• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.191-194
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• 2007

The paper clarifies all the noise sources of a CMOS image sensor, with which the GOCI (Geostationary Ocean Color Imager) is equipped, and analyzes their contribution to a nonlinear image sensor model. In particular, the noise PDF (Probability Density Function) is derived in terms of sensor-gain coefficients: a linear and a nonlinear gains. As a result, the relation between the noise characteristic and the sensor gains is studied.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.137-147
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• 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% of data volume could be saved by the data compression therefore another approach was required. First, we found the optimized number of bits per a pixel for the GOCI level-1 data from an idea that the quantization bit for the GOCI (i.e. 12 bit) was less than the number of bits per a pixel for the GOCI level-1 data (i.e. 32 bit). Experiments were conducted using the $R^2$ and the Modulation Transfer Function (MTF). It was quantitatively revealed that the data qualities were maintained although the number of bits per a pixel was reduced to 14. Also, we performed network simulations using the Network Simulator 2 (Ns2). The result showed that 57.7% of the end-toend delay for a GOCI level-1 data was saved when the number of bits per a pixel was reduced to 14 and 92.5% of the end-to-end delay for a GOCI level-2 data was saved when 92.9% of the data size was reduced due to the compression.

• Proceedings of KOSOMES biannual meeting
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• 2008.05a
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• pp.191-195
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• 2008

In Ansan (the headquarter of KORDI ; Korea Ocean Research & Development Institute), KOSC(Korea Ocean Satellite Center) is being prepared for acquisition, processing and distribution of sensor data via L-band from GOCI(Geostationary Ocean Color Imager) instrument which is loaded on COMS(Communication, Ocean and Meteorological Satellite); it will be launched in 2009. The basis equipment of KOSC(Electric power, Network, Security) has been constructed in 2007. KOSC is being constructed data processing and management system, GOCI L-band reception system, etc. The final object of KOSC is that maximize the application of GOCI.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.275-292
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• 2014

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 "Wetness" of the GOCI TCT data showed a relatively high correlation with Albedo ($R^2$ = 0.75), Normalized Difference Vegetation Index (NDVI) ($R^2$ = 0.97), and Normalized Difference Moisture Index (NDMI) ($R^2$ = 0.77), respectively. These results indicate the suitability of the GOCI TCT coefficients.

• Korean Journal of Remote Sensing
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• v.23 no.2
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• pp.137-144
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• 2007

In KORDI (Korea Ocean Research and Development Institute), the KOSC (Korea Ocean Satellite Center) construction project is being prepared for acquisition, processing and distribution of sensor data via L-band from GOCI (Geostationary Ocean Color Imager) instrument which is loaded on COMS (Communication, Ocean and Meteorological Satellite); it will be launched in 2008. Ansan (the headquarter of KORDI) has been selected for the location of KOSC between 5 proposed sites, because it has the best condition to receive radio wave. The data acquisition system is classified into antenna and RF. Antenna is designed to be $\phi$ 9m cassegrain antenna which has 19.35 G/T$(dB/^{\circ}K)$ at 1.67GHz. RF module is divided into LNA (low noise amplifier) and down converter, those are designed to send only horizontal polarization to modem. The existing building is re-designed and arranged for the KOSC operation concept; computing room, board of electricity, data processing room, operation room. Hardware and network facilities have been designed to adapt for efficiency of each functions. The distribution system which is one of the most important systems will be constructed mainly on the internet. and it is also being considered constructing outer data distribution system as a web hosting service for offering received data to user less than an hour.

• Korean Journal of Remote Sensing
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• v.24 no.6
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• pp.559-569
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• 2008

The Geostationary Ocean Color Imager (GOCI) on board the Communication Ocean Meteorological Satellite (COMS), the first geostationary ocean color sensor, requires accurate atmospheric correction since its eight bands are also affected by atmospheric constituents such as gases, molecules and atmospheric aerosols. Unlike gases and molecules in the atmosphere, aerosols can interact with sunlight by complex scattering and absorption properties. For the purpose of qualified ocean remote sensing, understanding of aerosol-radiation interactions is needed. In this study, we show micro-physical and optical properties of aerosols using the Optical Property of Aerosol and Cloud (OPAC) aerosol models. Aerosol optical properties, then, were used to analysis the relationship between theoretical satellite measured radiation from radiative transfer calculations and aerosol optical thickness (AOT) under various environments (aerosol type and loadings). It is found that the choice of aerosol type makes little different in AOT retrieval for AOT<0.2. Otherwise AOT differences between true and retrieved increase as AOT increases. Furthermore, the differences between the AOT and angstrom exponent from standard algorithms and this study, and the comparison with ground based sunphotometer observations are investigated. Over the northeast Asian region, these comparisons suggest that spatially averaged mean AOT retrieved from this study is much better than from standard ocean color algorithm. Finally, these results will be useful for aerosol retrieval or atmospheric correction of COMS/GOCI data processing.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.253-262
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• 2016

Here, we estimated daily Photosynthetically Available Radiation (PAR) from Geostationary Ocean Colour Imager (GOCI) and compared it with daily PAR derived from polar-orbiting MODIS images. GOCI-based PAR was also validated with in-situ measurements from ocean research station, Socheongcho. GOCI PAR showed similar patterns with in-situ measurements for both the clear-sky and cloudy day, whereas MODIS PAR showed irregular patterns at cloudy conditions in some areas where PAR could not be derived due to the clouds of sunglint. GOCI PAR had shown a constant difference with the in-situ measurements, which was corrected using the in-situ measurements obtained on the days of clear-sky conditions at Socheongcho station. After the corrections, GOCI PAR showed a good agreement excepting on the days with so thick cloud that the sensor was optically saturated. This study revealed that GOCI can estimate effectively the daily PAR with its advantages of acquiring data more frequently, eight times a day at an hourly interval in daytime, than other polar orbit ocean colour satellites, which can reduce the uncertainties induced by the existence and movement of the cloud and insufficient images to map the daily PAR at the seas around Korean peninsula.

• Korean Journal of Remote Sensing
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• v.28 no.3
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• pp.277-285
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• 2012

The GOCI imagery can be an effective alternative to monitor short-term changes over terrestrial environments. This study aimed to assess the radiometric characteristics of the GOCI multispectral imagery for land applications. As an initial approach, we compared GOCI at-sensor radiance with MODIS data obtained simultaneously. Dynamic range of GOCI radiance was larger than MODIS over land area. Further, the at-sensor radiance over various land surface targets were tested by vicarious calibration. Surface reflectance were directly measured in field using a portable spectrometer and indirectly derived from the atmospherically corrected MODIS product over relatively homogeneous sites of desert, tidal flat, bare soil, and fallow crop fields. The GOCI radiance values were then simulated by radiative transfer model (6S). In overall, simulated radiance were very similar to the actual radiance extracted from GOCI data. Normalized difference vegetation index (NDVI) calculated from the GOCI bands 5 and 8 shows very close relationship with MODIS NDVI. In this study, the GOCI imagery has shown appropriate radiometric quality to be used for various land applications. Further works are needed to derive surface reflectance over land area after atmospheric correction.

• Korean Journal of Remote Sensing
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• v.29 no.2
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• pp.173-182
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• 2013

The early Sea-viewing Wide Field-of-view Sensor(SeaWiFS) atmospheric correction algorithm which is the basis of the atmospheric correction algorithm for Geostationary Ocean Color Imager(GOCI) assumes that water-leaving radiances is negligible at near-infrared(NIR) wavelengths. For this reason, all of the satellite measured radiances at the NIR wavelengths are assigned to aerosol radiances. However that assumption would cause underestimation of water-leaving radiances if it were applied to turbid Case-2 waters. To overcome this problem, Management Unit of the North Sea Mathematical Models(MUMM) atmospheric correction algorithm has been developed for turbid waters. This MUMM algorithm introduces new parameter ${\alpha}$, representing the ratio of water-leaving reflectance at the NIR wavelengths. ${\alpha}$ is calculated by statistical method and is assumed to be constant throughout the study area. Using this algorithm, we can obtain comparatively accurate water-leaving radiances in the moderately turbid waters where the NIR water-leaving reflectance is less than approximately 0.01. However, this algorithm still underestimates the water-leaving radiances at the extremely turbid water since the ratio of water-leaving radiance at two NIR wavelengths, ${\alpha}$ is changed with concentration of suspended particles. In this study, we modified the MUMM algorithm to calculate appropriate value for ${\alpha}$ using an iterative technique. As a result, the accuracy of water-leaving reflectance has been significantly improved. Specifically, the results show that the Root Mean Square Error(RMSE) of the modified MUMM algorithm was 0.002 while that of the MUMM algorithm was 0.0048.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.209-220
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• 2010

One of the most difficult tasks in measuring oceanic conditions is to produce oceanic current information. In efforts to overcome the difficulties, various attempts have been carried out to estimate the speed and direction of ocean currents by utilizing sequential satellite images. In this study, we have estimated sea surface current vectors to the south of the Korean Peninsula, based on the maximum cross-correlation method by using sequential ocean color images of SeaWiFS chlorophyll-a. Comparison of surface current vectors estimated by this method with the geostrophic current vectors estimated from satellite altimeter data and in-situ ADCP measurements are good in that current speeds are underestimated by about 15% and current directions are show differences of about $36^{\circ}$ compared with previous results. The technique of estimating current vectors based on maximum cross-correlation applied on sequential images of SeaWiFS is promising for the future application of GOCI data for the ocean studies.