## 5 Discussion
### Relationship between Model Performance and NDVI  
The presence of biomass over soil may affect the model's prediction capability because the satellite data also carries information regarding the vegetation. To see the effect of the biomass, we calculated the NDVI from the S2 surface reflectance image during the testing periods and compared it with the MAE values of the model for the prediction dates.  
Figure 6(a) visualizes the distribution of MAE values for all available stations together with the NDVI\({}_{mean}\) and NDVI\({}_{max}\) values. The figure shows the correlation between the mean NDVI\({}_{mean}\) and MAE values. MAE values tend to increase with increasing NDVI\({}_{mean}\) values.  
The violin plot given in Figure 6(b) shows the distribution of the actual vs. predicted SM values at stations whose MAE values are lower (Station ID: 1569, 1541, 1577) with low soil moisture and higher (Station ID: 1527, 816, 1481) with high soil moisture. Here, we focused on finding out the origins of the variations in MAE values among these stations. For this purpose, the variation of the NDVI values were used. This analysis showed that the NDVI variation is one of the reasons for the deterioration of the SM prediction.  
The backscattered signals obtained from SAR data were strongly affected by high biomass due to the interaction between electromagnetic radiation, plants, and soil. Therefore, these findings show that the model's estimation performance is prone to uncertainties from the existing biomass. Similar findings also exist in previous studies [(35; 71; 72; 73)]. These studies found that the SM content in bare or low-density vegetation areas is more predictable than in high-density vegetation areas.  
Figure 6: Overall MAE for land cover classes.  
Figure 7: Model performance w.r.t. NDVI variation. (**a**) Scatter plot shows the distribution of MAE vs. NDVI relationship for each station. (**b**) Violin plots representing the statistical distribution of actual and predicted temporal SM data at the ISMN stations with their minimum and maximum NDVI values.  
Another investigation that we conducted on the impact of NDVI variation was using station-based time series. For this purpose, we focused on some stations that show a variation in NDVI over the years. We see that the growth cycle of NDVI values before seeding and after harvest is lower than crops' vegetative and reproductive phases. We believe that the prediction capability of the model thoughout the growth cycle is an important detail that needs to be investigated. Hence, we prepared the Figure 7(a) to show the model's performance in time. According to Figure 7(a), the model's performance on the SM forecasting dropped approximately between May 2020 to October 2020 due to very low SM values. During this period, we can see an increase in the NDVI values from \(\sim\)0.2 to \(\sim\)0.9. We observed a similar situation in the other stations as well. In the time series of stations 827 and 1572, given in Figure 7(b),c, the station has higher NDVI values from June to the end of December and from mid-April to the beginning of November, respectively. These three stations and the others with similar behavior have MAE values less than 0.075.  
Figure 8: Time series of SM predictions during the testing period for stations 816, 827, and 1572.