Spatiotemporal variability analysis of remote sensing–derived net primary productivity and its hydrological drivers

A refined understanding of how hydrological processes act as key drivers of terrestrial carbon dynamics is essential for improving assessments of atmospheric CO₂ fluxes and strengthening climate change mitigation strategies. This study characterizes the spatiotemporal dynamics of net primary productivity (NPP), a fundamental indicator of terrestrial CO₂ sequestration capacity, across South Korea from 2004 to 2019 using the Carnegie–Ames–Stanford Approach (CASA) model, with particular emphasis on hydrological drivers. The CASA framework was implemented by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing products with meteorological variables, enabling consistent estimation of vegetation productivity across diverse land-cover types. Pronounced increases in NPP were observed in deciduous broadleaf forests and croplands, whereas urban areas exhibited declining trends, reflecting contrasting trajectories in ecosystem productivity linked to land use patterns.

To quantify the roles of individual hydrological drivers, we evaluated the seasonal contributions of precipitation, temperature, and groundwater storage (GWS). Precipitation and temperature inputs were derived from ground-based meteorological station observations and spatially interpolated using the Barnes objective analysis method to generate continuous spatiotemporal datasets. GWS was derived from satellite observations, combined with machine learning model to capture spatiotemporal variability in subsurface water availability. Elevated temperatures and increased GWS during spring and autumn—corresponding to the major growing season—served as strong positive drivers of vegetation productivity, while summer NPP was predominantly influenced by precipitation variability. Beyond the widely recognized roles of temperature and precipitation, the analysis underscores groundwater as a critical and previously underappreciated driver of spatiotemporal NPP variability.

By clarifying the contribution of groundwater to terrestrial CO₂ uptake, the findings provide essential guidance for enhancing carbon-flux monitoring strategies and for managing ecosystems that rely substantially on subsurface water resources. More broadly, the results highlight the importance of incorporating groundwater dynamics and the full suite of hydrological drivers into assessments of carbon cycle processes and into comprehensive climate adaptation and mitigation frameworks.

(Acknowledgments) This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (RS-2022-NR072257). This work was also supported by the Management Technology for Groundwater Dams in Water Supply Vulnerable Areas Program of the Korea Environmental Industry & Technology Institute (KEITI), funded by the Ministry of Environment (MOE) (RS-2025-01842973).