## 4 Discussion
### HLS-Classification Uncertainty and Its on Impact Simulated Methane Flux  
There is a significant gap between the small scale of vegetation heterogeneity (scale of a few 10s of cm) and the HLS resolution (30 m \(\times\) 30 m). Therefore, HLS NDVI-based classification will assign each pixel to the majority of its vegetation cover, thus over-representing the dominant patch-type and under-representing less common types that do not form large patches, which could provide a source of bias in the classification results. In addition, another source of uncertainty is attributed to the increased cloud presence during the early growing season. A main component of the NDVI-based classification method is the NDVI timeseries derived from the \"pure pixels\" used as a standard to classify all other pixels to their dominant patch-type. It is common to have missing NDVI observations in the early growing season due to increased cloud cover, which coincides with the increase in the vegetation NDVI, defining the trend or signature of this specific patch-type compared to others. These high-cloud-cover data points are usually filtered out during data processing, which would contribute to the uncertainty coupled with the NDVI-based classification. In order to further analyze the uncertainty introduced with the use of the HLS-derived classification method, we compared its results to those of the WorldView-derived classification for year 2018 within our area of interest, i.e., \(480\times 480\) m\({}^{2}\) around the EC tower. Using the supervised classification of the WV data, we obtained the high-resolution (1.24 m), high-accuracy classification (WV\({}_{\text{High-Resolution}}\)). Then, we upscaled the WV high-resolution grid to the HLS resolution (30 m \(\times\) 30 m) and counted the majority patch-type in each upscaled pixel to obtain a WV\({}_{\text{Upscaled}}\) classification, matching the HLS resolution, but accounting for high-accuracy data. We compared these two classification results to those of the HLS-derived classification. Table 3 shows the results of the classification of each method and the difference (\(\Delta\)) or mismatch between the three different methods. Figure 8 shows the corresponding classification map of the three techniques. The results show that upscaling to the HLS resolution leads to an underestimation of open water patches in the area of interest (where open water patches are rare and small), but with a minimal effect on the final overall results (Table 3). In order to further investigate this impact on the total methane flux, we ran the ELM for 2018 using the WV\({}_{\text{High-Resolution}}\) classification results and obtained the RMSE between simulated methane flux using WV\({}_{\text{High-Resolution}}\) as input for the vegetation cover and simulated methane flux using HLS as input for vegetation cover. The RMSE between these two simulations is 3.14 nmol m\({}^{-2}\) s\({}^{-1}\), which we consider to be the error introduced to the total methane flux using the HLS-derived classification method. To have a better understanding of the uncertainty introduced by the HLS method, we compare it to the estimated error of the EC flux measurement, which in our case was 27 nmol m\({}^{-2}\) s\({}^{-1}\) at US-LA3 for the two years when measurements were available (2021-2022). Thus, the error introduced to the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Percent of Pixels (\%)**} & \multicolumn{3}{c}{**Classification Mismatch \(\Delta\) (\%)**} \\ \hline Patch-type & WV\({}_{\text{High-Resolution}}\) & WV\({}_{\text{Upscaled}}\) & HLS & \(\Delta\) (WV\({}_{\text{Upscaled}}\)\(-\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(\)\(methane flux simulation results due to the HLS classification error is minimal, especially when compared to the existing uncertainty of methane flux observations.