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1992年至2023年的全球年度模拟VIIRS夜间光数据集

Xiuxiu Chen^1,2,3,4, Zeyu Wang^1,3, Feng Zhang^5,6

¹School of Earth Sciences, Zhejiang University, Hangzhou, 310058, China.

Scientific data

|December 18, 2024

概括

一个新的夜光U-Net超分辨率网络创建了一个连续的500m全球数据集 (SVNL) 从1992-2023. 这些改进的夜间光线数据为城市化研究提供了更好的空间细节和时间一致性.

科学领域:

地球观测地球观测
地理空间科学地理空间科学
人工智能的人工智能

背景情况:

夜间照明 (NTL) 数据对于监测人类活动,城市化和社会经济因素至关重要.
现有的NTL数据集存在差异和有限的时间覆盖,阻碍了长期研究.
需要进行交叉传感器校准,以创建一致和可靠的NTL数据集.

研究的目的:

开发DMSP-OLS和NPP-VIIRS NTL数据之间的交叉传感器校准方法.
从1992年到2023年生成一个连续的,高分辨率的全球年度NTL数据集.
为长期监测人类活动和城市化提供可靠的数据来源.

主要方法:

一个夜光U-Net超分辨率网络被开发用于交叉传感器校准.
该网络使用DMSP-OLS和NPP-VIIRS NTL数据进行训练.
一个连续500米的全球年度模拟VIIRS NTL数据集 (SVNL) 为19922023.3生成.

主要成果:

生成的SVNL数据集显示了高数据质量和可靠性.
与现有的数据集相比,SVNL数据在捕获NTL动态,时间一致性和空间细节方面表现出卓越的性能.
验证证实了U-Net网络在NTL数据校准方面的有效性.

结论:

SVNL数据集提供了全球夜间照明的有价值,长期和一致的记录.
这一数据集可以显著增强全球城市化和人类活动监测的研究.
拟议的U-Net方法为协调来自不同传感器的NTL数据提供了一个强大的解决方案.

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