标题： 一种扩展VIIRS类似人工夜间灯光图像重建的新型建模框架和数据产品（1986-2024） 显示英文标题

标题： A Novel Modeling Framework and Data Product for Extended VIIRS-like Artificial Nighttime Light Image Reconstruction (1986-2024)

摘要： 人工夜间灯光（NTL）遥感是量化人类活动强度和空间分布的重要代理。 尽管NPP-VIIRS传感器提供了高质量的NTL观测数据，但其时间覆盖范围始于2012年，限制了延伸到更早时期的长期时间序列研究。 尽管在扩展VIIRS类似NTL时间序列方面取得了进展，但当前方法仍然存在两个显著缺点：光强低估和结构遗漏。 为了克服这些限制，我们提出了一种新颖的重建框架，包括两个阶段的过程：构建和优化。 构建阶段的特点是一个层次融合解码器（HFD），旨在增强初始重建的真实性。 优化阶段采用双特征优化器（DFR），利用高分辨率不透水面掩膜来引导和增强细粒度的结构细节。 基于此框架，我们开发了扩展的VIIRS类似人工夜间灯光（EVAL）产品，将标准数据记录向后延长了26年，从1986年开始。 定量评估显示，EVAL显著优于现有的最先进产品，将$\text{R}^2$从0.68提高到0.80，同时将RMSE从1.27降低到0.99。 此外，EVAL表现出良好的时间一致性，并与社会经济参数保持高度相关性，证实了其在长期分析中的可靠性。 由此产生的EVAL数据集为研究社区提供了一个有价值的新的资源，并可在https://doi.org/10.11888/HumanNat.tpdc.302930公开获取。 显示英文摘要

摘要： 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. Despite the progress in extending VIIRS-like NTL time-series, current methods still suffer from two significant shortcomings: the underestimation of light intensity and the structural omission. To overcome these limitations, we propose a novel reconstruction framework consisting of a two-stage process: construction and refinement. The construction stage features a Hierarchical Fusion Decoder (HFD) designed to enhance the fidelity of the initial reconstruction. The refinement stage employs a Dual Feature Refiner (DFR), which leverages high-resolution impervious surface masks to guide and enhance fine-grained structural details. Based on this framework, we developed the Extended VIIRS-like Artificial Nighttime Light (EVAL) product for China, extending the standard data record backwards by 26 years to begin in 1986. Quantitative evaluation shows that EVAL significantly outperforms existing state-of-the-art products, boosting the $\text{R}^2$ from 0.68 to 0.80 while lowering the RMSE from 1.27 to 0.99. Furthermore, EVAL exhibits excellent temporal consistency and maintains a high correlation with socioeconomic parameters, confirming its reliability for long-term analysis. The resulting EVAL dataset provides a valuable new resource for the research community and is publicly available at https://doi.org/10.11888/HumanNat.tpdc.302930.