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The study area (GCWS region; blue box) is located over oligotrophic waters to the south of Japan within the coverage area of GOCI (red box). The GCWS region covers 433 × 968 pixels, which is equivalent to 100,000 km².
Source publication
Short-term (sub-diurnal) biological and biogeochemical processes cannot be fully captured by the current suite of polar-orbiting satellite ocean color sensors, as their temporal resolution is limited to potentially one clear image per day. Geostationary sensors, such as the Geostationary Ocean Color Imager (GOCI) from the Republic of Korea, allow t...
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Detection of biological, physical and chemical parameters is needed for the determination of water quality. Some of these water quality parameters such as turbidity, chlorophyll-a, harmful algae, suspended sedi-ment, submerged habitat and temperature, can be derived directly via the satellite remote sensing facilities, particu-larly through the oce...
Citations
... A pixel was rendered invalid for the matching analysis when it encountered one of the following criteria: a negative R rs value, land, clouds, a high satellite zenith angle (>60°), or a high solar zenith angle (>75°). R rs values outside the maximum limit (Equation 2) were also discarded, which means that R rs needs to meet the following Equation (1) (Concha et al., 2019): ...
Timely and accurate observations of harmful algal blooms dynamics help to coordinate coastal protection and reduce the damage in advance. To date, predicting changes in the spatial distribution of algal blooms has been challenging due to the lack of suitable tools. The paper proposes that the development and disappearance of algal bloom can be monitored by satellite remote sensing in a large area from the diurnal variation of chlorophyll a. In this paper, 32 pairs of observed data in 2011–2020 showed that it was most appropriate to outline the areas where the diurnal variation (the standard deviation calculated from the daily chlorophyll a) in chlorophyll a was more than 2.2 mg/m³. Among them, 30 pairs of data showed that the high chlorophyll a diurnal variation could predict the growth of the algal bloom in the next days. In these events, the median area difference between the two spatial distributions was -0.08%. When there was a high diurnal variation in chlorophyll a in the area adjacent to where algal bloom was occurred, a new algal bloom region was likely to spread in subsequent days. Continuous multiday time series showed that the diurnal variation in chlorophyll a can reflect the algal bloom’s overall growth condition.
... The GOCI Data Processing System (GDPS) is the official data processing software for GOCI. The standard AC algorithm of GOCI evolved initially based on the algorithms of SeaWiFS and MODIS, then expanded and improved according to the characteristics of GOCI's own instrument design and research fields [70,[75][76][77][78]. This model followed a regional empirical relationship between the (660) and two NIR bands ( (745) and (865)) (denoted as SR660). ...
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.
... Fig. 10 shows the potential of PUK for observing the vertical migration of red tides when GOCI succeeds in acquiring serial cloud-free scenes during a day. The diurnal stability of GOCI R rs was assessed by Concha et al. (2019), which assures the diurnal stability of the results of our PUK algorithm. Fig. 10 shows that the concentration of Chl a in one location increased in most of the areas, and the overall extent of red tide expanded, which is consistent with previous field studies ( Kim et al., 2010;Park et al., 2001) showing that M. polykrikoides tends to migrate to the surface to graze more sunlight at around 3-4 pm in the afternoon by controlling its own buoyancy. ...
... This analysis, however, did not include water bodies, whose examinations are critical for cross-mission consistency in the aquatic remote sensing domain (Barnes et al., 2021;IOCCG, 2012;Kwiatkowska et al., 2008;Zibordi et al., 2022). The differences in downstream aquatic science products (like chla, a cdom , SPM) could be traced to discrepancies in TOA observations for physics-based processing algorithms (Concha et al., 2019;Gordon, 1990;Mélin, 2019;Pahlevan et al., 2019). As part of a recent community-wide round-robin exercise, Pahlevan et al., 2021 reported the performance of eight different AC processors for L8 and S2. ...
With an identical design and build, the Operational Land Imager-2 (OLI2) aboard Landsat-9 (L9) complements OLI observations by reducing the global revisit rate of Landsat to 8 days. This study takes advantage of near-coincident OLI2 and OLI observations obtained on 11-17 November 2021 to assess the relative quality of the standard United States Geological Survey (USGS) top-of-atmosphere (TOA) reflectance (ρ t) and atmospherically corrected reflectance (aquatic reflectance; ρ AR w) products over bodies of water. The TOA assessment was carried out for all the visible bands, including the panchromatic band, as well as the near-infrared (NIR) and shortwave infrared (SWIR) bands, whereas ρ AR w products were analyzed in the 443, 482, 561, and 655 nm bands. The overlapping areas of OLI-OLI2 ρ w product pairs were further analyzed for the rigor in corrections for the viewing geometry implemented in selected atmospheric correction (AC) processors, including SeaDAS, ACOLITE, and POLYMER, with products denoted as ρ AR w , ρ ac w , and ρ pol w , respectively. Overall, we found the OLI2-OLI ρ t products to be consistent within 0.4% in the visible-near-infrared (VNIR) bands except in the green band (561 nm), where OLI2 records ~0.8% larger values than OLI. The two SWIR bands (1610 and 2200 nm) were also found to agree within ~2.2% and 2.1%, respectively, with OLI2 being lower in magnitude. The median differences in the standard ρ w (ρ AR w) were estimated to be ~2.4%, 1.5%, 2.3%, and 2.5% in the 443, 482, 561, and 655 nm bands, respectively, which are well within the accepted differences in cross-mission data merging schemes. Further, we show that OLI2's signal-to-noise ratio (SNR) is 7-30% higher than that of OLI in all the bands, likely due to its 14-bit digitization rate, as compared to OLI's 12-bit digitization rate. Our self-consistency assessment of the AC processors for handling the differences in the view zenith angles (ΔVZA) and relative azimuth angles (ΔRAA) of OLI2 and OLI observations suggests that, overall, the processors account for the Sun-sensor geometry differently at different spectral bands. These differences (inconsistencies) amount to average median absolute percentage differences (MAPD) in ρ w from 0.3 to 5.3% (e.g., Δρ AR w (443 nm, ΔVZA, ΔRAA) = ρ AR,OLI w (443, +6.3,10) − ρ AR,OLI2 w (443, +3.9,− 13) < 0.5%).More specifically, we found that SeaDAS provides the most optimal corrections for the angular variability as a function of VZA, ρ ac w are most consistent for different ranges of RAAs, and POLYMER performs best in the 443 nm band. We surmise that future (and other existing) AC methods should be tested with Landsat-8/-9 underfly imagery to quantify their performance for tackling the variability in imaging geometry and minimize associated uncertainties. With several missions planned for launch by the end of this decade, it is further emphasized that post-launch tandem maneuvers are essential to creating harmonized multi-mission data products and, therefore, similar operations should be considered and extended to ensure a broad range of environmental conditions are captured for comprehensive cross-mission analyses.
... Related studies have shown that the accuracy of GOCI products obtained at noon is higher than that in the morning and evening, and the deviation of data in bands 5 to 8 is larger than that at noon (Lamquin et al., 2012;Moon et al., 2012;Qiao et al., 2021). On the one hand, the weak light during the twilight periods increases the difficulty of AC and inhibits the collection of water color information; on the other hand, the large solar zenith angle and the observation zenith angle reduce the ability of the water color satellite to detect chlorophyll (Concha et al., 2019;Li H et al., 2019;Li et al., 2018). In addition, under the large solar zenith angle in the twilight periods, due to the influence of the large solar zenith angle and the curvature of the earth, there is also a certain error in the Rayleigh-corrected reflectivity calculated by the standard AC algorithm (Gordon et al., 1988;Wang, 2002;He et al., 2018). ...
Introduction
In the last decade, the outbreak of large-scale green tides caused by Ulva prolifera has continuously occurred in the Yellow Sea. Satellite remote sensing techniques have been widely used to monitor the distribution area and duration of green tides due to their advantages of their large-area synchronous observation. Ulva prolifera in the Yellow Sea is mainly distributed in bands or large patches during its flourishing stage. Previous studies have rarely reported the quantitative analysis of a single Ulva prolifera patch and its changes in the short term.
Methods
Considering the high temporal resolution of the Geostationary Ocean Color Imager (GOCI) sensor and the patchy distribution of Ulva prolifera floating on the sea surface, we developed a feasible method for monitoringUlva prolifera by performing clustering analysis with density-based spatial clustering of applications with noise (DBSCAN) to capture the diurnal variation characteristics of a single Ulva prolifera patch.
Results
This new approach was used to extract informationfrom a single Ulva prolifera patch in the Yellow Sea in 2012 and 2017. The results showed that during the time of GOCI imaging, the tidal current was the main factor driving the drift of Ulva prolifera, and the drifting direction of Ulva prolifera was consistent with the direction of the local tidal current, with a coefficient of determination of 0.94.
Discussion
By changing the normalized difference vegetation index (NDVI) threshold, further more accurate atmospheric correction (AC) of GOCI data during the twilight periods was indirectly achieved. By comparing the areal change in the single patch before and after AC, we speculated that the daily change in signal intensity received by the GOCI sensor may be the main reason for the diurnal variation in the Ulva proliferacoverage area. The results showed the details of the diurnal variation in Ulvaprolifera patches in the dynamic marine environment, and the main reason that may cause this variation was speculated.
... Therefore, accurate, high-resolution ancillary data are required for climate research in the ocean-color remote sensing field. The higher MAPE values in the red and NIR bands ( Figure 6) were related to their low magnitude [52]. ...
... Therefore, accurate, high-resolution ancillary data are required for climate research in the ocean-color remote sensing field. The higher MAPE values in the red and NIR bands ( Figure 6) were related to their low magnitude [52]. Comparisons of the other three ocean-color products (CHL, CDOM, and TSS) yielded similar results to the R rs comparison, with a low bias and slight scattering of data points (Figure 7). ...
... Unlike R rs , CHL, CDOM, and TSS showed nonsignificant overall MAPEs (3.53%, 6.18%, and 7.71%, respectively) based on different TPW sources, considering that the GCOS requirement for CHL is 30%. However, because the improved accuracy of the GOCI-II ocean-color data by AMI TPW depended on the sunlight path length, the application of AMI TPW for GOCI-II ocean-color retrieval may offer advantages in the analysis of diurnal variation [52] and long-term data with spatiotemporal consistency [53]. ...
In remote sensing of the ocean color, in particular, in coarse-resolution global model simulations, atmospheric trace gases including water vapor are generally treated as auxiliary data, which create uncertainties in atmospheric correction. The second Korean geostationary satellite mission, Geo-Kompsat 2 (GK-2), is unique in combining visible and infrared observations from the second geostationary ocean color imager (GOCI-II) and the advanced meteorological imager (AMI) over Asia and the Pacific Ocean. In this study, we demonstrate that AMI total precipitable water (TPW) data to allow realistic water vapor absorption correction of GOCI-II color retrievals for the ocean. We assessed the uncertainties of two candidate TPW products for GOCI-II atmospheric correction using atmospheric sounding data, and then analyzed the sensitivity of four ocean-color products (remote sensing reflectance [Rrs], chlorophyll-a concentration [CHL], colored dissolved organic matter [CDOM], and total suspended sediment [TSS]) for GOCI-II water vapor transmittance correction using AMI and global model data. Differences between the TPW sources increased the mean absolute percentage error (MAPE) of Rrs from 2.97% to 6.43% in the blue to green bands, higher than the global climate observing system requirements (<5%) at 412 nm. By contrast, MAPE values of 3.53%, 6.18%, and 7.71% were increased to 6.63%, 13.53%, and 16.14% at high sun and sensor zenith angles for CHL, CDOM, and TSS, respectively. Uncertainty analysis provided similar results, indicating that AMI TPW produced approximately 3-fold lower error rates in ocean-color products than obtained using TPW values from the National Centers for Environmental Prediction. These results imply that AMI TPW can improve the accuracy and ability of GOCI-II ocean-color products to capture diurnal variability.
... For this reason, for example, the data from the GOCI radiometer located on a geostationary satellite, which makes it possible to survey the Sea of Japan every hour, are not used at observation times earlier than 9:00 or later than 16:00 local time. Even for this time interval, though, it is necessary to make measurement corrections depending on the local time [21]. Errors in bioparameter calculations using NIR correction at low sun angles can be significant. ...
The environmental disaster in Kamchatka in the autumn of 2020 was caused by an extensive bloom of harmful microalgae of the genus Karenia. A spectral shape algorithm was used to detect algae on satellite imagery. The algorithm calibration of in situ species composition data made it possible to identify areas where harmful algae dominated in biomass. The algorithm allowed evaluation of the dynamics of the distribution of the algae. The state of phytoplankton was estimated based on images of the specific capacity of photosynthesis. Specific fluorescence is the ratio of the height of the fluorescence line (flh) to the concentration of chlorophyll-a (chl-a). The parameter was used to recognize the stages of the algal bloom: intensive growth, blooming, and change in the dominant algal species. In addition, an increase in the concentration of harmful substances in the coastal zone due to wind impact was analyzed. After analyzing the available data, the events that caused the ecological disaster can be summarized as follows. After the stage of intensive growth of microalgae, nutrient deficiency stimulated the production of metabolites that have a harmful effect on the environment. The change of the dominant alga species in the second half of September and the past storm contributed to a sharp increase in the concentration of metabolites and dead organic matter in the coastal zone, which caused an ecological disaster. The subsequent mass bloom of alga species of the same genus, and the regular wind impact leading to the concentration of harmful substances in the coastal zone, contributed to the development of this catastrophic phenomenon.
... For this reason, for example, the data from the GOCI radiometer located on a geostationary satellite, which make it possible to survey the Sea of Japan every hour, are not used at observation times earlier than 9:00 or later than 16:00 local time. Even for this time interval, though, it is necessary to make measurement corrections depending on the local time [21]. Errors in bioparameter calculations using NIR correction at low sun angles can be significant. ...
The environmental disaster in Kamchatka in the autumn of 2020 was caused by an extensive bloom of harmful microalgae of the genus Karenia. A spectral shape algorithm was used to detect algae. The algorithm calibration of in situ species composition data made it possible to identify areas where harmful algae dominated in biomass. Satellite images of chlorophyll-a concentra-tion, turbidity, specific fluorescence, and spectral shape parameter were computed. The images were used to recognize the stages of algal bloom: intensive growth, blooming, and change in the dominant algal species. Cases of an increase in the concentration of harmful substances in the coastal zone due to wind impact were analyzed. The following explanation of events has been offered. After the stage of intensive growth of microalgae, nutrient deficiency stimulated the production of metabolites that have a harmful effect on the environment. The change of the dominant alga species in the second half of September and the past storm contributed to a sharp increase in the concentration of metabolites and dead organic matter in the coastal zone, which caused an ecological disaster. The subsequent mass bloom of alga species of the same genus, and the regular wind impact leading to the concentration of harmful substances in the coastal zone, contributed to the development of this catastrophic phenomena.
... Using coincident daily R rs from two sensors or matching satellite retrieved and in situ R rs , [14] established an approach based on collocation analysis to generate R rs uncertainty associated with random effects. Using geostationary measurement from Geostationary Ocean Color Imager (GOCI) collected over the course of a day, and with the assumption that no detectable changes occur in the optical properties over waters with low productivity during the daytime period, uncertainty in R rs is calculated as twice the standard deviation of multiple observations in one day [15]. Although some issues with validation using in situ data could be resolved by the image-based approaches, the uncertainty derived is either valid for a specific dataset or only includes the random uncertainty. ...
The spectral distribution of marine remote sensing reflectance, Rrs, is the fundamental measurement of ocean color science, from which a host of bio-optical and biogeochemical properties of the water column can be derived. Estimation of uncertainty in these derived properties is thus dependent on knowledge of the uncertainty in satellite-retrieved Rrs (uc(Rrs)) at each pixel. Uncertainty in Rrs, in turn, is dependent on the propagation of various uncertainty sources through the Rrs retrieval process, namely the atmospheric correction (AC). A derivative-based method for uncertainty propagation is established here to calculate the pixel-level uncertainty in Rrs, as retrieved using NASA’s multiple-scattering epsilon (MSEPS) AC algorithm and verified using Monte Carlo (MC) analysis. The approach is then applied to measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite, with uncertainty sources including instrument random noise, instrument systematic uncertainty, and forward model uncertainty. The uc(Rrs) is verified by comparison with statistical analysis of coincident retrievals from MODIS and in situ Rrs measurements, and our approach performs well in most cases. Based on analysis of an example 8-day global products, we also show that relative uncertainty in Rrs at blue bands has a similar spatial pattern to the derived concentration of the phytoplankton pigment chlorophyll-a (chl-a), and around 7.3%, 17.0%, and 35.2% of all clear water pixels (chl-a ≤ 0.1 mg/m³) with valid uc(Rrs) have a relative uncertainty ≤ 5% at bands 412 nm, 443 nm, and 488 nm respectively, which is a common goal of ocean color retrievals for clear waters. While the analysis shows that uc(Rrs) calculated from our derivative-based method is reasonable, some issues need further investigation, including improved knowledge of forward model uncertainty and systematic uncertainty in instrument calibration.
... Ocean surveying is breaking through the traditional limitation of time and space, entering into the new modern marine measurement stage [1,2]. In the age of digital measurement, 3S (GNSS, GIS [3,4], RS [5,6]) technology is representative. The new technology not only provides high-precision positioning and depth information [7][8][9], but also expands the technical means of obtaining information on oceanographic surveys. ...
Marine surveying is an important part of marine environment monitoring systems. In order to improve the accuracy of marine surveying and reduce investment in artificial stations, it is necessary to use high-precision GNSS for shipborne navigation measurements. The basic measurement is based on the survey lines that are already planned by surveyors. In response to the needs of survey vessels sailing to the survey line, a method framework for the shortest route planning is proposed. Then an intelligent navigation system for survey vessels is established, which can be applied to online navigation of survey vessels. The essence of the framework is that the vessel can travel along the shortest route to the designated survey line under the limitation of its own minimum turning radius. Comparison and analysis of experiments show that the framework achieves better optimization. The experimental results show that our proposed method can enable the vessel to sail along a shorter path and reach the starting point of the survey line at the specified angle.
