Enhancing the spatial resolution of satellite-derived Land surface temperature (LST) using an operational trapezoid interpolation disaggregation model (OTI_DisTrad)

This paper proposed an operational trapezoid interpolation disaggregation model (OTI_DisTrad) that disaggregates coarse resolution thermal data to fine spatial resolutions using the Vegetation fractional cover (VFC) versus Land surface temperature (LST) space. Developed model attempted to weaken the influences of soil moisture on LST variability at the finer-resolution level. The main assumption underlying our model is that the quantitative relationship between VFC, Bowen ratio and LST that is retrieved at a coarse resolution also works well at finer resolutions for the pixels inside the VFC-LST space. Moreover, the Support Vector Machine (SVM) regression was utilized to address the pixels outside the VFC-LST space by fitting the relationship between Index-based Built-up Index (IBI), surface albedo and LST. The OTI_DisTrad model was tested on two heterogeneous landscapes of the central Iowa, USA (watershed region) and Beijing, China (urban region). Statistical performance measures were used to compare OTI_DisTrad to other five classical approaches (M1, M2, M3, M4 and M5). Results revealed that OTI_DisTrad was more accurate than the three classical ones for the vegetated-dominated watershed region. The average Root mean square error (RMSE) and Mean absolute error (MAE) between disaggregated and reference LST is decreased from 1.83, 1.86 and 1.75 °C (M1, M2 and M3) to 1.59 °C (OTI_DisTrad) and from 1.32, 1.39 and 1.28 °C (M1, M2 and M3) to 1.10 °C (OTI_DisTrad), respectively. Statistical results also reported that OTI_DisTrad was slightly superior to other two classical methods for the urban regions, with the average RMSE and MAE between disaggregated and reference temperature decreased from 2.43 and 2.26 °C (M4 and M5) to 1.97 °C (OTI_DisTrad) and from 1.69 and 1.47 °C (M4 and M5) to 1.21 °C (OTI_DisTrad), respectively.