Abstract Mid-infrared reflectance spectroscopy (MIRS, 4000–400 cm−1) is being considered to provide accurate estimations of soil properties, including soil organic carbon (SOC) and soil inorganic carbon (SIC) contents. This approach has mainly been demonstrated by using datasets originating from the same area A, with similar geopedological conditions, to build, validate and test prediction models. The objective of this study was to analyse how MIRS performs when applied to predict SOC and SIC contents, from a calibration database collected over a region A, to predict over a region B, where A and B have no common area and different soil and climate conditions. This study used a French MIRS soil dataset including 2178 topsoil samples to calibrate SIC and SOC prediction models with partial least squares regression (PLSR), and a Tunisian MIRS topsoil dataset including 96 soil samples to test them. Our results showed that when using the French MIRS soil database, i) the SOC and SIC of French validation samples were successfully predicted using global models (R2val = 0.88 and 0.98, respectively), ii) the SIC of Tunisian samples was also predicted successfully both using a global model and using a selection of spectral neighbours from the French calibration database (R2test of 0.96 for both), iii) the SOC of Tunisian samples was predicted moderately well by global model (R2test of 0.64) and a transformation by natural logarithm of the calibration SOC values significantly improved the SOC prediction of Tunisian samples (R2test of 0.97), and iv) a transformation by natural logarithm of SOC values provided more benefit than a selection of spectral neighbours from the French calibration database for predicting Tunisian SOC values. Therefore, in the future, MIRS might replace conventional physico-chemical analysis techniques, or at least be considered as an alternative technique, especially when optimally exhaustive calibration databases will become available.

中文翻译：

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使用法国国家图书馆通过中红外反射光谱预测突尼斯样品中土壤有机和无机碳浓度

摘要 中红外反射光谱 (MIRS, 4000–400 cm-1) 被认为可以提供对土壤特性的准确估计，包括土壤有机碳 (SOC) 和土壤无机碳 (SIC) 含量。这种方法主要通过使用来自同一区域 A、具有相似地质地质条件的数据集来构建、验证和测试预测模型来证明。本研究的目的是分析 MIRS 在应用于预测 SOC 和 SIC 含量时的表现，从在区域 A 收集的校准数据库，以预测区域 B，其中 A 和 B 没有共同的区域，土壤和气候不同使适应。本研究使用法国 MIRS 土壤数据集，包括 2178 个表土样本，通过偏最小二乘回归 (PLSR) 校准 SIC 和 SOC 预测模型，和突尼斯 MIRS 表土数据集，包括 96 个土壤样本以进行测试。我们的结果表明，当使用法国 MIRS 土壤数据库时，i) 使用全球模型成功预测了法国验证样品的 SOC 和 SIC（分别为 R2val = 0.88 和 0.98），ii) 也成功预测了突尼斯样品的 SIC使用全球模型并使用法国校准数据库中的一系列光谱邻居（两者的 R2test 均为 0.96），iii) 通过全球模型（R2test 为 0.64）和通过自然对数的转换对突尼斯样品的 SOC 进行了适度预测校准 SOC 值显着改善了突尼斯样本的 SOC 预测（R2test 为 0.97），iv) SOC 值的自然对数转换比从法国校准数据库中选择光谱邻居提供了更多的好处，用于预测突尼斯 SOC 值。因此，在未来，MIRS 可能会取代传统的理化分析技术，或者至少被视为一种替代技术，尤其是当最详尽的校准数据库可用时。