from the contour line with dominant wavelengths of 500 nm and 560 nm. Third, the size of a dominant wavelength of 500 nm or more in the discolored water areas changed in a complicated manner within the range of almost 0 to 35 km2. The area of the dominant wavelength of 500 nm or more slightly increased just before the eruption. Finally, it was proven that the “dominant wavelength” from the SGLI proposed in this paper can be a very effective tool in understanding or predicting submarine volcanic activity

Evaluation of Chlorophyll-a Estimation Approaches Using Iterative Stepwise Elimination Partial Least Squares (ISE-PLS) Regression and Several Traditional Algorithms from Field Hyperspectral Measurements in the Seto Inland Sea, Japan

Harmful algal blooms (HABs) occur frequently in the Seto Inland Sea, bringing significant economic and environmental losses for the area, which is well known as one of the world&rsquo;s most productive fisheries. Our objective was to develop a quantitative model using in situ hyperspectral measurements in the Seto Inland Sea to estimate chlorophyll a (Chl-a) concentration, which is a significant parameter for detecting HABs. We obtained spectra and Chl-a data at six stations from 12 ship-based surveys between December 2015 and September 2017. In this study, we used an iterative stepwise elimination partial least squares (ISE-PLS) regression method along with several empirical and semi-analytical methods such as ocean chlorophyll, three-band model, and two-band model algorithms to retrieve Chl-a. Our results showed that ISE-PLS using both the water-leaving reflectance (RL) and the first derivative reflectance (FDR) had a better predictive ability with higher coefficient of determination (R2), lower root mean squared error (RMSE), and higher residual predictive deviation (RPD) values (R2 = 0.77, RMSE = 1.47 and RPD = 2.1 for RL; R2 = 0.78, RMSE = 1.45 and RPD = 2.13 for FDR). However, in this study the ocean chlorophyll (OC) algorithms had poor predictive ability and the three-band and two-band model algorithms did not perform well in areas with lower Chl-a concentrations. These results support ISE-PLS as a potential coastal water quality assessment method using hyperspectral measurements

Reproduction of the Marine Debris Distribution in the Seto Inland Sea Immediately after the July 2018 Heavy Rains in Western Japan Using Multidate Landsat-8 Data

Understanding the spatiotemporal environment of the ocean after a heavy rain disaster is critical for satellite remote sensing research and disaster prevention. We attempted to reproduce changes in marine debris distributions using multidate data of Landsat-8 spectral reflectance acquired immediately after a heavy rain disaster in western Japan in July 2018. Data from cleaning ships were used for screening the marine debris area. As most of the target marine debris consisted of plant fragments, a method based on the corrected floating algae index (cFAI) was applied to Landsat-8 data. Data from cleaning ships clarify that most of the marine debris accumulated in the waters in the northern part of Aki Nada, a part of the Seto Inland Sea. The spectral characteristics of the corresponding marine debris spectral reflectance obtained from the Landsat-8 data were explained by the FAI with band 5 (central wavelength: 865 nm) as the maximum value. Unlike traditional FAI, cFAI eliminated the effect of background water turbidity. The Otsu method was effective for the automatic threshold determination for cFAI. Although Landsat-8 data have limited spatial resolution and observation frequency, these data were useful for understanding marine debris distribution after a heavy rain disaster

A Simple Red Tide Monitoring Method using Sentinel-2 Data for Sustainable Management of Brackish Lake Koyama-ike, Japan

We proposed and validated a method for monitoring red tides in the brackish Lake Koyama-ike, Japan, using Sentinel-2 Multispectral Instrument (MSI) data with a 10 m spatial resolution. To achieve this objective, we acquired 36 spectral reflectance/Chla data points in the field from 2012 to 2018. We obtained a high correlation of Chla (R2 = 0.83) using the proposed red tide model (RIKY = [MSI Band 5 &#8722; MSI Band 4]/[MSI Band 5 + MSI Band 4]) and field data. Based on our results, the proposed model was also validated using five Sentinel-2/Chla datasets from April to August 2017. Chla and red tide distribution characteristics estimated from Sentinel-2 data hardly appeared from April to July, and then spread rapidly throughout the lake (more than 70%) in August. Thus, Sentinel-2 data proved to be a very powerful tool in monitoring red tides in Lake Koyama-ike