VegSim: A Geospatial World Model for Scenario-Conditioned Vegetation Simulation

Abstract:Vegetation monitoring under climate stress requires answering not only how it will evolve given the expected weather, but how it would respond to alternative meteorological conditions. Forecasting models return the expected vegetation state for the observed weather and cannot answer these scenario-conditioned questions, because future weather is fixed to the recorded trajectory. We present VegSim, a geospatial world model for scenario-conditioned vegetation simulation. VegSim infers a latent vegetation state from sparse satellite-derived NDVI histories, past meteorological covariates, and static spatial context, propagates it forward under future weather forcing through recurrent latent dynamics, and decodes predictive NDVI quantiles at each lead time. Because future forcing enters as a controllable input, the same trained model supports probabilistic forecasting under observed weather and conditional simulation under user-defined meteorological forcing, without supervision on scenario responses. We evaluate VegSim on GreenEarthNet across in-distribution data and spatial, temporal, and joint spatio-temporal shifts, where it achieves strong point and probabilistic accuracy against time series and Earth Observation forecasting baselines while using a compact architecture. We then simulate vegetation responses across Europe under two contrasting meteorological scenarios, obtaining a weak but broadly positive response under winter wet warming and a widespread negative response under summer drought. The code is available at this https URL.

Machine Learning (cs.LG)

arXiv:2606.21961 [cs.LG]

(or arXiv:2606.21961v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.21961 Focus to learn more arXiv-issued DOI via DataCite

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