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Soil moisture content (SMC) is a basic condition for crop growth, and a key parameter for crop yield prediction and drought monitoring. An advantage of large-scale synchronous observation using remote sensing technology is that it provides the possibility of dynamic monitoring of soil moisture content in a large area. This study aimed to explore the feasibility of accurately estimating soil moisture content at a regional scale by combining ground hyper-spectral data with multispectral remote sensing (Sentinel-2) data. The results showed that the different mathematical transformations increased the correlation between soil spectral reflectance and SMC to varying degrees. Hyper-spectral optimized index normalized difference index (NDI) ((B769~797 – B848~881/B769~797 + B848~881); (B842 – B740/B842 + B740)) derived from the transformed reflectance (the first-order derivate of reciprocal-logarithm (Log (1/R))′, second-order derivate of reciprocal-logarithm (Log (1/R)) ′′) showed significant correlation (correlation coefficient (r) = 0.61; r = 0.47) with SMC, and the correlation coefficient values higher than difference index (DI) and ratio index (RI). From the performance of 12 prediction models which were taken optimized indices as independent variables, the central wavelength reflectance model (Log (1/R))′′ and the average wavelength reflectance model ((Log (1/R)) ′ presented higher validation coefficients (coefficient of determination (R2) = 0.61, root mean square error (RMSE) = 4.09%, residual prediction deviation (RPD) = 1.82; R2 = 0.69, RMSE = 3.48%, RPD = 1.91) compared with other models. When verifying the accuracy, the model yields R2 values of 0.619 and 0.701. These results indicated that the two-band hyper-spectral optimized indices (NDI) as an optimal indicator for quickly and accurately soil moisture content estimation. Combining the ground hyper-spectral data and satellite remote sensing image regional scale soil moisture content prediction provides a scientific reference for land-space integrated soil moisture content remote sensing monitoring. [ABSTRACT FROM AUTHOR]