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An Extended VIIRS-like Artificial Nighttime Light Data Reconstruction (1986-2024).

Yihe Tian1, Kwan Man Cheng2, Zhengbo Zhang3

  • 1Department of Earth System Science, Ministry of Education, Ecological Field Station for East Asian Migratory Birds, Tsinghua University, Beijing, 100084, China.

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|January 23, 2026
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Summary
This summary is machine-generated.

A new Extended VIIRS-like Artificial Nighttime Light (EVAL) dataset provides annual nighttime light data for China from 1986-2024. This advanced dataset overcomes limitations of previous products, offering improved accuracy for human activity analysis.

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Area of Science:

  • Remote Sensing
  • Earth Observation
  • Geospatial Analysis

Background:

  • Night-time light (NTL) remote sensing quantifies human activities.
  • Existing NPP-VIIRS data (post-2012) limits long-term studies.
  • Current extended NTL products underestimate intensity and omit details.

Purpose of the Study:

  • Introduce the Extended VIIRS-like Artificial Nighttime Light (EVAL) dataset.
  • Provide annual NTL data for China (1986-2024).
  • Overcome limitations of existing NTL datasets.

Main Methods:

  • Developed a novel two-stage deep learning model.
  • Model estimates initial NTL and refines details using impervious surface data.
  • Generated annual NTL data for China from 1986 to 2024.

Main Results:

  • EVAL dataset shows superior performance compared to state-of-the-art products.
  • Demonstrates enhanced temporal consistency.
  • Exhibits a stronger correlation with socioeconomic indicators.

Conclusions:

  • The EVAL dataset offers a significant advancement for long-term NTL research in China.
  • It provides more accurate and detailed insights into human activity.
  • Facilitates improved socioeconomic and environmental studies.