【Objective】Rapid and accurate measurement of soil properties is fundamental to modern precision agriculture. CaCO₃ is a major component of the carbonate in soil and has some important effects on a series of physical, chemical and biological properties of soil, such as soil pH, characteristics of soil colloids, soil nutrition and soil heavy metals absorption capacity. The traditional method for measuring soil CaCO₃ content is mainly based on chemical analysis, which is often rather costly, time-consuming and destructive. Therefore, the method is far from efficient to meet the requirement of modern precision agriculture. The technology of soil reflection spectroscopy can be used to make up the shortages of the traditional method, and provide a new approach for the study of pedology. This paper is oriented to explore feasibility of using visible-near infrared reflection spectra to estimate CaCO₃ content in coastal soil in North Jiangsu, to evaluate impacts of reflection spectra data sets, pre-processing methods on accuracy of the estimation of CaCO₃ content in coastal soil and to compare different modeling methods on estimating CaCO₃ content in coastal soil. 【Method】A total of 142 coastal soil samples were collected from North Jiangsu and analyzed for soil spectra with a portable FieldSpec 3. Soil properties of the soil samples were also determined with chemical methods and characterized, and the characteristics of visible-near infrared reflection spectra of the CaCO₃in the soil were analyzed. On such a basis, reflection spectra data sets of various spectral bands (e.g. seven sensitive bands, 355~2 495 nm, 355~780 nm, 781~2 495 nm and 381~2 459 nm), reflectance (R) and its pre-processing methods (e.g. multiplicative signal correction (MSC), first derivative (FD), standard normal variate (SNV), log(1/R) and 1/log R), and three regressions methods (e.g. partial least squares regression method (PLSR), principal component regression method (PCR) and multiple stepwise linear regression method (SMLR)) were selected to perform modeling analysis. In addition, coefficient of determination (R²), root mean square error (RMSR) and relative percent deviation (RPD) were used to evaluate accuracy of the models. 【Result】Results shows that the coastal soil is quite low in soil organic matter (SOM), but very high in CaCO₃ content; that reflectance of the soil in visible-near infrared bands is positively related to soil CaCO₃ content. Moreover, the response of soil spectral reflectance to change of soil CaCO₃ content is stronger in near infrared bands than in visible bands. The estimation (R²= 0.86, RMSE = 6.48, RPD = 2.66) using the model based on spectral reflectance data sets with bands from 381 nm to 2 459 nm is higher in accuracy than those using the other four data sets; the estimations using the pre-processing methods, R, log(1/R), 1/log R, MSC and SNV are good (0.85 ≤R² ≤ 0.87, 6.55 ≤ RMSE ≤7.13, 2.55 ≤ RPD ≤ 2.77); and the estimation using the model based on SMLR has R²being 0.67, while the estimations using the model based on PLSR and PCR are better than the former (R²≥ 0.81). 【Conclusion】It could be concluded that it is feasible to use visible-near infrared reflection spectra in estimating CaCO₃ content in the coastal soil of North Jiangsu; what kind of reflection spectral data sets, pre-processing methods, and regression methods should be used has a certain impact on precision of the estimation of CaCO₃ content in the soil; and the application of the technology of soil reflection spectroscopy will have a bright prospect in determining properties of coastal soil.