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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Nature Portfolio
2025-01-01
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| 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. |
| format | Article |
| id | doaj-art-bb76e527f054442788e1f9d2becde16e |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| 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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