Simulating and Mapping the Root-to-Shoot Ratio of Npp in Natural Grasslands Across China

Grasslands in China are rich in resources and have a strong carbon sequestration capacity, thus being important in the carbon cycle of terrestrial ecosystems. The root-to-shoot ratio (R/S) reflects the aboveground and belowground carbon allocation patterns of vegetation and is an important parameter for estimating carbon stocks in grasslands. However, researchers looking at grassland R/S have relied primarily on statistical analyses conducted in local areas with limited samples, and there is a lack of relevant studies on the R/S of grassland prediction and spatialization. In this study, a high-accuracy R/S model was constructed based on the AutoGluon framework and traditional machine learning (ML) algorithms using 1,367 R/S of grassland samples in China, combined with climate, soil, terrain and spectral features. Then, the R/S of natural grasslands was simulated at a 1 km resolution, and the R/S spatial distribution characteristics of 18 types of natural grasslands in China were analyzed. The results showed that 1) the estimation accuracy of the R/S model based on the AutoGluon framework was significantly better than that of the other three traditional ML models (R2 = 0.93, RMSE = 18.12). 2) The optimal model consists of 12 variables that are sensitive to changes in the R/S of grasslands, including topographic, climate, spectral, and soil features. 3) Among the 18 types of natural grasslands, alpine steppe had the largest R/S value of 346.52, while warm-temperate tussock had the smallest R/S value of 0.11. For each grassland, the mean R/S values ranged from 0.42 to 14.20, and the median R/S values fell within the range of 0.36 to 11.85. In general, the R/S spatial distribution map of natural grasslands developed in this study is expected to improve the accuracy of net primary productivity and carbon stock estimations in grassland ecosystems.