This workshop originated from the "Korea-Japan Workshop on Ocean Color (KJWOC)," which began in Nagasaki in 2003. For over two decades, Japan and South Korea have hosted the event alternately to promote research collaborations among scientists and students. In 2011, the "Asian Workshop on Ocean Color (AWOC)" was launched for Southeast Asian nations, and has been held jointly with KJWOC. Today, KJWOC/AWOC has evolved into the most significant community for ocean color remote sensing in the Asian region.
Main Topics of the Workshop
Ocean color remote sensing is a technique for observing light reflected from the sea surface. By analyzing "ocean color," we can remotely measure essential components such as phytoplankton (chlorophyll-a) that are crucial for understand the marine environment.
This workshop provides participants with opportunities to present their latest studies on remote sensing of marine and coastal environments while covering a wide range of observation technologies, from satellites in space to cutting-edge drones. Expected topics are outlined below:
- 🛰️ Current and Future Ocean Observation MissionsReviewing the operational status and data products of major Earth observation satellites to identify challenges and future directions for ocean monitoring.
- 🎯 Sensor Calibration and ValidationAddressing the temporal degradation of satellite sensors to ensure the long-term reliability and accuracy of satellite data.
- 🐟 Satellite Data Applications for Marine Environment, Physical Oceanography, and FisheriesMonitoring and assessment of water quality, harmful algal blooms, and physical processes (e.g., currents, fronts, eddies), as well as their utilization for fisheries management and marine resource assessment.
- 🌱 Coastal Habitats Mapping with Satellite SensorsMapping of coastal blue carbon habitats such as seagrasses, tidal mashes and mangroves
- 💼 Operational System DevelopmentDevelopment of practical applications with satellite data to bridge the gap between research and societal needs.
- 🤖 Advanced Data Analysis with AI and Machine LearningLeveraging AI and machine learning algorithms to automate the detection of long-term environmental trends and anomalous phenomena from satellite data.