Contributing person datacite.contributor.ContactPerson	Chaonan Ji
Contributing person datacite.contributor.ContactPerson	Miguel Mahecha
Contributing person datacite.contributor.ContactPerson	Guido Kraemer
Documentation of the data datacite.description.TechnicalInfo	- Self-describing Zarr Data - Complete documentation: https://arxiv.org/abs/2406.18179 - see DeepExtremesCubes_Access_v2.ipynb
Additional geographical or spatial references datacite.geolocation	Global
References to related material datacite.relatedItem.IsCitedBy	https://arxiv.org/abs/2410.01770
References to related material datacite.relatedItem.IsDescribedBy	https://arxiv.org/abs/2406.18179
References to related material datacite.relatedItem.IsSupplementedBy	https://essd.copernicus.org/preprints/essd-2024-396/
Description of the data datacite.resourceType	This dataset is tailored to map around these extremes, focusing on persistent natural vegetation, and facilitate biosphere dynamics forecasting in response to the compound heatwave and drought extremes. It comprises over 40,000 globally sampled small data cubes (i.e. minicubes) in zarr format, 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.
Type of the data datacite.resourceTypeGeneral	Dataset
Total size of the dataset datacite.size	2541048837002
Author dc.contributor.author	Ji, Chaonan
Author dc.contributor.author	Fincke, Tonio
Author dc.contributor.author	Benson, Vitus
Author dc.contributor.author	Camps-Valls, Gustau
Author dc.contributor.author	Fernández-Torres, Miguel-Ángel
Author dc.contributor.author	Gans, Fabian
Author dc.contributor.author	Kraemer, Guido
Author dc.contributor.author	Martinuzzi, Francesco
Author dc.contributor.author	Montero, David
Author dc.contributor.author	Mora, Karin
Author dc.contributor.author	Pellicer-Valero, Oscar
Author dc.contributor.author	Robin, Claire
Author dc.contributor.author	Söchting, Maximilian
Author dc.contributor.author	Weynants, Melanie
Author dc.contributor.author	Mahecha, Miguel
Upload date dc.date.accessioned	2024-12-19T18:15:30Z
Publication date dc.date.available	2024-12-19T18:15:30Z
Data of data creation dc.date.created	2023
Publication date dc.date.issued	2024-12-19
Abstract of the dataset dc.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.
Public reference to this page dc.identifier.uri	https://opara.zih.tu-dresden.de/handle/123456789/1186
Public reference to this page dc.identifier.uri	https://doi.org/10.25532/OPARA-703
Publisher dc.publisher	Universität Leipzig
Licence dc.rights	Attribution 4.0 International	en
URI of the licence text dc.rights.uri	http://creativecommons.org/licenses/by/4.0/
Specification of the discipline(s) dc.subject.classification	3::34::313::313-01
Specification of the discipline(s) dc.subject.classification	2::21::202::202-02
Specification of the discipline(s) dc.subject.classification	4::44::409::409-05
Specification of the discipline(s) dc.subject.classification	3::34::315
Title of the dataset dc.title	DeepExtremeCubes
Research instruments opara.descriptionInstrument	ERA5
Research instruments opara.descriptionInstrument	Sentinel 2
Research instruments opara.descriptionInstrument	Landsat
Research instruments opara.descriptionInstrument	ESA CCI
Research instruments opara.descriptionInstrument	Copernicus DEM
Underlying research object opara.descriptionObject.NonPhysicalObject	Extreme Events
Software opara.descriptionSoftware.ResourceProcessing	Zarr
Software opara.descriptionSoftware.ResourceProcessing	https://github.com/DeepExtremes/minicube-generation
Software opara.descriptionSoftware.ResourceProcessing	python
Project abstract opara.project.description	Climate extremes are on the rise, which is one of the most critical manifestations of climate change. Compound events can drive a combination of spatial and/or temporal hazards, and the instantaneous effect and long-term impact on society and the environment are typically much higher than a single event on its own. However, the multi-dimensional nature of compound events leads to a series of methodological challenges. The Deep-Extremes project aims at - Focusing on compound heat and drought events on a global scale, detecting based on long-term climate and land-surface data, combining EO archives and other observation data, with methods tailored to multivariate event detection. - Sampling a subset of large events in the Sentinel era and zooming into the events and in unaffected areas around the event with high-dimensional “mini-cubes”. - Training complementary deep-learning methods for prediction and understanding dynamics in such events. - Implementing the tested and validated workflow in a cloud environment and developing it further based on community feedback. - Engaging with the community via workshops and science discussions to further develop the proposed framework. The project is funded by the European Space Agency (ESA), part of the AI4SCIENCE activity. The first AI4SCIENCE ITT focuses on Extreme Events, Multi-Hazards and Compound Events, and contributes to the ESA Extremes and Natural Disasters Science Cluster.
Public project website(s) opara.project.publicReference	https://eo4society.esa.int/projects/deep-extremes/
Public project website(s) opara.project.publicReference	https://rsc4earth.de/project/deepextremes/
Public project website(s) opara.project.publicReference	https://opensciencedata.esa.int/projects/deep-extremes/collection
Project title opara.project.title	DeepExtremes

DeepExtremeCubes

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