• Proceedings of the KSRS Conference
• /
• v.1
• /
• pp.487-490
• /
• 2006

Ocean Color Monitor (OCM) onboard the Indian Remote Sensing Satellite IRS-P4 has been used to retrieve chlorophyll concentration in the Bay of Bengal and the Arabian Sea using a bio-optical algorithm. Cloud masking and atmospheric corrections have been performed before applying mapping function to derive chlorophyll concentration from IRS-P4 OCM data. We have retrieved chlorophyll concentration from OCM, and MODIS during the summer and winter season along the eastern and western coast of India at every 1 degree latitude at increasing distance (25, 50, 100, 150 and 200km) away from the coast as well as near river mouths for the period 2000-2003. We have also studied spatial and temporal dynamics of monthly MODIS Aqua (for period July 2002-April 2004). The seasonal dynamics of chlorophyll concentration over the Bay of Bengal and the Arabian Sea have been discussed using OCM and MODIS for both the coastal region and the open sea.

• Korean Journal of Remote Sensing
• /
• v.31 no.5
• /
• pp.473-490
• /
• 2015

There are diverse sea areas within the coverage of GOCI which is observed around the Korea at one-hour intervals. It includes not only very clear ocean of East Sea, but also extremely turbid waters of the Yangtze River estuary. In this study, we analyzed the different optical characteristics of various sea areas using absorption coefficients of phytoplankton, Suspended Particulate Matter(SPM), Dissolved Organic Matter(DOM). Totally 959 sets of bio-optical and marine environmental data were obtained from 2009 to 2014 around the sea area of Korea. The East Sea, South Sea, East China Sea and offshore part of Yellow Sea showed similar pattern having high levels of contribution of phytoplankton and DOM. On the other hands, the coastal part of Mokpo and Gyeonggi Bay showed opposite pattern having high levels of contribution of SPM and DOM. As a result of the algorithm performance for chlorophyll-a(Chl-a) and SPM, Chl-a is mostly overestimated and SPM is mainly tended to be underestimated. Large amount of errors are induced by the SPM rather than the chl-a and DOM. These errors are primarily founded in the coastal waters having relatively high levels of $a_{SPM}$ contribution of more than 60%.

• Korean Journal of Remote Sensing
• /
• v.39 no.6_2
• /
• pp.1565-1576
• /
• 2023

An atmospheric correction algorithm based on the radiative transfer model is required to obtain remote-sensing reflectance (R_rs) from the Geostationary Ocean Color Imager-II (GOCI-II) observed at the top-of-atmosphere. This R_rs derived from the atmospheric correction is utilized to estimate various marine environmental parameters such as chlorophyll-a concentration, total suspended materials concentration, and absorption of dissolved organic matter. Therefore, an atmospheric correction is a fundamental algorithm as it significantly impacts the reliability of all other color products. However, in clear waters, for example, atmospheric path radiance exceeds more than ten times higher than the water-leaving radiance in the blue wavelengths. This implies atmospheric correction is a highly error-sensitive process with a 1% error in estimating atmospheric radiance in the atmospheric correction process can cause more than 10% errors. Therefore, the quality assessment of R_rs after the atmospheric correction is essential for ensuring reliable ocean environment analysis using ocean color satellite data. In this study, a Quality Assurance (QA) algorithm based on in-situ R_rs data, which has been archived into a database using Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Archive and Storage System (SeaBASS), was applied and modified to consider the different spectral characteristics of GOCI-II. This method is officially employed in the National Oceanic and Atmospheric Administration (NOAA)'s ocean color satellite data processing system. It provides quality analysis scores for R_rs ranging from 0 to 1 and classifies the water types into 23 categories. When the QA algorithm is applied to the initial phase of GOCI-II data with less calibration, it shows the highest frequency at a relatively low score of 0.625. However, when the algorithm is applied to the improved GOCI-II atmospheric correction results with updated calibrations, it shows the highest frequency at a higher score of 0.875 compared to the previous results. The water types analysis using the QA algorithm indicated that parts of the East Sea, South Sea, and the Northwest Pacific Ocean are primarily characterized as relatively clear case-I waters, while the coastal areas of the Yellow Sea and the East China Sea are mainly classified as highly turbid case-II waters. We expect that the QA algorithm will support GOCI-II users in terms of not only statistically identifying R_rs resulted with significant errors but also more reliable calibration with quality assured data. The algorithm will be included in the level-2 flag data provided with GOCI-II atmospheric correction.

• Journal of the Korean earth science society
• /
• v.43 no.5
• /
• pp.591-603
• /
• 2022

The color and optical properties of seawater are determined by the interaction between dissolved organic and inorganic substances and plankton contained in it. The Ieodo - Ocean Research Institute (I-ORS), located in the East China Sea, is affected by the low salinity of the Yangtze River in the west and the Tsushima Warm Current in the south. Thus, it is a suitable site for analyzing the fluctuations in circulation and optical properties around the Korean Peninsula. In this study, seawater surrounding the I-ORS was classified according to its optical characteristics using the satellite remote reflectance observed with Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua and National Aeronautics and Space Administration (NASA) bio-Optical Marine Algorithm Dataset (NOMAD) from January 2016 to December 2020. Additionally, the variation characteristics of optical water types (OWTs) from different seasons were presented. A total of 59,532 satellite match-up data (d ≤ 10 km) collected from seawater surrounding the I-ORS were classified into 23 types using the spectral angle mapper. The OWTs appearing in relatively clear waters surrounding the I-ORS were observed to be greater than 50% of the total. The maximum OWTs frequency in summer and winter was opposite according to season. In particular, the OWTs corresponding to optically clear seawater were primarily present in the summer. However, the same OWTs were lower than overall 1% rate in winter. Considering the OWTs fluctuations in the East China Sea, the I-ORS is inferred to be located in the transition zone of seawater. This study contributes in understanding the optical characteristics of seawater and improving the accuracy of satellite ocean color variables.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.12
• /
• pp.1935-1941
• /
• 2021

In order to develop an algorithm using deep learning, which has been recently applied to various fields, it is necessary to have rich, high-quality learning data. In this paper, we propose an acquisition system for biological signals that simultaneously collects bio-signal data such as optical videos, thermal videos, and voices, which are mainly used in developing deep learning algorithms and useful in derivation of information, and transmit them to the server. To increase the portability of the collector, it was made based on Raspberry Pi, and the collected data is transmitted to the server through the wireless Internet. To enable simultaneous data collection from multiple collectors, an ID for login was assigned to each subject, and this was reflected in the database to facilitate data management. By presenting an example of biological data collection for fatigue measurement, we prove the application of the proposed acquisition system.