Analysis of Economic Development in Representative Regions of China's Three Major Economic Belts Using Multi-Source Remote Sensing Data

Objectives: A comprehensive analysis of the economic development of China's three major economic belts is crucial for understanding the disparities in economic development among different economic belts, conducting policy evaluations and formulation, and improving people's living standards. Remote sensing imagery can extract data and spatial distribution information on land use, urban construction, and development activity from a macroscopic perspective, playing an irreplaceable role in regional economic research. Methods: In response to the current lack of studies utilizing multi-source remote sensing data for regional economic analysis, this paper proposes a comprehensive evaluation and analysis method for assessing regional economic development. This method employs Sentinel-2 data, NPP/VIIRS night-time light data, as well as population and Gross Domestic Product (GDP) data, extracting features that reflect regional economic development from different perspectives. By constructing a regional economic scoring system based on the Analytic Hierarchy Process (AHP), the method provides a comprehensive analysis of the economic development status of representative regions within economic belts. This approach offers strong data support for understanding the current state and disparities among the three major economic belts. Results: The results show that during the three study periods, the regional economic scores for Jiangsu Province are 0.8411, 0.9047, and 0.9894, for Hubei Province are 0.6796, 0.6924, and 0.7527, and for the Ningxia Hui Autonomous Region are 0.3852, 0.4218, and 0.4767 respectively. Simultaneously, comprehensive analysis with other data reveals that the economic foundations of Jiangsu Province and Hubei Province are solid and consistently improving overall, while the economic development of the western economic belt represented by the Ningxia Hui Autonomous Region still lags behind other regions but with a continuous increase in development momentum. Conclusions: This study effectively integrates multi-source remote sensing data and socio-economic survey data, applying them to regional economic research. This approach provides a scientific methodological reference for studying regional economic development.