## 1 Introduction  
The normalized difference vegetation index (\(\mathrm{NDVI}\)[1]) is a well-recognized proxy of canopy greenness [2; 3; 4] that relates to ecosystem photosynthetic carbon uptake. NDVI is widely used in ecosystem modelling studies because interannual and intra-seasonal dynamics of gross primary productivity (GPP) are driven by changes in ecosystem phenology that directly affect total annual ecosystem carbon budgets [5; 6; 7; 8]. Even though new vegetation remotely based indices have been proposed for the estimation of ecosystem productivity (i.e., enhanced vegetation index (EVI) [3], photochemical reflectance index (PRI) [9], and chlorophyll content index (CCI) [10]), NDVI is extensively employed in ecological models and landscape mapping due to its long record available from Advanced Very-High-Resolution Radiometer (AVHRR) sensors [11; 12; 13; 14]. A number of studies have analyzed the impact of climate change on GPP and ecosystem greenness phenology. Changes in ecosystem greenness have generally been described at local and regional scales using in situ observations provided by eddy covariance flux data of carbon dioxide exchange, webcams, and satellite data [15; 16; 17; 18; 19; 20; 21]. Global scale land surface models are parameterized using earth observation data, where the NDVI is widely used as an indicator of ecosystem greenness [22; 23] due to its linking of photosynthesis with carbon uptake in main terrestrial ecosystems [24; 25; 26; 27; 28]. A range of satellite products (MODIS/Terra or MODIS/Aqua, [[https://modis.gsfc.nasa.gov/](https://modis.gsfc.nasa.gov/)]([https://modis.gsfc.nasa.gov/](https://modis.gsfc.nasa.gov/)); SPOT/VGT, [[http://www.spot-vegetation.com/](http://www.spot-vegetation.com/)]([http://www.spot-vegetation.com/](http://www.spot-vegetation.com/)); and NOAA/AVHRR, [[https://www.ncdc.noaa.gov/](https://www.ncdc.noaa.gov/)]([https://www.ncdc.noaa.gov/](https://www.ncdc.noaa.gov/))) have been studied to increase understanding of changes in ecosystem greenness at the global scale; however, impacts of landscape heterogeneity and spatial scale on the relationship between greenness and ecosystem productivity are unclear [29; 30] because research tends to be based on local scale, single-site studies. Field-based measurements of the NDVI are time consuming, particularly in highly heterogeneous landscapes, and field sampling protocols tend to be based on the Validation of European Remote Sensing Instruments (VALERI [31], [[http://w3.avignon.inra.fr/valeri/](http://w3.avignon.inra.fr/valeri/)]([http://w3.avignon.inra.fr/valeri/](http://w3.avignon.inra.fr/valeri/))) protocol for the estimation of NDVI, leaf area index (LAI), or vegetation cover. The VALERI method was developed for various spatial scales, from small study plots to the size of a pixel, of coarse-resolution satellite data. The VALERI database is available on request for a large number of sites worldwide at the [[http://w3.avignon.inra.fr/valeri/fic_htm/database/main.phpwebpage](http://w3.avignon.inra.fr/valeri/fic_htm/database/main.phpwebpage)]([http://w3.avignon.inra.fr/valeri/fic_htm/database/main.phpwebpage](http://w3.avignon.inra.fr/valeri/fic_htm/database/main.phpwebpage)). Geostatistical techniques and pixel-based classification algorithms offer a valuable alternative for an accurate and precise classification of landscape [32; 33]. GPP and NDVI field measurements are scaled up using in situ FLUXNET [34] radiation data [35; 36], and subsequent regression analysis is often used to define transfer functions that allow the estimation of GPP from NDVI data. However, the uncertainties related to the scale mismatch between the in situ point measurements, flux footprint, and the pixel resolution remain a critical issue.