Validation for the Geostationary Ocean Color Imager (GOCI) : the presnet and future
In order to provide quantitative control of the standard and non-standard products of Geostationary Ocean Color Imager (GOCI), on-board radiometric correction, atmospheric correction, and bio-optical algorithm are obtained continuously by comprehensive ...
심재설 +5 more
core
Integrating Inland and Coastal Water Quality Data for Actionable Knowledge. [PDF]
El Serafy GYH +21 more
europepmc +1 more source
Estimating Photosynthetically Available Radiation from Geostationary Ocean Color Imager (GOCI) Data
Jihye Kim +4 more
openaire +2 more sources
Total suspended particulate matter (TSM) in estuarine and coastal regions usually exhibits significant diurnal and seasonal variability. The understanding of such variability can reveal the sedimentary processes in coastal turbid waters which play a central role in the water quality and primary productivity in the ocean.
openaire +2 more sources
Distribution Patterns of Sea Fog During the Daytime Using Geostationary Ocean Color Imager (GOCI)
Sea fog disturbs ship traffic, and may cause accidents due to low visibility in the coastal regions and over oceans. In the Yellow Sea, thick fog often appears during the spring season from April to July. Data on sea fog can be useful information for the
Kazuo, 양찬수
core
Radaiometric Calibration of Geostationary Ocean Color Imager (GOCI)
2009년 발사를 목표로 개발 중인 통신해양기상위성(COMS, Communication, Ocean and Meterological Satellite)의 주요 탑재체 가운데 하나인 정지궤도 해양위성(이하 GOCI, Geostationary Ocean Color Imager)은 저궤도에 위치한 기존의 다른 해양위성과 달리 세계 최초로 정지궤도에서 해양 관측을 수행할 예정이다.
양찬수 +4 more
core
Geometric Correction for the Geostationary Ocean Color Imager from a Combination of Shoreline Matching and Frequency Matching. [PDF]
Kim HG, Son JH, Kim T.
europepmc +1 more source
Consistency of Suspended Particulate Matter Concentration in Turbid Water Retrieved from Sentinel-2 MSI and Landsat-8 OLI Sensors. [PDF]
Wang H, Wang J, Cui Y, Yan S.
europepmc +1 more source
Red Tide Detection Method Based on a Time Series Fusion Network Model: A Case Study of GOCI Data in the East China Sea. [PDF]
Ding T +5 more
europepmc +1 more source
Incorporation of machine learning and deep neural network approaches into a remote sensing-integrated crop model for the simulation of rice growth. [PDF]
Jeong S, Ko J, Shin T, Yeom JM.
europepmc +1 more source

