Acquisition of the information of soil moisture regime is one of the hotspots in current researches. It is not an easy job to achieve inversion of regional soil moisture content just by depending on soil water estimation models established solely on near-ground hyper-spectra. The study is to explore feasible ways to forecast soil moisture contents by combining the use of narrow-band hyper-spectra and wide-band multi-spectral remote sensing images. Field surveys were conducted and soil samples collected during April 28 to April 30, 2014 in Kenli County, the research area in the Yellow River Delta. Soil water contents were measured in lab using the soil samples and oven-drying method; soil spectra of undisturbed soil samples collected from fields were determined under natural light outdoors with an American ASD Fieldspec4 spectrometer; and the first 7 bands of the OIL sensor were selected and used to collate the Landsat8 remote sensing images of May 1, 2014 for atmospheric radiation correction, geometric precision correction, clipping and other processing. And further on, based on the hyper-spectral narrow-band reflectances measured outdoors LandSat8 wide-band reflectances were simulated with two fitting methods, center wavelength reflectance and band average reflectance methods; by means of band combination in four modes, i.e., ratio, difference, sum dividing reduction, and reduction dividing sum, with sensitive spectral parameters selected according to correlativity; then hyper-spectral single-form band combination and multi-form band combination soil moisture estimation models were established with the multiple stepwise linear regression analysis method, and then screened with the two fitting methods for the best model. Soil information in the remote sensing images was obtained using the linear mixed pixel decomposition method after excluding the vegetation information; the soil information was compared with the measured hyper-spectral reflectance and remote sensing image reflectances were corrected with the ratio and mean method. On this basis, the best hyper-spectral model for estimation of soil moisture contents was applied to the LandSat8 satellite images. Hence, remote sensing inversion of soil moisture contents in the study area was realized; a soil moisture content distribution map based on remote sensing inversion was plotted which was compared with the interpolated soil moisture content map based on measured data at the monitoring sites; Based on spatial distribution and area percentage of each water content level, the results of the inversion were analyzed and verified. Results show that 1）the spectral curves generally proceeded gently in a similar shape; soil reflectance tended to decline with rising water content; and to a certain extent, the 7 bands of Landsat8 OIL were related with soil moisture; 2) the model based on average reflectance of the bands for estimation of soil moisture contents is better than that based on center wavelength reflectance and the model based on multi-form band combination is superior to that based on single-form band combination;3) the outdoor measured reflectance fitted with the band average reflectance method is quite consistent with the remote sensing image reflectance in variation trend with correlation coefficient being 0.989, up to an extremely significant level; and 4) soil moisture content distribution map based on remote sensing inversion and the interpolated soil moisture content distribution map based on measured data are quite consistent and uniform in spatial distribution and numerical statistics, indicating that the estimation of soil moisture contents based on remote sensing inversion is in conformity with the actual situation of study area, displaying good reliability and authenticity. The study explored feasibility of combining hyper-spectral estimation with remote sensing inversion in estimating soil moisture contents in the studied area, and provided some scientific basis and technical reference for quick acquisition of the information of soil moisture regime in the Yellow River Delta.