C. Split-Window Algorithm by Jiménez-Muñoz et al. [12]

A split-window algorithm was introduced by Jiménez-Muñoz et al. [12] to estimate LST for the TIRS image, that is,

$$T\_s = T\_{10} + c\_1(T\_{10} - T\_{11}) + c\_2(T\_{10} - T\_{11})^2 + c\_0 + (c\_3 + c\_4w)(1 - \varepsilon) + (c\_5 + c\_6w)\Delta\varepsilon \tag{6}$$

where, ε is the average emissivity of the two bands, and ∆ε is the band emissivity difference; they are similar to those in Equation (1). *w* is the CWV in g/cm<sup>2</sup> , and *c*<sup>0</sup> to *c*<sup>6</sup> are coefficients. Jiménez-Muñoz et al. [12] regressed those coefficients and calculated the error of the temperature on the basis of simulation data. Their results are shown in Table 2. Similar to the above generalized split-window, this algorithm also considers the quadratic term of brightness temperature difference of Bands 10 and 11, and obtains the coefficients by fitting the temperature simulation data directly.

**Table 2.** Coefficients for the split-window algorithm and the land surface temperature (LST) RMSE from the linear regression by Jiménez-Muñoz et al. [12].

