# Increasing Ecosystem Fluxes Observed from Eddy Covariance and Solar-Induced Fluorescence Data
## 3 Results
### Site-Level Validations of ML Models Across Various PFTs  
For each PFT, we constructed six data-driven models using 80% of the total data samples for all FLUXNET sites with the same PFT classification (see Section 2). We validated the derived GPP and ET averaged for the six (2 ML \(\times\) 3 SIF) models using the remaining 20% of observations (Figures 2 and 3). The GPP derived by six data-driven models showed a Nash-Sutcliffe Efficiency (NSE) of 0.82 \(\pm\) 0.11 and relative mean biases (RMB) of 0.34 \(\pm\) 1.91% against observations average for various PFTs (Figure 2). The highest NSE value of 0.91 was found for mixed forest (MF), while the lowest value of 0.54 was found at cropland (CRO), potentially due to missing human disturbances in ML models such as irrigation and fertilizer management [66; 67]. The largest bias of 4.55% was found for savanna (SAV), and the lowest bias was \(-\)0.09% at EBF sites. For ET, the data-driven models also exhibited a good performance, with an average NSE of 0.8 \(\pm\) 0.07 and an RMB of \(-\)0.33 \(\pm\) 1.68% across all PFTs (Figure 3). Among different PFTs, the highest NSE value of 0.87 was found for EBF, while the lowest NSE of 0.69 was found at GRA sites. The largest bias was 3.14% at SAV sites, while the smallest bias was 0.33% for EBF. Data-driven models showed slightly varied performances in estimating GPP and ET across two algorithms and three SIF products (Table 2). For GPP, the highest NSE value was 0.83 \(\pm\) 0.1 for the CSIF-RF model, while the lowest NSE value was 0.81 \(\pm\) 0.12 for the GOSIF-XGB model. The lowest RMB was \(-\)0.14 \(\pm\) 1.69% for the RTSIF-XGB model, and the highest bias was 0.69 \(\pm\) 2.35% for the GOSIF-RF model. Across various PFTs, all six data-driven models showed the lowest NSE values of 0.51-0.57  
Figure 2: The independent validations between derived and observed GPP across various plant functional types (PFTs). ML models are trained using 80% (randomly selected) of the total datasets and validated using the remaining 20% of the datasets at global FLUXNET sites. For each PFT, derived GPP are averaged for the ensemble prediction by six data-driven models (2 ML \(\times\) 3 SIF datasets). The ten PFTs include ENF, evergreen needleleaf forest; EBF, evergreen broadleaf forest; DNF, deciduous needleleaf forests; DBF, deciduous broadleaf forests; MF, mixed forest; CSH, closed shrulands; OSH, open shrulands; WSA, woody savannas; SAV, savannas; GRA, grassland; WET, permanent wetland; CRO, cropland. Statistical metrics are shown in each panel, including the Nash-Sutcliffe Efficiency (NSE), relative mean biases (RMB), and number of samples (N). The black lines represent linear regressions and the red lines denote 1:1 ratio between observations and derivations. The colors of the scatters represent the kernel density. for CRO sites and 0.69-0.8 for ENF sites. In contrast, high NSE values were found for MF (0.91-0.92) and SH (0.89-0.92) sites, with low biases of \(-\)0.34%-0.32% for EBF and \(-\)0.91%-0.34% for MF. For ET, the highest NSE was 0.82 \(\pm\) 0.05 for the CSIF-RF model, while the lowest NSE value was 0.73 \(\pm\) 0.2 for the RTSIF-XGB model. The lowest RMB was 0.04 \(\pm\) 1.82% for the COSIF-XGB model, and the highest RMB was \(-\)0.58 \(\pm\) 1.68% for the RTSIF-RF model. Based on the SHAP values, we further explored the contributions of SIF and other factors to the variability of site-level GPP and ET using either XGB (Figure 4) or RF (Figure S3) models. For both algorithms, SIF showed the highest contributions to GPP variability, averaging 24%-49% across most PFTs excluding EBF (16%), where the correlations between SIF and GPP (or ET) were usually low (Table 1). However, Tair acted as the dominant contributor, with 30%-31% for DBF, ENF, and WET. Diffuse radiation and VPD contributed to GPP variability by 2-12% and 5-11% across various PFTs, respectively, while other factors had no significant impacts. For ET variability, SIF showed lower mean absolute SHAP values, up to 10-42%, compared to GPP variability (Figures 4 and S3).