%0 Journal Article %1 Ji2025 %A Ji, Chaonan %A Fincke, Tonio %A Benson, Vitus %A Camps-Valls, Gustau %A Fernández-Torres, Miguel-Ángel %A Gans, Fabian %A Kraemer, Guido %A Martinuzzi, Francesco %A Montero, David %A Mora, Karin %A Pellicer-Valero, Oscar J. %A Robin, Claire %A Söchting, Maximilian %A Weynants, Mélanie %A Mahecha, Miguel D. %D 2025 %J Scientific Data %K Yaff topic_earthenvironment %N 1 %P 149 %R 10.1038/s41597-025-04447-5 %T DeepExtremeCubes: Earth system spatio-temporal data for assessing compound heatwave and drought impacts %U https://doi.org/10.1038/s41597-025-04447-5 %V 12 %X 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.

@article{Ji2025, 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.}, added-at = {2025-02-04T13:44:48.000+0100}, author = {Ji, Chaonan and Fincke, Tonio and Benson, Vitus and Camps-Valls, Gustau and Fern{\'a}ndez-Torres, Miguel-{\'A}ngel and Gans, Fabian and Kraemer, Guido and Martinuzzi, Francesco and Montero, David and Mora, Karin and Pellicer-Valero, Oscar J. and Robin, Claire and S{\"o}chting, Maximilian and Weynants, M{\'e}lanie and Mahecha, Miguel D.}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/21f28028feae9127c3ca712a14fcc86df/scadsfct}, day = 25, doi = {10.1038/s41597-025-04447-5}, interhash = {5cc5a92ec58009ff1d3bd3fad7ce73b7}, intrahash = {1f28028feae9127c3ca712a14fcc86df}, issn = {2052-4463}, journal = {Scientific Data}, keywords = {Yaff topic_earthenvironment}, month = jan, number = 1, pages = 149, timestamp = {2025-02-04T13:44:48.000+0100}, title = {DeepExtremeCubes: Earth system spatio-temporal data for assessing compound heatwave and drought impacts}, url = {https://doi.org/10.1038/s41597-025-04447-5}, volume = 12, year = 2025 }