【Objective】The Qi-Dynasty Great Wall is made up of rammed soil, in which soil moisture plays an important role. Excessive moisture in the soil will lead to partial collapse of the wall. Therefore, it is of great significance to estimate soil water content in the rammed earth of the Great Wall so as to protect the relics of Great Wall. Although the traditional soil moisture measuring method is quite high in precision, it is rather labor- and time-consuming and rigorous in measuring environment. The use of hyperspectral technology has the characteristics of rich data/information, high-efficiency and non-destructiveness, which makes up for the shortages of the traditional measuring methods. In recent years, scholars at home and abroad have found that the multiple scattering correction method can eliminate the scattering effect caused by particle size variation of the samples, and then the difference in physical scattering information between different spectra. However, so far little has been reported about researches on whether the spectra corrected with the MSC method can make wavelength optimization more accurate. 【Method】 In this paper, the Qi-Dynasty Great Wall in Huangdao District of Qingdao City was cited as object of the study, and samples were collected vertically along the Wall. Initial soil moisture content of the samples were determined with the oven-drying method, and soil hyperspectral data was obtained using the US ASD FieldSpec4 portable spectrometer. In order to study effect of soil moisture content on soil spectral characteristics, soil samples 6.16%, 8.94%, 10.27%, 14.10%, 18.03%, and 24.29% in moisture content were selected for acquisition of hyperspectral reflectivity curves. In order to verify the effect of MSC on the preferred sensitive wavelengths, the primary spectral reflectance of the soil was pretreated with Lg(R)' and MSC+Lg(R)', separately, and then correlation analysis was done between primary spectral reflectance and soil water content for screening sensitive wavelengths; based on the spectral data that had been pre-processed separately with Lg(R)' and MSC+Lg(R)', support vector machines (SVM)-based soil moisture content hyperspectral estimation models were constructed.【Results】Results show that spectral curves of the soil samples, regardless of soil moisture content, varied on the whole quite similarly, declining gradually with rising soil moisture content. For a specific band, the response of spectra to soil water content varied in characteristic with band region; when the soil moisture content was low, with rising soil moisture content, the reflectivity in the shortwave and infrared bands varied sharply. The sensitive bands of the spectral reflectance of the rammed Wall soils pre-treated with Lg(R)' and MSC+Lg(R)' were mainly concentrated in the range of 1 450～1 500nm, 1 850～1 900nm and 2 050～2100nm. After the logarithmic first order differential treatment of the original spectral data, only four wavelengths relatively high in correlativity were obtained, whereas after the pretreatment with MSC+Lg (R)' seven were, that is, 1 861nm, 1 866nm, 1 549nm, 1 885nm, 1 871nm, 1 895nm and 2 095nm, with significantly higher correlation coefficients, i.e. -0.72, -0.71, 0.7, -0.7, 0.69, 0.69 and 0.69, which indicates that the multi-dimensional scattering correction method can enhance the correlative absorption information between spectra and soil moisture content, thus increasing the correlation between soil spectral reflectance and soil moisture content; The model based on the spectral data pre-treated with Lg(R)' was verified with determination coefficient, R_v² = 0.679, RE = 0.143, RMSEP = 0.431, and RPD = 1.765, while the model based on the data pre-treated with MSC+Lg(R)′ was found to have R_v²= 0.764，RE = 0.062, RMSEP = 0.159, and RPD = 2.671. Obviously, the former is better than the latter in prediction. All demonstrate that SVM regression models based on the sensitive bands screened out through pretreatment vary somewhat in prediction effect with pretreatment method. 【Conclusion】 All the findings in this study demonstrate that the use of the MSC method to preprocess spectral data can enhance the absorption information related to spectrum and soil moisture content, screen sensitive wavelengths more accurately and have the established SVM estimation model more accurate in prediction.