## 4 Discussion  
This study aims to assess the differences and propose cross-sensor conversion factors derived from the integrated or exclusive use of OLS and RMA regression models between Landsat-7, Landsat-8, and Sentinel-2 surface reflectance products. The objective is to take advantage of the potential arising from the harmonization of these products. While the previous literature has primarily focused on integrating TOA reflectance products in different areas, this study, for the first time, concentrates on the highly heterogeneous Mediterranean Basin using surface reflectance products, representing a significant advancement in the integration of satellite products within this region. Despite the good agreement observed in the comparison (Figures 2 and 3) between Landsat-7/8 and Sentinel-2 products, significant variations exist between cross-sensor band pairs, as demonstrated by mean differences and RMSD (Tables 5, 9 and 13). The slope of the ODR shows equivalent results for MSI vs. OLI (Table 5) and MSI vs. ETM+ (Table 9) comparisons, with values close to 1 for the visible bands, particularly the blue band. A higher disagreement was observed for the blue band between the MSI vs. ETM+ sensor pairs where a notable mismatched band width was reported (Table 2). In the case of the NIR band, MSI bands 8 and 8a exhibit remarkably similar ODR slopes and RMSD values  
Figure 5: Comparison of NDVI from Sentinel 2 and Landsat-8 from test dataset; (**a**) with uncorrected surface reflectance; (**b**) with adjusted OLS factor (Table 6). In yellow are the regions that represent larger numbers of points. The solid line is 1-to-1, dashed line is the ODR regression slope. when compared to OLI and ETM+ bands 5 and 4, respectively. For the comparison between Landsat 7/8 and Sentinel-2 the greatest deviation (RMSD) is observed in the SWIR bands. The SWIR1 (wavelength range 1565-1655 nm) and SWIR2 (wavelength range) bands of MSI exhibit greater deviation compared to the SWIR bands of ETM+ (SWIR1: wavelength range 1570-1750 nm, SWIR2: wavelength range 2090-2350 nm) with respect to the OLI ones (SWIR1: wavelength range 1570-1650 nm, SWIR2: wavelength range 2110-2290 nm). Overall, the RMSD resulting from the MSI vs. ETM+ comparison is higher than the RMSD of MSI vs. OLI. As expected, the comparison between sensors from the same satellite family (OLI vs. ETM+) shows a higher degree of agreement, as confirmed by the ODR slope, which is still close to 1 (Table 13). In this case as well, the greatest RMSD is observed in the SWIR bands, despite slight differences in the wavelength range (Table 2). The comparison analysis between the bands of different sensors, supported by the calculated difference metrics, suggests that despite a general good level of agreement, the differences in the recorded values are statistically significant. Therefore, for the integrated use of the three sensors in the Mediterranean basin area, calibration and harmonization are necessary. This study presents the results of two regression models, OLS, and RMA, suggesting a mixed band adjustment based on the coefficients of both models and supported by statistical analyses. Taking into consideration the RMSD, the mean difference and the ODR slope values, integrating the coefficients of the two models is suggested for band adjustment. For the harmonization between OLI and MSI bands, integrating the use of RMA coefficients for Blue, Green, NIRa, SWIR1 and SWIR2 bands, and OLS coefficients for Red and NIR bands, is suggested. For the harmonization between ETM+ and MSI bands, integrating the use of RMA coefficients for Blue and SWIR1 bands, and OLS coefficients for Red, NIR, Green, NIRa and SWIR2 bands, is suggested. Finally, for the harmonization between ETM+ and OLI bands, integrating the use of RMA coefficients for Red, NIR and SWIR2 bands, and OLS coefficients for Blue, Green and SWIR1 bands, is suggested. However, this approach is not mandatory, as shown in the results tables (Tables 7, 11 and 15), where both models improve sensor calibration.