Investigating spatial and vertical patterns of wetland soil organic carbon concentrations in China's Western Songnen Plain by comparing different algorithms

Abstract

Investigating the spatial and vertical patterns of wetland soil organic carbon concentration (SOCc) is important for understanding the regional carbon cycle and managing the wetland ecosystem. By integrating 160 wetland soil profile samples and environmental variables from climatic, topographical, and remote sensing data, we spatially predicted the SOCc of wetlands in China's Western Songnen Plain by comparing four algorithms: random forest (RF), support vector machine (SVM) for regression, inverse distance weighted (IDW), and ordinary kriging (OK). The predicted results of the SOCc from the different algorithms were validated against independent testing samples according to the mean error, root mean squared error, and correlation coecient. The results show that the measured SOCc values at depths of 0-30, 30-60, and 60-100 cm were 15.28, 7.57, and 5.22 g • kg-1, respectively. An assessment revealed that the RF algorithm was the most accurate for predicting SOCc; its correlation coecients at the different depths were 0.82, 0.59, and 0.51, respectively. The attribute importance from the RF indicates that environmental variables have various effects on the SOCc at different depths. The land surface temperature and land surface water index had a stronger influence on the spatial distribution of SOCc at the depths of 0-30 and 30-60 cm, whereas topographic factors, such as altitude, had a stronger influence within 60-100 cm. The predicted SOCc of each vertical depth increased gradually from south to north in the study area. This research provides an important case study for predicting SOCc, including selecting factors and algorithms, and helps understanding the carbon cycles of regional wetlands.