Wu et al. (2026) Decoding the drivers of global desertification sensitivity from 2005 to 2020

Identification

• Journal: Journal of Environmental Management
• Year: 2026
• Date: 2026-03-27
• Authors: Qi Wu, Gangte Lin, Min He, Jianzhou Gong
• DOI: 10.1016/j.jenvman.2026.129384

Research Groups

• School of Geography and Remote Sensing, Guangzhou University, Guangzhou, 510006, China
• China Urban Construction Design & Research Institute Co., Ltd., Beijing, 100120, China

Short Summary

This study assessed global desertification sensitivity from 2005 to 2020 using the GEE-MEDALUS model, identifying climate and soil quality as dominant drivers and highlighting their co-limitation for targeted interventions.

Objective

• To assess global desertification sensitivity from 2005 to 2020 and quantify the drivers and causal linkages of desertification using a multi-model approach.

Study Configuration

• Spatial Scale: Global scale, with causality evaluation focused on China.
• Temporal Scale: 2005 to 2020.

Methodology and Data

• Models used:
  • Google Earth Engine-Mediterranean Desertification and Land Use (GEE-MEDALUS) model, integrating Climate Quality Index (CQI), Soil Quality Index (SQI), Vegetation Quality Index (VQI), and Management Quality Index (MQI).
  • Optimal Parameters-based Geographical Detector (OPGD).
  • Geographical Convergent Cross Mapping (GCCM) model.
• Data sources: Remote sensing data (via Google Earth Engine) and various environmental indicators for climate, soil, vegetation, and management quality (e.g., Aridity Index, Soil Organic Matter, Depth to Bedrock).

Main Results

• High to extreme desertification sensitivity was concentrated in arid, semi-arid, and sub-humid margins, particularly across North Africa, the Middle East, and Central Asia.
• Temporally, extreme sensitivity areas remained stable (11.5%–12.0% of global land area), while high sensitivity expanded slightly (20.8% to 21.8%).
• CQI and SQI were identified as dominant drivers (mean q = 0.74 and 0.65, respectively), surpassing VQI and MQI (mean q = 0.56 and 0.53).
• The Aridity Index (q = 0.80) and Soil Organic Matter (q = 0.79) were the most influential individual indicators.
• Interactions between drivers significantly amplified explanatory power (e.g., q = 0.77–0.80 for combined effects).
• GCCM confirmed causal linkages for all indicators except Depth to Bedrock.
• The findings highlight a distinct climate-soil co-limitation in driving desertification sensitivity.

Contributions

• Provides the first global-scale assessment of desertification sensitivity using the GEE-MEDALUS model, offering a consistent baseline for transboundary monitoring.
• Quantifies the dominant drivers and their interactions at a global scale using the OPGD model.
• Establishes causal linkages among desertification indicators using the GCCM model, enhancing understanding of complex system dynamics.
• Offers critical insights into climate-soil co-limitation, which can inform the prioritization of interventions for enhancing resilience under environmental change.
• Supports international mandates such as UN Sustainable Development Goal 15.3 and Land Degradation Neutrality targets by providing a consistent scientific baseline.

Funding

Not specified in the provided text.

Citation

@article{Wu2026Decoding,
  author = {Wu, Qi and Lin, Gangte and He, Min and Gong, Jianzhou},
  title = {Decoding the drivers of global desertification sensitivity from 2005 to 2020},
  journal = {Journal of Environmental Management},
  year = {2026},
  doi = {10.1016/j.jenvman.2026.129384},
  url = {https://doi.org/10.1016/j.jenvman.2026.129384}
}