【Objective】As iron is one of the nutrient elements essential to plant growth, the content of soil available plays an important role in evolution of forest environment. The technology of hyper-spectral remote-sensing (RS) provides anew means for determination of soil physical and chemical components in laboratory.【Method】In this study, the relationship between soil organic matter and available iron was used to predict the content of available iron in soil indirectly. Besides the traditional single factor prediction model has its own limitation. In order to solve the problem of errors with the single-factor model, this study brought forth a composite model to improve accuracy of the prediction of soil organic matter contents in forest soils at a regional scale with the Vis-NIR spectrum technique. A total of 190 soil samples were collected from the 0～20 cm soil layers of the forests typical of Lushan region in Jiangxi Province. An ASD FieldSpec3 spectrograph diameter equipped with a high intensity contact probe was used to measure original spectral reflectance of the samples in line with standard procedure of the laboratory conditions, and mean while, the soil samples were analyzed for physical and chemical properties. Out of the 190 soil samples, 143 were picked out as samples for modeling and the remaining 47 verification ones. 【Result】The results showed that a significant positive correlation was found between the contents of soil organic matter and soil available iron, and then the binomial model can be built. Based on the results of spectral inversion of soil organic matter content, the contents of soil available iron were retrievable indirectly. Among the spectral inversion models, based on the full band (400～2450 nm) of soil spectra in this study, PLSR (partial least square regression) of the optimal linear fitting model and RBF(Radial Basis Function)neural network of the nonlinear fitting model were selected to form a combination to figure out arithmetic mean weight coefficients and to project an optimal combination model based on squared, reciprocal and entropy weight coefficients. Accuracies of the predictions of soil available iron content were evaluated by root mean squared error (RMSE_p), ratio of partial deviation (RPD) and determination coefficients (R²). Results show that the combination model is superior to the two separate models in prediction accuracy. Among the combination models, the entropy weight coefficient combination model is the best, with determination coefficient (R²) in verification model, root mean squared error (RMSE_p) and ratio of standard deviation of determination to standard deviation (RPD) of the soil organic matter prediction being 0.81, 11.54 g kg^-1and 2.18, the soil available iron indirect prediction being 0.70, 21.60 mg kg^-1and 1.77, respectively. The combination model is able to make use to a maximum margin of various information of the samples for prediction, reduce effectively the impacts of random factors in using single prediction models, enhance prediction stability and raise prediction capability of the models. 【Conclusion】All the findings of the study demonstrate that it is feasible to in directly predict soil available iron contents in forest soils by making use of hyper-spectral RS data. In the end, it can be concluded that the combination model can play a pretty good role in predicting soil organic matter content and indirect predicting soil available iron content.