## 1 Introduction  
The far reaching effects and evidence of climate change, driven by increases in atmospheric greenhouse gas concentrations, motivated international mitigation focus at the 2015 United Nations Framework Convention on Climate Change Fifteenth Session of the Conference of the Parties [1]. Regional syntheses of carbon flux tower data [2; 3; 4] provide geographically referenced estimates from multiple flux towers and offer detailed summarization of carbon flux properties. Throughout modern history, scientists have been developing models to formulate generalized algorithms to expand geographically and temporally sparse field observations to a much larger landscape means [2; 3; 4] or to make up-scaled maps of the measured field characteristics [5]. Understanding, mapping, and quantifying regional carbon flux magnitudes and variability of terrestrial non-point carbon sinks (removal of atmospheric carbon) and sources (emission of carbon to the atmosphere) is important in the promotion of ecosystem sustainability. Methodologicaland technological advances in the areas of remote sensing, environmental monitoring devices, data management, and computer-aided analytics have led the way to more innovative modeling. With these advancements, scientists have been able to apply these modeling methods to make estimates about discrete environmental characteristics, such as, atmospheric composition and below ground characteristics [6; 7; 8; 9]. Remote sensing data [5; 10; 11], light use efficiency modeling [9; 12], data mining techniques [5; 10; 11; 13; 14], process-base modeling [15; 16], and greenhouse gas inventories [17] have enhanced regional understanding and monitoring capabilities. Mapping efforts are sometimes at coarse resolutions, long time step intervals, have large (continental or global) study areas which may miss local detail, and can have highly automated or general gap filling strategies for temporary flux tower instrument failures. Our analysis focused on two primary large ecosystem classes in the U.S. Great Plains, cropland and grassland. We developed and applied our models at the 250 m spatial resolution and the weekly temporal resolution to retain the detailed temporal dynamics of carbon fluxes in the output maps. The primary objective of this study was to combine measurements acquired at flux towers with applicable remote sensing, weather, and other biogeophysical data to develop separate grassland and cropland ecologically-based net ecosystem production (NEP) models and derive mean daily NEP maps at a weekly time step from 2000 to 2008 for the study area of interest, the U.S. Great Plains (Figure 1). Although a flux tower measures only its surrounding vicinity, numerous studies have shown that these recordings can be used in regional or land cover specific synthesis [3; 4] or up-scaled to much greater levels through analyses of geospatial data relationships using computer modeling [9; 13; 18]. Of particular interest is the use of data mining regression tree algorithms for carbon flux mapping [5; 10; 11; 13; 14; 18]. An acute capability to address nonlinear relationships, deal with complex high order interactions, easily interpret results, and utilize both categorical and continuous variables, lends data mining regression tree [10], which contrasts with process-based modeling approaches that are frequently heavily driven by precipitation, a difficult variable to map accurately, particularly in rural regions where weather stations are sparse. A vulnerability of regression tree approaches is a tendency of over fitting. However, this can be mitigated through cross validation [19] and generalization of sequential dichotomous tests (trees) into generalized rules [20]. Regional comparisons indicated a higher grassland carbon sink strength and a higher water use efficiency than non-irrigated crops across rainfall gradients in the Great Plains.