Improving Sub-Industry GDP Estimation With SDGSAT-1 Multispectral Nighttime Light and Thermal Infrared Data: Effectiveness and Potential
Accurate and timely estimation of gross domestic product (GDP) is essential for evaluating economic development. Nighttime light (NTL) data effectively estimate subindustry GDP, yet previous studies relied on single panchromatic bands. Whether multispectral nighttime remote sensing data, detecting s...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11112745/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849228493848576000 |
|---|---|
| author | Lingxian Zhang Zuoqi Chen Wenkang Gong Congxiao Wang Jing Xiong Linxin Dong Jingwen Ni Yan Huang Bailang Yu |
| author_facet | Lingxian Zhang Zuoqi Chen Wenkang Gong Congxiao Wang Jing Xiong Linxin Dong Jingwen Ni Yan Huang Bailang Yu |
| author_sort | Lingxian Zhang |
| collection | DOAJ |
| description | Accurate and timely estimation of gross domestic product (GDP) is essential for evaluating economic development. Nighttime light (NTL) data effectively estimate subindustry GDP, yet previous studies relied on single panchromatic bands. Whether multispectral nighttime remote sensing data, detecting spectral differences from economic activities, improves subindustry GDP estimates remains unverified. This article leverages multispectral NTL and thermal infrared data from the SDGSAT-1 satellite, combined with land cover data, to estimate subindustry GDP using machine learning models. We compare support vector machines, neural networks, and random forest (RF), identifying RF as the optimal model due to its lowest RMSE values (9.16, 171.06, and 180.51 for primary, secondary, and tertiary industries, respectively). Empirical results demonstrate that multispectral SDGSAT-1 data significantly outperforms its single panchromatic band counterpart, improving <italic>R</italic><sup>2</sup> values for secondary and tertiary industries from 0.58 to 0.88 and 0.68 to 0.90, respectively. Compared to VIIRS NTL data, SDGSAT-1 further reduces spatial misdistribution over farmland and industrial zones, achieving a 7.7% <italic>R</italic><sup>2</sup> improvement at smaller scale (industrial parks level). Key factors driving GDP estimation vary across industries: cropland area dominates for the primary industry; thermal infrared and red light intensity for the secondary industry; and blue light intensity for the tertiary industry. These findings validate the superiority of multispectral NTL data in subindustry GDP estimation and offer actionable insights for enhancing urban economic monitoring and policy formulation. |
| format | Article |
| id | doaj-art-2475b6fe868e4cf2825c8b9b08ca6428 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-2475b6fe868e4cf2825c8b9b08ca64282025-08-22T23:05:49ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118202792029310.1109/JSTARS.2025.359576411112745Improving Sub-Industry GDP Estimation With SDGSAT-1 Multispectral Nighttime Light and Thermal Infrared Data: Effectiveness and PotentialLingxian Zhang0Zuoqi Chen1https://orcid.org/0000-0002-3654-9658Wenkang Gong2Congxiao Wang3https://orcid.org/0009-0000-6882-1380Jing Xiong4Linxin Dong5https://orcid.org/0000-0002-4962-2420Jingwen Ni6https://orcid.org/0009-0000-0405-4346Yan Huang7https://orcid.org/0000-0001-6314-1802Bailang Yu8https://orcid.org/0000-0001-5628-0003Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai, ChinaKey Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National and Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Academy of Digital China, Fuzhou University, Fuzhou, ChinaKey Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai, ChinaKey Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai, ChinaChina Institute for Urban Governance, Shanghai Jiao Tong University, Shanghai, ChinaKey Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai, ChinaKey Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai, ChinaKey Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai, ChinaKey Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai, ChinaAccurate and timely estimation of gross domestic product (GDP) is essential for evaluating economic development. Nighttime light (NTL) data effectively estimate subindustry GDP, yet previous studies relied on single panchromatic bands. Whether multispectral nighttime remote sensing data, detecting spectral differences from economic activities, improves subindustry GDP estimates remains unverified. This article leverages multispectral NTL and thermal infrared data from the SDGSAT-1 satellite, combined with land cover data, to estimate subindustry GDP using machine learning models. We compare support vector machines, neural networks, and random forest (RF), identifying RF as the optimal model due to its lowest RMSE values (9.16, 171.06, and 180.51 for primary, secondary, and tertiary industries, respectively). Empirical results demonstrate that multispectral SDGSAT-1 data significantly outperforms its single panchromatic band counterpart, improving <italic>R</italic><sup>2</sup> values for secondary and tertiary industries from 0.58 to 0.88 and 0.68 to 0.90, respectively. Compared to VIIRS NTL data, SDGSAT-1 further reduces spatial misdistribution over farmland and industrial zones, achieving a 7.7% <italic>R</italic><sup>2</sup> improvement at smaller scale (industrial parks level). Key factors driving GDP estimation vary across industries: cropland area dominates for the primary industry; thermal infrared and red light intensity for the secondary industry; and blue light intensity for the tertiary industry. These findings validate the superiority of multispectral NTL data in subindustry GDP estimation and offer actionable insights for enhancing urban economic monitoring and policy formulation.https://ieeexplore.ieee.org/document/11112745/Nighttime light (NL) remote sensingnighttime thermal infraredSDGSAT-1 imagerysubindustry gross domestic product (GDP) estimation |
| spellingShingle | Lingxian Zhang Zuoqi Chen Wenkang Gong Congxiao Wang Jing Xiong Linxin Dong Jingwen Ni Yan Huang Bailang Yu Improving Sub-Industry GDP Estimation With SDGSAT-1 Multispectral Nighttime Light and Thermal Infrared Data: Effectiveness and Potential IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Nighttime light (NL) remote sensing nighttime thermal infrared SDGSAT-1 imagery subindustry gross domestic product (GDP) estimation |
| title | Improving Sub-Industry GDP Estimation With SDGSAT-1 Multispectral Nighttime Light and Thermal Infrared Data: Effectiveness and Potential |
| title_full | Improving Sub-Industry GDP Estimation With SDGSAT-1 Multispectral Nighttime Light and Thermal Infrared Data: Effectiveness and Potential |
| title_fullStr | Improving Sub-Industry GDP Estimation With SDGSAT-1 Multispectral Nighttime Light and Thermal Infrared Data: Effectiveness and Potential |
| title_full_unstemmed | Improving Sub-Industry GDP Estimation With SDGSAT-1 Multispectral Nighttime Light and Thermal Infrared Data: Effectiveness and Potential |
| title_short | Improving Sub-Industry GDP Estimation With SDGSAT-1 Multispectral Nighttime Light and Thermal Infrared Data: Effectiveness and Potential |
| title_sort | improving sub industry gdp estimation with sdgsat 1 multispectral nighttime light and thermal infrared data effectiveness and potential |
| topic | Nighttime light (NL) remote sensing nighttime thermal infrared SDGSAT-1 imagery subindustry gross domestic product (GDP) estimation |
| url | https://ieeexplore.ieee.org/document/11112745/ |
| work_keys_str_mv | AT lingxianzhang improvingsubindustrygdpestimationwithsdgsat1multispectralnighttimelightandthermalinfrareddataeffectivenessandpotential AT zuoqichen improvingsubindustrygdpestimationwithsdgsat1multispectralnighttimelightandthermalinfrareddataeffectivenessandpotential AT wenkanggong improvingsubindustrygdpestimationwithsdgsat1multispectralnighttimelightandthermalinfrareddataeffectivenessandpotential AT congxiaowang improvingsubindustrygdpestimationwithsdgsat1multispectralnighttimelightandthermalinfrareddataeffectivenessandpotential AT jingxiong improvingsubindustrygdpestimationwithsdgsat1multispectralnighttimelightandthermalinfrareddataeffectivenessandpotential AT linxindong improvingsubindustrygdpestimationwithsdgsat1multispectralnighttimelightandthermalinfrareddataeffectivenessandpotential AT jingwenni improvingsubindustrygdpestimationwithsdgsat1multispectralnighttimelightandthermalinfrareddataeffectivenessandpotential AT yanhuang improvingsubindustrygdpestimationwithsdgsat1multispectralnighttimelightandthermalinfrareddataeffectivenessandpotential AT bailangyu improvingsubindustrygdpestimationwithsdgsat1multispectralnighttimelightandthermalinfrareddataeffectivenessandpotential |