The quantification of above-ground biomass (AGB) and soil organic carbon (SOC) across landscape scales has become increasingly critical for understanding terrestrial carbon dynamics and climate change mitigation strategies. This paper examines the integration of multiple remote sensing technologies for comprehensive carbon assessment, focusing on the synergistic use of optical, radar, and LiDAR sensors. Recent advances in machine learning algorithms and multi-sensor fusion techniques have significantly improved the accuracy of biomass and soil carbon estimation. This review synthesizes current methodologies, evaluates their strengths and limitations, and discusses future directions for integrated remote sensing approaches in carbon monitoring. Our analysis reveals that integrated approaches can achieve estimation accuracies of 85-95% for AGB and 70-85% for SOC, representing substantial improvements over single-sensor methods.