This project focuses on developing technologies for soil-based climate resilience and evaluating their impacts on soil functions, ecosystem services, and biodiversity across agricultural, forest, and urban park systems. Our current modeling efforts involve developing multi-level conceptual mechanisms—spanning data, soil, and ecosystem scales—to predict impacts under extreme weather conditions such as drought, flooding, and extreme temperatures, integrating these uncertainty assessments into a national information service system. 

This project integrates crop growth surveys with hyperspectral sensing to predict the resilience of barley and red beans under extreme weather (drought, flooding, and extreme temperatures). Our core work involves constructing dedicated service databases and developing 2D and 3D CNN models to optimize predictive performance across spatial and high-dimensional spectral data. 

We developed a mid-infrared (MIR) spectral library of soils from paddy fields and other croplands in Korea. The new data from this library will eventually be integrated with existing datasets to support ecosystem modeling. 

We completed a project on low-carbon water management in paddy systems aimed at enhancing water use efficiency and mitigating methane (CH₄) emissions. This study examined the combined effects of mid-season drainage (MD) and intermittent drainage (ID) in commercial fields. Specifically, MD was implemented for two and four weeks starting 30 days after transplanting, followed by ID, with CH₄ fluxes actively monitored throughout the 2022–2025 growing seasons.