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Local, field-scale, VisNIR-DRS soil calibrations generally yield the most accurate predictions but require a substantial number of local calibration samples at every application site. Global to regional calibrations are more economically efficient, but don't provide sufficient accuracy for many applications. In this study, we quantified the value of augmenting a large global spectral library with relatively few local calibration samples for VisNIR-DRS predictions of soil clay content (clay), organic carbon content (SOC), and inorganic carbon content (IC). VisNIR models were constructed with boosted regression trees employing global, local+global, and local spectral data, using local samples from two low-relief, sedimentary bedrock controlled, semiarid grassland sites, and one granitic, montane, subalpine forest site, in Montana, USA. The local+global calibration yielded the most accurate SOC predictions for all three sites [Standard Error of Prediction (SEP)= 3.8, 6.7, and 26.2 g kg-1]. This was similarly true for clay (SEP=95.3 and 102.5 g kg-1) and IC (SEP=5.5 and 6.0 g kg-1) predictions at the two semiarid grassland sites. A purely local calibration produced the best validation results for soil clay content at the subalpine forest site (SEP=49.2 g kg-1), which also had the largest number of local calibration samples (N=210). Using only samples from calcareous soils in the global spectral library combined with local samples produced the best SOC and IC results at the more arid of the two semiarid sites. Global samples alone never achieved more accurate predictions than the best local+global calibrations. For the temperate soils used in this study, the augmentation of a large global spectral library with relatively few local samples generally improved the prediction of soil clay, SOC, and IC relative to global or local samples alone.