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

LIU Zhiyang; YIN Huijie; LI Mingyuan; ZONG Fu; DONG Yanni

doi:10.13203/j.whugis20240233

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

Photogrammetry and Remote Sensing | 更新时间：2026-11-05

- Analysis of Economic Development in Representative Regions of China's Three Major Economic Belts Using Multi-source Remote Sensing Data
- Geomatics and Information Science of Wuhan University Vol. 51, Issue 3, Pages: 531-543(2026)
- 作者机构：
  
  1.武汉大学资源与环境科学学院，湖北武汉，430079
  2.中国地质大学(武汉)地球物理与空间信息学院，湖北武汉，430074
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20240233
  CLC： P237
- Received：07 December 2024，
  
  Published：05 March 2026
- 稿件说明：
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刘志洋,尹慧杰,李明渊，等.利用多源遥感数据的中国三大经济带代表区域经济发展分析[J].武汉大学学报（信息科学版）,2026,51(3):531-543.

LIU Zhiyang,YIN Huijie,LI Mingyuan,et al.Analysis of Economic Development in Representative Regions of China's Three Major Economic Belts Using Multi-source Remote Sensing Data[J].Geomatics and Information Science of Wuhan University,2026,51(3):531-543.
刘志洋,尹慧杰,李明渊，等.利用多源遥感数据的中国三大经济带代表区域经济发展分析[J].武汉大学学报（信息科学版）,2026,51(3):531-543. DOI：10.13203/j.whugis20240233

LIU Zhiyang,YIN Huijie,LI Mingyuan,et al.Analysis of Economic Development in Representative Regions of China's Three Major Economic Belts Using Multi-source Remote Sensing Data[J].Geomatics and Information Science of Wuhan University,2026,51(3):531-543. DOI：10.13203/j.whugis20240233

摘要

对中国三大经济带的经济发展状况进行综合性分析，对于了解不同经济带间的经济发展差异、进行政策评估与制定、提升人民生活水平等有着重要意义。遥感影像可以从宏观尺度上提取区域内的土地利用、城市建设、发展活跃度等数据及空间分布信息，在区域经济研究中有着不可替代的作用。将多源遥感数据应用于三大经济带发展研究能够有效利用区域宏观特征，从不同方面探索整体发展特点。使用哨兵2号卫星数据、美国Suomi国家极轨伙伴卫星搭载的可见光红外成像辐射计获取的夜光数据及人口、国民生产总值数据，提取了能够反映区域经济发展状况的特征，并通过构建基于层次分析法的区域经济评分体系综合分析三大经济带中代表省份的经济发展状况，为了解三大经济带发展现状与差异提供有力的数据支撑。结果表明，3个研究时段内江苏省区域经济得分分别为0.660 5、0.706 1、0.767 1，湖北省得分分别为0.519 9、0.530 7、0.573 4，宁夏回族自治区得分分别为0.294 3、0.321 2、0.360 1。同时，综合其他数据发现，江苏省与湖北省经济发展基础扎实且整体持续向好，而以宁夏回族自治区为代表的西部经济带的经济发展状况仍旧落后于其他地区，但发展增速持续提升。将多源遥感数据和社会经济调查数据进行有效结合并应用于区域经济研究，能够为研究区域经济发展状况提供科学的方法参考。

Abstract

Objectives

China has formed three major economic belts according to regional resource condition

economic foundation

and development orientation

namely the eastern

central

and western economic belts. A comprehensive analysis of economic development across China's three major economic belts is essential. It helps reveal regional disparities

supports policy evaluation and formulation

and contributes to improvements in living standards. Traditional regional economic research mainly relies on socio-economic statistical data. Although effective

such data lack spatial continuity and fine-scale spatial representation. Remote sensing imagery can extract data and spatial distribution information on land use

urban construction

and development activity from a macroscopic perspective

playing an important role in regional economic research. However

current research still primarily uses the single data source

such as night-time light data

for economic development studies. The integration of multi-source remote sensing data with advanced feature extraction methods provides new opportunities for comprehensive regional economic assessment.

Methods

In response to the current lack of studies utilizing multi-source remote sensing data for regional economic analysis

a comprehensive evaluation and analysis method is proposed. 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. A vision transformer (ViT)-based semantic segmentation model is applied to Sentinel-2 imagery to generate detailed land use classification maps. The threshold method is also applied to extract built-up areas from night-time light data. Nine evaluation indicators are constructed by combining the results of multi-source remote sensing image extraction with GDP and population data. By constructing a regional economic scoring system based on the analytic hierarchy process

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 indicate that the regional economic scores for Jiangsu Province are 0.660 5

0.706 1

and 0.767 1 across the three study periods

followed by Hubei Province with scores of 0.519 9

0.530 7

and 0.573 4

and the Ningxia Hui Autonomous Region with scores of 0.294 3

0.321 2

and 0.360 1

respectively. All three regions show consistent upward trends

reflecting overall improvement in regional development levels. Jiangsu Province maintains the highest score throughout the study period

showing strong economic vitality and a solid development foundation. Hubei Province demonstrates steady growth and structural stability

although a temporary slowdown is observed from 2020 to 2021

corresponding to the impact of the pandemic. However

subsequent data also show that Hubei Province recovers relatively quickly after the pandemic. In contrast

the Ningxia Hui Autonomous Region remains at a comparatively lower development level. Nevertheless

its economic score increases steadily

and its average growth rate ranks highest among the three regions

indicating strengthening development momentum within the western economic belt despite persistent structural disparities.

Conclusions

The integration of multi-source remote sensing data and socio-economic indicators enhances the accuracy and spatial explicitness of regional economic evaluation. The combination of ViT-based land use classification

night-time light analysis

and a regional economic scoring system enables multi-temporal assessment of development disparities among economic belts. The results reflect structural differences consistent with regional development patterns and recent policy influences. This framework provides a scalable and data-driven reference for monitoring regional economic dynamics and supporting coordinated development planning.

关键词

Keywords

references

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