The alternations in soil physicochemical properties caused by the reclamation of coastal tidal land can strongly affect the activities of soil extracellular enzymes. Soil extracellular enzymes are one of the most active organic components in soil ecosystem, which is involved in almost all the biochemical reactions. Determining the importance of potential influencing factors of soil extracellular enzymes and thus estimating their activities are important for clarifying the biological mechanism of soil carbon and nitrogen cycling. In this study, the multiple linear regressions (MLR) and random forest (RF) models were conducted to estimate the activities of soil amylase and urease activities using covariates, such as soil water content (SWC), electrical conductivity (EC), total nitrogen (TN), total phosphorus (TP), and soil organic carbon (SOC) as well as the soil bulk density (BD) and pH. The results reveals that the amylase activity of fishpond was significantly higher than that of other land use types, while the urease activity of rape land, broad bean land, and fishpond were notably higher than that of bare flat, Spartina alterniflora, and uncultivated land. The RF model indicated that the SWC and TN is the main variable affecting amylase and urease activity, respectively. The RF model performed much better than MLR model in estimating the soil amylase and urease activity as it revealed much lower error indices (MAE and RMSE) and higher R² value. The superiority of RF model in estimating amylase and urease activity is due to its advantages to handle the nonlinear and hierarchical relationships between enzyme activities and covariates, and insensitivity to overfitting and the presence of noise in the data.

沿海潮汐土地的开垦引起的土壤理化性质的变化可以强烈影响土壤细胞外酶的活性。土壤细胞外酶是土壤生态系统中最活跃的有机成分之一，几乎参与所有生化反应。确定土壤细胞外酶的潜在影响因素的重要性并由此估算其活性对于阐明土壤碳氮循环的生物学机制很重要。在这项研究中，采用多元线性回归（MLR）和随机森林（RF）模型，使用协变量（例如土壤含水量（SWC），电导率（EC），总氮）估算土壤淀粉酶和脲酶的活性（TN），总磷（TP），土壤有机碳（SOC）以及土壤容重（BD）和pH。结果表明，鱼塘的淀粉酶活性明显高于其他土地利用类型，而油菜田，蚕豆田和鱼塘的尿素酶活性明显高于裸露的土地，互花米草和未耕地。RF模型表明SWC和TN分别是影响淀粉酶和脲酶活性的主要变量。在估计土壤淀粉酶和脲酶活性方面，RF模型的表现要好于MLR模型，因为它显示出更低的误差指数（MAE和RMSE）和更高的R ²值。RF模型在估计淀粉酶和脲酶活性方面的优越性是由于其具有处理酶活性和协变量之间的非线性和层次关系的优势，以及对数据过度拟合和噪声的不敏感性。