BAYAZIT et al. (2026) Climate-Induced Vegetation Stress Detected Through Remote Sensing of Hydroclimatic Indicators

Identification

• Journal: Sustainability
• Year: 2026
• Date: 2026-02-26
• Authors: Esra BAYAZIT, Veysi Kartal, Saad Sh. Sammen, Miklas Scholz
• DOI: 10.3390/su18052235

Research Groups

• Department of Landscape Architecture, Faculty of Fine Arts and Design, Siirt University, Siirt, Türkiye
• Department of Civil Engineering, College of Engineering, Diyala University, Deyala, Iraq
• Department of Water Management, Sector of Regional Development, Environment and Construction, District of Herzogtum Lauenburg, Ratzeburg, Germany
• Department of Civil Engineering Science, School of Civil Engineering, and the Built Environment, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa

Short Summary

This study investigated the effects of hydroclimatic variability and long-term trends on vegetation response in Türkiye's Meriç-Ergene Basin. Findings reveal that vegetation dynamics are increasingly driven by temperature anomalies, leading to heightened evapotranspiration and expedited phenological processes, underscoring the basin's vulnerability to warming and drying.

Objective

• To investigate the spatiotemporal interactions of key hydroclimatic and environmental indicators (Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Soil Moisture (SM), precipitation (pr), reference evapotranspiration (ET0), and average temperature (Tave)) across the Meriç-Ergene Basin.
• To capture the evolving dynamics and nonlinear trajectories of environmental stressors using drought anomaly analysis and advanced trend detection techniques.

Study Configuration

• Spatial Scale: Meriç-Ergene Basin, northwestern Türkiye, covering five prominent districts: Çorlu, Edirne, Kırklareli, Lüleburgaz, and Tekirdağ. Data resolution for satellite products is 1 km.
• Temporal Scale:
  • Precipitation (pr), average temperature (Tave), reference evapotranspiration (ET0), soil moisture (SM): Monthly data from 1975 to 2024.
  • Land surface temperature (LST), Normalized Difference Vegetation Index (NDVI): Monthly data from 2001 to 2024.
  • Seasonal analysis: Winter (December, January, February), Spring (March, April, May), Summer (June, July, August), Autumn (September, October, November).

Methodology and Data

• Models used:
  • FAO Penman–Monteith equation for daily reference evapotranspiration (ET0) calculation.
  • Mann–Kendall (MK) Test for monotonic trend detection.
  • Sen’s Slope Estimator for quantifying trend magnitude.
  • Innovative Trend Analysis (ITA) for detecting hidden or non-linear trends.
  • Innovative Polygon Trend Analysis (IPTA) for assessing symmetry, strength, and direction of trends.
  • Spearman’s rank correlation coefficient for non-parametric correlation analysis.
• Data sources:
  • In-situ observations from multiple meteorological stations (for pr, Tave, SM, ET0 inputs).
  • MODIS (Moderate Resolution Imaging Spectroradiometer) products via Google Earth Engine (GEE):
    • MOD11A2 for LST (8-day composite, aggregated to monthly, 1 km spatial resolution).
    • MOD13A2 for NDVI (16-day composite, aggregated to monthly, 1 km spatial resolution).
  • ERA5 reanalysis datasets for solar radiation (calibrated with local observations).
  • Türkiye General Directorate of Meteorology (MGM) for temperature, wind speed, and relative humidity.

Main Results

• Temperature and Evapotranspiration: Statistically significant increasing trends were observed in average temperature (Tave), land surface temperature (LST), and reference evapotranspiration (ET0) across all seasons and annually (e.g., Tave annual Sen's slope = 0.0611 °C/year, p < 0.0001). Frequent positive anomalies in Tave, LST, and ET0 were particularly evident in spring and summer.
• Soil Moisture: Declining trends in soil moisture (SM) were noted, especially during summer, with frequent negative anomalies. While most Sen's slopes for SM were negative, statistical significance was often uncertain, except for clear seasonal declines.
• Precipitation: No statistically significant trends were detected in precipitation (pr) (p > 0.1), exhibiting highly variable patterns with positive anomalies mainly in spring and autumn, but frequent negative anomalies in summer.
• Vegetation (NDVI): Weak and mostly negative trends in NDVI were found for spring, summer, and autumn. However, a significant positive trend was observed in winter (Sen's slope = 0.0029/year, 95% CI = [0.0018, 0.0044]), potentially linked to warming. Summer NDVI anomalies were consistently constrained.
• Correlations: Strong positive correlations were found between ET0, LST, and Tave (r > 0.90). Strong negative correlations existed between ET0 and SM (as low as −0.83 in Lüleburgaz). NDVI showed moderate positive correlations with LST in Edirne (0.73) and Kırklareli (0.60), but weak or negative associations with pr and SM across most districts (e.g., NDVI-pr: −0.12 in Edirne; NDVI-SM: −0.59 in Edirne).
• Overall Shift: Vegetation dynamics are increasingly temperature-driven, with warming trends amplifying evapotranspiration demand and accelerating phenological processes. This leads to stronger correlations between NDVI and thermal factors rather than precipitation, indicating the basin's heightened vulnerability to warming and drying, particularly during summer.

Contributions

• Integrates nearly five decades (1975–2024) of in-situ hydroclimatic observations with satellite-derived indices (NDVI, LST from 2001–2024) to establish a comprehensive diagnostic framework for the Meriç-Ergene Basin.
• Provides novel, region-specific insights into the coupled dynamics of climate, soil, and vegetation by employing a multi-method approach including multivariable anomaly analysis, complementary trend detection techniques (Mann–Kendall, Sen’s slope, ITA, IPTA), and non-parametric correlation analysis.
• Highlights the desynchronization of hydro-vegetation relationships and the exacerbation of warming-induced drought, particularly during the summer months, offering a more robust understanding of hydroclimatic variability and its ecological consequences.
• Offers a multidimensional decision-support framework for climate-sensitive landscape planning and urban adaptation strategies, providing data-driven insights for enhancing landscape resilience and urban design in vulnerable agroecological zones.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Citation

@article{BAYAZIT2026ClimateInduced,
  author = {BAYAZIT, Esra and Kartal, Veysi and Sammen, Saad Sh. and Scholz, Miklas},
  title = {Climate-Induced Vegetation Stress Detected Through Remote Sensing of Hydroclimatic Indicators},
  journal = {Sustainability},
  year = {2026},
  doi = {10.3390/su18052235},
  url = {https://doi.org/10.3390/su18052235}
}