Research Progress on Estimation Methods of Forest Evapotranspiration Based on Bibliometrics
Bibliographic record
Abstract
Forest evapotranspiration (ET), a core process of water vapor exchange between forest ecosystems and the atmosphere, is crucial for global carbon and water cycles and ecosystem stability. However, its high-precision estimation faces challenges arising from complex forest structures and multi-factor driving mechanisms. Based on bibliometrics, this study visually analyzed 1,427 relevant papers from the Web of Science Core Collection (2005-2025) to summarize research status, hotspots and frontiers. Results show continuous growth in publications over two decades, peaking during 2017-2022. Journal co-occurrence reveals that Agricultural and Forest Meteorology ranks first, contributing 212 papers and 10,438 total citations with an average of 49.24 cites per article. The Chinese Academy of Sciences, University of CAS and USDA form a close collaboration network led by 279 core authors. Hotspots concentrate on eddy covariance (460 occurrences), remote sensing inversion (134) and machine learning (124, rapidly rising since 2017). Eddy covariance remains the “gold standard”; remote sensing breaks spatiotemporal limits by integrating multi-source data; machine learning, exhibiting the greatest advances, improves accuracy by 45% in complex environments (burst intensity 17.91 since 2017), promoting hybrid “physical mechanism + data-driven” models. Research evolved through three stages: traditional observation dominance (2005-2010), physical model optimization (2010-2016), and intelligent algorithm innovation (2017-present), with applications spanning ecological assessment and water resource management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.054 | 0.061 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".