Li et al. (2026) Integrating multi-dimensional features for remote sensing–based drought monitoring and driver analysis during the vegetation growing season: a case study in northern Xinjiang

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Identification

• Journal: Geomatics Natural Hazards and Risk
• Year: 2026
• Date: 2026-02-23
• Authors: Dan Li, Li He, Zhengwei He, Wenqian Bai, Run Jin, Zhiyu Lin, Yuna Huang
• DOI: 10.1080/19475705.2026.2634962

Research Groups

Not specified in the provided text.

Short Summary

This study develops a novel three-dimensional drought index (kTVPDI) for Xinjiang, integrating kernel-based NDVI, land surface temperature, and precipitation, demonstrating improved accuracy over traditional indices and identifying key drivers of drought intensification from 2000 to 2024.

Objective

• To develop and validate a robust three-dimensional drought index (kTVPDI) for Xinjiang, overcoming limitations of traditional indices, and to assess the spatiotemporal evolution and dominant drivers of drought from 2000 to 2024.

Study Configuration

• Spatial Scale: Xinjiang region, China (with implications for other arid and semi-arid regions globally).
• Temporal Scale: 2000 to 2024.

Methodology and Data

• Models used: kTVPDI (kernel-based Temperature–Vegetation–Precipitation Drought Index), TVDI (Temperature–Vegetation Drought Index framework), Sen slope, Mann‒Kendall test, Loess decomposition, SHAP-based machine learning.
• Data sources: Kernel-based Normalized Difference Vegetation Index (kNDVI), Land Surface Temperature (LST), Precipitation, Soil moisture (for validation).

Main Results

• The kTVPDI exhibits a significantly stronger correlation with soil moisture (r = -0.875) compared to TVDI, indicating improved monitoring accuracy.
• Drought severity increases from peripheral to central regions of Xinjiang and reaches its peak at the beginning and end of the growing season.
• The mean annual drought intensity follows the order: desert > grassland > cropland > forest.
• Approximately 66% of northern Xinjiang shows a slight upward trend in drought intensity.
• Potential evapotranspiration and wind speed are identified as major contributors to drought intensification, while elevation and slope mitigate drought severity.

Contributions

• Development of kTVPDI, a novel three-dimensional drought index that integrates kNDVI, LST, and precipitation, offering enhanced sensitivity and stability compared to traditional indices.
• Improved accuracy in drought monitoring, particularly by mitigating vegetation saturation and soil background effects.
• Comprehensive spatiotemporal assessment of drought evolution in Xinjiang from 2000 to 2024.
• Identification of dominant drivers (potential evapotranspiration, wind speed, elevation, slope) influencing drought severity in the region using machine learning.
• Provides a more robust tool for ecological management and risk mitigation in arid and semi-arid regions globally.

Funding

Not specified in the provided text.

Citation

@article{Li2026Integrating,
  author = {Li, Dan and He, Li and He, Zhengwei and Bai, Wenqian and Jin, Run and Lin, Zhiyu and Huang, Yuna},
  title = {Integrating multi-dimensional features for remote sensing–based drought monitoring and driver analysis during the vegetation growing season: a case study in northern Xinjiang},
  journal = {Geomatics Natural Hazards and Risk},
  year = {2026},
  doi = {10.1080/19475705.2026.2634962},
  url = {https://doi.org/10.1080/19475705.2026.2634962}
}