Improving Forest Above-Ground Biomass Estimation by Integrating Individual Machine Learning Models
Most Cited Paper
in Forest ecology and management
18 Months Post-Publication
Class of 2026
“Improving Forest Above-Ground Biomass Estimation by Integrating Individual Machine Learning Models”
A research intelligence initiative by Pepkio
pri.pepkio.com
Topics
aboveground biomass (AGB) · biomass estimation · Machine learning · remote sensing data · Ensemble methods · CatBoost · LightGBM · Random Forest · XGBoost · Hybrid machine learning models · forest types · spatial autocorrelation · validation strategies · carbon cycle · estimation accuracy · model selection · Ensemble methods
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Pepkio Research Index (PRI). Topics and Trends in Most Cited Forest ecology and management Papers, Class of 2026. https://pri.pepkio.com/top-papers/forest-ecology-and-management/2026. Accessed 2026-07-23. Zheng Su, Tinsley Li, Thematic Shifts in Early-High-Impact Cancer Genomics and Diagnostics Research: A Bibliometric and Semantic Analysis. bioRxiv 2026.07.04.736459; doi: https://doi.org/10.64898/2026.07.04.736459