Overview of geostationary ocean color imager (GOCI) and GOCI data processing system (GDPS)

Ryu, Joo-Hyung; Han, Hee-Jeong; Cho, Seongick; Park, Young-Je; Ahn, Yu-Hwan

doi:10.1007/s12601-012-0024-4

Cited by 233 publications

(137 citation statements)

References 37 publications

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“…Compared with other ocean color satellite imagers, the GOCI has a distinct advantage in time resolution. Because it is geosynchronous, it can obtain satellite data at one hour intervals, which is well suited for monitoring the suspended sediment distribution and transport, and has an especially good resolution of diurnal variations [29,30].…”

Section: Retrieval Of Ssc From Gocimentioning

confidence: 99%

Using GOCI Retrieval Data to Initialize and Validate a Sediment Transport Model for Monitoring Diurnal Variation of SSC in Hangzhou Bay, China

Yang

Mao

Huang

et al. 2016

Water

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Abstract:The diurnal variation of the suspended sediment concentration (SSC) in Hangzhou Bay, China has been investigated using remotely-sensed SSC derived from the Geostationary Ocean Color Imager (GOCI) in combination with a coupled hydrodynamic-ecological model for regional and shelf seas (COHERENS). The SSC maps were inferred through a UV-AC atmospheric correction algorithm and an empirical inversion algorithm from the GOCI Level-1B data. The sediment transport model was initialized from maps of the GOCI-derived SSC and the model results were validated through a comparison with remotely-sensed data. The comparison demonstrated that the model results agreed well with the observations. The relationship between SSC distribution and hydrodynamic conditions was analyzed to investigate the sediment transport dynamics. The model's results indicate that the action of tidal currents dominate the sediment deposition and re-suspension in the coastal waters of the East China Sea. This is especially the case in Hangzhou Bay where the tidal currents are strongest. The satellite-derived sediment data product can not only dramatically improve the specification of the initial conditions for the sediment model, but can also provide valuable information for the model validation, thereby improving the model's overall performance.

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Section: Retrieval Of Ssc From Gocimentioning

confidence: 99%

Using GOCI Retrieval Data to Initialize and Validate a Sediment Transport Model for Monitoring Diurnal Variation of SSC in Hangzhou Bay, China

Yang

Mao

Huang

et al. 2016

Water

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“…The GOCI is the first geostationary satellite sensor for monitoring ocean color at regional scale (Ryu et al, 2012). The GOCI provides eight hourly images per day with 500m spatial resolution.…”

Section: Data Usedmentioning

confidence: 99%

A Multi-Temporal Approach for Detecting Snow Cover Area Using Geostationary Imagery Data

Lee

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VIII, WG VIII/6 KEY WORDS: snow cover, snow detection, multi-temporal, GOCI ABSTRACT:In this study, we attempt to detect snow cover area using multi-temporal geostationary satellite imagery based on the difference of spectral and temporal characteristics between snow and clouds. The snow detection method is based on sequential processing of simple thresholds on multi-temporal GOCI data. We initially applied a simple threshold of blue reflectance and then root mean square deviation (RMSD) threshold of near infrared (NIR) reflectance that were calculated from time-series GOCI data. Snow cover detected by the proposed method was compared with the MODIS snow products. The proposed snow detection method provided very similar results with the MODIS cloud products. Although the GOCI data do not have shortwave infrared (SWIR) band, which can spectrally separate snow cover from clouds, the high temporal resolution of the GOCI was effective for analysing the temporal variations between snow and clouds.

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“…연안 원격탐사를 위한 비축 삼반사경 설계와 성능 분석 Zone Color Scanner(CZCS)가 발사된 이후, 최근에 정지궤도 해양관측위성(Geostationary Ocean Color Imager, GOCI)을 이용한 연구까지 위성 자료를 이용한 연안 감시, 환경 변화 연구 등이 활발히 수행되어 오고 있다 [1][2][3] . 위성 기반의 원격 탐사 연구는 매우 넓은 관측 영역을 갖는다는 장점은 탁월하 나, 극궤도 위성의 경우 동일 영역을 하루에 최대 1~2회 관 측하는 정도의 낮은 시간해상도와 최대 수십 미터 급의 공간 해상도를 가지는 제한 사항이 있다.…”

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Design and Performance Analysis of an Off-Axis Three-Mirror Telescope for Remote Sensing of Coastal Water

Oh¹,

Kang²,

Hyun³

et al. 2015

Korean Journal of Optics and Photonics

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We report the design and performance analysis of an off-axis three-mirror telescope as the fore optics for a new hyperspectral sensor aboard a small unmanned aerial vehicle (UAV), for low-altitude coastal remote sensing. The sensor needs to have at least 4 cm of spatial resolution at an operating altitude of 500 m, 4° field of view (FOV), and a signal to noise ratio (SNR) of 100 at 660 nm. For these performance requirements, the sensor's optical design has an entrance pupil diameter of 70 mm and an F-ratio of 5.0. The fore optics is a three-mirror system, including aspheric primary and secondary mirrors. The optical performance is expected to reach 1/15λ in RMS wavefront error and 0.75 in MTF value at 660 nm. Considering the manufacturing and assembling phase, we determined the alignment compensation due to the tertiary mirror from the sensitivity, and derived the tilt-tolerance range to be 0.17 mrad. The off-axis three-mirror telescope, which has better performance than the fore optics of other hyperspectral sensors and is fitted for a small UAV, will contribute to ocean remote-sensing research.

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Overview of geostationary ocean color imager (GOCI) and GOCI data processing system (GDPS)

Cited by 233 publications

References 37 publications

Using GOCI Retrieval Data to Initialize and Validate a Sediment Transport Model for Monitoring Diurnal Variation of SSC in Hangzhou Bay, China

Using GOCI Retrieval Data to Initialize and Validate a Sediment Transport Model for Monitoring Diurnal Variation of SSC in Hangzhou Bay, China

A Multi-Temporal Approach for Detecting Snow Cover Area Using Geostationary Imagery Data

Design and Performance Analysis of an Off-Axis Three-Mirror Telescope for Remote Sensing of Coastal Water

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