Assessment of different kernel-driven models for daytime urban thermal radiation directionality simulation

Parametric kernel-driven models are crucial for operationally adjusting satellite-derived urban land surface temperatures (LSTs) obtained at slant angles to hemispherically-representative values. Various parametric models have been proposed to simulate urban thermal radiation directionality, but a comprehensive comparison of the performances of the published parametric models, especially over a variety of urban surfaces under different solar radiation conditions, remains lacking. It is also unknown whether the combination of the available hotspot and base shape kernels can be used to derive new parametric models with even better performances compared with existing models. Based on both forward-modelling and satellite datasets, here we systematically evaluate three single-kernel and eight dual-kernel parametric models. The main findings are as follows: (1) Amongst the three single-kernel models, the VIN model has the best overall performance, with an average rootmean-square error (RMSE) of 0.79 and 1.35 K, based on forward-modelling and satellite data, respectively. However, the ROU and RL models outperform the VIN model when the solar zenith angle is less than 30 degrees, and in particular it has a higher accuracy for hotspot description. (2) The dual-kernel models usually perform better than the single-kernel models. Amongst the eight dual-kernel models, those with the hotspot kernel KHotspot_rou (used by the ROU model) are more competent than those using KHotspot_vin (obtained from the Vinnikov model) as the hotspot kernel. The RVI model, in general, has the highest accuracy, with average RMSEs of 0.49 and 0.77 K based on forward-modelling and satellite data, respectively. (3) Compared with the single- and dual-kernel models, the multi-kernel models sometimes have better accuracies but the performance improvements are relatively limited. We also provide recommendations for model selection under various scenarios. Our systematic assessment improves our understanding of urban thermal radiation directionality regimes and potentially enables the improved correction of remotely-sensed urban LSTs, thus helping to advance thermal remote sensing of the urban environment.