【Objective】Soil organicmatter is an important index of soil properties, because it is vital to crop growth and soil quality.The technology of hyperspectral analysis is a rapid, convenient, low-cost and alternative method andexhibits an increasingly remarkable development potential in estimation ofsoil organic matter. However, when hyperspectral reflectance is usedin the field, there are several external environmentalfactors, including soil moisturecontent, temperature, and surface of the soil that may affect soil spectra. Especially soil moisture content, a major limit to field hyperspectralsurvey, might mask the absorption features ofsoil organic matter, and hence dramatically lower accuracy of the prediction of soil organic matter. Therefore, it is essential to find a method capable of removing the impact of soil moisture content on spectral reflectance, so as to improve the accuracy of quantitative prediction of soil organic matter. In this paper, the EPO (external parameter orthogonalization)algorithm was introducedfor that purpose.【Method】A total of 217 soil samples were collected from the 0～20 cm soil layer in theJianghan Plain.In the laboratory, the soil samples were air-dried andground to pass a sieve with mesh < 2 mm. Then the soil samples were analyzedseparately for soil organic mattercontentwith the potassium dichromate external heating method.The total of 217 soil samples werefurther divided into three non-overlapping subsets: a model calibration set (S0), consisting of 122 samples and dedicated to development of amultivariate model for soil organic matter; anEPO development subset (S1) consisting of 60 samples forEPO development; and a validation subset (S2) consisting of 35 samples for independentEPO validation. Then, the samples in S1 and S2 were rewetted in line with the following procedure: from each soil sample 150 g oven-dried soil was weighed out, put in a black cylindrical box and rewettedalong the gradient of soil moisture contentincrementwith interval being 4% each, making up a total of 9 treatments in soil moisture content along the gradienti.e. 0, 4%, 8%, 12%, 16%, 20%, 24%, 28% and 32%. An spectrometer was used to acquire hyperspectral reflectances of the samples of three subsets (S0, S1 andS2, including the rewetting samples)on 350 to 2500 nm. And then influences of the soil moisture content on the soil spectra were analyzed, and the scores of the first two principal components inthe principal component analysis were used for comparison to determine performance ofEPOalgorithmin removing the effects ofsoil moisture contenton spectral reflectance of the wet samples. In the end,modeling for the S0 subset was done using the partial least squares regressionand support vector machine regression, and the S2 subset of wet samples were used as external validation setbefore and after calibration with EPO. The coefficient of determination (R²), root mean squared error (RMSE) and the ratio of prediction to deviation (RPD) between the predicted and measured values of soil organic matterwere used to compare the 3 models in performance: High R², RPD and low RMSE were indicators of optimal models for partial least squares regression (before EPO calibration), EPO-partial least squares regression and EPO-support vector machine regression. 【Result】Results show that (1) Soil moisture content does have obvious influence on spectralreflectance, and the reflectance decreases in value across the entire wavelengthdomain with increasing soil moisture content, making it more challenging to identify usefulfeatures of soil organic matterwith spectra; (2) For Subset S2 before EPO calibration, no spectral overlaps are observed between the wet and dry samples, and spectra of the wet sample cluster in spaces free from those of the dry sample (mutual independent space). However, after EPOcalibrationof Subset S2 set, the spectra of the wet sample appear almost in the same positions as those of the dry sample do within the eigen space, demonstrating that the two groups of spectra are highly similar; (3) Before EPO calibration, the partial least squares regression model is the poorest in prediction accuracy (the validation RPD=1.16). EPO calibration has improved prediction accuracy of the model up to an acceptable level (the validation RPD=1.76). And EPO-support vector machine regression model performs better than the other two with validation R² reaching 0.78, and RPD = 2.15, which indicates that the effects of soil moisture content on spectra are successfully eliminated.【Conclusion】In the future, this approach willfacilitaterapidmeasurement ofsoil organic matter for this study area.