Estimation of surface soil moisture and distributions play a key role in the ecological, environmental, and topographical investigations for intertidal mudflats, which are characteristically wet and periodically submerged by sea water. Unfortunately, existing techniques of large-scale surface moisture monitoring have measurement limitations, and the efficiency and accuracy of traditional methods are difficult to be ensured simultaneously. Thanks to the advantages of high precision, high resolution, and strong flexibility, Terrestrial Laser Scanners (TLSs) have been widely used to study the geomorphological features of intertidal mudflats. However, merely geometrical information is not enough to derive the physical characteristics of a mudflat, it is also necessary to mine intensity data of points cloud. Aside from the 3D points cloud, TLS can record the intensity value of each point, which contains spectral characteristics of the scanned target and is of vital importance to the improvement of TLS data classification and feature extraction. Most TLSs emit near-infrared lasers that can be strongly absorbed by water. Thus, the intensity values of areas with high water moisture are theoretically smaller than those of the regions with low moisture. In this study, the intensity data of TLSs were corrected for the incidence angle and distance effects, and the corrected intensity data were utilized to quantitatively estimate the surface soil moisture of intertidal mudflats. The Riegl VZ-4000 long-distance terrestrial laser scanner with a near-infrared wavelength of 1550 nm was used to establish the indoor moisture model and to conduct a case study of a tidal flat in the Chongming Island, Shanghai, China. Additionally, 26 sediment samples were also collected by the traditional way of gravimetric measurements to get moisture. Results show that there was a power function relationship between the corrected intensity data and surface moisture, with the correction coefficient being 0.961 and the estimation accuracy reaching 91.94%. Our findings show that, compared with the traditional technique (i.e., gravimetric measurements), the corrected intensity data of long-range TLSs are effective for quick, accurate, and detailed estimation of surface soil moisture over large areas.