Global basic landform units derived from multi-source digital elevation models at 1 arc-second resolution

Abstract. Landforms are fundamental components of the Earth surface, providing the base on which surface processes operate. Understanding and classifying global landforms, which record the internal and external dynamics of the planet's evolution, constitutes a critical aspect of Earth system science. Advances in Earth observation technologies have enabled access to higher resolution data, for example remote sensing imagery and digital elevation models (DEMs). However, landform data with a resolution of approximately 1 arc-second (approximately 30 m) are lacking at the global scale, which limits the progress of geomorphologic studies at finer scales. Here, we propose a novel framework for global landform classification and release a unique dataset called Global Basic Landform Units (GBLU), which incorporates a comprehensive set of objects that constitute the range of landforms on Earth. Constructed from multiple 1 arc-second DEMs, GBLU ranks among the highest-resolution global geomorphology datasets to date. Its development integrates geomorphological ontologies and key derivatives to strike a balance between mitigating local noise and preserving valuable landform details. GBLU categorizes the Earth's landforms into three levels with 26 classes, yielding discrete vector units that record landform type and distribution. Comparative analyses with previous datasets reveal that GBLU enhances capture of landform details, enabling more precise depiction of geomorphological boundaries. This refinement facilitates the identification of novel spatial disparities in landform patterns, exemplified by marked contrasts between Asia and other continents, and highlights the distinct prominence of China in terms of landform diversity. Given that the fundamental data resolution of GBLU accords well with available remote sensing datasets, it is readily incorporated into analytical workflows, exploring the relationship between landforms, climate and land cover. The full data set is available on the Deep-time Digital Earth Geomorphology platform and Zenodo (Yang et al., 2024; https://doi.org/10.5281/zenodo.13187969).