Accurate estimation of plant biophysical properties is essential for understanding ecosystem functioning, monitoring forest health, productivity, and assessing the risk of disturbances. However, retrieval accuracy is often constrained by uncertainties in model parameterization, soil background effects, and spectral redundancy. This study systematically evaluates the influence of parameter range, band selection, and soil background on the inversion of leaf area index, chlorophyll content, leaf mass per area, and equivalent water thickness in Eucalyptus plantations throughout an entire growing season. Retrievals were conducted by inverting the PROSAIL model. Our approach led to three innovative findings: 1) the integration of trait-informed parameter ranges, tailored to Eucalyptus plantations, which substantially improves retrieval accuracy, especially for leaf mass per area; 2) the demonstration that band selection can reduce root mean square error by up 58% for Chlorophyll content, 52% leaf mass per area and 69% for equivalent water thickness; and 3) the performance of soil background datasets strongly depends on the sampling strategy used and whether band selection was applied. These findings highlight that combining spectral optimization with contextual prior knowledge provides a robust framework for improving PROSAIL inversion in forest ecosystems, advancing remote sensing applications for plantation monitoring and sustainable management of natural resources. Moreover, this work presents a rarely explored application of PROSAIL inversion to forest plantations, extending its use beyond crop studies.