Remote sensing and integration of machine learning algorithms for above-ground biomass estimation in Larix principis-rupprechtii Mayr plantations: a case study using Sentinel-2 and Landsat-9 data in northern China

Estimating above-ground biomass (AGB) is important for ecological assessment, carbon stock evaluation, and forest management. This research assesses the performance of the machine learning algorithms XGBoost, SVM, and RF using data from the Sentinel-2 and Landsat-9 satellites. The study assesses the influence of the significant spectral bands and vegetation indices on the accuracy of the AGB estimate. The results presented in the paper indicate that Sentinel-2 data were more effective than Landsat-9 data. This is mainly because it had higher spatial and spectral resolution, which enabled the model vegetation gradients and structural attributes more accurately. The XGBoost model performed the best with an R 2 of 0.82 and RMSE of 0.73 Mg/ha with Sentinel-2 and R 2 of 0.80 and RMSE of 0.71 Mg/ha with Landsat-9. In the current study, SVM also showed a substantial accuracy with an R 2 of 0.79 and RMSE of 0.73 Mg/ha for Sentinel-2 and R 2 of 0.76 and RMSE of 0.80 Mg/ha for Landsat-9. For Sentinel-2, the random forest achieved an R 2 of 0.74 and an RMSE of 0.93 Mg/ha, and Landsat 9 yielded an R 2 of 0.72 and an RMSE of 0.88 Mg/ha. Thus, using variable importance analysis, the results showed that vegetation indices and spectral bands have higher importance in predicting AGB. As expected from their application in biomass research, these predictors consistently emerged as highly significant across models and datasets. This study demonstrates the potential of integrating machine learning with remote sensing data to achieve accurate and efficient biomass assessment.