# Increasing Ecosystem Fluxes Observed from Eddy Covariance and Solar-Induced Fluorescence Data
## 3 Results
### Spatiotemporal Variations in ECSIF-Based GPP and ET  
We further applied PFT-dependent models to construct the ECSIF_GPP and ECSIF_ET datasets on a global scale and compared the spatiotemporal variations in average GPP and ET from six ECSIF products with the ensemble of satellite-based products (Figures 5 and 6). On the global scale, high GPP (>2000 gC m\({}^{-2}\) yr\({}^{-1}\)) and ET (>1000 mm yr\({}^{-1}\)) values were found over the tropical regions such as the Amazon, Central Africa, and Southeastern Asia (Figure 5a,d). In contrast, low GPP (<1000 gC m\({}^{-2}\) yr\({}^{-1}\)) and ET (500 \(<\) mm yr\({}^{-1}\)) values appeared at mid-high latitudes (e.g., boreal Asia), consistent with the observed spatial pattern (Figure 5b,e). Compared to the ensemble products, ECSIF_GPP showed a correlation of 0.93 and bias of 10.33% globally, with positive differences over the Amazon and Central Africa (Figure 5c). Similarly, ECSIF_ET exhibited a correlation of 0.94 and bias of 2.79% against observations (Figure 5f). The highest \(r\) for GPP (ET) was achieved up to 0.94 (0.92) with the CSIF-RF model (Table 3), and RF models showed better performance in both \(r\) and RMB than XGB-based models.  
Figure 4: Feature importance of different factors to (**a–j**) GPP and (**k–t**) ET across various PFTs for the RF model. The values of feature impacts are calculated as the mean absolutely SHAP (SHapley Additive exPlanation) value of each factor. The number of sites for each PFT is shown in the bracket. The errorbars represent one standard deviation of three SIF products.  
417-560 mm yr\({}^{-1}\) for ET as estimated by different studies based on varied methods and data sources (Table 4). Among the six sets of ECSIF products, the highest (lowest) GPP estimates were 130.8 (124.8) Pg C yr\({}^{-1}\) for the GOSIF-RF (CSIF-XGB) model (Table 3). The highest and lowest ET estimates were 582.4 mm yr\({}^{-1}\) for CSIF-XGB and 460.4 mm yr\({}^{-1}\) for CSIF-RF models, respectively. Regionally, the total GPP and area-weighed ET were 34.3 Pg C yr\({}^{-1}\) and 931 mm yr\({}^{-1}\) in South America, 34.4 Pg C yr\({}^{-1}\) and 548 mm yr\({}^{-1}\) in Asia, 25.7 Pg C yr\({}^{-1}\) and 745 mm yr\({}^{-1}\) in Africa, 17.4 Pg C yr\({}^{-1}\) and 492 mm yr\({}^{-1}\) in North America, 9.9 Pg C yr\({}^{-1}\) and 465 mm yr\({}^{-1}\) in Europe, and 6.1 Pg C yr\({}^{-1}\) and 481 mm yr\({}^{-1}\) in Oceania.  
For trends, ECSIF_GPP showed annual trends of 0.22 \(\pm\) 0.04 Pg C yr\({}^{-2}\), mainly resulting from positive trends over eastern China, India, and Europe (Figure 6a). This positive trend was similar to the 0.35 \(\pm\) 0.11 Pg C yr\({}^{-2}\) observed in ensemble GPP products (Figure 6b), though the ECSIF_GPP slightly underestimated the positive GPP trends in boreal regions (Figure 6c). Meanwhile, ECSIF_ET exhibited a positive trend of 0.64 \(\pm\) 0.14 mm yr\({}^{-2}\), with significant spatial heterogeneity, primarily driven by positive changes over eastern China and negative trends over Central Africa (Figure 6d). This trend was lower than that of 1.73 \(\pm\) 0.64 mm yr\({}^{-2}\) in the ensemble ET products (Figure 6e). The largest differences were found over South America and Central Africa (Figure 6f), potentially due to limitations in training sites in the southern hemisphere. For different ML algorithms, ECSIF showed higher trends of 0.16-0.26 Pg C yr\({}^{-2}\) for GPP and 0.71-0.87 mm yr\({}^{-2}\) for ET with XGB, as compared to 0.19-0.24 Pg C yr\({}^{-2}\) for GPP and 0.45-0.58 mm yr\({}^{-2}\) for ET with the RF algorithms (Table 3).