EarthMind: Persistence-Anchored Latent Transitions for Robust Multimodal Earth Observation Forecasting

Forecasting Earth observations is hampered by a practical fact: the inputs are routinely incomplete. In our globally distributed benchmark, only 90.3% of Sentinel-2 months and 82.6% of Sentinel-1 months are usable. We present EarthMind, a multimodal spatiotemporal world model that frames monthly forecasting as learning a transition from a persistence-anchored state rather than regenerating the scene from scratch. EarthMind fuses Sentinel-2 multispectral imagery, Sentinel-1 SAR, static terrain, and ten weather variables into latent Earth states. It predicts the next state with a Transformer and decodes that state into future Sentinel-2 and Sentinel-1 imagery and weather. Two extensions target real-world use. Structured modality masking (EarthMind-Robust) reproduces realistic sensor outages during training. Band-wise calibration toward persistence (EarthMind+) learns how much of the predicted change each spectral band can be trusted with. On 1,000 sites with site-disjoint splits (16,562 test sequences), EarthMind, trained from scratch, performs comparably to the pretrained Prithvi-EO-2.0 foundation model on clean inputs, with no significant difference on any metric, while additionally forecasting SAR and weather. EarthMind+ attains the lowest RMSE (0.0676), highest PSNR (23.40 dB) and SSIM (0.9704), and lowest SAM (4.68${}^{\circ }$) among the evaluated methods, while Prithvi retains the lowest MAE. When SAR and weather are both missing, structured masking reduces RMSE by 35.3%. In four-month closed-loop rollouts, EarthMind stays 42.8% below last-observation-carried-forward, and its frozen latent state improves land-cover balanced accuracy by 12.2 points over raw features without any land-cover supervision. The learned calibration coefficients further identify the shortwave-infrared bands as the least predictable.