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HLUH-5km: A high-resolution global historical land use harmonization dataset from 1900 to 2023 reconstructed based on remote sensing and statistic data

Global Historical Land Use Harmonization dataset (HLUH-5km) provides spatially explicit fractional coverage (0–1) of six major land-use types (water and snow, urban, cropland, grazing land, forest, and non-forest) at 0.05° spatial resolution for the period 1900–2023. Because water

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CreatorPeng, Siqi
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Published2026-03-02
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DOI10.5281/zenodo.18812020
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Downloads692
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Licensecc-by-4.0
File Size6.9 GB
Data TypeDataset
Published2026
Licensecc-by-4.0
Total Views317
Total Downloads692

Global Historical Land Use Harmonization dataset (HLUH-5km) provides spatially explicit fractional coverage (0–1) of six major land-use types (water and snow, urban, cropland, grazing land, forest, and non-forest) at 0.05° spatial resolution for the period 1900–2023. Because water and snow areas are assumed to remain constant over time, only a single baseline layer is provided for this class, while the other land-use categories vary through time. Before 1950 the dataset is reported at 10-year intervals, and after 1950 it is updated annually. National land-use areas were reconstructed primarily from statistics reported by the Food and Agriculture Organization of the United Nations and subsequently allocated to grid cells using a probability-based spatial allocation framework that integrates environmental and socioeconomic constraints.

The HLUH-5km dataset is distributed as five compressed ZIP archives containing the time-varying land-use fraction layers and one GeoTIFF file representing the static water and snow layer.

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

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HLUH-5km: A high-resolution global historical land use harmonization… (Full Dataset)6.9 GB
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Files are hosted on the source repository. Click download to access the full dataset.

Peng, Siqi (2026). HLUH-5km: A high-resolution global historical land use harmonization dataset from 1900 to 2023 reconstructed based on remote sensing and statistic data. https://doi.org/10.5281/zenodo.18812020