Bibliometric Analysis of Remote Sensing Research Trend in Crop Growth Monitoring: A Case Study in China
Bibliographic record
Abstract
Remote sensing of crop growth monitoring is an important technique to guide agricultural production. To gain a comprehensive understanding of historical progression and current status, and future trend of remote sensing researches and applications in the field of crop growth monitoring in China, a study was carried out based on the publications from the past 20 years by Chinese scholars. Using the knowledge mapping software CiteSpace, a quantitative and qualitative analysis of research development, current hotspots, and future directions of crop growth monitoring using remote sensing technology in China was conducted. Furthermore, the relationship between high-frequency keywords and the emerging hot topics were visually analyzed. The results revealed that Chinese researchers paid more attention on keywords such as “vegetation index”, “crop growth”, “winter wheat”, “leaf area index (LAI)”, and “model” in the field of crop growth monitoring, and “LAI” and “unmanned aerial vehicle (UAV)”, appeared increasingly in frontier research of this discipline. Overall, bibliometric results from this CiteSpace-aided study provide a quantitative visualization to enrich our understanding on the historical development, current status, and future trend of crop growth monitoring in China.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.050 | 0.264 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".