Optical reflectance across spatial scales—an intercomparison of transect-based hyperspectral, drone, and satellite reflectance data for dry season rangeland
Bibliographic record
Abstract
Drone-based multispectral sensing is a valuable tool for dryland spatial ecology, yet there has been limited investigation of the reproducibility of measurements from drone-mounted multispectral camera array systems or the intercomparison between drone-derived measurements, field spectroscopy, and satellite data. Using radiometrically calibrated data from two multispectral drone sensors (MicaSense RedEdge (MRE) and Parrot Sequoia (PS)) co-located with a transect of hyperspectral measurements (tramway) in the Chihuahuan desert (New Mexico, USA), we found a high degree of correspondence within individual drone data sets, but that reflectance measurements and vegetation indices varied between field, drone, and satellite sensors. In comparison to field spectra, MRE had a negative bias, while PS had a positive bias. In comparison to Sentinel-2, PS showed the best agreement, while MRE had a negative bias for all bands. A variogram analysis of NDVI showed that ecological pattern information was lost at grains coarser than 1.8 m, indicating that drone-based multispectral sensors provide information at an appropriate spatial grain to capture the heterogeneity and spectral variability of this dryland ecosystem in a dry season state. Investigators using similar workflows should understand the need to account for biases between sensors. Modelling spatial and spectral upscaling between drone and satellite data remains an important research priority.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".