Cross-Domain Transferability of Foliar Nitrogen Prediction in Sugarcane (<em>Saccharum officinarum</em>) Through the Integration of UAV and Simulated Spectral Data

Remotely Piloted Aircraft (RPAs) equipped with multispectral sensors have emerged as promising tools for estimating foliar nitrogen content (FNC). In this context, this study applied a methodological approach aimed at simulating UAV multispectral data using hyperspectral leaf data obtained in a controlled environment, with the objective of evaluating its predictive potential and its transferability to field data collected by UAVs for FNN estimation. To this end, spectral bands and indices equivalent to those of UAV-mounted sensors were simulated based on hyperspectral data acquired by a benchtop sensor, and subsequently used in modeling via Partial Least Squares Regres-sion (PLSR) and Random Forest (RF). The results showed similar performance across the levels, with R² values of 0.75 and 0.76 for PLSR and RF on the UAV data, and 0.75 and 0.74 for PLSR and RF on the simulated data, respectively. The RF model also performed well in cross-domain validation, with R² = 0.70 when calibrated with simulated data and ap-plied to UAV data. Furthermore, the simulated data maintained high predictive power even with a reduced sample size. It is concluded that spectral simulation constitutes a viable strategy for expanding the applicability of nutritional monitoring using multi-spectral sensors.