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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.
Scientific Data
|January 23, 2026
Summary
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.
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.
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