## Methods
### Data Pre-Processing and Computation of VIs  
Three different VI time series were constructed based on the MODIS satellite data: \(\mathrm{NDVI}_{\mathrm{maiac}}\) and \(\mathrm{sPRI}_{\mathrm{ref}}\) from the MAIAC dataset and \(\mathrm{NDVI}_{\mathrm{prod}}\) from the MOD13Q1 product. The data were initially screened to exclude pixels with erroneous values due to presence of snow, clouds and cloud shadows. Despite the CV-MVC algorithm applied to the MOD13Q1 product, the \(\mathrm{NDVI}_{\mathrm{prod}}\) values still had a considerable level of noise. Quality assurance data provided with the MOD13Q1 product were used to exclude observations that were flagged as lower quality based mainly on clouds, snow and aerosol quantity.. The MAIAC dataset was provided pre-screened with approximately 88% of the observations recorded as missing data values and thus the remaining non-erroneous 12% of the data could be used in the analysis.  
\(\mathrm{NDVI}_{\mathrm{maiac}}\) was calculated like \(\mathrm{NDVI}_{\mathrm{prod}}\) using Equation (1). Due to the lack of clarity regarding the optimal MODIS reference bands for the PRI, a number of different versions of the index using MODIS band 11 (546-556 nm) were calculated based on bands that have been used in previous studies [26; 27; 30]. These included bands 1 (620-670 nm), 4 (545-565 nm), 10 (483-493 nm) and 12 (546-556 nm), modifying the original PRI formula by [PERSON] et al. [17]:  
\[\mathrm{PRI}_{\mathrm{ref}}=\ \frac{R_{546-556}-R_{ref}}{R_{546-556}+R_{ref}} \tag{2}\]  
where \(R_{ref}\) indicates the reference band reflectance (i.e., reflectance in bands 1, 4, 10 or 12). To obtain only positive PRI values, which was a requirement in a later stage of the analysis, the PRIs were scaled using the following equation [32]:  
\[\mathrm{sPRI}_{\mathrm{ref}}=\ \frac{\mathrm{PRI}_{\mathrm{ref}}+1}{2} \tag{3}\]  
To determine the most useful reference band for the \(\mathrm{PRI}_{\mathrm{ref}}\), the MODIS indices were linked temporally with flux tower measurements of GPP and LUE. The satellite overpass times were matched with the nearest time stamp in the flux data. In cases where the time difference exceeded 10 min, the average of the two closest measurements was calculated. The statistical relationships between GPP/LUE and the differently configured PRIs and the NDVI were then explored through regression analysis to evaluate their suitability as indicators of photosynthetic activity. The PRI with the strongest relationship with GPP and LUE (highest \(R^{2}\)) was carried forward in the analysis.  
Several authors suggest setting a baseline for VI values for the winter months in boreal regions due to persistence of snow and low sun zenith angles which introduces a high degree of noise [(38; 50)]. A pixel-specific baseline VI value, assumed to be constant across years, can be used because boreal vegetation goes into a state of stable dormancy during the winter [38]. The winter VI values were estimated based on approximate lowest usable values over the full time series within a time window in each year proposed by [PERSON] et al. [38] in the autumn following senescence, but prior to persistent snow cover (mid-October to mid-November). The baseline for NDVI and PRI was set at 0.4 and 0.3 respectively in the months December to February. Any values below the winter baseline VI were replaced with these values.  
The final pre-processing steps involved calculating the mean VI on days with multiple overpasses and excluding satellite overpasses occurring before 10:00 a.m. to reduce effects of solar zenith angle variations. Days with no observations were assigned N/A values.