Remote sensing approaches for carbon stock estimation in optimising REDD+ benefits: a systematic review from 2015 to 2025.

Remote sensing technologies are transforming Measurement, Reporting, and Verification (MRV) systems for REDD+ projects, yet our systematic review reveals a critical misalignment between algorithmic advancement and institutional capacity across participating countries. We analysed 44 studies from 2015–2025, selected from 877 publications, examining how remote sensing approaches for carbon stock estimation correspond to varying institutional contexts. The technological landscape has evolved rapidly, with optical sensors providing the foundation for deforestation detection, while radar and LiDAR technologies increasingly enable fine-scale biomass assessment. Countries with high institutional capacity scores (>7.0/10) employ advanced algorithms in 43% of studies, while countries with low-capacity scores (<5.0/10) show no implementation of advanced algorithms despite facing complex forest monitoring challenges.Three critical disparities were identified: geographic imbalance in monitoring capabilities, with forest-rich nations in Africa and parts of Southeast Asia lacking infrastructure comparable to countries such as Brazil; a methodological bias toward above-ground biomass estimation, with only 4.5% of studies addressing soil carbon; and an operationalization gap where proven techniques lag in integration into national MRV systems. The most effective REDD+ monitoring systems are not necessarily those employing the most advanced algorithms, but those achieving optimal alignment between methodological sophistication and institutional capacity.We recommend strategic capacity building, South-South knowledge transfer, and tiered implementation approaches enabling countries to progressively adopt sophisticated methods. Addressing this technology-implementation gap will support more equitable forest carbon monitoring and optimise REDD+ benefits as a climate mitigation mechanism.