Radio-frequency (RF) reflection measurements provide a non-ionizing approach for estimating biological tissue thickness; however, accurately resolving deeper tissues is difficult because their reflections are attenuated and superimposed with those from overlying layers. This study presents a physics-guided stacked-regression framework for estimating subcutaneous adipose tissue (SAT), muscle tissue (MT), and visceral adipose tissue (VAT) thicknesses from simulated scattering parameters. A planar multilayer abdominal model containing 120 combinations of SAT, MT, and VAT thicknesses was evaluated. The input features consisted of the magnitude of S₁₁ at ten frequencies from 300 MHz to 2 GHz, including four intermediate frequencies at 600, 700, 800, and 900 MHz that were absent from the previous model. Target-specific frequency subsets were selected and evaluated using polynomial regression and five-fold cross-validation. Multilayer reflection theory was used to explain the nonlinear relationship between tissue thickness and the measured RF response. The extended-frequency models achieved errors of 1.96%, 7.60%, and 19.86% for SAT, MT, and VAT, respectively, compared with 2.56%, 11.42%, and 39.10% in the previous model. A selective stacked architecture was subsequently evaluated by using an estimated overlying tissue thickness as an additional input for a deeper-tissue estimator. Adding predicted SAT thickness degraded MT estimation, whereas adding predicted MT thickness improved VAT estimation. The final stacked VAT model achieved an RMSE of 0.14±0.11 mm, an R² of 0.97±0.02, and an error of 12.49%, corresponding to a 68.06% error reduction relative to the previous VAT model. These findings demonstrate that denser frequency sampling and selectively applied anatomical guidance can improve RF-based estimation of deeper tissue layers.