DeepExtremeCubes: Integrating Earth System Spatio-Temporal Data for Impact Assessment of Climate Extremes

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.

Publication
arXiv
Chaonan Ji
Chaonan Ji
Postdoctoral fellow / Earth System Data Science
Guido Kraemer
Guido Kraemer
Postdoctoral associate / Earth System Data Science
Francesco Martinuzzi
Francesco Martinuzzi
PhD candidate / Earth System Data Science
David Montero
David Montero
PhD candidate / Earth System Data Science
Karin Mora
Karin Mora
Postdoctoral fellow / Remote Sensing in Geo- and Ecosystem Research
Miguel D. Mahecha
Miguel D. Mahecha
Professor for Earth System Data Science