Simulating the root-to-shoot ratio of natural grassland biomass in China by the AutoGluon framework - Rui Guo; Xiaodong Huang; Yangjing Xiu; Minglu Che; Jinlong Gao; Shuai Fu; Qisheng Feng; Tiangang Liang (International Journal of Digital Earth)
China's grasslands, with abundant resources and high carbon sequestration capacity, play an important role in the terrestrial carbon cycle. The root-to-shoot ratio (R/S) reflects the aboveground and belowground carbon allocation patterns of vegetation and is an important parameter for estimating grassland carbon stocks. Previous studies have focused primarily on fixed R/S values from statistical analyses, leading to large uncertainties in areas with high spatial heterogeneity. In this study, a high-accuracy R/S model was constructed using the AutoGluon framework and traditional machine learning (ML) algorithms with 1,367 R/S samples of grassland in China, integrating climate, soil, terrain and spectral features. The results indicated the following: (1) The AutoGluon model achieved superior performance (R² = 0.93, RMSE = 18.12) compared to three traditional ML models. (2) Among the 17 natural grassland types, median R/S values ranged from 0.36 to 11.11 and were significantly lower than the corresponding means (0.44 to 13.38), indicating a right-skewed distribution. (3) In terms of climatic zones, the R/S values followed the trend: alpine grasslands > temperate grasslands > warm and tropical grasslands. This study has achieved spatial mapping of natural grassland R/S, offering valuable insights into carbon allocation, ecosystem functioning, and the sustainable management of grassland resources.