Purpose <p>Multispectral remote sensing plays an increasingly vital role in precision agriculture, with the green area index (GAI) being a key parameter due to its relevance for yield formation. However, sensor-specific GAI calibration is labor-intensive and time-consuming, contrasting with the rapid advancement of UAV-based spectral sensors and their short market life spans. Therefore, this study investigated exemplarily the feasibility of transferring GAI calibrations between two UAV-based sensors. </p> Methods <p>A multi-year, multi-crop dataset was used to evaluate three strategies for cross-calibrating MicaSense RedEdge-MX data to GAI produced by published Sequoia models rather than by destructive sampling: (1) band-to-band, (2) ratio-to-ratio, and (3) ratio-to-GAI. Each approach was tested using crop-specific and universal models. To assess the impact of prediction errors, GAI time series were generated for two crops over two years to compute radiation interception and radiation use efficiency (RUE), emphasizing that plausible RUE values provide an indirect verification.</p> Results <p>All methods showed high predictive accuracy (R² = 0.83–0.97), but only the ratio-to-GAI approach provided stable GAI dynamics and reliable RUE estimates, especially at low canopy densities. This approach benefited from the combined use of multiple spectral ratios and the inclusion of an additional band not provided by the Sequoia sensor. It also leveraged the RedEdge-MX’s superior wavelength positions for universal GAI calibration, resulting in minimal differences between crop-specific (R² = 0.88–0.99) and universal models (R² = 0.87–0.99). The extensive dataset revealed date-specific and phenology-driven changes in sensor correlations, emphasizing that concise, ratio-based GAI calibrations may be more robust than complex models.</p> Conclusion <p>These findings underline the importance of efficient cross-calibration strategies in a fast-evolving UAV sensor landscape.</p>