Results 61 to 70 of about 793 (204)

A review of the FATIMA Yellow Sea field campaign research

Quarterly Journal of the Royal Meteorological Society, Volume 152, Issue 777, April 2026 Part B.
The FATIMA marine fog project took place over the Sable Island region of eastern Canada (summer 2022) and the Yellow Sea of the Republic of Korea (ROK, summer 2023). Its goals are to improve marine fog monitoring and forecasting. Instrumented multiple research vessels, aircraft, tethered balloon and autonomous systems, ocean observation sites, and ...
Seok Lee, Ismail Gultepe, H. J. S. Fernando, Saša Gaberšek, Luc Lenain, Qing Wang, Edward D. Creegan, Eric Pardyjak, Clive Dorman, Lossif Lozovatsky, David G. Ortiz‐Suslow, Jesus Ruiz‐Plancarte, Ryan Yamaguchi, Eren Gultepe, S. Hoch, A. Perelet, D. Singh, Joo Wan Cha, Lian Shen, Anup Vaman Barve, Anne Dowling, Sen Wang, Gianina Giacosa, Laurent Grare, Peter Taylor, Zheqi Chen, Rachel Y.‐W. Chang, Piyush Teeloku, Nicholas Gapp, Seung‐Woo Lee, Jong‐Min Jeong, Hojin Kim, Jin‐Yong Jeong, Ki‐Young Heo, Minsang Kim, Myung‐Sook Park, Kyung‐Hee Oh, Joon‐Seong Park, Dong‐Chull Jeon, Kyu‐Min Song, Jae‐Yeob Kim, Dawoon Jung, Kyung‐Il Chang, Jae Hak Lee, Kyung Man Kim, Daehun Yu, Seong Soo Yum, Andrew J. Heymsfield, Martin Agelin‐Chaab, Tae‐Young Goo, Dong‐Gyun Ryu   +50 more
wiley   +1 more source

An Expanded Three Band Model to Monitor Inland Optically Complex Water Using Geostationary Ocean Color Imager (GOCI)

Frontiers in Remote Sensing, 2022
Due to strict spectral band requirements, the three-band (TB) chlorophyll-a concentration (Cchla) estimation algorithm cannot be applied to GOCI image, which has great potential in frequently monitoring inland complex waters. In this study, the TB algorithm was expanded and applied to GOCI data.
Yulong Guo, Yulong Guo, Changchun Huang, Yunmei Li, Chenggong Du, Yuan Li, Weiqiang Chen, Weiqiang Chen, Lingfei Shi, Lingfei Shi, Guangxing Ji, Guangxing Ji   +11 more
openaire   +2 more sources

A Cloud Screening Preprocessing Framework for GOCI‐II Aerosol Optical Depth: Development and Impact on Retrieval Errors

Journal of Geophysical Research: Atmospheres, Volume 131, Issue 1, 16 January 2026.
Abstract Accurate retrieval of satellite‐derived aerosol optical depth (AOD) is critical for air quality forecasting, especially when AOD is assimilated into models. However, if retrieval errors in the satellite‐derived AOD are not corrected or characterized, they can lead to false analysis increments and ultimately degrade the quality of data ...
Joonhee Kim, Ukkyo Jeong, Sang‐Woo Kim, Yonghee Lee, Taehee Kim, Hyeong‐Ahn Kwon, Sang‐Moo Lee   +6 more
wiley   +1 more source

Exploiting Machine Learning to Develop Ocean Color Retrievals From the Tropospheric Emissions: Monitoring of Pollution Instrument

Earth and Space Science, Volume 12, Issue 11, November 2025.
Abstract Retrievals of ocean color (OC) properties from space are important for understanding the ocean ecosystem, the carbon cycle, and monitoring events such as harmful algal blooms (HABs). The recently launched U.S. National Aeronautics and Space Administration (NASA) Earth Venture Instrument, the geostationary Tropospheric Emissions: Monitoring of ...
Z. Fasnacht, J. Joiner, M. Bandel, A. Ibrahim, A. Heidinger, M. D. Himes, J. Allen, J. Carr, X. Liu, H. Chong, N. Krotkov   +10 more
wiley   +1 more source

Radiometric Cross-Calibration of Tiangong-2 MWI Visible/NIR Channels over Aquatic Environments using MODIS

Remote Sensing, 2018
The Moderate-Resolution Wide-Wavelength Imager (MWI), onboard the Tiangong-2 (TG-2) Space Lab, is an experimental satellite sensor designed for the next-generation Chinese ocean color satellites.
Qu Zhou, Liqiao Tian, Jian Li, Qingjun Song, Wenkai Li   +4 more
doaj   +1 more source

Tide-Induced Variability and Mechanisms of Surface Suspended Sediment in the Zhoushan Archipelago along the Southeastern Coast of China Based on GOCI Data

Remote Sensing, 2021
The variations and dynamics of suspended sediment in the coastal waters around the Zhoushan Archipelago are complex due to strong tidal dynamics. This study aims to delineate the tide-induced variability of surface suspended sediment and reveal its ...
Yunfei Du, Huiyi Lin, Shuangyan He, Daosheng Wang, Ya Ping Wang, Jicai Zhang   +5 more
doaj   +1 more source

An Efficient Data Processing Method to Improve the Geostationary Ocean Color Imager (GOCI) Data Service

Korean Journal of Remote Sensing, 2014
We proposed and verified the methods to maintain data qualities as well as to reduce data volume for the Geostationary Ocean Color Imager (GOCI), the world’s first ocean color sensor operated in geostationary orbit. For the GOCI level-2 data, 92.9% of data volume could be saved by only the data compression. For the GOCI level-1 data, however, just 20.7%
Hyun Yang, Eunsong Oh, Tai-Hyun Han, Hee-Jeong Han, Jong-Kuk Choi   +4 more
openaire   +2 more sources

Diurnal Variability of Submesoscale Chlorophyll‐a Fronts Revealed by Hourly Geostationary Satellite Observations

Geophysical Research Letters, Volume 52, Issue 19, 16 October 2025.
Abstract The detection of submesoscale fronts is critical for understanding physical‐biological interactions and ecosystem variability. This study characterizes the diurnal variability of chlorophyll‐a (Chl‐a) fronts using hourly data from the geostationary satellite GK‐2B/GOCI‐II in the East/Japan Sea.
Hye‐Jin Woo, Kyung‐Ae Park, Peter C. Cornillon   +2 more
wiley   +1 more source

Diurnal Variability of Turbidity Fronts Observed by Geostationary Satellite Ocean Color Remote Sensing

Remote Sensing, 2016
Monitoring front dynamics is essential for studying the ocean’s physical and biogeochemical processes. However, the diurnal displacement of fronts remains unclear because of limited in situ observations.
Zifeng Hu, Delu Pan, Xianqiang He, Yan Bai   +3 more
doaj   +1 more source

Evaluation of GOCI Remote Sensing Reflectance Spectral Quality Based on a Quality Assurance Score System in the Bohai Sea

Remote Sensing, 2022
In the application of ocean color remote sensing, remote sensing reflectance spectral (Rrs(λ)) is the most important and basic parameter for the development of bio-optical algorithms.
Xiaoyan Liu, Qian Yang, Yunhua Wang, Yu Zhang   +3 more
doaj   +1 more source