Spatial and temporal characteristics and removal methodology of suspended particulate matter speckles from Geostationary Ocean Color Imager data

Lee, Min-Sun; Park, Kyung-Ae; Moon, Jai-Woon; Kim, Wonkook; Park, Young-Je

doi:10.1080/01431161.2018.1552818

Cited by 4 publications

(7 citation statements)

References 53 publications

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“…In contrast, the MFNN method exhibited high performance capability with regard to the abnormal pixel removal by eliminating 45% of the original pixels (Figure 14d). Previous studies have reported the high performance of the threshold-based scheme for removal speckles in ocean color images [14,16]. The accuracy obtained from the confusion matrix of the threshold method based on the spatial uniformity within 3 × 3 pixels was 91.7% for the image obtained on 13 January 2015 ( Figure 14).…”

Section: Discussionmentioning

confidence: 86%

“…In addition, high-altitude aerosol conditions around cloud edges are likely to be different from the aerosol type of the background field especially in the open ocean, which makes it difficult to choose the proper aerosol model in the atmospheric correction procedure. This type of improper selection in the aerosol models may also lead to the emergence of an unexpected low chl-a concentration distribution [16]. Thus, the spectral characteristics of such erroneous pixels should be investigated.…”

Section: Abnormal Chlorophyll-a Features Around Cloudsmentioning

confidence: 99%

“…Erroneous low-value pixels are also apparent in GOCI data. In GOCI chl-a images, these errors frequently appear as speckle noise in a single pixel, unexpectedly congregated patches, or array-like features; they may be associated with unsuccessful atmospheric corrections caused by factors such as aerosol and cloud contamination or instrumental-related artifacts such as slot-related stray light (e.g., [16]). Unlike traditional ocean color sensors, the target area of GOCI is covered by 16 consecutive acquisitions in 4 × 4 slots, which tends to induce inconsistencies and noise between the adjacent slots in GOCI products (Figure 1b) [17,18].…”

Section: Introductionmentioning

confidence: 99%

“…Methodologies for the removal of erroneous pixels in ocean color images has been proposed based on the spatial and temporal distribution of the data values [8,9,16,21,22]. Most of them were based on smoothing scheme or thresholds to eliminate speckles in the chl-a concentration images; however, this is mainly advantageous when the distribution of the image background field does not change much, or when the speckle values are extremely high compared with the surroundings.…”

Section: Introductionmentioning

confidence: 99%

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Application of Deep Learning for Speckle Removal in GOCI Chlorophyll-a Concentration Images (2012–2017)

Park

2021

Remote Sensing

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The detection and removal of erroneous pixels is a critical pre-processing step in producing chlorophyll-a (chl-a) concentration values to adequately understand the bio-physical oceanic process using optical satellite data. Geostationary Ocean Color Imager (GOCI) chl-a images revealed that numerous speckle noises with enormously high and low values were randomly scattered throughout the seas around the Korean Peninsula as well as in the Northwest Pacific. Most of the previous methods used to remove abnormal chl-a concentrations have focused on inhomogeneity in spatial features, which still frequently produce problematic values. Herein, a scheme was developed to detect and eliminate chl-a speckles as well as erroneous pixels near the boundary of clouds; for the purpose, a deep neural network (DNN) algorithm was applied to a large-sized GOCI database from the 6-year period of 2012–2017. The input data of the proposed DNN model were composed of the GOCI level-2 remote-sensing reflectance of each band, chl-a concentration image, median filtered, and monthly climatology chl-a image. The quality of the individual images as well as the monthly composites of chl-a data was improved remarkably after the DNN speckle-removal procedure. The quantitative analyses showed that the DNN algorithm achieved high classification accuracy with regard to the detection of error pixels with both very high and very low chl-a values, and better performance compared to the general arithmetic algorithms of the median filter and threshold scheme. This implies that the implemented method can be useful for investigating not only the short-term variations based on hourly chl-a data but also long-term variabilities with composite products of the GOCI chl-a concentration over the span of a decade.

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Section: Discussionmentioning

confidence: 86%

Section: Abnormal Chlorophyll-a Features Around Cloudsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Deep Learning for Speckle Removal in GOCI Chlorophyll-a Concentration Images (2012–2017)

Park

2021

Remote Sensing

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“…where NRTI refers to normalized RTI. Before applying the NRTI to GOCI, speckles in the GOCI images were first removed by using data on SPM [34] and then calculated chlorophyll-a concentration [52,53] using a 3-band ocean color algorithm (called OC3 algorithm) ( Figure 7). Pixels with negative values of R rs (λ) were regarded as failures of atmospheric correction and replaced with NaN values.…”

Section: Development Of a New Normalized Red Tide Intensity Index (Nrti)mentioning

confidence: 99%

Derivation of Red Tide Index and Density Using Geostationary Ocean Color Imager (GOCI) Data

2021

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Red tide causes significant damage to marine resources such as aquaculture and fisheries in coastal regions. Such red tide events occur globally, across latitudes and ocean ecoregions. Satellite observations can be an effective tool for tracking and investigating red tides and have great potential for informing strategies to minimize their impacts on coastal fisheries. However, previous satellite-based red tide detection algorithms have been mostly conducted over short time scales and within relatively small areas, and have shown significant differences from actual field data, highlighting a need for new, more accurate algorithms to be developed. In this study, we present the newly developed normalized red tide index (NRTI). The NRTI uses Geostationary Ocean Color Imager (GOCI) data to detect red tides by observing in situ spectral characteristics of red tides and sea water using spectroradiometer in the coastal region of Korean Peninsula during severe red tide events. The bimodality of peaks in spectral reflectance with respect to wavelengths has become the basis for developing NRTI, by multiplying the heights of both spectral peaks. Based on the high correlation between the NRTI and the red tide density, we propose an estimation formulation to calculate the red tide density using GOCI data. The formulation and methodology of NRTI and density estimation in this study is anticipated to be applicable to other ocean color satellite data and other regions around the world, thereby increasing capacity to quantify and track red tides at large spatial scales and in real time.

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Incidence of harmful algal blooms in pristine subtropical ocean: a satellite remote sensing approach (Jeju Island)

2023

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Despite the increasing numbers of red tide events in the pristine subtropical ocean, a paucity of previous observations has limited understanding of harmful algae in the seas around the Korean Peninsula. Therefore, using six years (2012–2017) of Geostationary Ocean Color Imager (GOCI) satellite data, we characterized the red tides around Jeju Island, a volcanic island located near the paths of the Jeju Warm Current and Tsushima Warm Current, using the Normalized Red Tide Index (NRTI) method. The seawater around Jeju Island has for a long time been considered to be very clear, with relatively low suspended particulate matter concentrations and few harmful algae. Nonetheless, the satellite-based NRTI detection method used in this study detected and supported the existence of red tides in the coastal region around Jeju Island. Analysis of the red tide distribution showed that red tide first began to appear near the western coast of Jeju Island, then developed in the northern and eastern coastal regions, and finally vanished in the eastern coastal region. The monthly averages of the NRTI demonstrated a bloom event from April to May in every year. Additional fall blooms were detected in August–September, particularly in 2013 and 2016. The NRTI revealed strong interannual variations. The longest blooms occurred in 2015, and the most comprehensive and strongest event occurred in the spring of 2016. The latter three years (2015–2017) had much higher NRTI than the former three years (2012–2014). The probability of red tide occurrence at a given point during the 6-year study period revealed spatial differences. Relatively high probability of 0.3–0.5 was determined along the northern coastal region, whereas low probability of less than 0.2 was found along the southern region. Ground truth data also showed more frequent observations and higher red tide cell densities along the northern coast. Changes in NRTI in spring are positively correlated with changes in ENSO indices in winter. This study is the first to use a satellite-based approach with a vast long-term satellite database to elucidate the existence and probability of red tides near Jeju Island. We anticipate that this study will provide a useful strategy for remote monitoring of harmful algal blooms over wide regions using optical data.

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Spatial and temporal characteristics and removal methodology of suspended particulate matter speckles from Geostationary Ocean Color Imager data

Cited by 4 publications

References 53 publications

Application of Deep Learning for Speckle Removal in GOCI Chlorophyll-a Concentration Images (2012–2017)

Application of Deep Learning for Speckle Removal in GOCI Chlorophyll-a Concentration Images (2012–2017)

Derivation of Red Tide Index and Density Using Geostationary Ocean Color Imager (GOCI) Data

Incidence of harmful algal blooms in pristine subtropical ocean: a satellite remote sensing approach (Jeju Island)

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