Find in Library

The CO₂ efflux from forest soil (FCO₂) is one of the largest components of the global carbon cycle. Accurate estimation of FCO₂ can help us better understand the carbon cycle in forested areas and precisely predict future climate change. However, the scarcity of field-measured FCO₂ data in the subtropical forested area greatly limits our understanding of FCO₂ dynamics at regional and global scales. This study used an automatic cavity ring-down spectrophotometer (CRDS) analyzer to measure FCO₂ in a typical subtropical forest of southern China in the dry season. We found that the measured FCO₂ at two experimental areas experienced similar temporal trends in the dry season and reached the minima around December, whereas the mean FCO₂ differed apparently across the two areas (9.05 vs. 5.03 g C m⁻² day⁻¹) during the dry season. Moreover, we found that both abiotic (soil temperature and moisture) and biotic (vegetation productivity) factors are significantly and positively correlated, respectively, with the FCO₂ variation during the study period. Furthermore, a machine-learning random forest model (RF model) that incorporates remote sensing data is developed and used to predict the FCO₂ pattern in the subtropical forest, and the topographic effects on spatiotemporal patterns of FCO₂ were further investigated. The model evaluation indicated that the proposed model illustrated high prediction accuracy for the training and testing dataset. Based on the proposed model, the spatiotemporal patterns of FCO₂ in the forested watershed that encloses the two monitoring sites were mapped. Results showed that the spatial distribution of FCO₂ is obviously affected by topography: the high FCO₂ values mainly occur in relatively high altitudinal areas, in slopes of 10–25°, and in sunny slopes. The results emphasized that future studies should consider topographical effects when simulating FCO₂ in subtropical forests. Overall, our study unraveled the spatiotemporal variations of FCO₂ and their driving factors in a subtropical forest of southern China in the dry season, and demonstrated that the proposed RF model in combination with remote sensing data can be a useful tool for predicting FCO₂ in forested areas, particularly in subtropical and tropical forest ecosystems.