Abstract: 【Objective】 Rapid and accurate quantitative analysis of soil organic matter is essential for sustainable development of precision agriculture. Visible and near-infrared (Vis-NIR) reflectance spectroscopy has been widely used for soil properties estimation and digital soil mapping. However, it is less exact in monitoring soil organic matter (SOM) in the field when compared to laboratory-based spectroscopic measurement mainly due to some factors, such as soil moisture, temperature, and soil surface texture. Among these three factors, soil moisture (SM) has the most pronounced effects on spectral reflectance. Therefore, it is urgently significant that a method for removing SM effects from spectral reflectance and improving the accuracy of quantitative prediction of SOM should be proposed. 【Method】 A total of 217 soil samples used in this study were collected at 0-20 cm depth from Gong'an County and Qianjiang City in Jianghan Plain. These soil samples were air-dried, ground, and sieved (less than 2 mm) in the laboratory, and the SOM of each soil sample was analyzed based on potassium dichromate external heating method. These 217 samples were further divided into three non-overlapping data-sets: the model calibration set (S⁰), this set consisted of 122 samples to develop multivariate models for SOM; The orthogonal signal correction (OSC) development set (S¹), this set consisted of 60 samples for OSC development; The validation set (S²), this set consisted of 35 samples for independent OSC validation. Then, sample rewetting (S¹ andS² set) was carried out: each soil sample was weighed 150 g oven-dried soil in a cylindrical black box, and then they were rewetted by 4% SM increment for each level in the laboratory. Total 9 treatments were obtained, corresponding to the following SM levels i.e. 0%, 4%, 8%, 12%, 16%, 20%, 24%, 28%, and 32%. Soil hyperspectral reflectance was measured in the laboratory with an ASD Fieldspec-Pro spectroradiometer for the three data-sets (S⁰, S¹ andS² , including the rewetting samples). Savitzky-Golay smoothing with a window size of 11 nm and polynomial order of 2 (SG) were applied to the three data-sets, then external parameter orthogonalization (EPO) and orthogonal signal correction (OSC) were conducted to remove the SM effects on reflectance spectra. In the next, the effect of SM on the reflectance spectra was analyzed, and the scores of the first two principal components from the principal component analysis (PCA) corrected by OSC method and spectral correlation coefficient were used to compare the performance in removing the effects of SM. Finally, the S⁰ data-sets were calibrated using the partial least squares regression (PLSR), and the S² data-sets were then examined as external validation sets. Using the coefficient of determination (R²), root mean squared error (RMSE) and the ratio of prediction to deviation (RPD) between the predicted and measured SOM to compare the performance of PLSR, EPO-PLSR and OSC-PLSR models, high R², RPD and low RMSE were indicators of the optimal model in the removal of SM effects.【Result】SM had an obvious influence on soil spectra reflectance, and the reflectance values across the entire wavelength domain decreased as the SM increased, making it more challenging to identify useful features of SOM by spectra, it dramatically degraded the prediction accuracy of SOM. No overlap before OSC was observed between the wet and dry ground spectra because the wet spectra grouped in an independent space from the dry ground spectra, and the range of the spectral correlation coefficients between different SM levels was large. However, after OSC, the wet spectra had nearly identical positions in the feature space to the corresponding dry ground spectra, which showed the spectral similarity between the two groups of spectra, and the range of the spectral correlation coefficients between different SM levels was small. The validation mean values of R²_pre, RPD for the nine SM levels of EPO-PLSR model were 0.72 and 1.89, respectively. OSC method could effectively remove the effects of SM on SOM estimation, OSC-PLSR model obtained a better performance than the PLSR, EPO-PLSR model, the validation mean values of R²_pre, RPD for the nine SM levels were 0.72 and 1.89, respectively. 【Conclusion】 OSC-PLSR method was recommended for a better quantitative prediction of SOM from the soil samples under different SM levels. In the future, this approach may facilitate the proximally sensed field spectra for rapidly measuring SOM for this study area.

Key words: Vis-NIR spectra')">