Preliminary verification results of the geostationary ocean color satellite data processing software system

Han, Hee-Jeong; Ryu, Joo-Hyung; Cho, Seongick; Yang, Chan-Su; Ahn, Yu-Hwan

doi:10.1117/12.869658

Cited by 3 publications

(3 citation statements)

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“…Processing System (GDPS)에서 처리할 수 있다 (Han et al, 2010;Ryu et al, 2012 Feb 6 7 1 2 4 5 6 8 1 3 1 2 14 15 9 10 12 13 14 16 9 11 9 10 22 23 17 18 20 21 22 24 17 19 17 18 30 25 26 28 29 30 25 27 25 Programme (IGBP)의 Land cover 그룹을 사용하여 지표 피복을 분류하였다 (Friedl et al, 2002). IGBP 자료는…”

Section: 결과 Goci 센서의 관측 영역 중 육상 영역은 약 38%를unclassified

A Study of Tasseled Cap Transformation Coefficient for the Geostationary Ocean Color Imager (GOCI)

Shin¹,

Park²,

Won³

2014

Korean Journal of Remote Sensing

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:The objective of this study is to determine Tasseled Cap Transformation (TCT) coefficients for the Geostationary Ocean Color Imager (GOCI). TCT is traditional method of analyzing the characteristics of the land area from multi spectral sensor data. TCT coefficients for a new sensor must be estimated individually because of different sensor characteristics of each sensor. Although the primary objective of the GOCI is for ocean color study, one half of the scene covers land area with typical land observing channels in Visible-Near InfraRed (VNIR). The GOCI has a unique capability to acquire eight scenes per day. This advantage of high temporal resolution can be utilized for detecting daily variation of land surface. The GOCI TCT offers a great potential for application in near-real time analysis and interpretation of land cover characteristics. TCT generally represents information of "Brightness", "Greenness" and "Wetness". However, in the case of the GOCI is not able to provide "Wetness" due to lack of ShortWave InfraRed (SWIR) band. To maximize the utilization of high temporal resolution, "Wetness" should be provided. In order to obtain "Wetness", the linear regression method was used to align the GOCI Principal Component Analysis (PCA) space with the MODIS TCT space. The GOCI TCT coefficients obtained by this method have different values according to observation time due to the characteristics of geostationary earth orbit. To examine these differences, the correlation between the GOCI TCT and the MODIS TCT were compared. As a result, while the GOCI TCT coefficients of "Brightness" and "Greenness" were selected at 4h, the GOCI TCT coefficient of "Wetness" was selected at 2h. To assess the adequacy of the resulting GOCI TCT coefficients, the GOCI TCT data were compared to the MODIS TCT image and several land parameters. The land cover classification of the GOCI TCT image was expressed more precisely than the MODIS TCT image. The distribution of land cover classification of the GOCI TCT space showed meaningful results. Also, "Brightness", "Greenness", and -2 75 -

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Section: 결과 Goci 센서의 관측 영역 중 육상 영역은 약 38%를unclassified

A Study of Tasseled Cap Transformation Coefficient for the Geostationary Ocean Color Imager (GOCI)

Shin¹,

Park²,

Won³

2014

Korean Journal of Remote Sensing

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“…The IMPS is used to generate level 1B data from raw data while GDPS is used with level 1B data to generate processed level 2 data which consist of ocean color products such as

, normalized water-leaving radiance (

), chlorophyll concentration, and total suspended sediment (TSS) concentration. GDPS also generates level 3 data which consist of fishery information, primary productivity, water quality level and water current vectors [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of GOCI Ocean Color Products

Amin¹,

Lewis

Lawson

et al. 2015

Sensors

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The Geostationary Ocean Color Imager (GOCI) is the first geostationary ocean color sensor in orbit that provides bio-optical properties from coastal and open waters around the Korean Peninsula at unprecedented temporal resolution. In this study, we compare the normalized water-leaving radiance (nLw) products generated by the Naval Research Laboratory Automated Processing System (APS) with those produced by the stand-alone software package, the GOCI Data Processing System (GDPS), developed by the Korean Ocean Research & Development Institute (KORDI). Both results are then compared to the nLw measured by the above water radiometer at the Ieodo site. This above-water radiometer is part of the Aerosol Robotic NETwork (AeroNET). The results indicate that the APS and GDPS processed nLw correlates well within the same image slot where the coefficient of determination (r2) is higher than 0.84 for all the bands from 412 nm to 745 nm. The agreement between APS and the AeroNET data is higher when compared to the GDPS results. The Root-Mean-Squared-Error (RMSE) between AeroNET and APS data ranges from 0.24 [mW/(cm2srμm)] at 555 nm to 0.52 [mW/(cm2srμm)] at 412 nm while RMSE between AeroNET and GDPS data ranges from 0.47 [mW/(cm2srμm)] at 443 nm to 0.69 [mW/(cm2srμm)] at 490 nm.

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“…GDPS also generates level 3 data which consist of fishery information, primary productivity, water quality level, and water current vectors. 21 However, in this study, we only used the standard GDPS 2,20 (version 1.2) processed GOCI nLw in the red and NIR regions since FLH and RBD use radiance/reflectance in these bands.…”

Section: Satellite Datamentioning

confidence: 99%

Hourly turbidity monitoring using Geostationary Ocean Color Imager fluorescence bands

Amin¹,

Shulman

2015

J. Appl. Remote Sens

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Abstract. The Geostationary Ocean Color imager (GOCI) is the first geostationary ocean color satellite sensor that collects hourly images eight times per day during daylight. This high frequency image acquisition makes it possible to study more detailed dynamics of red tide blooms, sediment plumes, and colored dissolved organic matter plumes, and can aid in the prediction of biophysical phenomena. We apply the red band difference and the fluorescence line height algorithms to GOCI imagery to separate waters with high algal and nonalgal particles and validate the results with the MODIS imagery. We also track optical features using hourly GOCI imagery and assess their movement through comparisons with predicted ocean currents derived from the navy coastal ocean model and tidal data.

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Preliminary verification results of the geostationary ocean color satellite data processing software system

Cited by 3 publications

References 0 publications

A Study of Tasseled Cap Transformation Coefficient for the Geostationary Ocean Color Imager (GOCI)

A Study of Tasseled Cap Transformation Coefficient for the Geostationary Ocean Color Imager (GOCI)

Comparative Analysis of GOCI Ocean Color Products

Hourly turbidity monitoring using Geostationary Ocean Color Imager fluorescence bands

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