Benchmarking machine learning and ensemble approaches for remote sensing–based forest aboveground carbon mapping

This dataset supports the manuscript entitled “Benchmarking machine learning and ensemble approaches for remote sensing–based forest aboveground carbon mapping.” The repository contains the processed datasets and source code used to develop and evaluate multiple machine learning models for estimating forest aboveground carbon (AGC) storage using multi-source remote sensing data. Specifically, the repository includes: Training and validation samplesField-derived aboveground carbon measurements linked with corresponding remote sensing predictor variables. Remote sensing–derived feature variablesProcessed spectral indices, environmental variables, and auxiliary predictors used in model development. Model performance resultsEvaluation metrics for different machine learning models, feature selection strategies, hyperparameter optimization methods, and ensemble configurations. Predicted AGC outputsModel-based AGC estimation results used to generate spatial carbon maps. Code