SpanWorld: A Variable-Span World Model for Vegetation Forecasting

World models learn internal representations of environments and their evolution, providing a way to forecast future environmental change. These models need to preserve the predictive value of their internal representations as rollouts progress. Evaluating this capability requires examining continued predictions from generated states alongside direct forecasts from history. To this end, we propose SpanWorld, a remote-sensing world model that learns vegetation dynamics from satellite observations and advances its latent spatial state through weather-conditioned transitions. The model uses given future weather as an external driver, and its subsequent predictions are evaluated against real remote-sensing imagery. SpanWorld combines baseline predictions derived from incomplete historical observations with a dynamic spatial state updated by a shared variable-span transition, supporting direct prediction and partitioned rollout within a finite forecast window. We further introduce a partitioned-path learning mechanism that retains supervision of predictions made directly from the same initial state at every future target date. It also samples temporal partitions and uses observed target images to supervise predictions made by continuing from model-generated intermediate states. On GreenEarthNet's 100-day dense normalized difference vegetation index (NDVI) image forecasting task, we compare SpanWorld with a control using the same architecture. Both models receive supervision of direct predictions at all forecast horizons. Using the same endpoint images, we additionally supervise predictions from partitioned rollouts in SpanWorld and direct predictions in the control. On the IID split, both models roll out to day 50 using one temporal partition unseen during training, then predict the image at each target date from day 55 to day 100 separately from their respective day-50 states. For these subsequent predictions, partitioned-path learning reduces mean squared error (MSE) by approximately 11% relative to the control, while direct-prediction errors over the same period remain similar.