Development of atmospheric correction algorithm for Geostationary Ocean Color Imager (GOCI)

Ahn, Jae-Seong; Park, Young-Je; Ryu, Joo-Hyung; Lee, Boram; Oh, Im Sang

doi:10.1007/s12601-012-0026-2

Cited by 108 publications

(70 citation statements)

References 32 publications

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“…To improve the performance of the atmospheric correction algorithm in turbid waters, the MUMM algorithm developed by Ruddick [29] was implemented in the latest version of GDPS (v.1.3.0). This algorithm calculates the contribution of aerosols and water to satellite reflectance on a per-pixel basis, with the assumption of spatially-constant Band-7: Band-8 ratios for aerosol reflectance (ε) and water reflectance (α) at 2 near-infrared (NIR) bands [30]. The limitation of this algorithm is that remote sensing reflectance (R rs ) from extremely turbid water (Rrs(660) ě 0.015 sr´1) can be underestimated due to the decrease of water reflectance after saturation by short wave lengths, as turbidity increases [27].…”

Section: Goci Imagesmentioning

confidence: 99%

“…The limitation of this algorithm is that remote sensing reflectance (R rs ) from extremely turbid water (Rrs(660) ě 0.015 sr´1) can be underestimated due to the decrease of water reflectance after saturation by short wave lengths, as turbidity increases [27]. In order to avoid this underestimation, Ahn [30] used a modified version of this algorithm (m-MUMM algorithm) in which the alpha value changes in proportion to turbidity by iterated calculations. This m-MUMM algorithm implemented by the current version of GDPS (which is also the algorithm adopted by the present study) can be applied in coastal turbid waters.…”

Section: Goci Imagesmentioning

confidence: 99%

“…To examine this effect, two dates that had minimal cloud coverage were chosen for analysis; maximum tidal ranges on 30 (Table 1).…”

Section: The Effect Of Spring-neap Tidal Cycle On Diurnal Variation Omentioning

confidence: 99%

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Application of the Geostationary Ocean Color Imager to Mapping the Diurnal and Seasonal Variability of Surface Suspended Matter in a Macro-Tidal Estuary

et al. 2016

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Abstract:Total suspended particulate matter (TSM) in estuarine and coastal regions usually exhibits significant natural variations. The understanding of such variations is of great significance in coastal waters. The aim of this study is to investigate and assess the diurnal and seasonal variations of surface TSM distribution and its mechanisms in coastal waters based on Geostationary Ocean Color Imager (GOCI) data. As a case study, dynamic variations of TSM in the macro-tidal Yalu River estuary (YRE) of China were analysed. With regard to diurnal variability, there were usually two peaks of TSM in a tidal cycle corresponding to the maximum flood and ebb current. Tidal action appears to play a vital role in diurnal variations of TSM. Both the processes of tidal re-suspension and advection could be identified; however, the diurnal variation of TSM was mainly affected by a re-suspension process. In addition, spring-neap tides can affect the magnitude of TSM diurnal variations in the YRE. The GOCI-retrieved TSM results clearly showed the seasonal variability of surface TSM in this area, with the highest level occurring in winter and the lowest in summer. Moreover, although river discharge to the YRE was much greater in the wet season than the dry season, TSM concentrations were significantly higher in the dry season. Wind waves were considered to be the main factor affecting TSM seasonal variation in the YRE.

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Section: Goci Imagesmentioning

confidence: 99%

Section: Goci Imagesmentioning

confidence: 99%

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Application of the Geostationary Ocean Color Imager to Mapping the Diurnal and Seasonal Variability of Surface Suspended Matter in a Macro-Tidal Estuary

et al. 2016

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“…The GOCI Level-1B data were processed to retrieve hourly TSM using the GOCI data process software (GDPS) offered by KOSC. In the data processing, we used the KOSC standard atmospheric correction algorithm proposed by Ahn et al [52,53]. In addition, the GOCI standard TSM algorithm (Yellow and East China Sea Ocean Color (YOC) algorithm) on the basis of Siswanto's TSM retrieval model for the YS and ECS was used as follows [54,55]:…”

Section: Goci Data Processing and Total Suspended Matter Retrievalmentioning

confidence: 99%

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

Pan

et al. 2016

Remote Sensing

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Abstract: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. Using the hourly satellite imageries from the Geostationary Ocean Color Imager (GOCI) with a spatial resolution of 500 m, we investigated the diurnal displacement of turbidity fronts in both the northern Jiangsu shoal water (NJSW) and the southwestern Korean coastal water (SKCW) in the Yellow Sea (YS). The hourly turbidity fronts were retrieved from the GOCI-derived total suspended matter using the entropy-based algorithm. The results showed that the entropy-based algorithm could provide fine structure and clearly temporal evolution of turbidity fronts. Moreover, the diurnal displacement of turbidity fronts in NJSW can be up to 10.3 km in response to the onshore-offshore movements of tidal currents, much larger than it is in SKCW (around 4.7 km). The discrepancy between NJSW and SKCW are mainly caused by tidal current direction relative to the coastlines. Our results revealed the significant diurnal displacement of turbidity fronts, and highlighted the feasibility of using geostationary ocean color remote sensing technique to monitor the short-term frontal variability, which may contribute to understanding of the sediment dynamics and the coupling physical-biogeochemical processes.

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“…The Geostationary Ocean Color Imager (GOCI) is the world's first ocean color observational satellite placed in geostationary orbit [27]. The monitored region spans from 116.08˝E to 143.92˝E and from 24.75˝N to 47.25˝N, and full coverage of this area is composed of 16 slot images.…”

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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Development of atmospheric correction algorithm for Geostationary Ocean Color Imager (GOCI)

Cited by 108 publications

References 32 publications

Application of the Geostationary Ocean Color Imager to Mapping the Diurnal and Seasonal Variability of Surface Suspended Matter in a Macro-Tidal Estuary

Application of the Geostationary Ocean Color Imager to Mapping the Diurnal and Seasonal Variability of Surface Suspended Matter in a Macro-Tidal Estuary

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

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

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