Sargassum Detection Using Machine Learning Models: A Case Study with the First 6 Months of GOCI-II Imagery

Shin, Jisun; Lee, Jong-Seok; Jang, Lee-Hyun; Lim, Jin-Wook; Khim, Boo‐Keun; Jo, Young‐Heon

doi:10.3390/rs13234844

Cited by 22 publications

(10 citation statements)

References 47 publications

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“…Experimental results showed that SRSe-Net is able to obtain more accurate segmentation with fewer network parameters. Shin et al [73] trained three machine learning models with Rayleigh-corrected reflection images and a ground truth map obtained from Geostationary Ocean Color Imager-II (GOCI-II) imagery during the agglomeration of Sargassum along the northern Jeju coast in Korea in 2021, including fine tree, fine Gaussian support vector machine (SVM), and gentle adaptive boosting (Gentle-Boost). Among these, the authors determined that Gentle-Boost was best at detecting the location of Sargassum (Figure 5).…”

Section: Oil Spillsmentioning

confidence: 99%

“…In the forecasting of oceanic variables, methodologies broadly fall into two types. The first type of methodology Figure 5: Ground-truth maps and Sargassum maps generated using traditional methods (DVI, SRG, and SI) and machine learning models (fine tree, fine Gaussian SVM, and GentleBoost) from GOCI-II images obtained on 27 January, adapted from Shin et al [73]. Ocean-Land-Atmosphere Research includes traditional numerical models that rely on expert knowledge to develop complex dynamic and thermodynamic equations.…”

Section: Oceanic Phenomena Forecastingmentioning

confidence: 99%

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Recent Developments in Artificial Intelligence in Oceanography

Han

Bethel

et al. 2022

Ocean-Land-Atmos Res

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With the availability of petabytes of oceanographic observations and numerical model simulations, artificial intelligence (AI) tools are being increasingly leveraged in a variety of applications. In this paper, these applications are reviewed from the perspectives of identifying, forecasting, and parameterizing ocean phenomena. Specifically, the usage of AI algorithms for the identification of mesoscale eddies, internal waves, oil spills, sea ice, and marine algae are discussed in this paper. Additionally, AI-based forecasting of surface waves, the El Niño Southern Oscillation, and storm surges is discussed. This is followed by a discussion on the usage of these schemes to parameterize oceanic turbulence and atmospheric moist physics. Moreover, physics-informed deep learning and neural networks are discussed within an oceanographic context, and further applications with ocean digital twins and physics-constrained AI algorithms are described. This review is meant to introduce beginners and experts in the marine sciences to AI methodologies and stimulate future research toward the usage of causality-adherent physics-informed neural networks and Fourier neural networks in oceanography.

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Section: Oil Spillsmentioning

confidence: 99%

Section: Oceanic Phenomena Forecastingmentioning

confidence: 99%

Recent Developments in Artificial Intelligence in Oceanography

Han

Bethel

et al. 2022

Ocean-Land-Atmos Res

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“…Subsequently, South Korea launched GOCI-II in 2020. GOCI-II has many improvements over GOCI, with more watercolor bands and higher spatial resolution [56][57][58].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of the Application of GOCI to Water Quality Monitoring of Inland and Coastal Waters

Shao,

Wang,

Liu

et al. 2024

Preprint

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In recent decades, as eutrophication in inland and coastal waters (ICWs) has increased due to anthropogenic activities and global warming, so has the need for timely monitoring. Compared with traditional sampling and laboratory analysis methods, satellite remote sensing technology can provide macro-scale, low-cost, and near real-time water quality monitoring services. The Geostationary Ocean Color Imager (GOCI), integrated onboard the Communication Ocean and Meteorological Satellite (COMS) from the Republic of Korea, was the first geostationary ocean color observation satellite and was operational from April 1, 2011 to March 31, 2021. Over ten years, GOCI has observed oceans, coastal waters, and inland waters within its 2,500 km×2,500 km target area centered on the Korean Peninsula. The most attractive feature of GOCI, compared with other commonly used watercolor sensors, was its high temporal resolution (1h, eight times daily from 0 UTC to 7 UTC), providing the opportunity to monitor the quality of ICWs, where optical properties can change rapidly throughout the day. This systematic review aims to comprehensively review GOCI features and applications in ICWs, analyzing progress in atmospheric correction algorithms and water quality monitoring. Analyzing 123 articles from the Web of Science and China National Knowledge Infrastructure (CNKI) through a bibliometric quantitative approach, we examined GOCI’s strength and performance with different processing methods. These articles reveal that GOCI played an essential role in monitoring the ecological health of ICWs in its observation area in East Asia. GOCI has led the way to a new era of geostationary ocean satellites, providing new technical means for monitoring water quality in oceans, coastal zones, and inland lakes. We also discuss the challenges encountered by geostationary satellites in monitoring water quality and provide suggestions for improvements.

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“…CEAN color remote sensing data have been widely used for short-and long-term monitoring of water quality [1], [2], [3], [4], ocean productivity [5], ocean fronts [6], and blooms such as red [7], [8], [9], green, and golden tide blooms [10], [11], [12]. Monitoring and assessing water changes are carried out using ocean color sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS; Terra:1999-present; Aqua:2002-present), Sea-viewing Wide Manuscript received XXX; revised XXX; accepted XXX.…”

Section: Introductionmentioning

confidence: 99%

U-Net Super-Resolution Model of GOCI to GOCI-II Image Conversion

Shin,

Jo,

Khim

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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The use of ocean color sensors presents limitations to monitoring coastal environmental changes and capturing fine spatial features below 1 km due to low spatial (0.5-1 km) and temporal (1 day) resolutions. The Geostationary Ocean Color Imager (GOCI)-II, launched on 18 February 2020, is a follow-up mission to GOCI, operated from June 27, 2010 to March 31, 2021. GOCI-II imagery, with a spatial resolution of 250 m, detects more detailed spatial structures of ocean dynamics compared to GOCI with a spatial resolution of 500 m. This study aims to develop a U-Net super-resolution model to enhance the GOCI remote-sensing reflectance (Rrs) imagery to the same spatial resolution as GOCI-II. The U-Net model is trained with eight paired bands (412, 443, 490, 555, 660, 680, 745, and 865 nm) of GOCI and GOCI-II Rrs around the waters of the Korean Peninsula. The consistency level between GOCI and GOCI-II images indicated GOCI sensor degradation, especially in the blue bands, during its last mission period from December 2020 to March 2021. Through quantitative and qualitative evaluations, we found that the U-Net Rrs image had greater spectral information with higher consistency compared to the G1-bicubic image by bicubic interpolation of GOCI. In particular, the U-Net results improved the consistency in the blue bands (412, 443, and 490 nm). Qualitative evaluations also showed that U-Net corrected the blue band underestimation in degraded GOCI images. In addition, chlorophyll-a concentration (CHL) map from the U-Net Rrs not only simulated spatial patterns, similar to GOCI-II CHL map, but also corrected the overestimated GOCI CHL map. The U-Net super-resolution model may help to produce more reliable and fine-scale Rrs products from GOCI similar to those of GOCI-II, and to enable long-term ocean color monitoring around the waters of the Korean Peninsula.

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Sargassum Detection Using Machine Learning Models: A Case Study with the First 6 Months of GOCI-II Imagery

Cited by 22 publications

References 47 publications

Recent Developments in Artificial Intelligence in Oceanography

Recent Developments in Artificial Intelligence in Oceanography

A Systematic Review of the Application of GOCI to Water Quality Monitoring of Inland and Coastal Waters

U-Net Super-Resolution Model of GOCI to GOCI-II Image Conversion

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