Multi-sensor remote sensing and machine learning for aboveground biomass mapping in Vietnam’s Melaleuca wetlands: A review

Accurate mapping of aboveground biomass in tropical peatland forests remains challenging due to the complexity of vegetation structure, hydrological regimes, and data heterogeneity across sensors. This review synthesizes multi‑sensor remote sensing and machine‑learning approaches for aboveground biomass estimation in Vietnam’s Melaleuca wetlands, aiming to establish a standardized framework of terminology, metrics, and environmental covariates for future research and applications. By harmonizing key indicators such as canopy height, texture, soil-hydro-geomorphological variables, and validation metrics (R², RMSE), the framework enhances reproducibility, comparability, and data integration across scales. The study further consolidates a practical roadmap encompassing data acquisition, feature engineering, modeling, and validation stages - culminating in uncertainty‑aware biomass mapping that bridges research and operational implementation. Beyond synthesizing existing studies, this work provides actionable guidance for open‑access workflows and policy‑oriented applications in carbon accounting and wetland restoration. The proposed standardized approach thus supports both scientific and managerial communities in advancing sustainable management of Vietnam’s Melaleuca peat ecosystems and will help standardize future aboveground biomass mapping across Southeast Asian wetlands.

L-band SAR, LiDAR and GEDI, Melaleuca aboveground biomass, Multi-sensor data fusion, Spatial and spatio-temporal cross-validation

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