DOI: 10.5281/zenodo.14992989
title: A high-quality daily nighttime light (HDNTL) dataset for global 600+ cities (2012-2024)

description: Introduction

Nighttime light (NTL) data at daily scales presents an innovative foundation for monitoring human activities, offering vast potential across various research domains such as urban planning and management, disaster monitoring, and energy consumption. The VNP46A2 dataset, sourced from NPP/VIIRS, has been providing globally corrected daily NTL data since 2012. However, persistent challenges, such as fluctuations in daily NTL series due to spatial mismatch and angular effects, as well as missing data holes, have significantly impacted the accuracy and comprehensiveness of extracting daily NTL changes. To address these challenges, a dataset production framework focusing on error correction, interpolation, and validation was developed. This framework led to the creation of a high-quality daily NTL dataset from 2012 to 2024, named HDNTL, which specifically targets 653 cities with populations predictably exceeding one million in 2025. Comparative analysis with the VNP46A2 dataset revealed that the HDNTL dataset effectively mitigates instability in daily series caused by spatial mismatch and angular effects observed in VNP46A2, improving data comparability across time and space dimensions. This dataset enhances the ability of the NTL to reflect the ground events, providing a more accurate reference for daily-scale nighttime light research. 

 

Data description

1. Data Overview

This product is generated based on the NASA Black Marble VNP46A2 dataset, inheriting its spatial resolution of 15 arc-seconds (approximately 500 meters). The data is provided in GeoTIFF format with a scale factor of 0.01, where a pixel value of 0 indicates invalid data (NaN).

2. File Structure

The data is organized in descending order of projected city population in 2025, with every 10 cities stored in a zip archive. For example, the zip archive named 001-010 contains data for the first to the tenth cities in the sequence. The city serial number can be referenced in the data table provided in the file “WUP2018-Pop2025_Cities_Over_1M.xls”. Each folder within a compressed package contains HDNTL data for a city from 2012 to 2024, comprising 13 images, with each image corresponding to one year of daily NTL data. File names use underscores _ as separators, following the format below:

Example of Image Name:088_204586_Kitakyushu-Fukuoka_2022

Field Descriptions:



Field 1: City serial number, ranked by projected population in 2025, ranging from 001 to 653 (refer to the “WUP2018-Pop2025_Cities_Over_1M.xls” ).

Field 2: City code (refer to the “WUP2018-Pop2025_Cities_Over_1M.xls” ).

Field 3: Urban Agglomeration Name (refer to the “WUP2018-Pop2025_Cities_Over_1M.xls” ).

Field 4: Year.


3. Image Internal Structure

Each image contains multiple bands, with each band corresponding to one day of data. The band naming format is “NTLYYYY_MM_DD”, where:



YYYY represents the year,

MM represents the month,

DD represents the day.


Example:NTL2022_01_01 represents NTL data for January 1, 2022.

4. Data Usage Recommendations



Data content: This dataset provides only the NTL value.

Scale factor: The scale factor of HDNTL is 0.01. The scale factor of VNP46A2 is 0.1.

Invalid Value Handling: A pixel value of 0 indicates invalid data (NaN), and masking is recommended before use.

Quality Control: Quality control, cloud cover, and snow flag information are inherited from the corresponding bands of VNP46A2.

Viewing zenith angle: Users can refer to the band “Sensor_Zenith” of VNP46A1.


 

Acknowledgments

This research was supported by the Research Grants Council of Hong Kong (project No.15229222), the National Natural Science Foundation of China (No. 42401474), and the Hong Kong Polytechnic University (project No. Q-CDBP).