Liu et al. (2026) GloSVeT: a global 0.05° monthly mean surface soil and vegetation component temperature dataset (2003–2023)

Identification

• Journal: Earth system science data
• Year: 2026
• Date: 2026-09-16
• Authors: Xiangyang Liu, Zhao‐Liang Li, Chen Ru, Si‐Bo Duan, Pei Leng
• DOI: 10.5194/essd-18-6885-2026

Research Groups

• State Key Laboratory of Efficient Utilization of Arable Land in China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
• Hebei International Joint Research Center for Remote Sensing of Agricultural Drought Monitoring/ School of Land Science and Space Planning, Hebei GEO University, Shijiazhuang 050031, China
• State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Short Summary

This study presents GloSVeT, a global dataset of monthly mean surface soil and vegetation component temperatures from 2003 to 2023 at 0.05° spatial resolution. The dataset was generated using the FuSVeT framework, which integrates multi-temporal MODIS observations with ERA5-Land reanalysis data.

Objective

• To develop a physically consistent global product that simultaneously provides surface soil and vegetation component temperatures at high spatial resolution.
• To overcome the limitations of current satellite-derived land surface temperature (LST) products by separating LST into its constituent components.

Study Configuration

• Spatial Scale: Global, 0.05° spatial resolution.
• Temporal Scale: Monthly mean, 2003-2023 period.

Methodology and Data

• Models used: FuSVeT framework, GOT09 model for sub-daily temperature dynamics.
• Data sources:
  • MODIS Collection 6.1 monthly LST products (MOD11C3 and MYD11C3).
  • ERA5-Land reanalysis dataset for skin temperature and ancillary information.
  • GLASS FVC product for fractional vegetation cover.

Main Results

• GloSVeT achieves reliable accuracy with coefficients of determination mostly at or above 0.9 and root mean square errors generally around 2 K for both components.
• TC analysis demonstrates globally consistent performance, with advantages in humid tropics and transitional ecosystems compared with reanalysis products.
• Soil temperature anomalies are predominantly negatively correlated with soil moisture, while vegetation temperature aligns with solar-induced fluorescence along a clear gradient from energy-limited to water-limited biomes.

Contributions

• GloSVeT provides the first global product that simultaneously represents soil and vegetation component temperatures at high spatial resolution.
• The dataset offers new opportunities for quantifying land-atmosphere energy exchange, monitoring ecosystem hydrothermal responses, and improving the representation of land surface processes in Earth system models.

Funding

• This study was funded by [project/program name] with reference code [reference code].

Citation

@article{Liu2026GloSVeT,
  author = {Liu, Xiangyang and Li, Zhao‐Liang and Ru, Chen and Duan, Si‐Bo and Leng, Pei},
  title = {GloSVeT: a global 0.05° monthly mean surface soil and vegetation component temperature dataset (2003–2023)},
  journal = {Earth system science data},
  year = {2026},
  doi = {10.5194/essd-18-6885-2026},
  url = {https://doi.org/10.5194/essd-18-6885-2026}
}