# Monitoring Spatial and Temporal Variabilities of Gross Primary Production Using MAIAC MODIS Data
## 1 Introduction  
The continuous development of remote-sensing techniques has increased their popularity in the fields of earth science, ecology, conservation, and nature and land management [1; 2; 3; 4; 5; 6; 7; 8]. Remotely sensed vegetation indices (RSVIs) are frequently used to broadly and efficiently monitor spatial and temporal variations in terrestrial primary productivity (e.g., ecosystem photosynthesis). The accuracies of the RSVIs, however, have only been evaluated using few ecosystems using one or two indices [9; 10; 11; 12]. Broad-scale and global studies often cannot accurately assess the relationships between productivity and vegetation indices [1; 13; 14; 15], so how well these relationships between vegetation indices and productivity can be transferred to different ecosystems is still not clear. Terrestrial primary productivity (in a broad sense) has many facets and sources of variability, and RSVIs may not be equally good at monitoring all of them. Several studies have reported good relationships between the spatial variability of biomass stocks or the production of aboveground biomass and RSVIs such as the normalised difference vegetation index (NDVI) and the enhanced vegetation index (EVI) for boreal forests [16], shrublands [17], grasslands [10; 18; 19], and croplands [20]. Interannual variability of tree growth and fruit production have also been satisfactorily monitored by various RSVIs in Mediterranean evergreen broadleaved forests [2; 12], but few studies have compared the performances of RSVIs across multiple ecosystems. Other sources of variability of terrestrial productivity have been studied less. Little effort has been devoted to testing the relationship between the spatial variability of RSVI seasonality and the seasonality of terrestrial photosynthesis, despite the extensive focus on the study of phenology using RSVIs [6; 9; 21]. The performance of RSVIs across sites with different seasonalities therefore requires further study, comparing their performances using raw values and deseasonalised time series (anomalies). Several factors can disrupt this relationship and require further investigation, even though trend analyses using RSVIs are relatively common and have often identified a positive relationship with actual greenness or increase in biomass [1; 15]. The choice of RSVI to use in a study is not always straightforward, because all RSVIs have both advantages and disadvantages. NDVI and EVI are currently two of the most widely used vegetation indices. NDVI is useful for comparing seasonal and interannual changes in vegetation growth and activity [12; 19]. NDVI, however, can saturate in conditions of large biomass [22], a problem that EVI, which was designed to improve sensitivity in regions with large biomass, overcomes [23]. Recent studies of RSVIs have provided two new indices with very promising properties: the near-infrared reflectance index, NIRv, [24], and the chlorophyll-carotenoid index, CCI [9]. NIRv addresses the confounding effects of leaf area, background brightness, and the distribution of photosynthetic capacity with depth in canopies and has recently been demonstrated to correlate well with gross primary production (GPP) across biomes [24]. CCI, which is based on changes in the content of chlorophyll and carotenoid pigments in leaves, can effectively monitor photosynthetic phenology in evergreen conifers, despite their small changes in crown cover [9]. We identified the features of terrestrial GPP that can be reliably monitored using RSVIs, comparing the temporal patterns of the indices and GPP derived from 58 eddy-flux towers distributed throughout Europe.