## 3 Results
### Assessing the Relationships between the Predictors for Different Vegetation Types
C3 crop vegetation GPP, on the other hand, is characterized by two significant correlation improvements: one when precipitation is introduced into GPP quantification (Figure 5c) and the other related to the addition of lead-lag (Figure 5e). Figure 5: Relationship of mean monthly GPP (gC/m\({}^{2}\)/day) measured at FLUXNET stations with C3 crop vegetation (Table 1) used in this study and (**a**) GOME-2 SIF; (**b**) SIF-based GPP; (**c**) SIF- and precipitation-based GPP; (**d**) MLR-based GPP (SIF, precipitation, soil moisture as predictors); (**e**) MLR-based GPP with lead–lag. The [PERSON]’s correlation coefficient \(R\) and root-mean-square error \(RMSE\) (gC/m\({}^{2}\)/day) are shown. Figure 6: Relationship of mean monthly GPP (gC/m\({}^{2}\)/day) measured at FLUXNET stations with C3 non-arctic grass vegetation (Table 1) used in this study and (**a**) GOME-2 SIF; (**b**) SIF-based GPP; (**c**) SIF- and precipitation-based GPP; (**d**) MLR-based GPP (SIF, precipitation, soil moisture as predictors); (**e**) MLR-based GPP with lead–lag. The [PERSON]’s correlation coefficient \(R\) and root-mean-square error \(RMSE\) (gC/m\({}^{2}\)/day) are shown. Figure 7: Relationship of mean monthly GPP (gC/m\({}^{2}\)/day) measured at FLUXNET stations with C4 grass vegetation (Table 1) used in this study and (**a**) GOME-2 SIF; (**b**) SIF-based GPP; (**c**) SIF- and precipitation-based GPP; (**d**) MLR-based GPP (SIF, precipitation, soil moisture as predictors); (**e**) MLR-based GPP with lead–lag. The [PERSON]’s correlation coefficient \(R\) and root-mean-square error \(RMSE\) (gC/m\({}^{2}\)/day) are shown. Overall, GPP estimates derived with the use of MLR while taking the lead-lag relationship into consideration are close to those from FLUXNET network: at the crop stations peak mean monthly GPP can be up to 20 gC/m\({}^{2}\)/day, while for the grass sites mean monthly GPP does not exceed 10 gC/m\({}^{2}\)/day. These findings agree well with those reported by [PERSON] et al. (2020). We have also explored how the inclusion of lead-lag into the relationship between predictors influences GPP estimates for individual plant species. Alfafla and rice, both C3 crop species, cultivated at US-Tw3 and US-Twt stations, respectively, were selected for this purpose. Figures 8a and 9a reveal that the correlation between SIF and flux tower GPP estimates demonstrates a moderate strength of relationship (0.57 and 0.41 for alfalfa and rice, respectively). It can be seen that for the rice station, the addition of precipitation (compare Figure 9b and Figure 9c) into the GPP equations has played a substantial role in the improvement of the correlation between MLR GPP estimates and respective FLUXNET GPP values, while such a change in \(R\) is not noted for alfalfa vegetation. These results imply that the sensitivity of crop production to various environmental parameters such as precipitation is an essential factor to consider when investigating crop production. Moreover, it is necessary to analyze the interplay between such sensitivities: e.g., rice, which is often planted during the tropical rainy season, has a water demand of 450-700 mm/total growing season while alfalfa water need is about two times higher and comprises 800-1600 mm/total growing season. However, rice is known to be less drought-resistant than alfalfa ([PERSON] et al., 2019) and also to be more significantly impacted by precipitation levels than other crops such as wheat and maize ([PERSON] et al., 2019), which might explain why the addition of precipitation to the MLR GPP equation leads to the pronounced improvement in GPP estimates for rice but not for alfalfa.