# Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes
## 4 Discussion  
Even though several VIs (e.g., red-edge band-related, Table 4) fitted moderately with _DMY_ (_NSE_ values for model calibration were higher than 0.5), the overall VI-based UR performed inadequately (Table 4). The evaluation accuracies were much higher than expected, which was likely derived from the small size of the evaluation dataset (\(n=9\)); however, the accuracies of calibration and validation were low, which further indicates that the overall accuracy of UR models was not convincing. [PERSON] et al. [38] reported similar results where the VIs calculated from satellite images were proven to have been insufficient to capture the submeter variability of rangelands due to the relatively coarse spatial resolution. Several VIs, such as _NDVI_, also have saturation issues when the LAI reaches three [40, 66], which could contribute to the poor correlation as well. Indeed, in northern Sweden, crops in general and grasslands in particular grow exceedingly fast once temperature is not limiting due to long daylight hours, and canopy closure is rapidly reached. Another reason could be that the datasets used in this study included several sites and years, resulting in different conditions (Tables 1 and 2), which made it challenging to build a robust model to accommodate this heterogeneity based on simple VIs. In contrast to the relatively poor performance of UR, MR performed notably better with higher calibration _NSE_ values, thus emphasizing the advantages of PLSR and especially nonparametric machine learning-based regression approaches (SVR and RFR), which do not have distributional assumptions and variance requirements [26, 67]. The most notable enhancement of MR over UR is that it can manage multiple variables in a single model and take advantage of more explanatory variables, and thus, information. PLSR and RFR can identify the importance of predictor variables based on either principal components analysis and consequent components selection [68] or the OOB indices calculation [19, 67]. Thus, less important and collinear variables can be discarded, which could significantly improve the model performance. In this study, RFR outperformed PLSR to a certain extent in model calibration, validation and evaluation (Table 5) and the possible reason would be that RFR has a stronger ability to produce relatively robust models [69]. For SVR, unlike PLSR and RFR, it cannot distinguish the importance of variables and select the variables by statistical analysis, but the model accuracy would be advanced to a large extent once the parameters of the kernel are correctly set [3, 38]. However, it is also the main drawback for SVR as it needs computation and time to find the right kernel and there is a risk that the found kernel is not optimal [26]. RFR has apparent merits, such as easy operation, high efficiency and reliable robustness [29]. Several remote sensing-based studies in other areas have found similar results, i.e., simple VIs were not able  
Figure 6: Observed versus estimated dry matter yield (_DMY_, t ha\({}^{-1}\)) for selected random forest regression (RFR) model with a calibration _NSE_ value of 0.92 (average value of 300 runs, Table 5). The timothy contents (%) are marked with different colors and the black color indicates that the botanical compositions of the samples were not measured, hence there was no data. to produce sufficiently robust models for prediction, but RFR could improve performance, such as potato N status estimation [19] and lake transparency monitoring [70]. It was observed in this study that the BC had little effect on the selected RFR model. RF has a strong ability to integrate different datasets from different sources into a single model for classification or regression analyses [19; 71]. A previous study reported that the effect from BC on the Sentinel-2 satellite data-based regression modeling for pasture biomass estimation was unclear [9]. This study answered this question to a certain extent and it should be continued to be explored and quantified in future studies.