## 4 Discussion  
All of the six Sentinel-derived indices were used to develop hybrid models, which were used as proxies for fPAR to model GPP along with field measured PAR and literature-based LUE parameters using the light use efficiency model approach. The global fixed annual value [21] and growing season values [20; 22] of the LUE parameter (\(\varepsilon\)) were used to model GPP, and these values were then compared with the EC-derived GPP to validate model performance. Model 2, which included NDVI and NDWI2, showed the highest correlation (0.96) with EC-derived GPP; one of the reasons for this might be that it was able to identify vegetation through NDVI and water bodies through NDWI2. Model 4, which included NDVI and EVI, showed a correlation of 0.89 with the EC-derived GPP and was able to detect the vegetation well. A study by [PERSON] et al. [56] found that using a single index to model GPP underestimated the flux values by 12% with EVI and overestimated the flux values by 20% with NDVI at an agricultural site. Similar studies highlight the importance of using hybrid models instead of single index for estimating GPP. Model 1, 3, 5, and 6 showed poor to moderate correlation with EC-derived GPP. [PERSON] et al. [57] and [PERSON] et al. [58; 25] also highlighted the difference in spectral signatures of raised bogs and challenges for identifying ecotopes. A recent study by [PERSON] et al. [23] found out that MOD17A2H GPP product overestimates GPP modelled from data collected by EC towers situated at two ex-forestry sites undergoing restoration in Scotland and at a near-natural blanket bog site in Ireland [23]. Site-level validation studies have also shown moderate to low performance of MOD17A2H in capturing the seasonal and inter-annual variation in flux estimation [59]. Even though some models [60; 61] have tried to incorporate remotely sensed variables to enhance performance, such as land surface  
Figure 6: The correlation matrix of modelled and EC-derived GPP for 2019 and 2020: (**a**) Correlation matrix for \(\varepsilon=0.61\), (**b**) Correlation matrix for \(\varepsilon=0.83\), (**c**) Correlation matrix for \(\varepsilon=1.2\) (warmer colors represent stronger positive correlation, cold colors represent moderate positive correlation, and grey color represents correlation values lower than 80%). temperature (LST) and LSWI in the MODIS products, access to clear cloud-cover-free imagery is a challenge that limits the application of these models across various scales. In this study, the hybrid models were developed using three years of data that align with the deployment of the EC tower at this site in 2018, and this work represents a novel attempt to develop hybrid models exclusively for raised bog systems. The proposed Models 2 and 4 in this study performed well with limited remote sensing data for a near-natural peatland and highlights the potential to apply the models developed across raised bogs with different management practices. Furthermore, these models could have utility for other wetland systems and could be tested on blanket bogs, fans, and turloughs, and they could also be used to spatially and temporally upscale GPP models using the site-specific LUE parameter developed here or the literature-based approach of LUE parameter integration. One of the limitations in this study was the availability of LUE parameter values in the wider literature for sites similar to the one in this study, as there are limited studies on peatlands that have reported seasonal, growing season, and annual LUE parameter values [8; 21; 22]. Even though Sentinel-2 provides relatively high-resolution imagery, there is a need to use \(\varepsilon\) values from similar sites while using LUE models. Several studies used photochemical reflectance index (PRI) as a direct proxy for \(\varepsilon\); however, it is not possible with Sentinel 2 data due to limited band availability [8; 13].