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.
@article{arxiv.2508.00590,
title = {An Extended VIIRS-like Artificial Nighttime Light Data Reconstruction (1986-2024)},
author = {Yihe Tian and Kwan Man Cheng and Zhengbo Zhang and Tao Zhang and Junning Feng and Zhehao Ren and Suju Li and Dongmei Yan and Bing Xu},
journal= {arXiv preprint arXiv:2508.00590},
year = {2026}
}