## 3 Results  
### NEE Estimations  
The linear regression analysis showed a large range of variation of R\({}^{2}\) values of the correlations between the investigated SVIs and NEE (Table 3). Traditional VIS-NIR SVIs showed highly variable performances when considering flights on a separate basis (model 1: R\({}^{2}\) up to 0.89). Red-edge based SVIs showed R\({}^{2}\) values ranging from 0.41-0.79. For model 1, NIDI indices showed extremely variable R\({}^{2}\) values (up to 0.75).  
Considering the model 2, a strong increase of the predictive performance was observed. The R\({}^{2}\) of the model fed with NIDI1 reached 0.90 and PRMSE 14.54 % (Table 3), and red-edge SVIs R\({}^{2}\) values were very high (MTCI was the second-best index and reached an R\({}^{2}\) value of 0.85).  
The PLSR (model 3) demonstrated that 95% of the variation in NEE could be explained by algorithms derived from AisaEAGLE spectra (7 latent components; PRMSE = 10.25%), with a heterogeneous statistical contribution of the various wavelengths (Figure 2). The most relevant region of the spectrum was at the very end of the chlorophyll absorption wavelengths (VIP peak at 741.9 nm; VIP value 3.14). The adjacent bands also showed high VIP values: For wavelengths <741.9 nm, the VIP values were still higher than 2 for bands 737.3, 732.6, 727.9, and 723.3 nm. Also, considering the NIR shoulder bands >741.9 nm (towards the NIR-shoulder region), band 746.6 and 751 showed VIP values higher than 2. VIP values were higher than 1 up to 800 nm, indicating a relevant contribution of the NIR spectral domain.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{6}{c}{**Model 1**} & \multicolumn{6}{c}{**Model 2**} \\ \cline{2-9}
**SVI** & \multicolumn{2}{c}{**F1**} & \multicolumn{2}{c}{**F2**} & \multicolumn{2}{c}{**F3**} & \multicolumn{2}{c}{**F1F2F3***} \\ \cline{2-9}  & **R2** & **PRMSE (\%)** & **R2** & **PRMSE (\%)** & **R2** & **PRMSE (\%)** & **R2** & **PRMSE (\%)** & **R2** & **PRMSE (\%)** \\ \hline NDVI & 0.26 & 13.21 & 0.25 & 37.68 & 0.89 & 6.84 & 0.77 & 22.07 \\ NDVIg & 0.67 & 8.85 & 0.70 & 23.90 & 0.72 & 10.89 & 0.80 & 20.61 \\ NDVIre & 0.64 & 9.18 & 0.41 & 33.36 & 0.60 & 12.91 & 0.80 & 20.47 \\ MTCI & 0.75 & 7.68 & 0.52 & 30.20 & 0.79 & 9.40 & 0.85 & 17.89 \\ Clre & 0.65 & 9.02 & 0.42 & 33.23 & 0.61 & 12.81 & 0.82 & 19.34 \\ Clg & 0.70 & 8.37 & 0.66 & 25.28 & 0.73 & 10.52 & 0.81 & 20.17 \\ NIDI1 & 0.73 & 7.93 & 0.52 & 30.22 & 0.75 & 10.19 & 0.90 & 14.54 \\ NIDI2 & 0.00 & 15.33 & 0.20 & 39.05 & 0.00 & 20.43 & 0.75 & 23.14 \\ EVI & 0.84 & 6.21 & 0.25 & 37.65 & 0.74 & 10.51 & 0.80 & 20.84 \\ \hline \end{tabular}
\end{table}
Table 3: Summary of the statistics (R2—coefficient of determination, PRMSE—percentage root mean square error) of the linear regression between measured Net Ecosystem Exchange (NEE) and spectral vegetation indices (SVIs; model 1) for single flights (F1, F2 and F3) and the product of SVIs and photosynthetically active radiation for all flights together (F1F2F3*: SVIs*PAR; model 2).  
Figure 2: Variable importance of projection (VIP) statistics for the modeling coefficients of partial least squared regression (7 components; R2 = 0.95; RMSE 10.25%) based on AisaEAGLE ROI reflectance data (circular ROIs with a radius of 30m), fitted to the Eddy Covariance measured Net Ecosystem Exchange (NEE). The Sentinel-2 bands used in the study are highlighted, as well as the chlorophyll absorption, the leaf and canopy scattering ranges ([PERSON] et al., 2016), and the NIR water absorption peak ([PERSON] et al., 2016).