Timely and precise crop monitoring is essential for precision agriculture, yet spaceborne remote sensing frequently suffers from temporal gaps, cloud cover, and high subscription costs. Consumer-grade smartphone photography offers a low-cost alternative for localized monitoring, though its implementation remains doubtful due to lack of standardization and calibration. This study evaluates the cross-platform consistency of visible spectrum (RGB) vegetation indices between Sentinel-2A satellite imagery and ground-truth smartphone photography. Field trials were conducted across four winter wheat (Triticum aestivum L.) fields, located in the semi-arid zone of Ukraine, systematically capturing crop canopy development across three major phenological stages: tillering, stem elongation, and earing. Six visible spectrum vegetation indices (ExG, ExGR, GLI, VARI, MGRVI, and CIVE) were evaluated. A comparison of static absolute index values revealed strong cross-platform correlation for ExGR, VARI, MGRVI and CIVE (R = 0.74–0.77). Switching to the daily index change rates analysis dramatically changed cross-platform interoperability. The MGRVI emerged as the only robust metric, achieving a superior dynamic linear correlation (R = 0.89) and accounting for 78% of spaceborne variance with RMSE of 0.014. Other smartphone-derived vegetation indices provided moderate to weak (R = 0.26–0.66) correlation with satellite-borne indices, that makes them less suitable for implementation in cross-platform crop monitoring systems. The regression model for smartphone to satellite calibration of MGRVI serves as a pre-condition of cross-platform interoperability in digital systems of precise crop monitoring.