Mapping Crop Fractional Green Canopy Cover Using High Spatial Resolution Thermal and Optical Remote Sensing in Southern Ontario, Canada
Bibliographic record
Abstract
Fractional green canopy cover (FGCC) is a commonly used diagnostic parameter to describe plant growth and vitality. Remote sensing estimates of crop FGCC have been used to support a wide range of agricultural applications, including assessing management practices designed to promote crop productivity and maintain environmental sustainability. This study investigated the potential of using affordable thermal and optical remote sensing sensors to map FGCC at the Agriculture Agri-Food Canada experimental farm located in Woodslee, Ontario, Canada. Airborne RGB (Red-Green-Blue) colour and thermal images and ground-level red-edge images were collected in August 2019 over crop fields with different treatment plots. The 20 cm resolution RGB images and 5 cm red-edge images were used to extract reference data for FGCC, which were then linked to thermal data and red-edge vegetation indices. We found that the airborne thermal data explained 77% of the variation in FGCC at a range of 0-100%. However, the airborne thermal image could only explain 17% of the variation within the cornfields, which have low FGCC values ranging from 20% to 40%. In contrast, the simple ratio derived from the ground-level red-edge image could explain 81% of the FGCC variation in the cornfields. These results suggest that both the thermal and red-edge images are capable of estimating crop FGCC, with the red-edge outperforms the thermal images in estimating low FGCC for single-crop fields. In conclusion, this study demonstrates an efficient and affordable means to map crop FGCC, and these maps could play an essential role in generating insights relating to crop canopy development, light interception, and evapotranspiration partitioning, which can be used as indicators for soil health and nutrient status.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".