# Improving Forest Above-Ground Biomass Estimation by Integrating Individual Machine Learning Models

*PRI Rank #14 · Topics and Trends in Most Cited Forest ecology and management Papers, Class of 2026*

*Canonical URL: https://pri.pepkio.com/top-papers/forest-ecology-and-management/2026/rank-14*

| Field | Value |
| --- | --- |
| Rank | #14 |
| 18m citations | 42 |
| Journal | Forests |
| Year | 2024 |
| DOI | 10.3390/f15060975 |
| Corresponding authors | Qiuyan Huang |
| Institution | Nanning Normal University, China |

**Ranking page:** [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)

**Paper link:** [10.3390/f15060975](https://doi.org/10.3390/f15060975)

## 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

## Cite this ranking

```
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 2026-09-06.

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
```