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.