Simulating the root-to-shoot ratio of natural grassland biomass in China by the AutoGluon framework
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 estimatin...
Sábháilte in:
| Príomhchruthaitheoirí: | , , , , , , , |
|---|---|
| Formáid: | Alt |
| Teanga: | Béarla |
| Foilsithe / Cruthaithe: |
Taylor & Francis Group
2025-12-01
|
| Sraith: | International Journal of Digital Earth |
| Ábhair: | |
| Rochtain ar líne: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2538220 |
| Clibeanna: |
Cuir clib leis
Níl clibeanna ann, Bí ar an gcéad duine le clib a chur leis an taifead seo!
|
| Achoimre: | 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. |
|---|---|
| ISSN: | 1753-8947 1753-8955 |