Gutiérrez-Cabrera et al. (2026) Climate-Smart Framework for Olive Yield Estimation: Integrating Soil Properties, Thermal Time, and Remote Sensing NDVI Time Series

Identification

• Journal: Agronomy
• Year: 2026
• Date: 2026-03-30
• Authors: Rosa Gutiérrez-Cabrera, J. Borondo, Ana Maria Tarquis
• DOI: 10.3390/agronomy16070722

Research Groups

• Grupo de Sistemas Complejos, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas (ETSIAAB), Universidad Politécnica de Madrid, Madrid, Spain
• AgrowingData, Almeria, Spain
• ICAI Engineering School, Universidad Pontificia de Comillas, Madrid, Spain
• CEIGRAM, ETSIAAB, Universidad Politécnica de Madrid, Madrid, Spain

Short Summary

This study developed a climate-smart framework integrating soil properties, thermal time, and remote sensing NDVI to improve olive yield estimation at the parcel scale in southern Spain, revealing key relationships between early-season vegetation, rainfall during specific thermal windows, and subsequent year's yield.

Objective

• To develop an integrated smart-farming framework for olive yield estimation by linking soil properties, thermal time, and remote sensing NDVI time series, thereby enhancing the interpretability and transferability of yield indicators at the parcel scale in southern Spain.

Study Configuration

• Spatial Scale: Parcel scale in southern Spain.
• Temporal Scale: Multi-year analysis of seasonal canopy dynamics and interannual yield variability, with phenology-aligned comparisons across campaigns.

Methodology and Data

• Models used: A climate-smart framework integrating soil context, thermal time (Growing Degree Days, GDD), and remote sensing. Parcels were classified into three edaphic clusters.
• Data sources: SoilGrids root-zone properties, Sentinel-2 Normalized Difference Vegetation Index (NDVI) time series, and rainfall data.

Main Results

• Parcels were successfully classified into three distinct edaphic clusters using SoilGrids root-zone properties and Sentinel-2 NDVI time series.
• Canopy development was effectively expressed in thermal time using Growing Degree Days (GDD), enabling phenology-aligned comparisons.
• A consistent negative association was found between cumulative NDVI up to 520 GDD and both biomass and oil yield, indicating an early-season vegetation trade-off and carry-over effects typical of perennial systems.
• Rainfall accumulated during a thermally defined window (120–480 GDD) strongly estimated the yield in the subsequent year, achieving high coefficients of determination (R² = 0.83–0.97) across all soil clusters.

Contributions

• Presents an integrated smart-farming framework that links soil context, climate forcing, and satellite-observed canopy dynamics for olive yield estimation.
• Enhances the interpretability, cross-year comparability, and scalability of yield indicators by anchoring vegetation and precipitation metrics to physiologically meaningful thermal milestones (GDD), avoiding arbitrary calendar windows.
• Offers a robust basis for early yield forecasting, cooperative-level production planning, and adaptive management strategies in Mediterranean olive systems, particularly in the face of increasing drought frequency and heat extremes.

Funding

Not specified in the provided text.

Citation

@article{GutiérrezCabrera2026ClimateSmart,
  author = {Gutiérrez-Cabrera, Rosa and Borondo, J. and Tarquis, Ana Maria},
  title = {Climate-Smart Framework for Olive Yield Estimation: Integrating Soil Properties, Thermal Time, and Remote Sensing NDVI Time Series},
  journal = {Agronomy},
  year = {2026},
  doi = {10.3390/agronomy16070722},
  url = {https://doi.org/10.3390/agronomy16070722}
}