Abstract: Abstract: Soil moisture content has great influence on the prediction accuracy of soil organic matter (SOM) content using hyperspectral data. The purpose of this study was to find the threshold of soil moisture content suitable for using hyperspectral data to predict SOM content. A total of 63 soil samples including black soil, chernozem and meadow soil were collected from crop fields in Lishu and Gongzhuling county, Jilin province and in Binxin county, Heilongjiang province. The soil samples were air-dried and sieved through a 2-mm sieve. SOM contents were measured in the laboratory. The soil samples were divided into two groups including 42 samples for calibration and 21 for validation. Reflectance of soil samples with over-dried, air-dried and 5% to 40% soil moisture contents (the interval of 5%) were measured using ASD Fieldspec Pro High Spectrometer in a dark room. Soil spectral reflectance (R) was mathematically transformed into first derivatives of reflectance (R') and the logarithm of the inverse of the reflectance (Log (1/R)). SOM content spectral prediction models were set up respectively by using partial least squares regression (PLSR) method. The method of variable importance in projection (VIP) was used to analyze which spectral ranges were important to explain SOM content under different soil moisture contents by using PLSR. The results showed that soil spectral reflectance had a larger decline with soil moisture content increasing from 5% to 25%, but the decline trend slowed down when soil moisture content increased from 25% to 40%. That means the soil moisture content with less than 25% had more obvious effect on soil spectral reflectance change than soil moisture content with higher than 25%. With the increase of soil moisture content, moisture absorption valley appeared a large tendency on bands of 1 450 and 1 900 nm. It indicated that effects of soil moisture content on soil spectral reflectance happened mainly in the near infrared wavelength range. SOM content spectral prediction model for air-dried soil samples had better accuracy. When the soil moisture content was less than 25%, the accuracy of SOM content estimation model was affected by soil moisture content largely, and the highest prediction accuracy was Log (1/R) spectral data transformation model. When the soil moisture content was or more than 25%, it was not suitable to be used for hyperspectral SOM content estimation, because SOM spectral characteristics was covered by soil moisture spectral characteristics. The VIP values of reflectance bands from 1 870 to 2 400 nm with higher than 25% soil moisture contents were less than 1. That means those wavelength had weak explanation ability of SOM content. This study can provide valuble information for SOM content spectral estimation in the crop field that has different soil moisture conditions.