【Objective】 High~precision soil organic carbon mapping is the basis for studying the spatiotemporal pattern of cultivated soil organic carbon and its influencing mechanism. Results of the relevant research can provide decision support for the designation of farmland management regarding "carbon sequestration and emission reduction". Agricultural management activities are an important influencing factor of soil organic carbon changes in farmland, but soil organic carbon mapping based on agricultural management activities is relatively rare. The phenological parameters extracted from remote sensing images are a direct reflection of agricultural management activities and have great application potential in studying the impact of agricultural management activities on farmland soil organic carbon. 【Method】 This study selected the paddy fields in Zhangzhou City, Fujian Province as the research object. We used the random forest algorithm, based on five different variable combinations (Group A:only natural environment variables; Group B:natural environment variables + early rice phenological parameters:Group C:natural environment variables + late rice phenological parameters; Group D:natural environment variables + early rice phenological parameters + late rice phenological parameters; Group E:early rice phenological parameters + late rice phenological parameters), to build soil organic carbon content prediction models. By comparing the prediction accuracy of the five groups of models, the spatial distribution characteristics of predicted values, the importance of related influencing factors, and the influence of phenological parameters on the accuracy of soil organic carbon mapping were analyzed. Also, the main influencing factors of soil organic carbon mapping in paddy fields in Zhangzhou City were excavated. Agricultural management activities that have an important impact on soil organic carbon in paddy fields in Zhangzhou City were also identified. 【Result】 The results showed that the differences in the spatial distribution of soil organic carbon in paddy fields in Zhangzhou resulted from the combined effect of natural environmental factors and agricultural management measures. Phenological parameters can effectively improve the mapping accuracy of soil organic carbon in paddy fields in Zhangzhou City. Compared with the prediction model based only on natural factors, the addition of phenological parameters can reduce the error of the prediction model and improve the ability of the model to explain variance. The phenological parameters that had the greatest impact on soil organic carbon in paddy fields in Zhangzhou City were the rate of increase at the beginning of the early rice growing season (h1), the time for the start of the early rice growing season (a1), and the rate of decrease at the end of the early rice growing season (i1). These three most important phenological parameters were positively, negatively, and negatively correlated with soil organic carbon content, respectively. 【Conclusion】 The adoption of water and fertilizer management measures that can promote early growth and rapid germination of the early rice, accelerate the tillering rate of the early rice, and slow down the senescence rate of the early rice will increase the soil organic carbon content in the cultivated land. Building a prediction model based on phenological parameters can effectively improve the accuracy of farmland soil organic carbon mapping. The research on farmland soil organic carbon mapping based on phenological parameters can provide decision support for farmland management. The results of this study can provide a theoretical basis for related research.