Evaluation of forest carbon uptake in South Korea using the national flux tower network, remote sensing, and data-driven technology

Abstract

Forests provide most of the carbon sequestration of atmospheric carbon dioxide (CO2); however, accurately quantifying the uptake amount over a region remains challenging. For reginal or national estimates, the forest productivity model and forest inventories are used which provide information for national greenhouse gas inventories. However, it has some limitations, such as not considering below-ground biomass, its lack of species-specific allometric models, the restrictions it places during fieldwork, and the long period it takes to complete the survey. In contrast to inventory-based biomass estimates, the eddy covariance (EC) method can assess net CO2 exchange of a whole ecosystem continuously and automatically with a high temporal resolution. Since, these measurements only represent a site-level observation scale (∼ 1 km2), upscaling via linkages with observation data, remote sensing, and modeling methods has been used to estimate regional or national land-atmosphere carbon fluxes. In this study, we employ a data-driven method to estimate the national-scale gross primary production (GPP) and net ecosystem CO2 exchange (NEE) by combining EC flux data from 10 sites in South Korea with remote sensing data through a machine learning algorithm based on support vector regression (SVR) for the period 2000–2018. Site-level evaluation of estimated GPP and NEE from the SVR-based model shows equivalent performance compared to other continental and global upscaled models. The mean estimated annual GPP and NEE of the South Korea forests region over the period 2000–2018 were 1465 ± 37 and −243 ± 32 g C m2 year−1, respectively. The SVR-based net primary production (NPP) was consistent with the biometric-based NPP (r2 = 0.46, p < 0.05). This study shows that combining data from a national flux network and remote sensing using a data-driven approach can be used to estimate forest CO2 fluxes on a national scale.