Understanding Vegetation–Climate Relationships Using GeoAI (Eirini Trypidaki)
“Understanding Vegetation–Climate Relationships Using GeoAI: A Spatiotemporal Analysis in the Ebro River Basin” by Eirini Trypidaki The Open-Earth Monitor Global Workshop 2026 "Next-Generation EO & GeoAI Built on Openness and Distributed Data" 7-9 October 2026, Barcelona, Spain https://earthmonitor.org/global-workshop-2026/ Vegetation in terrestrial ecosystems plays a key role in the carbon cycle, and understanding its spatiotemporal patterns and associated drivers is crucial for ecological research. This study explores the relations between remote sensing vegetation Gross Primary Production (GPP) and climate explanatory variables such as the Standardized Precipitation Evapotranspiration Index (SPEI) and soil moisture anomalies (SMA). The study focused on the climatically diverse Ebro River basin (85,600 km²), Spain's river largest catchment, using monthly data from 2016 to 2024. The area is bounded between the three meteorological domains of this region of SW Europe: Atlantic, European continental and Mediterranean. During the processing phase, harmonized monthly products at 1 km spatial resolution were generated from multiple satellite and in-situ sources. GPP was aggregated from the MOD17A2HGF product, SPEI was derived in-situ meteorological data (Trypidaki et al 2024) by AEMET, and monthly SMA were computed from Sentinel-1 synthetic aperture radar (SAR) data using a dual-polarization algorithm (DPA) (Fan et al. 2025). We explore vegetation–climate relationships using correlation and GeoAI ML approaches, including Random Forest (caret R package) and Accumulated Local Effects (ALEPlots R package) between GPP and climate variables. Model stability and variable importance were evaluated using multiple metrics. Our findings highlight the potential, requirements and limitations of GeoAI tools compared to classical statistical methods, in handling nonlinear relationships and multicollinearity. References: Fan, D., Zhao, T., Jiang, X., García-García, A., Schmidt, T., Samaniego, L., Attinger, S., Wu, H., Jiang, Y., Shi, J., Fan, L., Tang, B.-H., Wagner, W., Dorigo, W., Gruber, A., Mattia, F., Balenzano, A., Brocca, L., Jagdhuber, T., … Peng, J. (2025). A Sentinel-1 SAR-based global 1-km resolution soil moisture data product: Algorithm and preliminary assessment. Remote Sensing of Environment, 318, 114579. https://doi.org/10.1016/j.rse.2024.114579 Trypidaki E., Pesquer L., Domingo-Marimon C, "Spatiotemporal Analysis for Enhanced Drought Monitoring and Agricultural Applications in the Ebro Basin, Spain," 2024 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Padua, Italy, 2024, pp. 603-608, https://doi.org/10.1109/MetroAgriFor63043.2024.10948835