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 (R-2), 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.