Wonkook Kim

Pusan National University | PNU · Department of Civil and Environmental Engineering

For the past three decades, polar-orbiting ocean color satellites have provided local, regional to global scale estimates of oceanic net primary production that have greatly aided studies of ocean carbon cycling, food web dynamics and climate change. Despite considerable progress, accurate estimates of daily ocean productivity from space have not b...

Questions of whether diurnal changes in carbon fixation affect the global carbon budget cannot be answered using the present generation of polar orbiting ocean color sensors that can only retrieve one image daily. Here, we present novel satellite-derived indices of chlorophyll-based production based on the Geostationary Ocean Color Imager (GOCI), w...

Airborne hyperspectral data play an important role in remote sensing of coastal waters. However, before their application, atmospheric correction is required to remove or reduce the atmospheric effects caused by molecular and aerosol scattering and absorption. In this study, we first processed airborne hyperspectral CASI-1500 data acquired on 4 May...

Pixel-wise classification in remote sensing identifies entities in large-scale satellite-based images at the pixel level. Few fully annotated large-scale datasets for pixel-wise classification exist due to the challenges of annotating individual pixels. Training data scarcity inevitably ensues from the annotation challenge, leading to overfitting c...

Geostationary Ocean Color Imager (GOCI) observations are applied to marine fog (MF) detection in combination with Himawari-8 data based on the decision tree (DT) approach. Training and validation of the DT algorithm were conducted using match-ups between satellite observations and in situ visibility data for three Korean islands. Training using dif...

Volumetric generation of earth-observing data from the recent geostationary satellite can provide synergy of spatio-temporal retrieval of surface reflectance as well as aerosol optical thickness (AOT). Implementing the algorithm to determine the AOT based on the pre-defined geometry dependent surface reflectance data is an effective way to monitor...

Plain Language Summary The primary question that motivated this work was how to evaluate near‐surface submesoscale (i.e., between mesoscale and microscale) turbulence in an Eulerian framework over an area of a few hundred kilometers. As this has not been possible with conventional observational platforms, we suggested a novel approach using a geost...

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...

A key on-orbit calibration step for satellite remote sensing of ocean color is the vicarious calibration. This establishes the final gains for each spectral band on the sensor that minimize bias in the retrieved ocean color signal. The vicarious calibration is specific to the instrument and the atmospheric correction algorithm. The vicarious calibr...

This study assessed the uncertainty in remote sensing reflectance derived from the four widely-used hyperspectral above-water radiometers. The results showed that the uncertainty varies by radiometers, ranging from 10 % to 30 %.

Currently, accurate detection of natural phenomena, such as red tide, that adversely affect wildlife and human, using satellite images has been increasingly utilized. However, red tide detection on satellite images still remains a very hard task due to unpredictable nature of red tide occurrence, extreme sparsity of red tide samples, difficulties i...

Reviewing six years of Geostationary Ocean Color Imager (GOCI) suspended particulate matter (SPM) concentration images from 2011 to 2016 revealed unexpected and some enormously high or low values. These speckles are randomly scattered throughout the entire study area or congregated at a certain part, which has strongly restricted the scientific app...

Short-term (hours) 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 the stu...

The climate-induced decrease in fish catches in South Korea has been a big concern over the last decades. The increase in sea surface temperature (SST) due to climate change has led to not only a decline in fishery landings but also a shift in the fishing grounds of several fish species. The habitat suitability index (HSI), a reliable indicator of...

Min, S.-H.; Park, M.-O.; Kim, S.-W.; Han, I-S.; Kim, W., and Park, Y-J., 2018. Using GOCI Data for Detection of Coastal Upwelling at East/Japan Sea. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 1471–...

Accurate and timely quantification of widespread harmful algal bloom (HAB) distribution is crucial to respond to the natural disaster, minimize the damage, and assess the environmental impact of the event. Although various remote sensing-based quantification approaches have been proposed for HAB since the advent of the ocean color satellite sensor,...

Green tides that developed along the coast of China in 2015 were detected and tracked using vegetation indices from GOCI and Landsat images. Green tides first appeared near the Jiangsu Province on May 14 before increasing in size and number and moving northward to the Shandong Peninsula in mid-June. Typhoon Cham-hom passed through the Yellow Sea on...

Remote sensing has been successfully utilized to distinguish and quantify sediment properties in the intertidal environment. Classification approaches of imagery are popular and powerful yet can lead to site- and case-specific results. Such specificity creates challenges for temporal studies. Thus, this paper investigates the use of regression mode...

An estimation of the aerosol multiple-scattering reflectance is an important part of the atmospheric correction procedure in satellite ocean color data processing. Most commonly, the utilization of two near-infrared (NIR) bands to estimate the aerosol optical properties has been adopted for the estimation of the effects of aerosols. Previously, the...

The Geostationary Ocean Color Imager (GOCI) is the world's first ocean color sensor in geostationary orbit. Although the GOCI has shown excellent radiometric performance with little long-term radiometric degradation and a high signal-to-noise ratio, there are radiometric artefacts in GOCI Level 1 products caused by stray light detected within the G...

Opaque masses (e.g., cloud and haze) are the main obstacles interrupting remote observations of ocean color using optical sensors. We performed a statistical analysis for 1 year of ocean color data derived from the Geostationary Ocean Color Imager (GOCI), which performs eight observations per day. We discovered that the valid ranges of the data var...

The radiometric calibration of satellite data is critical in many environmental studies and applications that are based on remote sensing data. The Geostationary Ocean Color Imager (GOCI) has suffered from what is called an interslot radiometric discrepancy (ISRD), which creates clear inconsistency between the adjacent slots in GOCI Level 1B (L1B)...

Measurements of ocean color from Geostationary Ocean Color Imager (GOCI) with a moderate spatial resolution and a high temporal frequency demonstrate high value for a number of oceanographic applications. This study aims to propose and evaluate the calibration of GOCI as needed to achieve the level of radiometric accuracy desired for ocean color st...

The monitoring of top-of-atmosphere (TOA) reflectance time series provides useful information regarding the long-term degradation of satellite sensors. For a precise assessment of sensor degradation, the TOA reflectance time series is usually corrected for surface and atmospheric anisotropy by using bidirectional reflectance models so that the angu...

Radiometric sensor calibration is critical for quantitative use of the data obtained from the FeungYun-3A MEdium-Resolution Spectral Imager (MERSI) sensor. To meet the required calibration criteria, several vicarious calibration (VC) techniques are being employed in the current operational calibration of MERSI. This letter presents the independent...

The Sonoran Desert, which is located in North America, has been frequently used for vicarious calibration of many optical sensors in satellites. Although the desert area has good conditions for vicarious calibration (e.g. high reflectance, little vegetation, large area, low precipitation), its adjacency to the sea and large variability in atmospher...

Sonoran Desert is a potential pseudo-invariant site that can be used for vicarious calibration of satellite sensors. However, the surface and atmospheric anisotropy of the site first needs to be characterized for the precise evaluation of the long-term stability of the sensors. In this study, the anisotropy of the desert site is investigated by app...

Increased availability of hyperspectral data and greater access to advanced computing have motivated development of more advanced methods for exploitation of nonlinear characteristics of these data. Advances in manifold learning developed within the machine learning community are now being adapted for analysis of hyperspectral data. This chapter in...

Accurate land cover classification that ensures robust mapping under diverse acquisition conditions is important in environmental studies where the identification of the land cover changes and its quantification have critical implications for management practices, functioning of ecosystems, and impact of climate. While remote sensing data have serv...

Localized training data typically utilized to develop a classifier may not be fully representative of class signatures over large areas but could potentially provide useful information which can be updated to reflect local conditions in other areas. An adaptive classification framework is proposed for this purpose, whereby a kernel machine is first...

Hyperspectral imagery (HSI) derived from remote sensing can delineate surface properties of substrates such as type, moisture, and grain size. These are critical parameters that determine the substrate bearing strength. Although HSI only sees the surface layer, statistics can be derived that relate surface properties to the likely bearing strength...

Introduction: In September 2007, NRL, in part-nership with multiple institutions, undertook a com-bined airborne multi-sensor remote sensing campaign and in situ validation effort. The experiment, VCR'07, took place at the Virginia Coast Reserve (VCR), a National Science Foundation–funded Long Term Eco-logical Research (LTER) Site on the Eastern Sh...

Remote sensing data sets are often difficult to compare directly due to environmental changes between acquisitions of two data sets. This paper proposes an adaptive framework for robust classification when no reference data are available in a new area or time period. Labels of test data are recovered during iterative applications of kernel machines...

Statistical classification of hyperspectral data is challenging because the inputs are high in dimension, while the quantity of labeled data is typically limited. The resulting classifiers are often unstable and have poor generalization. Nonlinear manifold learning algorithms assume that the original high dimensional data actually lie on a low dime...

Introduction: In September 2007, NRL, in part-nership with multiple institutions, undertook a com-bined airborne multi-sensor remote sensing campaign and in situ validation effort. The experiment, VCR'07, took place at the Virginia Coast Reserve (VCR), a National Science Foundation–funded Long Term Eco-logical Research (LTER) Site on the Eastern Sh...

A classifier derived from labeled samples acquired over an extended area may not perform well for a specific sub-region if the spectral signatures of classes vary across the image. However, characterizing the local effects are an ill-posed problem, particularly for hyperspectral data, since an adequate number of labeled samples is not typically ava...

In September 2007, NRL, in partnership with multiple institutions, undertook a combined airborne multi-sensor remote sensing campaign and in situ validation effort. The experiment took place at the Virginia coast reserve (VCR'07), an NSF funded long term ecological research site (LTER) on the Eastern shore of Virginia [10]. The study area comprised...

Nonlinear manifold learning algorithms assume that the original high dimensional data actually lie on a low dimensional manifold defined by local geometric distances between samples. Most of the traditional methods have focused only on the spectral distances in calculating the local dissimilarity of samples, whereas in the case of image data, the s...