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

TL;DR

This paper introduces a new dataset of artificial nighttime light in China from 1986 to 2024, using a deep learning model to improve accuracy and detail.

Contribution

A novel two-stage deep learning model generates a high-quality extended VIIRS-like NTL dataset for China from 1986 to 2024.

Findings

The EVAL dataset shows superior temporal consistency compared to existing products.

EVAL has a stronger correlation with socioeconomic indicators than current datasets.

The model effectively reconstructs fine-grained structural details using impervious surface data.

Abstract

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. Current extended VIIRS-like NTL data products suffer from two significant shortcomings: the underestimation of light intensity and the omission of structural details. To overcome these limitations, we present the Extended VIIRS-like Artificial Nighttime Light (EVAL) dataset, a new annual NTL dataset for China spanning from 1986 to 2024. This dataset was generated using a novel two-stage deep learning model designed to address the aforementioned shortcomings. The model first constructs an initial estimate and subsequently refines fine-grained structural details using high-resolution impervious surface data as guidance. Quantitative evaluations demonstrate that EVAL significantly outperforms state-of-the-art products, exhibiting superior temporal consistency and a stronger correlation with socioeconomic indicators.

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