## 1 Introduction  
Soil total carbon (TC) is composed of organic (all organic components mainly derived from the decomposition of plants and animals; and including living organisms) and inorganic (non-living C, typically as carbonates) carbon forms. Due to the short-term cycle of soil organic carbon (SOC) and its key role for soil functions, the quantitative evaluation of SOC is essential for determining a suitable management practice to conserve or increase soil carbon stock [1; 2; 3; 4]. Monitoring SOC over large areas or long periods of time requires analysis of substantial numbers of samples which can be labor-intensive and expensive. Under those circumstances, the soil spectroscopy technique is an effective method to predict SOC rapidly at minimal cost [5; 6]. Soil spectroscopy uses the visible and near-infrared (VIS-NIR, 400-2500 nm) and mid-infrared (2500-25,000 nm) spectral reflectance to infer soil properties from a scanned sample [7]. This technique has been used mainly under laboratory conditions, but it can also be applied in the field for a specific site or in an instrument setup for ongoing scanning [6]. Spectral reflectance of soil in the VIS-NIR has been used to predict soil C in different soil types. [PERSON] et al. [8] reported high correlation values for total and organic C (\(R^{2}\) = 0.85 and \(R^{2}\) = 0.86, respectively) with an error of 5.33 g\(\cdot\)kg\({}^{-1}\) for total C and 2.88 g\(\cdot\)kg\({}^{-1}\) for organic C in an entisol in the first 50-cm layer of soil. [PERSON] et al. [9] found higher correlation values for inorganic C (\(R^{2}\) = 0.76) than organic C (\(R^{2}\) = 0.67) in a cortisol to a depth of 90 cm. In addition, successful predictions of soil carbon fractions have been made by VIS-NIR spectroscopy [10; 11], suggesting this technique as a reliable alternative for assessing the impact of land use change on soil carbon pools. Other related studies show the performance of different multivariate methods in calibrating models [12; 13; 14]. Classical methods for analyzing soil C require a rigorous sample preparation and expensive chemical supplies (e.g., dry combustion). As a complement to these methods, soil spectroscopy can be used to build empirical models to predict soil properties from spectral data. The potential of this technique to predict soil C has been extensively reported [15; 16; 17; 18; 19], and important considerations for the effective application of soil spectroscopy have been also reviewed by [PERSON] [7]. However, to our knowledge, there are no studies that describe potential issues such as overfitting in model calibration procedures in a realistic field example. In general, better correlations and fewer errors have been reported for sieved (<2 mm) and dry (air/oven) samples [9; 20]. [PERSON] et al. [16] concluded that grinded samples notably improve the predictions of total soil carbon. In contrast, [PERSON] [15] found that the quality of prediction was not benefited by grinding samples. Soil darkness is the most evident effect of water on spectral variability, but strong effects occur in the infrared spectral reflectance where the wavelengths of 1450 nm and 1950 nm are absorption bands [21; 22]. Because water molecules in soil are dispersed, broad bands are usually seen at these wavelengths [21]. Andisols have special properties such as low density and relatively high amounts of organic carbon within the soil profile [23], which have an important effect on soil reflectance. Before model calibration, a pre-processing step is needed to reduce random noise and dimensionality of the spectral data [12]. The purpose of pre-processing techniques in spectroscopy is to eliminate the portion of reflectance that does not come from the desired properties of the target. In soil spectroscopy, the spectra variability includes incident light reflected in multiple directions (diffuse reflectance) due to soil roughness, soil aggregates, soil structure, and particle size. These physical properties scatter the incident energy in many directions.