A robust gap-filling method for predicting missing observations in daily Black Marble nighttime light data

ABSTRACTNighttime light (NTL) remote sensing data plays a crucial role in comprehending changes in human activities. The availability of the daily lunar BRDF-corrected Black Marble NTL product (VNP46A2) enables the use of NTL data to detect and assess the impact of short-term emergencies. However, d...

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Bibliographic Details
Published in:GIScience & Remote Sensing
Main Authors: Xiangyu Hao, Jinxiu Liu, Janne Heiskanen, Eduardo Eiji Maeda, Si Gao, Xuecao Li
Format: Article
Language:English
Published: Taylor & Francis Group 2023-12-01
Subjects:
Nighttime light
gap-filling
deep learning
NASA black marble product
Online Access:https://www.tandfonline.com/doi/10.1080/15481603.2023.2282238

Internet

https://www.tandfonline.com/doi/10.1080/15481603.2023.2282238

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