Multiple areas from semiconductors to packed bed energy storage have a critical need to understand thermal transport across multiple layers. Current state-of-the-art techniques typically employ a direct temperature measurement that is difficult to apply and suffers inaccuracies due to contact resistance and probe size. Recently a technique called thermoreflectance has gained prominence in measuring thin film and bulk thermal conductivity, using the reflectance of a well-known coating as a temperature transducer. Radiometric techniques for temperature measurement differ, collecting and measuring radiated infrared power and directly correlating to the temperature of the sample, allowing for measurement across a larger temperature range. Modulated Photothermal Radiometry builds off this concept, increasing its accuracy and allowing for thermal conductivity measurement across multiple layers. The system uses a modulated laser source causing a damped periodic heat flux, resulting in a frequency and thermal property-dependent surface temperature and phase. Lock-In radiometric techniques are used to extrapolate the amplitude and phase of the radiative signal compared to the incident laser reference. The signal generated depends on the modulation frequency that causes the heat wave to probe through the depth of the sample, resulting in a complex relationship between the signal and the thermal properties of that layer. Current state-of-the-art work has utilized this technique for thin layers (~5-10um) with simpler thermal properties, featuring a linear approximation where a ratio of slopes is utilized to determine thermal properties. However, this technique limits the scope of materials that can be analyzed. In our work we seek to expand this technique to thicker (~10mm) and complex (porous) materials, where different modes of transport may dominate at higher temperatures. We observe experimentally that as the thickness expands the linearity breaks down and the signal saturates. In our work we solve the complete 2D model to apply MPTR beyond the linear region. The 2D model does not have an analytical solution, requiring numerical methods to approximate the solution. The parameters of the solution are algorithmically modified until the model matches with experimental results.