DeepExtremeCubes: Integrating Earth system spatio-temporal data for impact assessment of climate extremes

• URL: http://arxiv.org/abs/2406.18179v1
• Date: Wed, 26 Jun 2024 08:53:26 GMT
• Title: DeepExtremeCubes: Integrating Earth system spatio-temporal data for impact assessment of climate extremes
• Authors: Chaonan Ji, Tonio Fincke, Vitus Benson, Gustau Camps-Valls, Miguel-Angel Fernandez-Torres, Fabian Gans, Guido Kraemer, Francesco Martinuzzi, David Montero, Karin Mora, Oscar J. Pellicer-Valero, Claire Robin, Maximilian Soechting, Melanie Weynants, Miguel D. Mahecha,
• Abstract summary: Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. Here, we introduce the DeepExtremes database, tailored to map around heatwave and drought extreme impact. It comprises over 40,000 spatially sampled small data cubes (i.e. minicubes) globally, with a spatial coverage of 2.5 by 2.5 km.
• Score: 5.736700805381591
• License: http://creativecommons.org/licenses/by/4.0/
• 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 spatially sampled small data cubes (i.e. minicubes) globally, 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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