## 4 Discussion  
This research study aims specifically at investigating how already existing satellite SIF products (on the example of GOME-2) can be complemented by other parameters, such as precipitation and soil moisture, in order to produce reasonable GPP estimates. To our knowledge, this is also the first study to quantitatively assess the lead-lag relationship between water availability in the critical plant development period and its reflection in GPP and SIF at later stages of the plant lifecycle. In this research, we have derived separate equations quantifying relationships between SIF and GPP  
Figure 14: Gross primary production (gC/m\({}^{2}\)/month) from BESS data product in summer 2007 (**a–c**), summer 2011 (**d–f**), and summer 2012 (**g–i**). Figure 15: Gross primary production (gC/m\({}^{2}\)/day) over select FLUXNET stations in 2007–2012. More detailed information about the stations can be found in Table 1.  
based on the differences in grass and crop plant functional types and taking into account precipitation and soil moisture conditions over the contiguous US. In this regard, a factor that has the potential to complicate the relationship between SIF itself and GPP is the sun-satellite view observation geometry, which could introduce unwanted variation in observational SIF and cause large uncertainties in the SIF-GPP relationship. [PERSON] et al. [93] demonstrated that angular normalization of SIF by hot spot direction could improve the correlation between SIF and GPP by \(0.04\pm 0.03\)-\(0.07\pm 0.04\) on average, especially in deciduous broadleaf forests. The angular normalization as a prerequisite step in using satellite SIF products will be addressed in future studies. We have used well established MODIS products as an observational reference along with more recent BESS data. Other newly available GPP products such as Vegetation Photosynthesis Model (VPM) [71] and FLUXCOM [94] can potentially serve as additional benchmarks for the evaluation of GPP estimates and might be useful for future research. Near-infrared reflectance of vegetation (NIRv, [95]) will also be used in future studies, as it has demonstrated a strong relationship with both measured and modeled GPP and mostly resolves the mixed-pixel problem arising from the need to disentangle the contribution of vegetation to the observed spectral signal in remotely sensed data. We have performed GPP estimation and employed precipitation and soil moisture datasets pertinent to the contiguous US only. Similarly, as seen from this study, FLUXNET measurements from other regions such as Europe, East and Southeast Asia, South America can be used, depending on prevalent biome types, precipitation, and soil moisture content to establish such SIF-GPP relationships. The lead-lag relationship discussed in this study is likely to differ for various biomes represented by different PFTs, as shown in our analysis. This fact is to be taken into account if similar research is to be conducted for vegetation that cannot be classified as crops or grasses. For example, deciduous trees might exhibit more resistance to drought conditions compared to crops due to their trunk water storage, which may result in a longer delay between precipitation and fluorescence levels. Hence, there might be a different lead-lag relationship between tree production and precipitation, which warrants further investigation. Our findings indicate that SIF-GPP relationships tend to vary not only between vegetation types, but also between crop species specifically. This is reflected in a significant improvement in agreement between FLUXNET and predicted MLR GPP when precipitation is considered for rice, but not for alfalfa. These results imply that differing sensitivity of crop production to various environmental parameters such as precipitation is an important factor to be taken into account when examining and quantifying crop production.