• Title/Summary/Keyword: 해색원격탐사

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Spatial Downscaling of Ocean Colour-Climate Change Initiative (OC-CCI) Forel-Ule Index Using GOCI Satellite Image and Machine Learning Technique (GOCI 위성영상과 기계학습 기법을 이용한 Ocean Colour-Climate Change Initiative (OC-CCI) Forel-Ule Index의 공간 상세화)

  • Sung, Taejun;Kim, Young Jun;Choi, Hyunyoung;Im, Jungho
    • Korean Journal of Remote Sensing
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    • v.37 no.5_1
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    • pp.959-974
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    • 2021
  • Forel-Ule Index (FUI) is an index which classifies the colors of inland and seawater exist in nature into 21 gradesranging from indigo blue to cola brown. FUI has been analyzed in connection with the eutrophication, water quality, and light characteristics of water systems in many studies, and the possibility as a new water quality index which simultaneously contains optical information of water quality parameters has been suggested. In thisstudy, Ocean Colour-Climate Change Initiative (OC-CCI) based 4 km FUI was spatially downscaled to the resolution of 500 m using the Geostationary Ocean Color Imager (GOCI) data and Random Forest (RF) machine learning. Then, the RF-derived FUI was examined in terms of its correlation with various water quality parameters measured in coastal areas and its spatial distribution and seasonal characteristics. The results showed that the RF-derived FUI resulted in higher accuracy (Coefficient of Determination (R2)=0.81, Root Mean Square Error (RMSE)=0.7784) than GOCI-derived FUI estimated by Pitarch's OC-CCI FUI algorithm (R2=0.72, RMSE=0.9708). RF-derived FUI showed a high correlation with five water quality parameters including Total Nitrogen, Total Phosphorus, Chlorophyll-a, Total Suspended Solids, Transparency with the correlation coefficients of 0.87, 0.88, 0.97, 0.65, and -0.98, respectively. The temporal pattern of the RF-derived FUI well reflected the physical relationship with various water quality parameters with a strong seasonality. The research findingssuggested the potential of the high resolution FUI in coastal water quality management in the Korean Peninsula.

Estimation of High Resolution Sea Surface Salinity Using Multi Satellite Data and Machine Learning (다종 위성자료와 기계학습을 이용한 고해상도 표층 염분 추정)

  • Sung, Taejun;Sim, Seongmun;Jang, Eunna;Im, Jungho
    • Korean Journal of Remote Sensing
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    • v.38 no.5_2
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    • pp.747-763
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    • 2022
  • Ocean salinity affects ocean circulation on a global scale and low salinity water around coastal areas often has an impact on aquaculture and fisheries. Microwave satellite sensors (e.g., Soil Moisture Active Passive [SMAP]) have provided sea surface salinity (SSS) based on the dielectric characteristics of water associated with SSS and sea surface temperature (SST). In this study, a Light Gradient Boosting Machine (LGBM)-based model for generating high resolution SSS from Geostationary Ocean Color Imager (GOCI) data was proposed, having machine learning-based improved SMAP SSS by Jang et al. (2022) as reference data (SMAP SSS (Jang)). Three schemes with different input variables were tested, and scheme 3 with all variables including Multi-scale Ultra-high Resolution SST yielded the best performance (coefficient of determination = 0.60, root mean square error = 0.91 psu). The proposed LGBM-based GOCI SSS had a similar spatiotemporal pattern with SMAP SSS (Jang), with much higher spatial resolution even in coastal areas, where SMAP SSS (Jang) was not available. In addition, when tested for the great flood occurred in Southern China in August 2020, GOCI SSS well simulated the spatial and temporal change of Changjiang Diluted Water. This research provided a potential that optical satellite data can be used to generate high resolution SSS associated with the improved microwave-based SSS especially in coastal areas.

Spatial Distribution of Pigment Concentration Around the East Korean Warm Current Region Derived from Satellite Data - Satellite Observation in May 1980 - (위성원격탐사에 의한 동한난류 주변 해역의 색소농도 공간적 분포 -1980년 5월 관측을 중심으로 -)

  • Kim Sang Woo;Saitoh Sei-ich;Kim Dong Sun
    • Korean Journal of Fisheries and Aquatic Sciences
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    • v.35 no.3
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    • pp.265-272
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    • 2002
  • Spatial distribution of Phytoplankton Pigment Concentration (PPC) and Sea Surface Temperature (SST) around the East Korean Warm Current (EKWC) was described, using both Coastal Zone Color Scanner (CZCS) images and Advanced Very High Resolution Radiometer (AVHRR) images in May, 1980. Water mass in this region can be classified into five categories in the horizontal profile of PPC and SST, nLw (normalized water-leaving radiance) images: (1) coastal cold water region associated with concentrations of dissolved organic material or yellow colored substances and suspended sediments, (2) cold water region of thermal frontal occurred by a combination of phytoplankton absorption and suspended materials, (3) warm water overlay region by the phytoplankton absorption than the suspended materials; (4) warm water region occurred by the low phytoplankton absorption, and (5) offshore region occurred by the high phytoplankton absorption. In particular, the highest PPC (>2.0 mg/m^3) area appeared in the CZCS and AVHRR images with a band shaped distribution of the thermal front and ocean color front region, which is located the coastal cold waters alonB western thermal front of the warm streamer of the EKWC. In this region, the highest PPC occurred by a combination of the high absorption of the phytoplankton (443 nm) and highest reflectance of suspended materials (550 nm). Another high PPC ($\simeq$$6\;mg/m^3$) appeared in the warm water overlay region inside warm streamer. High phytoplankton pigment concentration of this region was corresponding to the short wavelength of 443 nm, which represented phytoplankton absorption of the CZCS image.

Determining Spatial and Temporal Variations of Surface Particulate Organic Carbon (POC) using in situ Measurements and Remote Sensing Data in the Northeastern Gulf of Mexico during El $Ni\tilde{n}o$ and La $Ni\tilde{n}a$ (현장관측 및 원격탐사 자료를 이용한 북동 멕시코 만에서 El $Ni\tilde{n}o$와 La $Ni\tilde{n}a$ 기간 동안 표층 입자성 유기탄소의 시/공간적 변화 연구)

  • Son, Young-Baek;Gardner, Wilford D.
    • The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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    • v.15 no.2
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    • pp.51-61
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    • 2010
  • Surface particulate organic carbon (POC) concentration was measured in the Northeastern Gulf of Mexico on 9 cruises from November 1997 to August 2000 to investigate the seasonal and spatial variability related to synchronous remote sensing data (Sea-viewing Wide Field-of-view Sensor (SeaWiFS), sea surface temperature (SST), sea surface height anomaly (SSHA), and sea surface wind (SSW)) and recorded river discharge data. Surface POC concentrations have higher values (>100 $mg/m^3$) on the inner shelf and near the Mississippi Delta, and decrease across the shelf and slope. The inter-annual variations of surface POC concentrations are relatively higher during 1997 and 1998 (El Nino) than during 1999 and 2000 (La Nina) in the study area. This phenomenon is directly related to the output of Mississippi River and other major rivers, which associated with global climate change such as ENSO events. Although highest river runoff into the northern Gulf of Mexico Coast occurs in early spring and lowest flow in late summer and fall, wide-range POC plumes are observed during the summer cruises and lower concentrations and narrow dispersion of POC during the spring and fall cruises. During the summer seasons, the river discharge remarkably decreases compared to the spring, but increasing temperature causes strong stratification of the water column and increasing buoyancy in near-surface waters. Low-density plumes containing higher POC concentrations extend out over the shelf and slope with spatial patterns and controlled by the Loop Current and eddies, which dominate offshore circulation. Although river discharge is normal or abnormal during the spring and fall seasons, increasing wind stress and decreasing temperature cause vertical mixing, with higher surface POC concentrations confined to the inner shelf.