DeepExtremeCubes: Earth system spatio-temporal data for assessing compound heatwave and drought impacts

Abstract With climate extremes’ rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. Earth observation datasets...

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Main Authors: Chaonan Ji, Tonio Fincke, Vitus Benson, Gustau Camps-Valls, Miguel-Ángel Fernández-Torres, Fabian Gans, Guido Kraemer, Francesco Martinuzzi, David Montero, Karin Mora, Oscar J. Pellicer-Valero, Claire Robin, Maximilian Söchting, Mélanie Weynants, Miguel D. Mahecha
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Data
Online Access:https://doi.org/10.1038/s41597-025-04447-5
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author Chaonan Ji
Tonio Fincke
Vitus Benson
Gustau Camps-Valls
Miguel-Ángel Fernández-Torres
Fabian Gans
Guido Kraemer
Francesco Martinuzzi
David Montero
Karin Mora
Oscar J. Pellicer-Valero
Claire Robin
Maximilian Söchting
Mélanie Weynants
Miguel D. Mahecha
author_facet Chaonan Ji
Tonio Fincke
Vitus Benson
Gustau Camps-Valls
Miguel-Ángel Fernández-Torres
Fabian Gans
Guido Kraemer
Francesco Martinuzzi
David Montero
Karin Mora
Oscar J. Pellicer-Valero
Claire Robin
Maximilian Söchting
Mélanie Weynants
Miguel D. Mahecha
author_sort Chaonan Ji
collection DOAJ
description Abstract With climate extremes’ rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. Earth observation datasets comprehensively monitor ecosystem dynamics and responses to climatic extremes, yet the data complexity can challenge the effectiveness of machine learning models. Despite recent progress in deep learning to ecosystem monitoring, there is a need for datasets specifically designed to analyse compound heatwave and drought extreme impact. Here, we introduce the DeepExtremeCubes database, tailored to map around these extremes, focusing on persistent natural vegetation. It comprises over 40,000 globally sampled small data cubes (i.e. minicubes), with a spatial coverage of 2.5 by 2.5 km. Each minicube includes (i) Sentinel-2 L2A images, (ii) ERA5-Land variables and generated extreme event cube covering 2016 to 2022, and (iii) ancillary land cover and topography maps. The paper aims to (1) streamline data accessibility, structuring, pre-processing, and enhance scientific reproducibility, and (2) facilitate biosphere dynamics forecasting in response to compound extremes.
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institution Kabale University
issn 2052-4463
language English
publishDate 2025-01-01
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spelling doaj-art-bb76e527f054442788e1f9d2becde16e2025-01-26T12:14:49ZengNature PortfolioScientific Data2052-44632025-01-0112111010.1038/s41597-025-04447-5DeepExtremeCubes: Earth system spatio-temporal data for assessing compound heatwave and drought impactsChaonan Ji0Tonio Fincke1Vitus Benson2Gustau Camps-Valls3Miguel-Ángel Fernández-Torres4Fabian Gans5Guido Kraemer6Francesco Martinuzzi7David Montero8Karin Mora9Oscar J. Pellicer-Valero10Claire Robin11Maximilian Söchting12Mélanie Weynants13Miguel D. Mahecha14Remote Sensing Centre for Earth System Research (RSC4Earth), Leipzig UniversityBrockmann Consult GmbHMax Planck Institute for BiogeochemistryImage Processing Laboratory (IPL), Universitat de ValènciaImage Processing Laboratory (IPL), Universitat de ValènciaMax Planck Institute for BiogeochemistryRemote Sensing Centre for Earth System Research (RSC4Earth), Leipzig UniversityRemote Sensing Centre for Earth System Research (RSC4Earth), Leipzig UniversityRemote Sensing Centre for Earth System Research (RSC4Earth), Leipzig UniversityRemote Sensing Centre for Earth System Research (RSC4Earth), Leipzig UniversityImage Processing Laboratory (IPL), Universitat de ValènciaMax Planck Institute for BiogeochemistryRemote Sensing Centre for Earth System Research (RSC4Earth), Leipzig UniversityMax Planck Institute for BiogeochemistryRemote Sensing Centre for Earth System Research (RSC4Earth), Leipzig UniversityAbstract With climate extremes’ rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. Earth observation datasets comprehensively monitor ecosystem dynamics and responses to climatic extremes, yet the data complexity can challenge the effectiveness of machine learning models. Despite recent progress in deep learning to ecosystem monitoring, there is a need for datasets specifically designed to analyse compound heatwave and drought extreme impact. Here, we introduce the DeepExtremeCubes database, tailored to map around these extremes, focusing on persistent natural vegetation. It comprises over 40,000 globally sampled small data cubes (i.e. minicubes), with a spatial coverage of 2.5 by 2.5 km. Each minicube includes (i) Sentinel-2 L2A images, (ii) ERA5-Land variables and generated extreme event cube covering 2016 to 2022, and (iii) ancillary land cover and topography maps. The paper aims to (1) streamline data accessibility, structuring, pre-processing, and enhance scientific reproducibility, and (2) facilitate biosphere dynamics forecasting in response to compound extremes.https://doi.org/10.1038/s41597-025-04447-5
spellingShingle Chaonan Ji
Tonio Fincke
Vitus Benson
Gustau Camps-Valls
Miguel-Ángel Fernández-Torres
Fabian Gans
Guido Kraemer
Francesco Martinuzzi
David Montero
Karin Mora
Oscar J. Pellicer-Valero
Claire Robin
Maximilian Söchting
Mélanie Weynants
Miguel D. Mahecha
DeepExtremeCubes: Earth system spatio-temporal data for assessing compound heatwave and drought impacts
Scientific Data
title DeepExtremeCubes: Earth system spatio-temporal data for assessing compound heatwave and drought impacts
title_full DeepExtremeCubes: Earth system spatio-temporal data for assessing compound heatwave and drought impacts
title_fullStr DeepExtremeCubes: Earth system spatio-temporal data for assessing compound heatwave and drought impacts
title_full_unstemmed DeepExtremeCubes: Earth system spatio-temporal data for assessing compound heatwave and drought impacts
title_short DeepExtremeCubes: Earth system spatio-temporal data for assessing compound heatwave and drought impacts
title_sort deepextremecubes earth system spatio temporal data for assessing compound heatwave and drought impacts
url https://doi.org/10.1038/s41597-025-04447-5
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