The soil specific surface area (SSA) is a fundamental property governing a range of soil processes relevant to engineering, environmental, and agricultural applications. A method for SSA determination based on a combination of visible near‐infrared spectroscopy (vis‐NIRS) and vapor sorption isotherm measurements was proposed. Two models for water vapor sorption isotherms (WSIs) were used: the Tuller–Or (TO) and the Guggenheim–Anderson–de Boer (GAB) model. They were parameterized with sorption isotherm measurements and applied for SSA estimation for a wide range of soils (N = 270) from 27 countries. The generated vis‐NIRS models were compared with models where the SSA was determined with the ethylene glycol monoethyl ether (EGME) method. Different regression techniques were tested and included partial least squares (PLS), support vector machines (SVM), and artificial neural networks (ANN). The effect of dataset subdivision based on EGME values on model performance was also tested. Successful calibration models for SSA_TO and SSA_GAB were generated and were nearly identical to that of SSA_EGME. The performance of models was dependent on the range and variation in SSA values. However, the comparison using selected validation samples indicated no significant differences in the estimated SSA_TO, SSA_GAB, and SSA_EGME, with an average standardized RMSE (SRMSE = RMSE/range) of 0.07, 0.06 and 0.07, respectively. Small differences among the regression techniques were found, yet SVM performed best. The results of this study indicate that the combination of vis‐NIRS with the WSI as a reference technique for vis‐NIRS models provides SSA estimations akin to the EGME method.

中文翻译：

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结合可见光近红外光谱法和水蒸气吸附法估算土壤比表面积

土壤比表面积（SSA）是控制一系列与工程，环境和农业应用有关的土壤过程的基本属性。提出了一种基于可见近红外光谱（vis-NIRS）和蒸气吸附等温线测量相结合的SSA测定方法。使用了两种水蒸气吸附等温线（WSI）模型：Tuller-Or（TO）模型和Guggenheim-Anderson-de Boer（GAB）模型。通过吸附等温线测量对它们进行参数化，并将其用于各种土壤的SSA估算（N = 270），来自27个国家/地区。将生成的vis-NIRS模型与通过乙二醇单乙醚（EGME）方法确定SSA的模型进行比较。测试了不同的回归技术，包括偏最小二乘（PLS），支持向量机（SVM）和人工神经网络（ANN）。还测试了基于EGME值的数据集细分对模型性能的影响。成功地建立了SSA _TO和SSA _GAB的校准模型，几乎与SSA _EGME的校准模型相同。模型的性能取决于SSA值的范围和变化。但是，使用选定的验证样本进行的比较表明，估算的SSA _TO，SSA没有显着差异_GAB和SSA _EGME，其平均标准RMSE（SRMSE = RMSE /范围）分别为0.07、0.06和0.07。发现回归技术之间的细微差异，但SVM表现最佳。这项研究的结果表明，vis-NIRS与WSI的组合作为vis-NIRS模型的参考技术，可以提供类似于EGME方法的SSA估算。