Ensemble machine learning for sub-daily downscaling of satellite-derived gross primary productivity

Gross Primary Productivity (GPP) plays a central role in regulating terrestrial carbon uptake, yet commonly used satellite-based GPP products are provided at multi-day temporal resolutions, limiting their ability to capture rapid ecosystem responses to short-term environmental variability. This temporal constraint is particularly critical under increasing occurrences of extreme weather events, where sub-daily vegetation dynamics remain poorly understood. In this study, we propose a machine-learning-based framework to generate hourly GPP estimates at moderate spatial resolution across the Korean Peninsula. The approach integrates satellite-derived vegetation indices with reanalysis-based hydrometeorological variables and explicitly accounts for land-cover heterogeneity by constructing independent models for major land-cover classes. To enhance model interpretability and efficiency, a feature selection strategy was applied to identify key environmental drivers of GPP variability for each land-cover type. Model performance was evaluated using temporally independent datasets, demonstrating that hourly GPP estimates aggregated to multi-day scales are consistent with existing satellite GPP products, while additionally capturing realistic diurnal cycles and seasonal patterns. The results indicate that a reduced set of influential variables can preserve predictive skill while improving computational efficiency. The proposed framework provides a practical pathway for temporally downscaling widely available satellite GPP products to sub-daily resolution in regions with limited ground observations. This capability offers new opportunities to investigate vegetation productivity responses to short-term climatic extremes such as heatwaves and droughts, contributing to improved understanding of ecosystem carbon dynamics under a changing climate.

 

Acknowledgement

This work was supported by the Korea Environmental Industry & Technology Institute (KEITI) through Wetland Ecosystem Value Evaluation and Carbon Absorption Value Promotion Technology Development Project, funded by Korea Ministry of Climate, Energy and Environment (MCEE) (RS-2022-KE002066), and supported by the National Research Foundation of Korea(NRF) funded by the Ministry of Education (RS-2024-00465925) and by the Korea government (MSIT) (RS-2024-00334564 & RS-2021-NR060085).