Accompanying software and data for the paper "A Comparison between Machine Learning Methods for Carbon Sequestration Estimation based on Remote Sensing Data". Abstract Forests play a crucial role in storing a substantial amount of the world's carbon. Accurately estimating carbon storage is essential information for addressing and mitigating the impacts of global warming. While many studies have used machine learning models to estimate carbon storage (CS) in forests based on remote sensing data, this research also further examines carbon sequestration (i.e., the annual carbon uptake by trees; CSE). The objective of this study is two-fold: firstly, to identify the best machine learning models for estimating CSE and CS by testing various methods, and secondly, to examine the effect of climatic data and the canopy height model (CHM) on the estimation of CSE. To achieve the first objective, we compare the performance of seven competitive machine learning models including Support Vector Regression, K-Nearest Neighbors, Multilayer Perceptron, Random Forest, Gradient Boosting Decision Tree, extreme Gradient Boosting, and Categorical Boosting. As regards the second objective, we study the effect of four input configurations: the first one is a baseline configuration based solely on attributes extracted from satellite images (Sentinel-2) and geomorphology, the second configuration combines the satellite features with climatic data, the third one uses a CHM derived from LiDAR instead of climatic data, and the fourth combines all available features, satellite images, climatic data, and CHM. The results show that adding climatic data does not improve the estimation of CSE and CS, however adding CHM features highly improves the models’ performance for both targets. Random Forest and Categorical Boosting were found to have the best performance in comparison with other models in all the considered configurations.