An efficient method for aurora and noise reduction with a harmonized nighttime light dataset

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment Pub Date : 2025-06-30 DOI:10.1016/j.rse.2025.114891

Mengqing Geng , Xuecao Li , Shirao Liu , Guojiang Yu , Yuyu Zhou , Peng Gong

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引用次数: 0

Abstract

The availability of long-term, annually harmonized nighttime light (NTL) data is pivotal for monitoring human activities across past decades. The Defense Meteorological Satellite Program (DMSP) has provided over 20 years of NTL observations, facilitating extensive global and regional studies. With the termination of DMSP NTL in 2013 and the subsequent introduction of the Visible Infrared Imaging Radiometer Suite (VIIRS) NTL data, we previously developed the global harmonized NTL dataset (H-NTL-v1), which offers a temporally extended and consistent time series data from 1992 to 2022. Despite its widespread use, the H-NTL-v1 dataset has been affected by noise from auroras and transient lights, particularly in high-latitude areas. In this study, we present an innovative method that employs temporal frequency analysis and Pareto surface optimization to address these challenges, resulting in an improved global harmonized NTL dataset (H-NTL-v2). This enhanced dataset markedly improves the consistency between historical DMSP (1992–2013) and the DMSP-like estimates post-2014. For aurora affected city lights, here we did not implement a specific correction algorithm. Our results indicate that the improved H-NTL-v2, spanning 1992 to 2022, significantly reduces auroral noise and inter-annual variability. Compared to the original H-NTL-v1, the improved H-NTL-v2 demonstrates strong agreement with DMSP observation in 2012 and 2013. It exhibits significantly diminished fluctuation in total lit pixels and digital numbers, particularly for low-luminance pixels. This refined dataset minimizes noise impacts from auroras and other sources, enhancing the application potential of NTL data in studies of light pollution, urban slums, and poverty and inequality in developing regions.

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一种利用协调的夜间灯光数据集有效地减少极光和噪声的方法

摘要长期的、每年统一的夜间灯光（NTL）数据的可用性对于监测过去几十年的人类活动至关重要。国防气象卫星计划（DMSP）提供了超过20年的NTL观测，促进了广泛的全球和区域研究。随着2013年DMSP NTL项目的终止以及随后可见光红外成像辐射计套件（VIIRS） NTL数据的引入，我们先前开发了全球统一的NTL数据集（H-NTL-v1），该数据集提供了1992年至2022年的时间序列数据。尽管H-NTL-v1数据集被广泛使用，但它受到极光和瞬变光噪声的影响，特别是在高纬度地区。在这项研究中，我们提出了一种创新的方法，利用时间频率分析和帕累托曲面优化来解决这些挑战，从而改进了全球统一的NTL数据集（H-NTL-v2）。这个增强的数据集显著提高了历史DMSP（1992-2013）和2014年后类似DMSP估计之间的一致性。对于极光影响的城市灯光，这里我们没有实施特定的校正算法。结果表明，改进后的H-NTL-v2在1992 ~ 2022年期间显著降低了极光噪声和年际变率。与原来的H-NTL-v1相比，改进的H-NTL-v2与2012年和2013年的DMSP观测结果具有较强的一致性。它在总点亮像素和数字数字上表现出显著的减少波动，特别是对于低亮度像素。该精细化数据集最大限度地减少了极光和其他来源的噪声影响，增强了NTL数据在发展中地区光污染、城市贫民窟和贫困与不平等研究中的应用潜力。

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来源期刊

Remote Sensing of Environment 环境科学-成像科学与照相技术

CiteScore

25.10

自引率

8.90%

发文量

455

审稿时长

53 days

期刊介绍： Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.