## 5 Discussion
[69] noted that their conclusions in terms of temporal matching may only be appropriate for man-made reservoirs and the difference here may relate to greater surface water movement in lakes exposed to winds vs. reservoirs in deeper, wind-protected valleys. [PERSON] and [PERSON] [73] also tested different time windows in applying Sentinel-2 data to estimating Chl-a in Baltic lakes and had strong model-fit when there was no more than a three-day time difference between in situ and satellite data. Their study used 1346 sample points, 44% of which had light to moderate algal blooms. Our study had a much smaller number of sample points and the increase in available data by expanding the time window may have led to better training of the one-day model, but the rapidly changing conditions may have limited further temporal expansion. These findings underscore the importance of dataset size in model performance and highlight the potential benefits of expanding the dataset when feasible, either through expanding the temporal match, but also through more intentionally aligning field-sampling schedules with the overpass time of Sentinel-2 imagery. The results presented in Table 4 highlighted that when using imagery filtered using the QA60 bitmask approach, a portion of the testing prediction values were erroneously categorized as having low values, despite the corresponding in situ values being above 8 \(\upmu\)g/L. Further examination of the corresponding Sentinel-2 images for the erroneous points identified cloud contamination as the key contributor to these errors. In response to the cloud-related challenges, we explored the potential of an alternate cloud masking approach, Cloud Score+ developed by [PERSON] et al. [58], to mitigate cloud contamination and enhance the accuracy of phycocyanin estimation. The selection of the optimal threshold is important in the Cloud Score+ cloud masking approach. [PERSON] et al. [58] initially proposed a threshold ranging between 50% and 65%. [PERSON] et al. [74] filtered Sentinel-2 images with more than 80% cloud coverage and 10% snow coverage, and subsequently applied the Cloud Score+ algorithm using a 60% threshold. In a recent study, [PERSON] et al. [53] compared a new cloud mitigation approach with four other methods, including Cloud Score+. Their proposed approach surpassed all other methods tested and may be worth exploring for future studies, though it increases complexity by using time-series data. [PERSON] et al. [53] noted that while Cloud Score+ demonstrated improvement over existing techniques in masking unusable data like clouds and cloud shadows, it still necessitated manual adjustments to establish the optimal threshold and struggled to define the boundaries between clouds, thin clouds, and cloud shadows. We tested various thresholds and found using our optimal Cloud Score+ threshold (50%) resulted in significant improvements in model fit compared to employing the QA60 bitmask approach. However, while model fit improved with the Cloud Score+ approach to cloud mitigation, our analysis (Table 8) still demonstrated prediction values in the testing datasets that were categorized as low, despite the corresponding in situ values exceeding 8 \(\upmu\)g/L. In examining the corresponding Sentinel-2 L2A images we found cloudy points that led to erroneous predictions. The persistence of cloudy pixels even after applying the Cloud Score+ approach underscores the need for further examination of its application to inland waterbodies. Other errors may be attributed to the small size of the lakes and confusion through pixels mixing water with surrounding land cover. Additional exploration is needed to identify the exact source of error in cloud mitigation, which will be a key focus of our future work. Previous studies (e.g., [75; 76; 77]), have highlighted the limitations of empirical models calibrated for specific locations and dates, emphasizing their lack of generalizability across different geographical areas and timeframes.