Guo et al. (2025) Towards regional drought monitoring with Sentinel-3 vegetation temperature condition index in the Sichuan Basin, PR China

Identification

• Journal: Remote Sensing Applications Society and Environment
• Year: 2025
• Date: 2025-10-17
• Authors: Fengwei Guo, Pengxin Wang, Kevin Tansey, Mingqi Li, Yuanfei Sun, Ji Zhou
• DOI: 10.1016/j.rsase.2025.101765

Research Groups

• College of Information and Electrical Engineering, China Agricultural University, Beijing, PR China
• Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications, Ministry of Agriculture and Rural Affairs, Beijing, PR China
• School of Geography, Geology and the Environment, University of Leicester, Leicester, UK
• School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, PR China

Short Summary

This study evaluates the potential of the Vegetation Temperature Condition Index (VTCI) derived from Sentinel-3 satellite data for regional drought monitoring in the Sichuan Basin, PR China, demonstrating its capacity to accurately reflect spatiotemporal drought variations and its consistency with MODIS VTCI and meteorological data.

Objective

• To explore the potential of the novel Sentinel-3 satellite for monitoring drought based on the Vegetation Temperature Condition Index (VTCI).
• To quantitatively and qualitatively verify if multiyear Sentinel-3 VTCI meets the needs for drought monitoring in the Sichuan Basin, PR China.
• To validate the drought monitoring potential of Sentinel-3 VTCI through correlation analysis with meteorological data and consistency analysis with multiyear MODIS VTCI.

Study Configuration

• Spatial Scale: Sichuan Basin, PR China (regional scale).
• Temporal Scale: Multiyear data, with specific drought events analyzed in early July and mid-August 2022.

Methodology and Data

• Models used: Vegetation Temperature Condition Index (VTCI) calculation.
• Data sources:
  • Sentinel-3 reflectance product
  • Sentinel-3 land surface temperature product
  • Multiyear Moderate Resolution Imaging Spectroradiometer (MODIS) VTCI (for consistency analysis)
  • Meteorological data (cumulative precipitation, maximum temperature, minimum temperature, relative humidity)

Main Results

• Two severe droughts were identified in early July and mid-August 2022 in the Sichuan Basin, consistent with actual drought conditions.
• The Root Mean Square Error (RMSE) between MODIS VTCI and Sentinel-3 VTCI ranged from 0.08 to 0.18, indicating reasonable consistency but also differences.
• Past 10-day cumulative precipitation demonstrated the strongest correlation with multiyear Sentinel-3 VTCI compared to other time scales.
• Sentinel-3 VTCI showed a strong negative correlation with maximum temperature and minimum temperature.
• Sentinel-3 VTCI exhibited a strong positive correlation with relative humidity.
• Multiyear VTCI derived from Sentinel-3 data enabled monitoring the spatiotemporal variation of drought conditions in the Sichuan Basin.

Contributions

• First exploration and validation of the potential of the novel Sentinel-3 satellite for regional drought monitoring using the Vegetation Temperature Condition Index (VTCI).
• Provides quantitative and qualitative verification of Sentinel-3 VTCI's suitability for drought monitoring by comparing it with actual conditions, meteorological data, and established MODIS VTCI.
• Highlights the capability of Sentinel-3 VTCI to monitor spatiotemporal drought variations, demonstrating its potential for large-region drought monitoring tasks with high-quality and high-resolution data.

Funding

• Not explicitly stated in the provided text.

Citation

@article{Guo2025Towards,
  author = {Guo, Fengwei and Wang, Pengxin and Tansey, Kevin and Li, Mingqi and Sun, Yuanfei and Zhou, Ji},
  title = {Towards regional drought monitoring with Sentinel-3 vegetation temperature condition index in the Sichuan Basin, PR China},
  journal = {Remote Sensing Applications Society and Environment},
  year = {2025},
  doi = {10.1016/j.rsase.2025.101765},
  url = {https://doi.org/10.1016/j.rsase.2025.101765}
}