Spatially adaptive estimation of multi-layer soil temperature at a daily time-step across China during 2010–2020

Wang, Xuetong; He, Liang; Li, Peng; Ma, Jiageng; Shi, Yu; Tian, Qi; Zhao, Gang; He, Jianqiang; Feng, Hao; Shi, Hao; Yu, Qiang

doi:10.5194/essd-18-97-2026

Articles | Volume 18, issue 1

https://doi.org/10.5194/essd-18-97-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-18-97-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 1

Data description paper

|

06 Jan 2026

Data description paper |

| 06 Jan 2026

Spatially adaptive estimation of multi-layer soil temperature at a daily time-step across China during 2010–2020

Xuetong Wang, Liang He, Peng Li, Jiageng Ma, Yu Shi, Qi Tian, Gang Zhao, Jianqiang He, Hao Feng, Hao Shi, and Qiang Yu

Supplement

https://doi.org/10.5194/essd-18-97-2026-supplement

Data sets

Daily multi-layer soil temperature dataset with 1 km resolution in China from 2010 to 2020 Xuetong Wang et al. https://doi.org/10.11888/Terre.tpdc.302333

Model code and software

Rotated-quadtree (v1.0.0) X. Wang https://doi.org/10.5281/zenodo.17996349

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Short summary

This study integrates ground observations, satellite remote sensing, and reanalysis data, applying machine learning techniques to generate a high-resolution, daily multi-layer soil temperature dataset in China from 2010 to 2020. The dataset accurately captures the spatiotemporal variations in soil temperature at multiple depths, offering valuable scientific insights for agricultural management and ecological research.