## 2 Materials and Methods
### Models for NEE Estimation  
In order to estimate NEE, three different approaches were used: (i) linear regression between NEE and SVIs (model 1); (ii) linear regression between NEE and a product of SVIs and incoming PAR (model 2); and (iii) partial least squares regression (PLSR) using the full set of AisaEAGLE hyperspectral reflectance spectra (model 3). The first two models utilized S2 simulated spectral bands, while in the third approach the full set of hyperspectral AisaEAGLE reflectance spectra (130 bands between 400 and 1000 nm) was used simultaneously to estimate NEE. The PLSR method, which is commonly adopted to analyze hyperspectral datasets [7], iteratively transforms predictor (reflectance data) and response (NEE) variables into latent vectors and generates band-wise calibration factors used to create a predictive linear model [19; 42]. Model 1 was tested for each single flight on a separate basis (as radiation was assumed to be constant at all five towers), while models 2 and 3 (both including PAR) were tested for all the three flights together using the PAR data averaged for the same time period used for EC flux data (Figure 1). [PERSON]'s correlation analysis was used to test the significance of the relationships between SVIs and NEE and SVIs*PAR and NEE. The PLSR analyses were carried out by means of the PLS package of the R software environment [43]. The optimal number of components in the PLSR analysis was determined by minimizing the leave-one-out cross-validated root mean square error of predictions (RMSE\({}_{\text{CV}}\)). The variable importance projection (VIP) statistic was computed in order to evaluate the \begin{table}
\begin{tabular}{c c c c} \hline
**Band Number** & **Central Wavelength (nm)** & **Bandwidth (nm)** & **Spatial Resolution (m)** \\ \hline B1 & 443 & 20 & 60 \\
**B2** & **490** & **65** & **10** \\
**B3** & **560** & **35** & **10** \\
**B4** & **665** & **30** & **10** \\
**B5** & **705** & **15** & **20** \\
**B6** & **740** & **15** & **20** \\
**B7** & **783** & **20** & **20** \\
**B8** & **842** & **115** & 10 \\
**B8a** & **865** & **20** & **20** \\ B9 & 945 & 20 & 60 \\ B10 & 1380 & 30 & 60 \\ B11 & 1610 & 90 & 20 \\ B12 & 2190 & 180 & 20 \\ \hline \end{tabular}
\end{table} 
Table 1: Specification of the Multi-Spectral Instrument (MSI) of the Sentinel-2 (S2) satellite system. The bands which were simulated for spectral vegetation indices (SVIs) and for the S2 CV calculations are shown in bold. \begin{table}
\begin{tabular}{c c c} \hline
**SVI** & **Formulation** & **Reference** \\ \hline NDVI & (R865-R665)/(R865+R665) & [37] \\ NDVIg & (R865-R560)/(R865+R560) & [38] \\ NDIVE & (R865-R705)/(R865+R705) & [38] \\ MTCI & (R865-R705)/(R705+R665) & [39] \\ CIre & (R865/R705)-1 & [40] \\ CIg & (R865/R561)-1 & [40] \\ NIDI1 & (R865-R740)/(R865+R740) & Proposed in this study \\ NIDI2 & (R865-R783)/(R865+R783) & [26] \\ EVI & 2.5*(R865-R665)/(1+R865+6*R665-7.5*R490) & [41] \\ \hline \end{tabular}
\end{table} 
Table 2: Spectral vegetation indices (SVIs) presented in this study: normalized difference vegetation index (NDVIg), green normalized difference vegetation index (NDVIg), red-edge normalized difference vegetation index (NDVIre), MERIS terrestrial chlorophyll index (MTCI), red-edge chlorophyll index (CIre), green chlorophyll index (CIg), and enhanced vegetation index (EVI). The list includes two near-infrared difference indices (NIDI1, NIDI2) using NIR bands \(\geq\)740 nm. R refers to reflectance at a specific S2 simulated band (nm). relative importance of reflectance at different wavelengths in the PLSR model. VIP values >1 indicated high importance to the PLSR model [19]. The model's coefficients were obtained by fitting each model against the measured NEE. The main goodness-of-fit statistics (coefficient of determination--R\({}^{2}\), percentage root mean square error (PRMSE)) were computed to compare the performances of the different models.