Monitoring the Characteristics of the Bohai Sea Ice Using High-Resolution Geostationary Ocean Color Imager (GOCI) Data

Satellite remote sensing data, such as moderate resolution imaging spectroradiometers (MODIS) and advanced very high-resolution radiometers (AVHRR), are being widely used to monitor sea ice conditions and their variability in the Bohai Sea, the southernmost frozen sea in the Northern Hemisphere. Monitoring the characteristics of the Bohai Sea ice can provide crucial information for ice disaster prevention for marine transportation, oil field operation, and regional climate change studies. Although these satellite data cover the study area with fairly high spatial resolution, their typically limited cloudless images pose serious restrictions for continuous observation of short-term dynamics, such as sub-seasonal changes. In this study, high spatiotemporal resolution (500 m and eight images per day) geostationary ocean color imager (GOCI) data with a high proportion of cloud-free images were used to monitor the characteristics of the Bohai Sea ice, including area and thickness. An object-based feature extraction method and an albedo-based thickness inversion model were used for estimating sea ice area and thickness, respectively. To demonstrate the efficacy of the new dataset, a total of 68 GOCI images were selected to analyze the evolution of sea ice area and thickness during the winter of 2012–2013 with severe sea ice conditions. The extracted sea ice area was validated using Landsat Thematic Mapper (TM) data with higher spatial resolution, and the estimated sea ice thickness was found to be consistent with in situ observation results. The entire sea ice freezing–melting processes, including the key events such as the day with the maximum ice area and the first and last days of the frozen season, were better resolved by the high temporal-resolution GOCI data compared with MODIS or AVHRR data. Both characteristics were found to be closely correlated with cumulative freezing/melting degree days. Our study demonstrates the applicability of the GOCI data as an improved dataset for studying the Bohai Sea ice, particularly for purposes that require high temporal resolution data, such as sea ice disaster monitoring

Algorithm to estimate daily PAR at the ocean surface from GOCI data: description and evaluation

Photosynthetically available radiation (PAR) reaching the ocean surface controls phytoplankton growth, primary productivity, and evolution within marine ecosystems. Therefore, accurate daily PAR estimates are important for a broad range of marine biology and biogeochemistry applications. In this study, hourly data from Geostationary Ocean Color Imager (GOCI), the world’s first geostationary ocean color sensor, was employed to estimate daily mean PAR at the ocean surface around the Korean Peninsula using a budget model based on plane-parallel theory. In situ PAR data collected from two ocean research stations (Socheong-cho and Ieodo) were used to evaluate the accuracy of the GOCI PAR estimates. First, the instantaneous in situ measurements were checked for calibration and exposure errors against Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer calculations under the clearest sky conditions and adjusted to eliminate biases. After adjustment, the root-means-square error (RMSE) between 6S and in situ PAR data was reduced from 6.08 (4.81%) and 3.82 (3.93%) mol/m2/day to 2.85 (2.26%) and 1.74 (1.21%) mol/m2/day at the Socheong-cho and Ieodo stations, respectively, and the coefficient of determination R2 was 0.99. Then, the GOCI daily mean PAR estimated by the initial algorithm were corrected using the 2015 adjusted in situ daily PAR measurements collected under clear-sky conditions. The daily mean PAR values derived from GOCI data in all conditions were improved after the correction, with RMSE reduced from 4.58 (8.30%) to 2.57 (4.65%) mol/m2/day and R2 = 0.97. The comparison statistics were similar for 2015 and 2016 combined, with RMSE of 2.52 (4.38%) and mean bias error (MBE) of –0.40 (–0.70%), indicating that the correction was also effective in cloudy conditions. On the other hand, daily PAR estimates from Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Himawari Imager (AHI) yielded larger RMSE of 6.24 (10.40%) mol/m2/day and MBE of –2.49 (–4.15%) mol/m2/day (MODIS) and RMSE of 3.71 (6.51%) mol/m2/day and MBE of –2.65 (–4.65%) mol/m2/day (AHI) against in situ measurements. The GOCI-based daily PAR model developed in this study is reliable and suitable for investigating the marine environment around the Korean Peninsula

Ocean remote sensing techniques and applications: a review (Part II)

As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.Peer ReviewedPostprint (published version

Monitoring multi-temporal and spatial variations of water transparency in the Jiaozhou Bay using GOCI data

Water transparency, commonly measured as Secchi disk depth (SDD), is essential for describing the optical properties of coastal waters. We proposed a regional linear corrected SDD estimation model based on the North Sea Mathematical Models for GOCI and the mechanical model developed by Lee et al. (2015) in the Jiaozhou Bay. Combined with the multiple variable linear regression analysis, the diurnal SDD variations of the bay inside and the bay mouth are controlled by the solar zenith angle (SZA) and tides. The bay outside mainly varies with SZA. From GOCI observations between 2011 and 2021, wind force influenced the entire area on the inner-annual SDD variations. It exhibits an increasing trend in the inter-annual dynamics, which was more stable inside the bay with an annual increase of 0.035 m, and air temperature was the most significant contribution. However, human activities cannot be ignored in causing water environment changes

Exploring Himawari-8 geostationary observations for the advanced coastal monitoring of the Great Barrier Reef

Larissa developed an algorithm to enable water-quality assessment within the Great Barrier Reef (GBR) using weather satellite observations collected every 10 minutes. This unprecedented temporal resolution records the dynamic nature of water quality fluctuations for the entire GBR, with applications for improved monitoring and management

Optical in situ and geostationary satellite-borne observations of suspended particles in coastal waters

Les particules en suspension dans l'eau de mer incluent les sédiments, le phytoplancton, le zooplancton, les bactéries, les virus et des détritus. Ces particules sont communément appelés matière en suspension (MES). Dans les eaux côtières, la MES peut parcourir de longues distances et être transportée verticalement à travers la colonne d'eau sous l'effet des vents et des marées favorisant les processus d'advection et de resuspension. Ceci implique une large variabilité spatio-temporelle de MES et quasiment impossible à reconstituer à travers les mesures traditionnelles des concentrations de MES [MES], par filtration de l'eau de mer à bord de bateaux. La [MES] peut être obtenue à partir de capteurs optiques enregistrant la diffusion et déployés soit de manière in-situ, soit à partir d'un satellite dans l'espace. Depuis la fin des années 70, par exemple, les satellites "couleur de l'eau" permettent d'établir des cartes de [MES] globales. La fréquence d'une image par jour pour la mer di Nord de ces capteurs polaires représente un obstacle non négligeable pour l'étude de variabilité de la [MES] dans les eaux côtières où la marée et les vents engendrent des variations rapides au cours de la journée. Cette limitation est d'autant plus importante pour les régions avec une couverture nuageuse fréquente. Les méthodes in-situ à partir d'un navire autonome ou d'une plateforme amarrée permettent d'enregistrer des données en continu mais leur couverture spatiale reste néanmoins limitée. Ce travail a pour objectif de mettre en avant les techniques de mesures in-situ et satellite de la [MES] en se concentrant principalement sur deux points. Premièrement, d'acquérir une meilleure connaissance de la variabilité de la relation entre la [MES] et la lumière diffuse, et deuxièmement, d'établir des cartes de [MES] dans la mer du Nord avec le capteur géostationnaire météorologique Européen (SEVIRI) qui donne des images chaque 15 minutes.La variabilité de la relation entre la [MES] et la lumière diffuse est étudiée à l'aide d'une banque de données in-situ. Nous démontrons que la [MES] est le mieux estimée à partir des mesures dans l'intervalle rouge du spectre de lumière rétro-diffuse. Par ailleurs, la relation entre la [MES] et la rétrodiffusion est gouvernée par la composition organique/inorganique des particules, ce qui représente des possibilités d'amélioration pour les algorithmes d'estimation de [MES] à partir de la couleur de l'eau. Nous démontrons aussi qu'avec SEVIRI il est possible d'estimer la [MES], la turbidité et le coefficient d'atténuation, deux variables étroitement liées à la [MES], avec généralement une bonne précision. Bien qu'il y ait d'importantes incertitudes dans les eaux claires, cette réussite est remarquable pour un capteur météorologique initialement conçu pour le suivi des nuages et des masses glaciaires, cibles beaucoup plus brillantes que la mer! Ce travail démontre pour la première fois que la variabilité de la [MES] à l'échelle temporelle des marées dans les eaux côtières au sud de la mer du Nord peut être capturée et mesurée par le biais de la télédétection de la couleur de l'eau ; ce qui ouvre des opportunités pour le monitoring de la turbidité et pour la modélisation des écosystèmes. Le premier capteur géostationnaire couleur de l'eau a été lancé en juin 2012, donnant des images multispectrale des eaux coréennes chaque heure. D'autres capteurs vont probablement suivre dans l'avenir, couvrant le reste des eaux du globe. Ce travail nous permet donc de préparer, de façon optimale, l'arrivée de ces capteurs qui vont révolutionner l'océanographie optique.Particles suspended in seawater include sediments, phytoplankton, zooplankton, bacteria, viruses, and detritus, and are collectively referred to as suspended particulate matter, SPM. In coastal waters, SPM is transported over long distances and in the water column by biological, tide or wind-driven advection and resuspension processes, thus varying strongly in time and space. These strong dynamics challenge the traditional measurement of the concentration of SPM, [SPM], through filtration of seawater sampled from ships. Estimation of [SPM] from sensors recording optical scattering allows to cover larger temporal or spatial scales. So called ocean colour satelittes, for example, have been used for the mapping of [SPM] on a global scale since the late 1970s. These polar-orbiting satellites typically provide one image per day forthe North Sea area. However, the sampling frequency of these satellites is a serious limitation in coastal waters where [SPM] changes rapidly during the day due to tides and winds.Optical instruments installed on moored platforms or on under-water vehicles can be operated continuously, but their spatial coverage is limited. This work aims to advance in situ and space-based optical techniques for [SPM] retrieval by investigating the natural variability in the relationship between [SPM] and light scattering by particles and by investigating whether the European geostationary meteorological SEVIRI sensor, which provides imagery every 15 minutes, can be used for the mapping of [SPM] in the southern North Sea. Based on an extensive in situ dataset, we show that [SPM] is best estimated from red light scattered in the back directions (backscattering). Moreover, the relationship between [SPM]] and particulate backscattering is driven by the organic/inorganic composition of suspended particles, offering opportunities to improve [SPM] retrieval algorithms. We also show that SEVIRI successfully retrieves [SPM] and related parameters such as turbidity and the vertical light attenuation coefficient in turbid waters. Even though uncertainties are considerable in clear waters, this is a remarkable result for a meteorological sensor designed to monitor clouds and ice, much brighter targets than the sea! On cloud free days, tidal variability of [SPM] can now be resolved by remote sensing for the first time, offering new opportunities for monitoring of turbidity and ecosystem modelling. In June 2010 the first geostationary ocean colour sensor was launched into space which provides hourly multispectral imagery of Korean waters. Other geostationary ocean colour sensors are likely to become operational in the (near?) future over the rest of the world's sea. This work allows us to maximally prepare for the coming of geostationary ocean colour satellites, which are expected to revolutionize optical oceanography.De in zeewater aanwezige zwevende materie zoals sedimenten, fytoplankton, zooplankton, bacteriën, virussen en detritus, worden collectief "suspended particulate matter" (SPM) genoemd. In kustwateren worden deze deeltjes over lange afstanden en in de waterkolom getransporteerd door biologische processen of wind- of getijdenwerking, waardoor SPM sterk varieert in ruimte en tijd. Door deze sterke dynamiek wordt de traditionele bemonstering van de concentratie van SPM, [SPM], door middel van filtratie van zeewaterstalen aan boord van schepen ontoereikend. Optische technieken die gebruik maken van de lichtverstriioongseigenschappen van SPM bieden een gebieds- of tijdsdekkend alternatief. Zogenaamde "ocean colour" satellieten bijvoorbeeld leveren beelden van o.a. [SPM] aan het zeeoppervlak op globale schaal sinds eind 1970, met een frequantie van één beeld per dag voor de Noordzee. Deze frequentie is echter onvoldoende in onze kustwateren waar [SPM] drastisch kan veranderen in enkele uren tijd. Optische instrumenten aan boord vann schepen of op onderwatervoertuigen kunnen continu meten, maar de gebiedsdekking is deperkt. Dit werk heeft tot doel de lichtverstriioongseigenschappen van SPM te karakterizeren en te onderzoeken of de Europese geostationaire weersatelliet, die elk kwartier een beeld geeft, kan worden gebruikt voor de kartering van [SPM] in de zuidelijke Noordzee. Op basis van een grote dataset van in situ metingen tonen wij aan dat [SPM] het nauwkeurigst kan worden bepaald door de meting van de verstrooiing van rood licht in achterwaartse richtingen (terugverstrooiing). Bovendien blijkt de relatie tussen [SPM] en terugverstrooiing afhankelijk van de organische-anorganische samenstelling van zwenvende stof, wat mogelijkhenden biedt tot het verfijnen van teledetectiealgoritmen voor [SPM]. Voorts tonen woj aan dat de Europese weersatelliet, SEVIRI, successvol kan worden aangewend voor de kartering van [SPM] en gerelateerde parameters zoals troebelheid en lichtdemping in de waterkolom. Hoewel met grote meetonzekerheid in klaar water toch een opmerkelijk resultaat voor een sensor die ontworpen werd voor detectie van wolken en ijs! Op wolkenvrije dagen wordt hierdoor de getijdendynamiek van [SPM] in de zuidelijke Noordzee voor het eerst detecteerbaar vanuit de ruimte, wat nieuwe mogelijkheden biedt voor de monitoring van waterkwaliteit en verbetering van ecosysteellodellen. Sinds juni 2010 is de eerste geostationaire ocean colour satelliet een feit : elk uur een multispectraal beeld van Koreaanse wateren. Vermoedelijk zullen er in de (nabije?) toekomst meer volgen over Europa en Amerika. Dit werk laat toe ons maximaal voor te bereiden op te komst van zo'n satellieten, waarvan verwacht wordt dat zij een nieuwe revolutie in optische oceanografie zullen ontketenen

Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters

The Earth's surface waters are a fundamental resource and encompass a broad range of ecosystems that are core to global biogeochemical cycling and food and energy production. Despite this, the Earth's surface waters are impacted by multiple natural and anthropogenic pressures and drivers of environmental change. The complex interaction between physical, chemical and biological processes in surface waters poses significant challenges for in situ monitoring and assessment and often limits our ability to adequately capture the dynamics of aquatic systems and our understanding of their status, functioning and response to pressures. Here we explore the opportunities that Earth observation (EO) has to offer to basin-scale monitoring of water quality over the surface water continuum comprising inland, transition and coastal water bodies, with a particular focus on the Danube and Black Sea region. This review summarises the technological advances in EO and the opportunities that the next generation satellites offer for water quality monitoring. We provide an overview of algorithms for the retrieval of water quality parameters and demonstrate how such models have been used for the assessment and monitoring of inland, transitional, coastal and shelf-sea systems. Further, we argue that very few studies have investigated the connectivity between these systems especially in large river-sea systems such as the Danube-Black Sea. Subsequently, we describe current capability in operational processing of archive and near real-time satellite data. We conclude that while the operational use of satellites for the assessment and monitoring of surface waters is still developing for inland and coastal waters and more work is required on the development and validation of remote sensing algorithms for these optically complex waters, the potential that these data streams offer for developing an improved, potentially paradigm-shifting understanding of physical and biogeochemical processes across large scale river-sea continuum including the Danube-Black Sea is considerable

A review of ocean color remote sensing methods and statistical techniques for the detection, mapping and analysis of phytoplankton blooms in coastal and open oceans

The need for more effective environmental monitoring of the open and coastal ocean has recently led to notable advances in satellite ocean color technology and algorithm research. Satellite ocean color sensors' data are widely used for the detection, mapping and monitoring of phytoplankton blooms because earth observation provides a synoptic view of the ocean, both spatially and temporally. Algal blooms are indicators of marine ecosystem health; thus, their monitoring is a key component of effective management of coastal and oceanic resources. Since the late 1970s, a wide variety of operational ocean color satellite sensors and algorithms have been developed. The comprehensive review presented in this article captures the details of the progress and discusses the advantages and limitations of the algorithms used with the multi-spectral ocean color sensors CZCS, SeaWiFS, MODIS and MERIS. Present challenges include overcoming the severe limitation of these algorithms in coastal waters and refining detection limits in various oceanic and coastal environments. To understand the spatio-temporal patterns of algal blooms and their triggering factors, it is essential to consider the possible effects of environmental parameters, such as water temperature, turbidity, solar radiation and bathymetry. Hence, this review will also discuss the use of statistical techniques and additional datasets derived from ecosystem models or other satellite sensors to characterize further the factors triggering or limiting the development of algal blooms in coastal and open ocean waters

Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities

With the increasing amount of spatial-temporal~(ST) ocean data, numerous spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, e.g., climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated with some unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models. Unfortunately, an overview of these studies is still missing, hindering computer scientists to identify the research issues in ocean while discouraging researchers in ocean science from applying advanced STDM techniques. To remedy this situation, we provide a comprehensive survey to summarize existing STDM studies in ocean. Concretely, we first summarize the widely-used ST ocean datasets and identify their unique characteristics. Then, typical ST ocean data quality enhancement techniques are discussed. Next, we classify existing STDM studies for ocean into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate the techniques for these tasks. Finally, promising research opportunities are highlighted. This survey will help scientists from the fields of both computer science and ocean science have a better understanding of the fundamental concepts, key techniques, and open challenges of STDM in ocean