# Increasing Ecosystem Fluxes Observed from Eddy Covariance and Solar-Induced Fluorescence Data
## 4 Discussion
Additionally, [PERSON] [16] found negative global ET trends up to \(-0.65\) mm yr\({}^{-2}\) during 2001-2011, while other studies indicated no significant trends [76; 93]. For WUE, ECSIF products showed positive trends of 0.0001-0.0024 g C kg\({}^{-1}\) H\({}_{2}\)O yr\({}^{-2}\) during 2001-2020, similar to 0.0025 g C kg\({}^{-1}\) H\({}_{2}\)O yr\({}^{-2}\) and 0.002 g C kg\({}^{-1}\) H\({}_{2}\)O yr\({}^{-2}\) as estimated by [PERSON] et al. [90] and [PERSON], et al. [44]. Furthermore, previous studies revealed substantial increases in WUE from regional [94; 95] and global [96] scales, confirming the reasonableness of our derived positive trends of WUE. Meanwhile, we also compared the performances of GPP and ET among ECSIF and FLUXCON X-BASE with global FLUXNET sites (Figure S6), and we found ECSIF showed NSE (bias) by 0.51 (\(-\)16.12%) and 0.62 (3.2%) with site-level GPP and ET, close to FLUXCOM X-BASE products. Overall, ECSIF products showed good performance in capturing the magnitudes and trends in GPP, ET, and WUE compared to ground and satellite-based observations and other studies. Considering uncertainties in various ECSIF products, we suggested that the average of six ECSIF products be used as benchmark GPP and ET with lowest uncertainties. Previous studies have found that rising atmospheric CO\({}_{2}\) concentrations could enhance plant photosynthesis, driving the trend in global GPP upward through the CO\({}_{2}\) fertilization effect [97; 98; 99]. The coupling effects of leaf stomata and plant transpiration further amplify this process, as increased greening leads to higher global ET due to the expanded leaf area [92; 100]. However, increases in plant photosynthesis typically outpace those in ET [101], resulting in an overall increase in WUE attributed to the CO\({}_{2}\) fertilization effect. Meanwhile, elevated CO\({}_{2}\) concentrations can partially inhibit leaf stomata, further enhancing WUE [95]. Variations in GPP were often influenced by surface radiation and temperature, particularly at the mid-high latitudes [98], while ET variations were mainly controlled by water exchange processes, such as precipitation [92; 102]. Our results show that WUE trends were positively correlated with GPP enhancements driven by rising temperatures and negatively correlated with ET responses to precipitation variations (Figures 7d and S8c,d). Similar findings have been reported by [PERSON] et al. [90]. As a result, changes in WUE were determined by the competitions between regional GPP and ET, with GPP increases dominating WUE enhancements in most areas. Nevertheless, there are some limitations and uncertainties in this study. First, the selection of SIF datasets may influence GPP and ET estimates of ECSIF products. For example, GOSIF-based datasets showed higher GPP and ET magnitudes and trends than CSIF- and RTSIF-based datasets (Table 3), due to differences among various SIF products. In addition, the time spans of SIF datasets limited the temporal extensions of ECSIF products, such as CSIF (2001-2020) and RTSIF (2001-2020). Based on that, we plan to update the annual ECSIF_GPP and ECSIF_ET datasets in the future, considering the continuously updated GOSIF dataset. Second, we selected only XGB and RF algorithms as the data-driven models to derive ECSIF products. Our analyses showed that the RF model caused larger biases than the XGB algorithm (Table 3). Other machine (or deep) learning algorithms may potentially perform better than the current ECSIF datasets. Third, the time spans and spatial unevenness of global FLUXNET sites may influence the accuracy of the upscaled GPP and ET estimates [21], as well as the effectiveness of ECSIF datasets. For example, the data used for training in this paper are only available up to 2014, and over 86% of the sites analyzed in this study were located in the Northern Hemisphere, with 64% in America and Europe. Therefore, more evenly distributed flux tower sites are needed to improve the accuracy of ML models in future studies.