SPEAR-SIM: Process-Guided Weak Supervision for Agricultural Foundation Models Using Mechanistic Crop Simulations

Earth observation foundation models have demonstrated strong transferability across remote sensing tasks, yet adapting them to agricultural applications remains challenging because large-scale supervision for intermediate crop physiological processes is largely unavailable. Existing downstream adaptation therefore relies primarily on sparse labels such as crop yield, providing limited guidance for learning physiologically meaningful representations. An observation-constrained process-guided adaptation framework, SPEAR-SIM, is proposed to leverage mechanistic crop simulations as weak supervision for Earth observation foundation models. The SPEAR-SIM framework first generates ensembles of APSIM crop growth trajectories and selects simulations that best agree with satellite-observed crop development, producing observationally validated teacher signals for multitask adaptation of a pretrained Earth observation foundation model. The resulting representations are evaluated through downstream U.S. county-level crop yield prediction across multiple forecasting cutoffs. Experimental results demonstrate consistent improvements over both handcrafted-feature baselines and conventional foundation model adaptation. The proposed framework increases early-season corn prediction performance from an $R^2$ of 0.308 to 0.623 when evaluated beyond the regions where simulator-derived supervision was available, while providing consistent improvements for soybean during mid-to-late season forecasting. Additional analyses further demonstrate that both observation-constrained teacher selection and physiological auxiliary supervision contribute substantially to learning informative crop representations. More broadly, these findings demonstrate that observation-constrained process-guided weak supervision provides an effective paradigm for integrating mechanistic crop simulations with foundation models and offers a general strategy for combining scientific simulators with modern self-supervised learning.