Seongjun Lee SeongjunLee4473

🛰️ About Me

Remote sensing researcher at the National Institute of Forest Science (NIFoS), South Korea 🇰🇷

🌲 Working on CAS500-4 Cal/Val, running UAV multispectral and LiDAR field campaigns to validate agricultural and forestry satellite products
🌊 Research interests: SAR-based flood mapping, deep learning for water quality prediction, and Earth observation
🎓 M.S. in Earth Sciences and Environmental Engineering from GIST (HydroAI Lab), working on AI for hydrology and remote sensing

Project	Description
remote-sensing-toolkit	Python utilities for satellite and geospatial data: raster and vector GIS helpers, NASA HLS L30/S30 and MODIS vegetation index readers, and tutorial notebooks from raw downloads to analysis-ready data.
dji-m3m-elm-calibration	Empirical Line Method calibration for DJI Mavic 3 Multispectral: converts DN orthomosaics to surface reflectance with four reference tarps and validates the result per band.

Journal articles (first author)

S. Lee et al., "Enhancing Detection of Inundated Areas Using Novel Hybrid PolSAR-Metaheuristic-Deep Learning Models," IEEE JSTARS, 2026. [DOI]
S. Lee et al., "Cross-Domain Flood Mapping with PolSAR-Informed Deep Learning in Data-Denied Regions: Evidence from Contrasting Hydroclimatic Regimes," IEEE JSTARS. (under review)
S. Lee et al., "A satellite-reanalysis deep-learning nationwide 1 km gridded inland water quality dataset for South Korea (2016-2024)," Scientific Data. (under review)

Selected conference presentations

S. Lee and H. Kim, "Development of Chlorophyll-a Prediction Model for Inland Reservoirs Using Satellite and Land Surface Model: Applying Deep Learning Approach," AGU 2024, Washington, D.C., 2024. (Poster)
S. Lee and H. Kim, "Advancing Deep Learning-Based Prediction of Water Quality for Inland Water," Korea International Water Week 2024, Daegu, 2024. (Oral)
S. Lee and H. Kim, "Estimation of Inland Water Quality Indicators Using Satellite and Machine Learning," COSPAR 2024, Busan, 2024. (Poster)