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 sub-industry GDP, yet previous studies relied on single panchromatic bands. Whether multispectral nighttime remote sensing data, detecting spectral differences from economic activities, improves sub-industry GDP estimates remains unverified. This study leverages multispectral nighttime light and thermal infrared data from the SDGSAT-1 satellite, combined with land cover data, to estimate sub-industry GDP using machine learning models. We compare Support Vector Machines (SVM), Neural Networks (NN), 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 R² 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% R² 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 sub-industry GDP estimation and offer actionable insights for enhancing urban economic monitoring and policy formulation.