A computational framework for processing time-series of earth observation data based on discrete convolution: global-scale historical Landsat cloud-free aggregates at 30 m spatial resolution

A computational framework for processing time-series of earth observation data based on discrete convolution: global-scale historical Landsat cloud-free aggregates at 30 m spatial resolution

Processing large collections of earth observation (EO) time-series, often petabyte-sized, such as NASA’s Landsat and ESA’s Sentinel missions, can be computationally prohibitive and costly. Despite their name, even the Analysis Ready Data (ARD) versions of such collections can rarely be used as direc...

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Bibliographic Details
Main Authors: Davide Consoli, Leandro Parente, Rolf Simoes, Murat Şahin, Xuemeng Tian, Martijn Witjes, Lindsey Sloat, Tomislav Hengl
Format: Article
Language:English
Published: PeerJ Inc. 2024-12-01
Series:PeerJ
Subjects:
Earth observation data
Time-series processing
Landsat
Discrete convolution
Gap-filling
Time-series reconstruction
Online Access:https://peerj.com/articles/18585.pdf
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