# Monitoring Spatial and Temporal Variabilities of Gross Primary Production Using MAIAC MODIS Data
## 2 Materials and Methods  
### Carbon Flux, Weather, and Remotely Sensed Data  
We downloaded daily data for 58 European sites from the FLUXNET 2015 ([[http://fluxnet.fluxdata.org/data/download-data](http://fluxnet.fluxdata.org/data/download-data)]([http://fluxnet.fluxdata.org/data/download-data](http://fluxnet.fluxdata.org/data/download-data))) Tier 1 data set containing data for weather and CO\({}_{2}\) flux from the eddy-covariance towers. The 58 sites (Figure 1, Table S1) covered boreal (six sites), temperate (43), and Mediterranean (nine) biomes and eight vegetation types (evergreen needle-leaved forest (16 sites), evergreen broad-leaved forest (three), deciduous broad-leaved forest (ten), mixed forest (three), open shrubland (two), grassland (nine), cropland (ten), and wetland (five)). We used all daily values of GPP from methods of daytime partitioning following [24]. We then calculated weekly, monthly, and annual values as the sum of GPP for each period (Figure 2). We also calculated average meteorological parameters for each site (mean temperature, total precipitation, and mean vapour-pressure deficit (VPD)) to use as predictors in statistical models (see descriptions below).  
Figure 1: Map of the sites. Blue, green, and red dots correspond to boreal, temperate, and Mediterranean sites, respectively.  
We downloaded all images for Europe for 2000-2016 from a MODIS (Moderate Resolution Imaging Spectroradiometer) composite processed by the MAIAC (Multi-Angle Implementation of Atmospheric Correction) [25; 26; 27] data set with a resolution of 1 km. The coarse resolution of the MAIAC dataset is not a problem when monitoring data coming from eddy covariance towers because land-use area around the towers is normally homogeneous [28]. We used raw data from the MAIAC data set, because this data set has less atmospheric noise and detects clouds better than previous data sets [25; 26; 27]. We therefore did not further filter our data. We calculated NDVI, EVI [23], NIR\({}_{v}\) as defined by [24] and [29], and CCI [9] using the central pixel from all records from the flux towers (Aqua + Terra images) by combining reflectance bands as:  
\[NDVI=\frac{B2-B1}{B2+B1} \tag{1}\]  
\[EVI=\frac{2.5*(B2*0.0001-B1*0.0001)}{B2*0.0001+6*B1*0.0001-7.5*B3*0.0001+1} \tag{2}\]  
\[NIR_{v}=(NDVI-0.08)*B2 \tag{3}\]  
\[CCI=\frac{B11-B1}{B11+B1} \tag{4}\]  
where the bandwidths for bands B1, B2, B3, B4, and B11 are 620-670, 841-876, 459-479, 545-565, and 526-536 nm, respectively. We calculated the RSVIs on weekly and monthly bases by first calculating the averages per day (more than one satellite overpass was often recorded, but calculating the median would have been nonsensical for two or three images per day) and then calculating the median of each index per week and month (Figure 2). We did not use maximum or mean values to estimate weekly and monthly values to avoid the interference of outliers. We also used weekly and monthly time resolutions instead of a daily resolution to prevent outliers and artefacts in the data. The CCI values were adjusted by (1 + CCI)/2, and NIR\({}_{v}\) was divided by its maximum value, for comparing to the NDVI and EVI values (which range from 0 to 1).  
Figure 2: Flowchart indicating the steps followed to perform the analyses presented in this study. _Acronyms_: Gross primary production (GPP), remotely sensed vegetation indices (RSVI).