*4.3. Accuracy of LST Retrieval Algorithms and LSE Models for Daytime*

In Figure 5, the accuracy results of the LST retrieval methods for daytime Landsat 8 data are illustrated based on the six NDVI-based LSE models of Table 3. In this validation test at the nine SURFRAD and ARM stations, the Landsat 8 image pixel covering the pyrgeometers was selected, and the estimated LST compared with the corresponding ground LST measurement.

− − The accuracy varied between 2.17 K RMSE and 5.47 K RMSE considering all LST methods and LSE models. MWA method with LSE4 and LSE6 presented similar and best results for the daytime. Using MWA and LSE4, the RMSE, STD of Error, and Bias were 2.17 K, 1.86 K, and −1.13 K, respectively. Furthermore, the same statistical metrics, in the same order, were 2.17 K, 1.79 K, and −1.24 for MWA with LSE6. In general, the daytime results revealed that for all LSE models, except for LSE2, MWA showed slightly better results than RTE, and RTE demonstrated slightly better results than SCA. LSE1 and LSE2 did not offer satisfying results with any of the LST retrieval algorithms. Apart from that, the other LSE models presented acceptable daytime LST results with MWA, RTE, and SCA. The Bias is always negative regardless of the approach, highlighting a general overestimation of the Landsat 8 retrieval with respect to the in-situ measurements, especially for higher LST values.

**Figure 5.** Daytime LST from Landsat 8, period 2013–2019 (see Appendix A): Accuracy assessment of MWA, RTE, and SCA retrieval methods with different LSE models at the nine SURFRAD and ARM stations.
