Abstract: Clay content is an important soil property that affects the structure, nutrient supply and other characteristics of soils. Variations in clay content can indicate the degree of soil development or soil age. In traditional chemical analyses of soil properties, the extractant interacts in the solution and at the solution-particle interface, thus altering the equilibrium between the soil solid and solution phases. Soil reflectance spectroscopy has been developed as an effective alternative method of measuring soil properties primarily because it requires minimal sample preparation and it is fast, cost-effective, non-destructive and non-hazardous to the soil. In recent decades, research on the use of reflectance spectroscopy in soil science has achieved rapid advances. Reflectance spectroscopy can be successfully applied to estimate the soil clay content. However, the mechanisms of soil clay content estimation using reflectance spectroscopy are not very clear. The goals of this study were to identify the bands within the range of 360-2490 nm that can be used to estimate the clay content and explore the mechanisms of the clay content estimation using reflectance spectroscopy. A total of 150 coastal soil samples were collected. The soil reflectance spectra were measured in a dark room using a FieldSpec 3 portable spectrometer. Raw spectral data were pre-processed by smoothing (R) and then by first derivative (FD), continuum removal (CR) or reciprocal transformation (DS). Calibration (75 soil samples) and validation datasets (75 soil samples) were obtained from 1,000 random selections of the data. Stepwise multiple linear regression (SMLR) and partial least squares regression (PLSR) were performed to estimate the soil clay content and to further identify the bands useful for modeling this parameter. The results indicated that the SMLR analysis of CR and R spectra and the PLSR analysis of R and FD spectra were characterized by good calibration and validation accuracies regarding the soil clay content. The frequency of a SMLR-selected band and the regression coefficient in the PLSR regression equation indicated the impact of the clay content on the reflectance spectroscopy. The bands of 360-900 nm and 1800-2490 nm were important for the clay content estimation. The absorption bands near 1900 nm were caused by crystal water in phyllosilicates, whereas the absorption at 2325 nm was attributed to the combined effects of absorption by chlorite and vermiculite. The 500-800 nm absorption bands were caused by the soil organic matter (SOM). And the relationship between the clay content and reflectance spectra was a secondary relationship attributed to the high correlation between SOM and clay content. The absorption near 410 nm was produced by the iron ions. The mutual adsorption of iron ions and clay was consistent with the relatively high model contribution. The regression coefficients in the PLSR yielded dual peaks of absorption near 1400 nm, which was attributed to the dual absorption peaks of kaolinite. In summary, the clay mineral composition, adsorption of clay and iron ions and correlations between clay and organic matter were the causes of the clay content estimation using reflectance spectroscopy.