Korean J. Remote Sens. 2023; 39(6): 1255-1272
Published online: December 31, 2023
https://doi.org/10.7780/kjrs.2023.39.6.1.7
© Korean Society of Remote Sensing
GOCI-II 및 극궤도 위성 자료를 병합한 Chlorophyll-a 산출물 생산방법 소개 및 활용 가능성 평가
신혜경 1),2)·권재엽 3)·김평중4)·김태호 5)*
1) (주)유에스티21 해양부 과장(General Manager, Oceanic Research Division, Underwater Survey Technology 21 Inc., Incheon, Republic of Korea) 2) 인하대학교 공간정보공학과 석사과정생(Master Student, Department of Geoinformatic Engineering, Inha University, Incheon, Republic of Korea) 3) (주)유에스티21 해양부 사원(Employee, Oceanic Research Division, Underwater Survey Technology 21 Inc., Incheon, Republic of Korea) 4) (주)유에스티21 해양부 부서장(Department Leader, Oceanic Research Division, Underwater Survey Technology 21 Inc., Incheon, Republic of Korea) 5) (주)유에스티21 해양부 부장(Department Manager, Oceanic Research Division, Underwater Survey Technology 21 Inc., Incheon, Republic of Korea)
Introduction and Evaluation of the Production Method for Chlorophyll-a Using Merging of GOCI-II and Polar Orbit Satellite Data
Satellite-based chlorophyll-a concentration, produced as a long-term time series, is crucial for global climate change research. The production of data without gaps through the merging of timesynthesized or multi-satellite data is essential. However, studies related to satellite-based chlorophyll-a concentration in the waters around the Korean Peninsula have mainly focused on evaluating seasonal characteristics or proposing algorithms suitable for research areas using a single ocean color sensor. In this study, a merging dataset of remote sensing reflectance from the geostationary sensor GOCI-II and polar-orbiting sensors (MODIS, VIIRS, OLCI) was utilized to achieve high spatial coverage of chlorophyll-a concentration in the waters around the Korean Peninsula. The spatial coverage in the results of this study increased by approximately 30% compared to polar-orbiting sensor data, effectively compensating for gaps caused by clouds. Additionally, we aimed to quantitatively assess accuracy through comparison with global chlorophyll-a composite data provided by Ocean Colour Climate Change Initiative (OC-CCI) and GlobColour, along with in-situ observation data. However, due to the limited number of in-situ observation data, we could not provide statistically significant results. Nevertheless, we observed a tendency for underestimation compared to global data. Furthermore, for the evaluation of practical applications in response to marine disasters such as red tides, we qualitatively compared our results with a case of a red tide in the East Sea in 2013. The results showed similarities to OC-CCI rather than standalone geostationary sensor results. Through this study, we plan to use the generated data for future research in artificial intelligence models for prediction and anomaly utilization. It is anticipated that the results will be beneficial for monitoring chlorophyll-a events in the coastal waters around Korea.
Keywords Chlorophyll-a, Merged, Remote sensing reflectance, GOCI-II, Geostationary satellite, Ocean color, OC-CCI
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Published online December 31, 2023 https://doi.org/10.7780/kjrs.2023.39.6.1.7
Copyright © Korean Society of Remote Sensing.
GOCI-II 및 극궤도 위성 자료를 병합한 Chlorophyll-a 산출물 생산방법 소개 및 활용 가능성 평가
신혜경 1),2)·권재엽 3)·김평중4)·김태호 5)*
1) (주)유에스티21 해양부 과장(General Manager, Oceanic Research Division, Underwater Survey Technology 21 Inc., Incheon, Republic of Korea) 2) 인하대학교 공간정보공학과 석사과정생(Master Student, Department of Geoinformatic Engineering, Inha University, Incheon, Republic of Korea) 3) (주)유에스티21 해양부 사원(Employee, Oceanic Research Division, Underwater Survey Technology 21 Inc., Incheon, Republic of Korea) 4) (주)유에스티21 해양부 부서장(Department Leader, Oceanic Research Division, Underwater Survey Technology 21 Inc., Incheon, Republic of Korea) 5) (주)유에스티21 해양부 부장(Department Manager, Oceanic Research Division, Underwater Survey Technology 21 Inc., Incheon, Republic of Korea)
Introduction and Evaluation of the Production Method for Chlorophyll-a Using Merging of GOCI-II and Polar Orbit Satellite Data
신혜경 1),2)·권재엽 3)·김평중4)·김태호 5)*
Abstract
Satellite-based chlorophyll-a concentration, produced as a long-term time series, is crucial for global climate change research. The production of data without gaps through the merging of timesynthesized or multi-satellite data is essential. However, studies related to satellite-based chlorophyll-a concentration in the waters around the Korean Peninsula have mainly focused on evaluating seasonal characteristics or proposing algorithms suitable for research areas using a single ocean color sensor. In this study, a merging dataset of remote sensing reflectance from the geostationary sensor GOCI-II and polar-orbiting sensors (MODIS, VIIRS, OLCI) was utilized to achieve high spatial coverage of chlorophyll-a concentration in the waters around the Korean Peninsula. The spatial coverage in the results of this study increased by approximately 30% compared to polar-orbiting sensor data, effectively compensating for gaps caused by clouds. Additionally, we aimed to quantitatively assess accuracy through comparison with global chlorophyll-a composite data provided by Ocean Colour Climate Change Initiative (OC-CCI) and GlobColour, along with in-situ observation data. However, due to the limited number of in-situ observation data, we could not provide statistically significant results. Nevertheless, we observed a tendency for underestimation compared to global data. Furthermore, for the evaluation of practical applications in response to marine disasters such as red tides, we qualitatively compared our results with a case of a red tide in the East Sea in 2013. The results showed similarities to OC-CCI rather than standalone geostationary sensor results. Through this study, we plan to use the generated data for future research in artificial intelligence models for prediction and anomaly utilization. It is anticipated that the results will be beneficial for monitoring chlorophyll-a events in the coastal waters around Korea.
Keywords: Chlorophyll-a, Merged, Remote sensing reflectance, GOCI-II, Geostationary satellite, Ocean color, OC-CCI
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