Multi-Source Remote Sensing and GIS-Driven Forest Carbon Monitoring for Carbon Neutrality: Integrating Data, Modeling, and Policy Applications

Forest carbon monitoring represents a cornerstone of global climate mitigation strategies, particularly under the accelerating push toward carbon neutrality. Recent advances in remote sensing technologies, geographic information systems (GIS), and artificial intelligence (AI) have transformed the capacity to quantify forest carbon dynamics across scales. This review synthesizes developments from 2020 to 2025, focusing on the integration of multi-source remote sensing data—including optical, SAR, and LiDAR—into scalable, GIS-based carbon monitoring systems. We systematically evaluate modeling frameworks ranging from empirical regression to machine learning, deep learning, and process-based ecological models, emphasizing their performance, scalability, and uncertainty management. Furthermore, we examine the operationalization of these technologies in national carbon accounting systems, REDD+ monitoring frameworks, and voluntary carbon markets. Case studies from Brazil and the Congo Basin illustrate diverse implementation pathways. Key challenges—including data standardization, model transferability, and high-resolution cost barriers—are critically assessed. Finally, future directions highlight the need for AI-augmented modeling, cloud-native workflows, and integrated sky-to-ground monitoring networks to enable real-time, policy-relevant carbon assessments. Together, these elements frame a next-generation forest carbon monitoring paradigm that is scientifically robust, operationally scalable, and aligned with global carbon neutrality goals.