FUT-NTL: A global dataset for future nighttime light (2025-2050) at 1 km gridded level under shared socio-economic...

Nighttime light (NTL) remote sensing data serves as a vital data source for monitoring urbanization processes and assessing the level of sustainable development from multidimensional aspects. Predicting NTL data offers a more comprehensive reflection of future urbanization, with the potential to provide deeper insights into future human activities and capture environmental indicators. To address the lack of globally consistent and dynamically evolving SSP-based NTL projections, we generated a global future NTL dataset (FUT-NTL) from 2025 to 2050 (at 5-year intervals) under five Shared Socio-economic Pathways (SSPs) with a spatial resolution of 1 km through the random forest regression models. The prediction models perform well globally, achieving the highest regional R² of 0.92 compared to the observed NTL intensity in 2020, with RMSE values ranging from 2.73 to 7.83 nWcm^-2sr^-1. Predicted NTL datasets align well with SSP narratives, showing the highest growth rates of NTL in scenarios of rapid development, particularly under SSP5. Sub-Saharan Africa stands out with the highest growth rates in NTL intensity across scenarios, except SSP4. Generally, our predicted datasets can be easily updated and provide valuable proxies for analyzing future urbanization, socioeconomic activities, and environmental indicators.