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.

气候极端事件对陆地生态系统的影响预测需要用到结构化、高质量和经过筛选的分析准备数据集。本研究致力于构建 DeepExtremeCubes 数据库，该数据库专门设计用于分析复合热浪和干旱极端事件对持续自然植被的影响，包括 40,000 个地理采样的小数据块，每个块都包含 Sentinel-2 L2A 影像、ERA5-Land 变量、从 2016 年到 2022 年的极端事件数据块，以及辅助土地覆盖和地形地图。该数据库的建设旨在提高数据获取和处理的效率，增强科学的可重复性，并促进对生物圈动态在复合极端事件下的预测研究。