We developed a new Object-based Disturbance Agent Classification Approach (ODACA) to characterize land disturbance agents based on Landsat time series. Seven major disturbance agents were characterized, including harvest, mechanical, stress, debris, hydrology, and fire. We first created the land disturbance map by using a modified COntinuous monitoring of Land Disturbance (COLD) algorithm (Zhu et al., 2020), and then established a semi-automated disturbance agent training dataset extraction framework based on existing open-source datasets, with very limited human intervention. The modified COLD algorithm was implemented based on Landsat time series from a single Landsat path to reduce the bidirectional reflectance distribution function effect and issues caused by data density disparity, and the model updating frequency was reduced from every new observation to every three percent of the number of observations used in the previous model updating to improve computational efficiency. Finally, disturbance agents were classified based on ODACA using a Random Forest model with a total of 175 predictor variables that contain rich information in the spectral, temporal, and spatial domains. Accurate land disturbance agent maps were created for five sites in the United States, with an overall accuracy of approximately 99%, and producer’s and user’s accuracies range from 57 to 100%, depending on specific disturbance agents.