New Insights in Crop Monitoring and Management Using Remote Sensing Data
Bibliographic record
Abstract
A Special Issue titled 'New Insights in Crop Monitoring and Management Using Remote Sensing Data' was launched in November 2022. It invited scientific contributions focused on the quantitative assessment of crop growth, management, and yield using innovative and integrated remote sensing techniques. To date, it has published 11 peer-reviewed research articles covering topics such as crop growth trajectory reconstruction, the retrieval of biophysical traits of leaves and crop canopies, crop aboveground biomass and yield prediction, and field irrigation amount estimation. The contributors come from research institutes and universities distributed in Russia, Canada, Brazil, Turkey, Germany, and the USA. Scholars from Xinjiang University proposed new algorithms to reconstruct Landsat EVI time series of crop growth seasons that are strongly polluted by clouds. Dr. Konstantin Muzalevskiy effectively estimated wheat height and aboveground biomass jointly using the MHz- and GHz-frequency band impulses when employing contactless ground penetration radar (GPR). A paper published in May of 2024 explored the use of VIS-NIR-SWIR hyperspectral and chlorophyll a fluorescence sensors to predict leaf structures and compounds and has received nine citations. An article estimating the aboveground biomass of winter wheat using a three-dimensional conceptual model with the help of UAV remote sensing images has 20 citations. This issue has garnered significant attention from agronomists, ecologists, and remote sensing scientists, as evidenced by its 30,569 web views.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".