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UID:pretalx-global-workshop-2026-9WB3A7@pretalx.earthmonitor.org
DTSTART;TZID=Europe/Amsterdam:20261007T153000
DTEND;TZID=Europe/Amsterdam:20261007T154500
DESCRIPTION:Vegetation in terrestrial ecosystems plays a key role in the ca
 rbon cycle\, and understanding its spatiotemporal patterns and associated 
 drivers is crucial for ecological research. This study explores the relati
 ons between remote sensing vegetation Gross Primary Production (GPP) and c
 limate explanatory variables such as the Standardized Precipitation Evapot
 ranspiration Index (SPEI) and soil moisture anomalies (SMA). \n\nThe study
  focused on the climatically diverse Ebro River basin (85\,600 km²)\, Spa
 in's river largest catchment\, using monthly data from 2016 to 2024. The a
 rea is bounded between the three meteorological domains of this region of 
 SW Europe: Atlantic\, European continental and Mediterranean. \n\nDuring t
 he processing phase\, harmonized monthly products at 1 km spatial resoluti
 on were generated from multiple satellite and in-situ sources. GPP was agg
 regated from the MOD17A2HGF product\, SPEI was derived in-situ meteorologi
 cal data (Trypidaki et al 2024) by AEMET\, and monthly SMA were computed f
 rom Sentinel-1 synthetic aperture radar (SAR) data using a dual-polarizati
 on algorithm (DPA) (Fan et al. 2025).\n\nWe explore vegetation–climate r
 elationships 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 importan
 ce were evaluated using multiple metrics.\n\nOur findings highlight the po
 tential\, requirements and limitations of GeoAI tools compared to classica
 l statistical methods\, in handling nonlinear relationships and multicolli
 nearity.\n\nReferences: \n\nFan\, D.\, Zhao\, T.\, Jiang\, X.\, García-Ga
 rcía\, A.\, Schmidt\, T.\, Samaniego\, L.\, Attinger\, S.\, Wu\, H.\, Jia
 ng\, 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 soi
 l moisture data product: Algorithm and preliminary assessment. Remote Sens
 ing of Environment\, 318\, 114579. https://doi.org/10.1016/j.rse.2024.1145
 79\n\nTrypidaki E.\, Pesquer L.\, Domingo-Marimon C\, "Spatiotemporal Anal
 ysis for Enhanced Drought Monitoring and Agricultural Applications in the 
 Ebro Basin\, Spain\," 2024 IEEE International Workshop on Metrology for Ag
 riculture and Forestry (MetroAgriFor)\, Padua\, Italy\, 2024\, pp. 603-608
 \, https://doi.org/10.1109/MetroAgriFor63043.2024.10948835
DTSTAMP:20260624T070125Z
LOCATION:Aula Magna
SUMMARY:Understanding Vegetation–Climate Relationships Using GeoAI: A Spa
 tiotemporal Analysis in the Ebro River Basin - Eirini Trypidaki
URL:https://pretalx.earthmonitor.org/global-workshop-2026/talk/9WB3A7/
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