ArticleScientific data2026
An Extended VIIRS-like Artificial Nighttime Light Data Reconstruction (1986-2024).
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Spatiotemporal evolution and coupling of "Forest-Cropland-Water" ecosystem vulnerability in the Yangtze River Economic Belt.PloS one · 2026Article
- Spatiotemporal Variation of Ecological Quality in the Yinshan Mountains Detected by MODIS Remote Sensing Indicators.Ecology and evolution · 2026Article
- Joint Learning of Emotion and Singing Style for Enhanced Music Style Understanding.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
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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