Quantifying spatial variability of crop water use by combining eddy covariance observations with high-resolution UAV-based remote sensing
Bibliographic record
Abstract
Applications of precision agriculture enable water management decisions to be made at increasingly fine resolutions—smaller than which we can routinely estimate the water use from crops. Direct measurement of crop evapotranspiration (ET) using the eddy covariance technique can provide continuous flux observations but cannot provide insight at smaller scales than the measurement footprint (~100-200m). On the other hand, remote sensing techniques based on the energy balance approach are capable of quantifying spatial patterns of ET at high resolutions but are limited to particular instances in time and cannot provide the temporal information required. In this research, we develop and evaluate a method to spatially disaggregate continuous ET measurements from eddy covariance systems. The approach is based on developing high resolution spatial maps of ET derived from a combination of UAV-thermal remote sensing energy balance modeling that is parameterized using UAV-LiDAR to measure crop canopy characteristics (leaf area index, canopy height, and vegetation fraction). Through consideration of the eddy covariance flux footprint, and the remotely-sensed spatial detail, temporally continuous spatial estimates of ET were developed. The application of this method is demonstrated for barley grown in the Canadian prairies under strongly heterogeneous field conditions.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".