Remote sensing for aboveground biomass monitoring in terrestrial ecosystems: A systematic review

Monitoring aboveground biomass (AGB) in terrestrial ecosystems is crucial for understanding carbon dynamics, assessing ecosystem health, and informing climate change mitigation strategies. Over the past two decades, advancements in remote sensing (RS) technologies, including optical, synthetic aperture radar (SAR), and light detection and ranging (LiDAR), combined with integrated approaches, have significantly improved AGB estimation accuracy. This systematic review examines spatiotemporal trends in AGB research, evaluates the scale of studies and field plots, and assesses the effectiveness of RS types and their synergies globally. It also addresses uncertainties, prospects, and constraints in RS-based AGB estimation, identifying key research gaps for future investigations. The literature survey reveals a growing use of RS methods and synergies over the last decade. North America and Europe are leading in LiDAR applications, while Asia contributes significantly to optical applications. China and the United States have the highest number of field plots, reflecting extensive sampling efforts and large-scale execution. Research trends focus on temperate, tropical, and boreal forests, with tropical forests exhibiting the highest mean AGB (245 ton ha⁻¹). Quantification of data sources shows 30 % of studies used LiDAR, followed by optical (27 %), optical-LiDAR (16 %), SAR-LiDAR (10 %), optical-SAR (7 %), SAR (6 %), and optical-SAR-LiDAR (5 %) combinations. Optical-SAR-LiDAR synergy demonstrated the highest efficiency (R² > 0.60), highlighting its potential when integrated with climatic, topographic, and biophysical data using advanced modeling techniques and comprehensive methodologies. This review provides valuable insights for researchers and policymakers focused on carbon cycles, RS, and climate change.