Abstract: Abstract: Soil moisture is one of the important components of soil and plays an important role in the material and vegetative nutrient transport process in the soil system. Soil moisture is also an essential soil physical parameter in the study on water cycle in ecological system, and a key variable of drought monitoring, soil erosion and surface evaporation studying. Therefore, soil moisture monitoring is very important. Remote sensing technology has been applied to soil moisture monitoring with its advantage of high efficiency and rapidness. The soil hyper-spectral ground experiment and the soil hyper-spectral characteristics are the basis for the inversion of soil moisture. In this paper, soil samples collected in field were mixed to achieve the purpose of keeping approximately constant soil properties. Then mixed soil sample was divided into 16 independent samples in order to ensure that the effects of soil properties on reflectance of each soil sample were at the same level, such as soil organic matter, soil texture, and soil salinity. After that, the samples were slowly irrigated with distilled water to get different levels of moisture. And the spectral data of each sample were measured at the same time under laboratory conditions. Based on this dataset, a remote sensing inversion model of soil moisture content based on exponential function was established and the parameters of model were fitted by using the experimental spectrum data. Fitted parameters illustrated the effects of soil moisture on soil reflected energy at each single band from 350 to 2 500 nm. A larger value of the fitted parameter indicated that more energy was absorbed by water and less energy was reflected. Result showed that there were 2 peaks near 1 400 and 1 900 nm after a steady trend less than 1 300 nm. And this fitted result was consistent with the absorption coefficients of pure water. It indicates that the exponential model with physically definable parameters can be used to describe the characteristics of soil reflectance changing with soil moisture conditions. Then this inversion model was used to estimate the soil moisture based on laboratory spectral data. The accuracy varied with soil moisture level, and it was lower for samples with soil moisture larger than 32.75% and lower than 5.52%. When soil moisture was 32.75%, the maximum absolute error and the minimum absolute error were 134.89% and 25.44%, respectively. When soil moisture was equal and lower than 5.52%, the maximum absolute error was larger than 200%. The estimation accuracy was better when the soil moisture was between 5.52% and 32.75%. The mean absolute error was less than 30% and the maximum absolute error was 83.81%. The determination coefficient and RMSE (root mean square error) between estimated and measured soil moisture content at 640 nm were 0.706 2 and 3.49%, respectively. The results indicate that the inversion model based on the exponential function can be used for soil moisture content estimation with good accuracy. This work provides new methods and ideas for monitoring topsoil moisture content by using remote sensing technology.