Peng et al. (2026) A global, historical, harmonized land-use dataset spanning 1900 to 2023 at 0.05° resolution reconstructed based on remote sensing and statistical data

Identification

• Journal: Scientific Data
• Year: 2026
• Date: 2026-09-15
• Authors: Siqi Peng, Xiao Zhang, Liangyun Liu
• DOI: 10.1038/s41597-026-08281-1

Research Groups

• Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences
• International Research Center of Big Data for Sustainable Development Goals
• University of Chinese Academy of Sciences
• School of Remote Sensing, Beijing Normal University

Short Summary

This study presents a global, historical, harmonized land-use dataset spanning 1900 to 2023 at 0.05° resolution, reconstructed based on remote sensing and statistical data. The dataset provides spatially explicit fractions of six major land-use types.

Objective

• Reconstruct a high-resolution global land-use dataset for the period 1900–2023

Study Configuration

• Spatial Scale: 0.05° resolution (approximately 5.6 km at the equator)
• Temporal Scale: 1900 to 2023

Methodology and Data

• Models used: Top-down land-use reconstruction framework integrating fine-resolution remote-sensing land-cover information with Food and Agriculture Organization of the United Nations (FAO) cropland and grazing-land statistics
• Data sources: Remote sensing data, FAOSTAT cropland and grazing-land data

Main Results

• The reconstructed dataset shows strong agreement with FAO Global Forest Resources Assessment (FRA) national forest area statistics.
• The dataset captures within-country spatial variability consistent with reported subnational land-use records across multiple regions.

Contributions

• Provides a coherent basis for land-use analysis, terrestrial carbon accounting, and Earth system modeling

Funding

• This work was supported by the National Key Research and Development Program of China [grant number 2023YFB3907403]

Citation

@article{Peng2026global,
  author = {Peng, Siqi and Zhang, Xiao and Liu, Liangyun},
  title = {A global, historical, harmonized land-use dataset spanning 1900 to 2023 at 0.05° resolution reconstructed based on remote sensing and statistical data},
  journal = {Scientific Data},
  year = {2026},
  doi = {10.1038/s41597-026-08281-1},
  url = {https://doi.org/10.1038/s41597-026-08281-1}
}