Abstract
Artificial Night-Time Light (NTL) remote sensing is a vital proxy for quantifying the intensity and spatial distribution of human activities. Although the NPP-VIIRS sensor provides high-quality NTL observations, its temporal coverage, which begins in 2012, restricts long-term time-series studies that extend to earlier periods. Current extended VIIRS-like NTL data products suffer from two significant shortcomings: the underestimation of light intensity and the omission of structural details. To overcome these limitations, we present the Extended VIIRS-like Artificial Nighttime Light (EVAL) dataset, a new annual NTL dataset for China spanning from 1986 to 2024. This dataset was generated using a novel two-stage deep learning model designed to address the aforementioned shortcomings. The model first constructs an initial estimate and subsequently refines fine-grained structural details using high-resolution impervious surface data as guidance. Quantitative evaluations demonstrate that EVAL significantly outperforms state-of-the-art products, exhibiting superior temporal consistency and a stronger correlation with socioeconomic indicators.
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Data availability
The Extended VIIRS-like Artificial Nighttime Light (EVAL) dataset generated in this study is available at the National Tibetan Plateau Data Center (TPDC) (https://doi.org/10.11888/HumanNat.tpdc.30293044).
Code availability
The programs used to generate all the results were Python 3.12. The code and scripts used for training, testing, and predicting the NTL data are available in the open GitHub repository “https://github.com/Rookie1miao/EVAL_framework”.
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Acknowledgements
This study is supported by National Key Research and Development Program of China (2022YFB3903703).
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Yihe Tian: Conceptualization, Methodology, Software, Validation, Formal analysis, Visualization, Writing Kwan Man Cheng: Conceptualization, Methodology, Resources, Validation, Formal analysis Zhengbo Zhang: Resources, Validation Tao Zhang: Review & Editing Zhehao Ren: Review & Editing Junning Feng: Review & Editing Suju Li: Review & Editing Dongmei Yan: Review & Editing Bing Xu: Review, Editing, Formal analysis, Funding acquisition, Supervision.
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Tian, Y., Cheng, K.M., Zhang, Z. et al. An Extended VIIRS-like Artificial Nighttime Light Data Reconstruction (1986–2024). Sci Data (2026). https://doi.org/10.1038/s41597-026-06549-0
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DOI: https://doi.org/10.1038/s41597-026-06549-0
