## 3 Results and Discussion
### Algorithm Performance for LST Forecasts
#### 3.1.3 The Chiese and Capitanata Consortia Test-Beds  
The fairly good performance found in the LST simulations and forecasts pushes us to carry on further investigations over different types of surfaces and climate regimes using the same algorithm. Hence, the algorithm performance has been verified over two surfaces different from asphalt: a maize and a tomato field of the Chiese and Capitanata consortia, in northern and southern Italy, respectively, where the Politecnico di Milano has carried out soil monitoring surveys during the SIM project. To test the sensitivity of the MEC algorithm even in cultivated fields, where energy fluxes play a relevant role between earth surface and atmosphere, and the LST prediction may affect the management of water resources, we decided to extend the analysis during growing seasons. Using the same equations described in Section 3, the algorithm reliability leads to poor results as highlighted in Table 11 (red colors). However, by calibrating Equation (4), a remarkable improvement is achieved (green color, in Table 11). In particular, the change of the \(\beta\) coefficient corresponds to a change in the amount of energy required to raise the surface temperature by 1 \({}^{\circ}\)C over one square meter of vegetated soil. After an appropriate tuning, we set the \(\beta\) parameter at a constant value of 250 W/m\({}^{2}\).  
The Figures 7 and 8 highlight the LST trend for observed data in comparison with simulated and forecasted values before and after the calibration of the \(\beta\) coefficient over the Chiese (Figure 7) and Capitanata (Figure 8) consortia, respectively. In both the cultivated fields, the applied tuning sensibly improved the performance and both simulations and forecasts match more closely the measured  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Statistical Index before/after the Algorithm Calibration**} & \multicolumn{2}{c}{**Chiese Consortium**} & \multicolumn{2}{c}{**Capitanata Consortium**} \\  & **Observations vs.** & **Observations vs.** & **Observations vs.** & **Observations vs.** \\  & **Simulation** & **Forecasts** & **Simulation** & **Forecasts** \\ \hline
**Nash-Sutcliffe (NSE) [–]** & \(-\)1.24/0.82 & \(-\)0.87/0.82 & \(-\)1.22/0.84 & \(-\)1.07/0.76 \\
**Root Mean Square Error (RMSE) [\({}^{\circ}\)C]** & 7.68/2.43 & 6.94/3.14 & 7.97/2.06 & 7.70/2.63 \\
**Pearson Correlation Coefficient (r) [–]** & 0.86/0.95 & 0.88/0.87 & 0.92/0.98 & 0.93/0.97 \\
**Determination Coefficient (R\({}^{2}\)) [–]** & 0.74/0.91 & 0.77/0.76 & 0.84/0.96 & 0.86/0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Statistical indexes obtained before and after the calibration of the \(\beta\) coefficient.  
Figure 6: Scatterplot between observed vs. forecasted LST over the “Linate” station point during clear (left) and cloudy (right) sky days.  
data at the two station points. For the sake of brevity, this comparison has been carried out only during clear sky days in the growing season, when the incoming solar radiation is higher; hence, ten non-consecutive days are here selected during two different growing seasons: 2018 for the Chiese and 2017 for the Capitanata, whose average duration is generally about five-six months from April till September both for maize and tomato growing season.  
Figure 7: Comparison between observed, simulated and forecasted data over the Chiese basin for ten non-consecutive days during the 2018 growing season before (**top**) and after (**bottom**) the calibration.  
Indeed, these high skill scores found after tuning the MEC-algorithm, give the proof of a high versatility of the implemented model even over different surfaces.