反射光谱技术具有快速、 便捷等特点, 过去几十年中将其应用于土壤科学的研究呈指数增长, 且广泛用于土壤属性估算。 土壤全氮含量是一项非常重要的肥力指标, 光谱估算全氮含量可以为实现精准农业提供重要支持。 但反射光谱估算土壤全氮含量是基于全氮与有机碳的相关性还是基于氮本身的吸收特征仍然存在争议。 本文以江苏滨海土壤为研究对象, 利用偏最小二乘法分别构建全氮和有机碳在相同建模样本量、 不同全氮含量及变异程度情况下的估算模型, 通过分析模型精度变化规律及全氮与有机碳估算模型系数的相关性, 探讨土壤全氮反射光谱估算机理。 结果表明, 该地区土壤为1 000年来滨海滩涂经人为耕作发育形成, 全氮含量不高, 有机碳含量偏低。 全氮与有机碳之间存在较强的相关性, 相关系数高达0.98。 土壤全氮含量估算精度随样本集全氮含量的平均值、 标准差增大出现先增加后略有减小的变化规律, 与变异系数的变化规律相一致。 当全氮含量较低时(样本平均值小于0.27 g·kg-1), 土壤全氮与有机碳相关系数也较小, 实现反射光谱估算全氮是基于氮的吸收特征; 当全氮含量较高时(样本平均值大于0.29 g·kg-1), 全氮与有机碳相关性较强且有机碳模型精度高于全氮, 说明有机碳对光谱曲线的影响随其含量增加而增大, 并掩盖了氮的吸收特征, 实现反射光谱估算全氮是基于其与有机碳的相关性。 该研究揭示了土壤反射光谱估算全氮含量的机理, 从而为反射光谱快速估算土壤全氮含量提供理论依据。

Reflectance spectroscopy has been widely used for predicting soil properties due to its rapidity and convenience. In past decades, the application of soil spectroscopy on soil science studies has increased exponentially. The total nitrogen (TN) content in soil is an important index for soil fertility and the rapid prediction of TN content with spectroscopy serves an important function in precision agriculture. However, whether the TN content in soil is predicted through its relationship with soil organic carbon (SOC) or on its specific absorption is still debatable. The objective of this study was to explore the mechanism of reflectance spectroscopy for predicting TN in soils. Soils used for calibration were sampled from coastal soil in the north of Jiangsu province. Partial least squares regression (PLSR) analysis was used for the calibration datasets with different TN content when the sample number is the same in every dataset. In order to explore the mechanism of reflectance spectroscopy for predicting total nitrogen in soil, the changes of model accuracies and the correlation of TN and SOC were analyzed. The results indicated that the contents of TN and SOC in soil were relatively lower because the soil was derived from coastal sediments in the past 1 000 years and formed during cultivation. There was strong correlation between TN and SOC (R=0.98). The prediction accuracy of TN increased at first and then decreased slightly with the increase of mean, standard deviation of TN content. Meanwhile, the changes of prediction accuracy comply well with coefficients of variation. In conclusion, when the TN content is relatively low (mean TN<0.27 g·kg-1), the correlation coefficient between TN and SOC was moderately-high and TN was predicted on the basis of N absorbers. When the TN content is relatively high (mean TN>0.29 g·kg-1), strong correlation coefficients were obtained for TN and SOC and the model accuracy of SOC were better than TN. The effect of SOC to spectroscopy enhanced with the increase of SOC content, which masked the spectral features of N. Therefore, TN was predicted through the correlation with SOC when the TN content is high. This study revealed the mechanism of reflectance spectroscopy for predicting TN in soil and it could provide a theoretical basis for predicting soil TN content rapidly using reflectance spectroscopy.