## 1 Introduction  
Since the turn of the century, the public's perception that significant changes in climate may be underway has increased following unparalleled outbreaks of fires, insects and pathogens in western North America [1; 2; 3]. Satellite-borne instruments document the extent of these disturbances and quantify the rates of change since the acquisition of Landsat imagery first began in 1972 [4]. With the launch of the National Aeronautics and Space Administration's Moderate Resolution Imaging Spectroradiometers (MODIS), daily changes in the Earth's terrestrial surface are currently being monitored. Satellite observations allow global monitoring of the state of vegetation, both as gradual changes occur over time and following disturbances [5]. The question arises, how much disturbance is natural and how much can be attributed to changes in climate? Without periodic disturbances, many forests become overly dense and predisposed to fire, insects and disease [6; 7]. A lengthening in the frequency of a disturbance, such as the interval between wildfires, by itself, may allow longer-lived species to replace more short-lived, fire-adapted ones, altering forest composition for centuries [8]. Alternatively, too short of a fire-return interval may prevent fire-adapted species from reaching maturity and producing seed. Ideally, it would be desirable to know the composition, density and age class distribution of trees in every forest [9]. More realistically, we might select a property of forests that is known to respond quickly to changing environmental conditions, such as maximum leaf area index, LAI\({}_{\text{max}}\)[10]. Forest ecologists recognize that there is a close relationship between LAI\({}_{\text{max}}\) and the growth potential of vegetation in a particular region [10; 11; 12]. The natural range in both variables is large in western North America. Across a 250-km transect in Oregon, LAI\({}_{\text{max}}\) varies from \(<\)1.0 in a juniper woodland to 12.0 m\({}^{2}\)m\({}^{-2}\) in a coastal Sitka spruce-western hemlock forest [13; 14] with a similar range and spatial distribution of the maximum mean annual increment (MAI\({}_{\text{max}}\)) from 1.5 to 15 m\({}^{3}\) ha\({}^{-1}\) yr\({}^{-1}\)[15]. A number of process-based models have been developed that quantify the link between LAI\({}_{\text{max}}\) and productivity by simulating gross photosynthesis, plant respiration and the allocation of growth to leaf area, as well as to stems and roots [16; 17]. Most process-based models take into account constraints imposed by seasonal variation in incident radiation, temperature and the evaporative demand of the atmosphere. Some also include a soil water balance to account for observed differences in growth and mortality during periods of extended drought [18; 19]. Few models, however, account for spatial variation in soil fertility [20], although the above-ground growth responses to rising atmospheric CO\({}_{2}\) are recognized to be severely constrained on infertile soils [21], as well as on soils with extreme water deficits [22]. [PERSON] _et al._[23] investigated the implications of variation in both of these soil properties by performing sensitivity analyses on modeled gross photosynthesis (GPP) among nine broadly defined ecoregions of the contiguous USA. Most forested areas in the western part of the United States were shown to be moderately to highly sensitive to variation in estimates of water availability and fertility. Even within a small area of southwestern Oregon, accurate information of soils proved necessary to ensure that model predictions were in close agreement with the measured growth [12]. The above cited authors concluded that conventional soil maps were insufficient to parameterize forest growth models across large areas. As an alternative to intensive field surveys, [PERSON] _et al._[24] applied model inversion and optimization techniques to derive and map soil properties across western North America at a spatial resolution of 1 km.