The Supporting Role and Challenges of Remote Sensing Technology in Whole-Process Carbon Emission Accounting - Shi, Hanlin (Scientific Journal of Technology, 2026)
Traditional carbon emission accounting has long been constrained by insufficient spatiotemporal resolution, limited coverage, and inadequate dynamic monitoring capabilities. Remote sensing technology, leveraging its inherent advantages of non-contact observation, synchronous wide-area sensing, and multi-scale dynamic tracking, has been deeply integrated into the entire process of carbon emission accounting, emerging as a pivotal force for addressing these challenges. Combined with practical application examples of technologies such as night light remote sensing and LiDAR, it deeply analyzes the core technical challenges in accounting accuracy control, multi-source heterogeneous data fusion, and industry-specific accounting method adaptation. Research has shown that remote sensing technology can construct a 1km-scale high-resolution carbon emission spatial grid. The correlation coefficient between the vegetation net primary productivity inverted by the CASA model and the ground measured values ranges from 0.6–0.9. However, factors including observation environmental interferences, inversion algorithm limitations, and industrial emission heterogeneity render the issue of data uncertainty highly pronounced. This article proposes a targeted technological optimization path to enhance the scientific and accurate accounting of carbon emissions throughout the entire process, and provide practical reference for the implementation of the "dual carbon" strategic goals.