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Understanding when major crops face water deficits and the magnitude of the resulting yield impact is becoming increasingly important; however, large-scale, crop-specific evaluations of when drought stress occurs and how severe it becomes remain limited, particularly those that connect stress timing and severity to the physiological processes determining yield. Our study addresses this gap by using a process-based model integrated with remote sensing data to derive a physiologically grounded drought indicator from point scale to grids and ultimately to district-level resolution. More specifically, we used gridded historical and projected climate data, along with crop, soil, and terrain information. Our first step was to examine how stress timing and severity have historically influenced silage maize and winter wheat yields across Germany. The analysis revealed that drought during shooting-tasselling and tasselling to flowering for silage maize, and grain filling for winter wheat had the strongest association with major yield losses. These crop-specific windows highlighted the importance of stage-dependent stress assessment. The next step involved benchmarking of our physiologically based drought indicator against Sentinel-3 based drought hazard products to compare the simulated and remotely sensed drought-affected areas. Finally, we conducted scenario-based exploration of climate and irrigation conditions to assess how different management and environmental scenarios alters future drought exposure and yield outcomes. In this process, we incorporated Sentinel-2 derived irrigation maps to spatially distinguish irrigated from rainfed areas, improving the representation of actual water management practices. By combining process-based crop models with Earth observation data, our framework provides a foundation for digital twin applications in agriculture showcasing a virtual replication of crop-climate interactions that enables systematic evaluation of how future stress patterns, management decisions and policy interventions may shape agricultural productivity at a larger scale.