Soil calcium carbonate (CaCO₃) content is an important soil property. The prediction of soil CaCO₃ content is necessary for the sustainable management of soil fertility. In this work, we attempted to incorporate environmental variables directly and through regression models into the framework of Bayesian maximum entropy (BME) to predict CaCO₃ content. Firstly, multiple linear regression (MLR) and geographically weighted regression (GWR) were used to establish a relationship between sampling data and environmental variables, including Digital Elevation Model, pH, temperature, rainfall, and fluvo-aquic soils. Prediction results of MLR and GWR served as soft data and were incorporated into the framework of BME to estimate the CaCO₃ content. Secondly, soil samples and environmental variables were combined to generate probability distributions of CaCO₃ at unsampled points. These probability distributions were used as soft data for the BME to predict the CaCO₃ content. The results showed that the GWR method (r = 0.84, RMSE = 24.0 g kg⁻¹) performed better than the MLR method (r = 0.73, RMSE = 30.1 g kg⁻¹). The BME-GWR method outperformed the BME-EV and BME-MLR methods. The r values of BME-GWR, BME-EV, and BME-MLR methods were 0.87, 0.86, and 0.82, respectively, and the RMSEs of the three methods were 22.2, 23.9, and 25.2 g kg⁻¹, respectively. The spatial distribution of CaCO₃ content predicted by the above methods was similar and significantly higher in the southwest than in the northeast.

土壤碳酸钙（CaCO ₃）含量是重要的土壤性质。对土壤CaCO ₃含量的预测对于土壤肥力的可持续管理是必要的。在这项工作中，我们尝试将环境变量直接并通过回归模型合并到贝叶斯最大熵（BME）框架中，以预测CaCO ₃含量。首先，使用多元线性回归（MLR）和地理加权回归（GWR）建立采样数据与环境变量之间的关系，这些环境变量包括数字高程模型，pH，温度，降雨量和潮土。MLR和GWR的预测结果用作软数据，并被纳入BME的框架中以估计CaCO ₃内容。其次，将土壤样品和环境变量结合起来，以在未采样点生成CaCO _3的概率分布。这些概率分布用作BME的软数据，以预测CaCO ₃含量。结果表明，GWR方法（r = 0.84，RMSE = 24.0 g kg ^-1）比MLR方法（r = 0.73，RMSE = 30.1 g kg ^-1）表现更好。BME-GWR方法优于BME-EV和BME-MLR方法。BME-GWR，BME-EV和BME-MLR方法的r值分别为0.87、0.86和0.82，三种方法的RMSE分别为22.2、23.9和25.2 g kg ^-1。CaCO ₃的空间分布 通过上述方法预测的含量相似，西南地区显着高于东北地区。