Diffuse reflectance spectroscopy in the visible and near-infrared (VNIR) spectral range has increasingly been utilised in recent years to measure soil properties, including organic carbon and macronutrient content, cation exchange capacity, pH, and even trace metal concentration. Proponents have even suggested that conventional laboratory soil testing methods can ultimately be replaced by these less laborious and more rapid spectroscopic methods. However, to this point in time, reflectance spectroscopy has not demonstrated accuracy comparable to that of conventional physical–chemical testing methods. This review explains why correlations between VNIR spectral features and specific chemical properties within selected sets of soil samples are indirect at best and therefore cannot be trusted more universally to predict soil chemical properties in larger and less homogeneous sample sets. Because mathematical manipulation of the raw spectral data has become increasingly complex and obscure to the users, particularly when machine learning is employed, the possibility exists that the correlations found and the predictions made are based on chance relationships resulting from overfitting the spectral data to include random noise in addition to any real trends. It is concluded that, for real-world soil testing for agronomic and environmental purposes (e.g. available nutrient and trace metal levels), spectral reflectance methods are not sufficiently reliable to replace conventional testing. Conversely, these reflectance methods show utility and convenience in measuring general trends across landscapes in levels of soil organic matter, carbonates, soluble salts and specific clay minerals.

中文翻译：

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通过漫反射光谱估计土壤化学性质：承诺与现实

近年来，可见光和近红外 (VNIR) 光谱范围内的漫反射光谱已越来越多地用于测量土壤特性，包括有机碳和常量营养素含量、阳离子交换容量、pH 值，甚至微量金属浓度。支持者甚至建议，传统的实验室土壤测试方法最终可以被这些不那么费力和更快速的光谱方法所取代。然而，到目前为止，反射光谱还没有显示出与传统物理化学测试方法相媲美的准确性。这篇综述解释了为什么 VNIR 光谱特征和选定土壤样本集中的特定化学性质之间的相关性充其量是间接的，因此不能更普遍地被信任来预测更大和不太均匀的样本集中的土壤化学性质。由于原始光谱数据的数学操作对用户来说变得越来越复杂和模糊，特别是在使用机器学习时，存在发现的相关性和做出的预测是基于过度拟合光谱数据以包括随机除了任何真正的趋势之外，还有噪音。得出的结论是，对于用于农艺和环境目的的实际土壤测试（例如可用养分和微量金属水平），光谱反射率方法的可靠性不足以替代传统测试。相反，这些反射率方法在测量整个景观中土壤有机质、碳酸盐、可溶性盐和特定粘土矿物水平的一般趋势方面显示出实用性和便利性。