A new spatiotemporal fusion model for integrating VIIRS and SDGSAT-1 Nighttime light data to generate daily SDGSAT-1 like observations
Nighttime light (NTL) data is a critical indicator for understanding social and environmental dynamics, offering unique insights into human activities after dark. However, while providing high temporal resolution, existing NTL datasets like VIIRS suffer from low spatial resolution, limiting their ca...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2472912 |
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| author | Jinhu Bian Touseef Ahmad Khan Ainong Li Jinping Zhao Yi Deng Guangbin Lei Zhengjian Zhang Xi Nan Amin Naboureh Muhib Ullah Khan |
| author_facet | Jinhu Bian Touseef Ahmad Khan Ainong Li Jinping Zhao Yi Deng Guangbin Lei Zhengjian Zhang Xi Nan Amin Naboureh Muhib Ullah Khan |
| author_sort | Jinhu Bian |
| collection | DOAJ |
| description | Nighttime light (NTL) data is a critical indicator for understanding social and environmental dynamics, offering unique insights into human activities after dark. However, while providing high temporal resolution, existing NTL datasets like VIIRS suffer from low spatial resolution, limiting their capability for detailed monitoring. There is a growing demand for NTL data with high spatial and temporal resolutions (HSTR). This study proposed a new HSTR NTL data fusion model named Nighttime Light Spatiotemporal Fusion (NTLSTF). This model generated HSTR NTL radiance values similar to SDGSAT-1 by reconstructing NTL features using a combination of spectral, spatial, and temporal weighting from VIIRS and SDGSAT-1 NTL data. Results demonstrated that the predicted SDGSAT-1 images were consistent with real SDGSAT-1 observations from both visual effect and radiance prediction accuracy. The validation of results was further supported by a high Structural Similarity Index (SSIM) of 0.976, with other quantitative metrics such as Coefficient of Determination (R²), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE), with the values of 0.941, 7.701, and 17.171, respectively. The predicted daily SDGSAT-1-like NTL data for flood disaster emergency response case in Pakistan showed the application potential of the proposed model. |
| format | Article |
| id | doaj-art-eabd7ecad85f456c8e4f0cdb61625e5f |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-eabd7ecad85f456c8e4f0cdb61625e5f2025-08-25T11:31:30ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2472912A new spatiotemporal fusion model for integrating VIIRS and SDGSAT-1 Nighttime light data to generate daily SDGSAT-1 like observationsJinhu Bian0Touseef Ahmad Khan1Ainong Li2Jinping Zhao3Yi Deng4Guangbin Lei5Zhengjian Zhang6Xi Nan7Amin Naboureh8Muhib Ullah Khan9Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, Sichuan, People’s Republic of ChinaInstitute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, Sichuan, People’s Republic of ChinaInstitute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, Sichuan, People’s Republic of ChinaInstitute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, Sichuan, People’s Republic of ChinaInstitute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, Sichuan, People’s Republic of ChinaInstitute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, Sichuan, People’s Republic of ChinaInstitute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, Sichuan, People’s Republic of ChinaInstitute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, Sichuan, People’s Republic of ChinaInstitute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, Sichuan, People’s Republic of ChinaInstitute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, Sichuan, People’s Republic of ChinaNighttime light (NTL) data is a critical indicator for understanding social and environmental dynamics, offering unique insights into human activities after dark. However, while providing high temporal resolution, existing NTL datasets like VIIRS suffer from low spatial resolution, limiting their capability for detailed monitoring. There is a growing demand for NTL data with high spatial and temporal resolutions (HSTR). This study proposed a new HSTR NTL data fusion model named Nighttime Light Spatiotemporal Fusion (NTLSTF). This model generated HSTR NTL radiance values similar to SDGSAT-1 by reconstructing NTL features using a combination of spectral, spatial, and temporal weighting from VIIRS and SDGSAT-1 NTL data. Results demonstrated that the predicted SDGSAT-1 images were consistent with real SDGSAT-1 observations from both visual effect and radiance prediction accuracy. The validation of results was further supported by a high Structural Similarity Index (SSIM) of 0.976, with other quantitative metrics such as Coefficient of Determination (R²), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE), with the values of 0.941, 7.701, and 17.171, respectively. The predicted daily SDGSAT-1-like NTL data for flood disaster emergency response case in Pakistan showed the application potential of the proposed model.https://www.tandfonline.com/doi/10.1080/17538947.2025.2472912Data fusionVIIRSSDGSAT-1nighttime lightNTLSTF model |
| spellingShingle | Jinhu Bian Touseef Ahmad Khan Ainong Li Jinping Zhao Yi Deng Guangbin Lei Zhengjian Zhang Xi Nan Amin Naboureh Muhib Ullah Khan A new spatiotemporal fusion model for integrating VIIRS and SDGSAT-1 Nighttime light data to generate daily SDGSAT-1 like observations International Journal of Digital Earth Data fusion VIIRS SDGSAT-1 nighttime light NTLSTF model |
| title | A new spatiotemporal fusion model for integrating VIIRS and SDGSAT-1 Nighttime light data to generate daily SDGSAT-1 like observations |
| title_full | A new spatiotemporal fusion model for integrating VIIRS and SDGSAT-1 Nighttime light data to generate daily SDGSAT-1 like observations |
| title_fullStr | A new spatiotemporal fusion model for integrating VIIRS and SDGSAT-1 Nighttime light data to generate daily SDGSAT-1 like observations |
| title_full_unstemmed | A new spatiotemporal fusion model for integrating VIIRS and SDGSAT-1 Nighttime light data to generate daily SDGSAT-1 like observations |
| title_short | A new spatiotemporal fusion model for integrating VIIRS and SDGSAT-1 Nighttime light data to generate daily SDGSAT-1 like observations |
| title_sort | new spatiotemporal fusion model for integrating viirs and sdgsat 1 nighttime light data to generate daily sdgsat 1 like observations |
| topic | Data fusion VIIRS SDGSAT-1 nighttime light NTLSTF model |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2472912 |
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