近几十年来, 光谱技术在土壤科学中的应用越来越受到重视。 利用土壤反射光谱可以快速获取土壤信息, 了解土壤理化属性, 对土壤铁含量进行估算。 在已有的研究中, 土壤铁的光谱估算多选用表层土壤, 大多进行全铁的估算, 忽略了不同形态的土壤铁, 且估算结果并不十分理想。 为了得到不同形态土壤铁最佳模型的处理方法, 并探讨有机质含量和土壤深度对不同形态土壤铁估算精度的影响, 以江苏省东台市为研究区, 选取20个地点共采集了160份土壤样品, 将每份样品分别研磨至10目和100目, 在室内进行光谱数据采集之后, 在使用八种不同方法进行预处理的同时将每种方法都选取多种参数, 利用偏最小二乘回归法将全反射波段分别与土壤中的全铁、 游离铁、 无定形铁的含量进行建模回归, 并评价模型精度。 结果表明: （1）建立三种不同形态土壤铁的最优模型的处理方式都是将土壤研磨成100目, 采用多元散射矫正法, 全铁的决定系数（R2）只有0.6, 而且模型不够稳定; 游离铁和无定形铁估算结果比较好, R2分别达到了0.77和0.69, 而且误差小, 模型稳定; （2）由于全铁中的硅酸铁很容易受到外界环境的影响, 有机质和土壤深度对全铁的估算精度影响都很大, 对游离铁的估算精度影响最小; 无定形铁由于含量少, 其估算模型也比较容易受到有机质和土壤深度的影响。

In recent decades, the application of spectral technology in soil science is getting more and more attention. Soil information can be obtained quickly by using soil reflectance spectra to understand the physical and chemical properties of soil and to estimate soil iron content. In previous studies, the surface soil always is selected for the estimation of soil iron content by using spectroscopy. It needs to estimate total iron and, the different forms of soil iron is ignored, therefore, the estimation result is not ideal. In order to gets a different form of soil iron processing method of optimal model to evaluate the accuracy of models, as well as discuss the organic matter content and soil depth on the influence of different forms of soil iron estimation accuracy. A total of 160 soil samples were collected from 20 sites in Dongtai city, Jiangsu province. These samples were ground to 10 meshes and 100 meshes. In the use of 8 different methods for the pretreatment of the same time each method will be selected by a variety of parameters, using partial least squares regression method to model the total reflection band and the total iron, free iron, amorphous iron content in the soil respectively, then evaluation model precision. The results showed that: (1) the optimal model of three kinds of soil iron was all ground to 100 meshes and the best pretreatment method was MSC. The prediction accuracy of total iron was acceptable and R2 was less than 0.6. The results of free iron and amorphous iron inversion were better and the R2 was 0.77 and 0.69, respectively. The errors were small and the models were stable. (2) Because the ferric metasilicate in total iron is easily affected by external environment, the organic matter and soil depth are of great influence on the estimate precision of total iron the most. But the estimation accuracy of free iron is the least affected. Because of the low content of amorphous iron, the estimated model is also susceptible to the influence of organic matter and soil depth.