Accurate modelling of soil and grass temperatures is essential for improving weather prediction models. The soil and grass temperatures are used to determine the surface temperature, which is a key parameter in latent and sensible heat flux calculations.

The surface temperature is often estimated using land surface parametrisation schemes, such as empirical skin resistance models. These parametrisations often lead to deviations and temporal shifts in the heat flux at the surface, causing a discrepancy in the closure of the surface energy balance (SEB) on short time scales. Addressing these inconsistencies requires a more refined approach to model heat transfer processes within the vegetation-soil continuum.

This research investigates the accuracy of a two-layer diffusive model with uniform thermal parameters in capturing temperature dynamics within the vegetation-soil continuum. The results indicate that a purely diffusive model accurately describes temperature dynamics within the soil. However, this approach is too simplistic to capture the complexity of heat transfer within the vegetation layer. Within the soil, the thermal diffusivity remains relatively constant over time. An optimal value is determined as $\kappa_{soil} = 3.0 \pm 0.3 \cdot 10 ^{-7} \text{ m}^2 \text{ s}^{-1}$, in line with values reported in previous research. In contrast, heat transfer within the grass is influenced by additional processes beyond pure diffusion. Preliminary analysis shows an improvement in the model performance with the introduction of a linear source term, likely accounting for radiative effects.

A diffusive approach to in-canopy heat transfer, combined with a source term, presents a promising step in describing the vegetation layer in surface heat transfer models. However, further research is necessary to refine the formulation of the source term, whether through a physically motivated or data-driven approach.

From a broader perspective, further additional observational and numerical research into the physical processes behind heat transfer within the grass layer is advised to assess their influence. Additionally, generalisation of the model will enhance its applicability in weather forecasting models to improve the prediction of thermal effects near the surface.