## 1 Introduction  
Productivity estimates are important measures to characterize the mass budget of a forest ecosystem. In this context, the carbon balance and carbon storage of the earth's ecosystems and their spatial and temporal development is important. A measure for the carbon flux is Net Primary Production (NPP) which describes the carbon uptake by vegetation through photosynthesis. In principle, large scale carbon measures are currently provided by National Forest Inventories, flux towers and remotely sensed methods, such as the MODIS (MODerate resolution Imaging Spectroradiometer) NPP algorithm. Researchers in previous studies have compared MODIS satellite-driven NPP with NPP estimates derived using terrestrial data [1; 2; 3]. [PERSON] _et al._[1] found that for Russia the two estimates comply on average. [PERSON] _et al._[2] observed deviations between MODIS and terrestrial NPP using inventory data from the mid-Atlantic region of the USA and suggest that differences in water availability explain this variation. [PERSON] _et al._[3] also found similar deviations and concluded that the different data sources for predicting NPP compare well after addressing forest management impacts. The authors further suggest that a combination of \"ground-based\" forest data with \"space-based\" forest productivity estimates would utilize the advantages of both methods. National forest inventories are established for a continuous monitoring of the forest situation across countries. They are often based on a systematic permanent grid design. For each sample plot, a limited number of trees compared to the total forest area [4] is recorded to provide information on the standing growing stock, increment, species composition and other information needs relevant for forest management [5; 6]. Such forest inventories are usually repeated every five to 10 years. Examples for national long term forest inventories can be found in countries such as Austria, Norway, Finland, Germany, _etc._[4]. The MODIS NPP algorithm is based on remote sensing techniques and was developed for estimating large-scale forest productivity [7; 8]. MODIS is a satellite-mounted sensor operated by the National Aeronautics and Space Administration of the United States (NASA) that provides large-scale information on global dynamics and processes. The MODIS algorithm MOD17 uses satellite data, climate data and biophysical properties of various cover types to derive eight-day GPP (Gross Primary Production) and NPP estimates on a 1 \(\times\) 1 km pixel resolution. This data source may be seen as a \"space-based\" approach and provides continuous coverage across the global land area to assess forest productivity. The differences of the two approaches are as follows:  
1. The MODIS satellite-driven NPP is based on the principles of carbon uptake following light use efficiency logic [9] using a simplification of the NPP algorithms implemented in BIOME-BGC [10]. According to the hypothesis put forth by [PERSON] _et al._[3] the MODIS NPP algorithm assumes a fully stocked forest for a given vegetation class or biome type and provides eight-day results NPP estimates on a 1 \(\times\) 1 km grid. 2. NPP estimates derived from terrestrial forest growth data (permanent sampling plots or forest inventory data) employ biomass expansion factors or biomass functions based on repeated observations such as breast height diameter (_dbh_) and/or tree height (_h_). Depending on the measurement interval the derived increment results represent a mean periodic average. Since this method is based on individual tree observations (e.g., _dbh, h_), potential changes in tree growth due to stand age, stand density or environmental effects such as weather patterns or CO\({}_{2}\) concentration are included as they affect _dbh_ and \(h\) development. Several studies have compared satellite to terrestrial inventory data [1,2,11-16], however, no analysis has compared forest inventory data to MODIS NPP in Central Europe.