# Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes
## 4 Discussion
By applying the selected RFR model shown in Figure 6, it is possible to map the instantaneous _DMY_ of key fields before harvest (Figure 7). From the map, farmers could potentially identify the variation within and among fields, which could help to determine the optimal harvest time and plan the harvesting sequence. However, it should be mentioned that due to the coverage of clouds and shadows, there was some important missing information. As the example in Figure 8 shows, the time-series _DMY_ estimation from Sentinel-2 images using the RFR model reflected the growth pattern of leys, including the reduction in biomass following harvests; however, as key information was missing due to the presence of clouds or shadows (the interval between available cloud-free Sentinel-2 images before the second harvest was 15 days), the time-series _DMY_ estimation could not precisely track the growth conditions between the first and second harvests. Figure 7: Layout of the estimated dry matter yield (_DMY_) for the first harvest from Sentinel-2 imagery obtained on 09 June 2019, one week before the first harvest using a selected RFR model, at Röbäcksdalen field research station. The background imagery is obtained from Google Earth. Table 6 further illustrates the mean intervals between cloud-free Sentinel-2 images and the corresponding standard deviations for each experimental field and year. Mean intervals between available cloud-free Sentinel-2 images ranged from 4-9 days and 3-7 days, depending on the location in 2019 and 2020, respectively. Furthermore, there were larger gaps (e.g., 20 days) between Sentinel-2 images in June and July (Figure 9), which makes it hard to track the information at the most needed time. Therefore, including more datasets, such as Sentinel-1 imagery, which is insensitive to clouds, would be helpful in the future [72, 73]. Statistical methods such as the weighted ensemble of radial basis function (RBF) convolution filters [74] can be used to detect time-series imagery outliers (e.g., clouds and shadows) and approximate missing data to create more frequent time-series data, and should be tried in future research. Including multispectral data from Landsat-8 could be another good solution to fill in the missing data. However, because of the coarser spatial resolution (30 m) and the lack of red-edge bands from Landsat-8 data [75], more caution should be taken and the effect from these differences on the modeling should be explored. Figure 8: Example of forage dry matter yield (_DMY_) during the growing season, estimated from Sentinel-2 imagery in 2020 at Röbäcksdalen field research station using a selected random forest regression (RFR) model. The red-dashed vertical lines indicate the timing of the first and second harvests. In this study, even though MR algorithms created relatively robust models, especially for model calibration and evaluation, there were obvious overfitting problems when the validation was taken into consideration. The relatively small dataset size (Table 3) is a potential reason, and the reason why SVR and RFR produced better results than PLSR was most likely because both SVR [76] and RFR [29] could function with small-sized model training datasets. The signal from soil could be another reason, especially at earlier growing stages. [PERSON] et al. [38] found that the forage _DMY_ prediction models derived from machine learning algorithms can be much improved by using satellite pixels with over 50% canopy cover. This study attempted to use individual bands as predictor variables, but the results were poorer (data not shown), and the possible reason as to why involving VIs as predictor variables improved the model accuracy could be that several soil-sensitive VIs (e.g., _OSAVI_ and _GOSAVI_) contributed to overcome the soil effects. Physical inversion [77; 78] and hybrid inversion models combining physical modeling and machine learning algorithms [79] are also relevant approaches to account for multiple