Aboveground biomass estimation in a grassland ecosystem using Sentinel-2 satellite imagery and machine learning algorithms
Problem
Aboveground biomass of grasslands must be estimated accurately to track carbon stocks and the carbon cycle.
Why it matters
Grassland biomass stores carbon, so accurate estimates matter for climate regulation accounting.
What the AI did
Random forest and gradient boosting models were trained on GEDI biomass density data using Sentinel-2 bands, NDVI, and SRTM DEM products to estimate grassland aboveground biomass.
Results
not stated in source (abstract truncated before accuracy metrics)
Impact
ML-based biomass estimation workflow is described, but quantitative accuracy is not visible in the abstract.
Record
| Venue | Environmental Monitoring and Assessment |
|---|---|
| Published | 2025 |
| Work type | article |
| Authors | Andisani Netsianda, Paidamwoyo Mhangara |
| Citations | 20 (OpenAlex, at import time) |
| References | 26 |
| Model or method | Random forest and gradient boosting |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1007/s10661-024-13610-1 |
| Open access | hybrid |
| Keywords | aboveground biomass, Sentinel-2, random forest, grassland, remote sensing |
Importance rating
significant — thousands to millions affected
Abstract states grasslands cover 15-26% of Earth's land surface and significantly impact the carbon cycle and climate regulation, making accurate biomass estimation relevant to global carbon accounting.
More in Climate & Environment
| # | Paper | Year | Citations | Importance |
|---|---|---|---|---|
| 1 | Learning skillful medium-range global weather forecasting | 2023 | 1,416 |
8
|
| 2 | Accurate medium-range global weather forecasting with 3D neural networks | 2023 | 1,631 |
8
|
| 3 | FuXi: a cascade machine learning forecasting system for 15-day global weather forecast | 2023 | 355 |
8
|
| 4 | Skilful nowcasting of extreme precipitation with NowcastNet | 2023 | 451 |
8
|
| 5 | Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects | 2023 | 378 |
8
|